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<h1 class="title">Mexico temperature notebook</h1><p class="subtitle">Kodi B. Arfer<br>Created 10 Sep 2018 • Last modified 26 Jan 2021</p>
</header>

<nav id="table-of-contents">
<h2>Contents</h2>
<div id="text-table-of-contents">
<ul>
<li><a class="internal" href="#sec--how-things-were">How things were</a></li>
<li><a class="internal" href="#sec--time-shenanigans">Time shenanigans</a></li>
<li><a class="internal" href="#sec--metereological-missingness">Metereological missingness</a></li>
<li><a class="internal" href="#sec--satellitetemperature-missingness">Satellite-temperature missingness</a></li>
<li><a class="internal" href="#sec--station-metadata">Station metadata</a></li>
<li><a class="internal" href="#sec--crossvalidation-results">Cross-validation results</a>
<ul>
<li><a class="internal" href="#sec--with-vs-without-training">With vs. without training Wunderground</a></li>
</ul>
</li>
<li><a class="internal" href="#sec--learning-curve">Learning curve</a></li>
<li><a class="internal" href="#sec--new-predictions">New predictions</a></li>
<li><a class="internal" href="#bibliography">References</a></li>
</ul>
</div>
</nav><div class="outline-2">
<h2 id="sec--how-things-were">How things were</h2>
<div class="outline-text-2">
<p>
Stages:
</p>

<ol class="org-ol">
<li>Mixed effects for grid cells with both satellite and ground data</li>
<li>Predict temperature in cells with satellite data but no ground data, using the mixed model(s) fit at stage 1</li>
<li>Predict temperature in cells with no satellite data using a spatial smoother</li>
</ol>

<p>
<a class='internal bibref' href='#bibref--rosenfelddsnj_2017'>Rosenfeld et al. (2017)</a> describes the method as applied to Israel.
</p>
</div>
</div>

<div class="outline-2">
<h2 id="sec--time-shenanigans">Time shenanigans</h2>
<div class="outline-text-2">
<p>
<a class='internal bibref' href='#bibref--hubmbw_2014'>Hu, Brunsell, Monaghan, Barlage, and Wilhelmi (2014)</a>: "The overpass times provided by MODIS LST product are in local solar time, which is defined as the MODIS observation time in coordinated universal time (UTC) plus longitude in degrees divided by 15 (<a class='internal bibref' href='#bibref--williamsonhgkk_2013'>Williamson, Hik, Gamon, Kavanaugh, &amp; Koh, 2013</a>)."
</p>

<p>
MODIS documentation: "Note that the Day_view_time and Night_view_time are in local solar time, which is the UTC time plus grid’s longitude in degrees / 15 degrees (in hours , +24 if local solar time &lt; 0 or - 24 if local solar time &gt;= 24). The data day in the name of all the daily MOD11A1 files is in UTC so the data day in local solar time at each grid may be different from the data day in UTC by one day."
</p>
</div>
</div>

<div class="outline-2">
<h2 id="sec--metereological-missingness">Metereological missingness</h2>
<div class="outline-text-2">
<p>
Are there any days on which one of the ground-station meterological variables is missing from every station?
</p>

<div class="org-src-container">
<pre class="src src-R">sapply(
    subset(select = -date, ground[,
        by = date,
        .SDcols = c(temp.ground.vars, nontemp.ground.vars),
        lapply(.SD, <span style="color: #cd0000; font-weight: bold;">function</span>(v) all(is.na(v)))]),
    any)
</pre>
</div>

<table>


<colgroup>
<col  class="left">

<col  class="left">
</colgroup>
<thead>
<tr>
<th scope="col" class="left">&#xa0;</th>
<th scope="col" class="left">x</th>
</tr>
</thead>
<tbody>
<tr>
<td class="left">ground.temp.lo</td>
<td class="left">FALSE</td>
</tr>

<tr>
<td class="left">ground.temp.mean</td>
<td class="left">FALSE</td>
</tr>

<tr>
<td class="left">ground.temp.hi</td>
<td class="left">FALSE</td>
</tr>

<tr>
<td class="left">wind.speed.mean</td>
<td class="left">FALSE</td>
</tr>
</tbody>
</table>

<p>
Fortunately, no.
</p>
</div>
</div>

<div class="outline-2">
<h2 id="sec--satellitetemperature-missingness">Satellite-temperature missingness</h2>
<div class="outline-text-2">
<div class="org-src-container">
<pre class="src src-R">r = rbindlist(lapply(available.years, <span style="color: #cd0000; font-weight: bold;">function</span>(y)
   {d = model.dataset(y, mrow.set = <span style="background-color: #ffefd5;">"pred.area"</span>)
    cbind(year = y, d[,
        .SDcols = c(<span style="background-color: #ffefd5;">"satellite.temp.day.imputed"</span>, <span style="background-color: #ffefd5;">"satellite.temp.night.imputed"</span>),
        lapply(.SD, mean)])}))
setnames(r, c(<span style="background-color: #ffefd5;">"year"</span>, <span style="background-color: #ffefd5;">"day"</span>, <span style="background-color: #ffefd5;">"night"</span>))
</pre>
</div>

<div class="org-src-container">
<pre class="src src-R">rd(d = 2, as.data.frame(r))
</pre>
</div>

<table id="tab--sat-temp-missingness">


<colgroup>
<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">
</colgroup>
<thead>
<tr>
<th scope="col" class="right">&#xa0;</th>
<th scope="col" class="right">year</th>
<th scope="col" class="right">day</th>
<th scope="col" class="right">night</th>
</tr>
</thead>
<tbody>
<tr>
<td class="right">1</td>
<td class="right">2003</td>
<td class="right">0.30</td>
<td class="right">0.35</td>
</tr>

<tr>
<td class="right">2</td>
<td class="right">2004</td>
<td class="right">0.52</td>
<td class="right">0.50</td>
</tr>

<tr>
<td class="right">3</td>
<td class="right">2005</td>
<td class="right">0.30</td>
<td class="right">0.30</td>
</tr>

<tr>
<td class="right">4</td>
<td class="right">2006</td>
<td class="right">0.31</td>
<td class="right">0.36</td>
</tr>

<tr>
<td class="right">5</td>
<td class="right">2007</td>
<td class="right">0.28</td>
<td class="right">0.33</td>
</tr>

<tr>
<td class="right">6</td>
<td class="right">2008</td>
<td class="right">0.29</td>
<td class="right">0.30</td>
</tr>

<tr>
<td class="right">7</td>
<td class="right">2009</td>
<td class="right">0.26</td>
<td class="right">0.30</td>
</tr>

<tr>
<td class="right">8</td>
<td class="right">2010</td>
<td class="right">0.32</td>
<td class="right">0.33</td>
</tr>

<tr>
<td class="right">9</td>
<td class="right">2011</td>
<td class="right">0.23</td>
<td class="right">0.27</td>
</tr>

<tr>
<td class="right">10</td>
<td class="right">2012</td>
<td class="right">0.31</td>
<td class="right">0.35</td>
</tr>

<tr>
<td class="right">11</td>
<td class="right">2013</td>
<td class="right">0.32</td>
<td class="right">0.37</td>
</tr>

<tr>
<td class="right">12</td>
<td class="right">2014</td>
<td class="right">0.32</td>
<td class="right">0.35</td>
</tr>

<tr>
<td class="right">13</td>
<td class="right">2015</td>
<td class="right">0.34</td>
<td class="right">0.37</td>
</tr>

<tr>
<td class="right">14</td>
<td class="right">2016</td>
<td class="right">0.34</td>
<td class="right">0.36</td>
</tr>

<tr>
<td class="right">15</td>
<td class="right">2017</td>
<td class="right">0.27</td>
<td class="right">0.28</td>
</tr>

<tr>
<td class="right">16</td>
<td class="right">2018</td>
<td class="right">0.29</td>
<td class="right">0.34</td>
</tr>

<tr>
<td class="right">17</td>
<td class="right">2019</td>
<td class="right">0.25</td>
<td class="right">0.29</td>
</tr>
</tbody>
</table>
</div>
</div>

<div class="outline-2">
<h2 id="sec--station-metadata">Station metadata</h2>
<div class="outline-text-2">
<div class="org-src-container">
<pre class="src src-R">as.data.frame(station.metadata())
</pre>
</div>

<table>


<colgroup>
<col  class="right">

<col  class="left">

<col  class="left">

<col  class="left">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="left">

<col  class="left">
</colgroup>
<thead>
<tr>
<th scope="col" class="right">&#xa0;</th>
<th scope="col" class="left">network</th>
<th scope="col" class="left">station</th>
<th scope="col" class="left">region</th>
<th scope="col" class="right">lon</th>
<th scope="col" class="right">lat</th>
<th scope="col" class="right">elev.m</th>
<th scope="col" class="right">date.min</th>
<th scope="col" class="right">date.max</th>
<th scope="col" class="right">n.obs</th>
<th scope="col" class="left">land.cover</th>
<th scope="col" class="left">climate.type</th>
</tr>
</thead>
<tbody>
<tr>
<td class="right">1</td>
<td class="left">emas</td>
<td class="left">ALTZOMONI</td>
<td class="left">Valle de México</td>
<td class="right">-98.655</td>
<td class="right">19.119</td>
<td class="right">3,959</td>
<td class="right">2012-11-01</td>
<td class="right">2019-12-30</td>
<td class="right">2,335</td>
<td class="left">High mountain meadow</td>
<td class="left">Subhumid semi-cold</td>
</tr>

<tr>
<td class="right">2</td>
<td class="left">emas</td>
<td class="left">APAN</td>
<td class="left">&#xa0;</td>
<td class="right">-98.466</td>
<td class="right">19.728</td>
<td class="right">2,475</td>
<td class="right">2015-05-13</td>
<td class="right">2019-12-30</td>
<td class="right">1,513</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">3</td>
<td class="left">emas</td>
<td class="left">ATLACOMULCO</td>
<td class="left">&#xa0;</td>
<td class="right">-99.877</td>
<td class="right">19.799</td>
<td class="right">2,568</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">5,722</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">4</td>
<td class="left">emas</td>
<td class="left">CERRO CATEDRAL</td>
<td class="left">Toluca</td>
<td class="right">-99.519</td>
<td class="right">19.542</td>
<td class="right">3,385</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">5,533</td>
<td class="left">Oyamel-fir forest</td>
<td class="left">Subhumid semi-cold</td>
</tr>

<tr>
<td class="right">5</td>
<td class="left">emas</td>
<td class="left">ECOGUARDAS</td>
<td class="left">Valle de México</td>
<td class="right">-99.204</td>
<td class="right">19.271</td>
<td class="right">2,578</td>
<td class="right">2008-02-14</td>
<td class="right">2019-12-30</td>
<td class="right">2,991</td>
<td class="left">Secondary (bushy type) vegetation of pine-oak forest</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">6</td>
<td class="left">emas</td>
<td class="left">EL CHICO</td>
<td class="left">&#xa0;</td>
<td class="right">-98.716</td>
<td class="right">20.186</td>
<td class="right">3,007</td>
<td class="right">2012-11-02</td>
<td class="right">2019-12-30</td>
<td class="right">2,448</td>
<td class="left">Oyamel-fir forest</td>
<td class="left">Subhumid semi-cold</td>
</tr>

<tr>
<td class="right">7</td>
<td class="left">emas</td>
<td class="left">ESCUELA NACIONAL DE CIENCIAS BIOLÓGICAS II, IPN.</td>
<td class="left">Valle de México</td>
<td class="right">-99.145</td>
<td class="right">19.499</td>
<td class="right">2,241</td>
<td class="right">2010-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">2,930</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">8</td>
<td class="left">emas</td>
<td class="left">ESCUELA NACIONAL DE CIENCIAS BIOLÓGICAS, IPN.</td>
<td class="left">Valle de México</td>
<td class="right">-99.171</td>
<td class="right">19.454</td>
<td class="right">2,247</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">4,955</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">9</td>
<td class="left">emas</td>
<td class="left">HUAMANTLA</td>
<td class="left">&#xa0;</td>
<td class="right">-97.966</td>
<td class="right">19.386</td>
<td class="right">2,455</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">5,776</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">10</td>
<td class="left">emas</td>
<td class="left">HUAUCHINANGO</td>
<td class="left">&#xa0;</td>
<td class="right">-98.066</td>
<td class="right">20.178</td>
<td class="right">1,565</td>
<td class="right">2008-05-02</td>
<td class="right">2017-12-08</td>
<td class="right">2,352</td>
<td class="left">Human settlements</td>
<td class="left">Temperate humid</td>
</tr>

<tr>
<td class="right">11</td>
<td class="left">emas</td>
<td class="left">HUICHAPAN</td>
<td class="left">&#xa0;</td>
<td class="right">-99.664</td>
<td class="right">20.389</td>
<td class="right">2,087</td>
<td class="right">2006-09-01</td>
<td class="right">2019-07-11</td>
<td class="right">4,048</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">12</td>
<td class="left">emas</td>
<td class="left">HUIMILPAN</td>
<td class="left">&#xa0;</td>
<td class="right">-100.283</td>
<td class="right">20.390</td>
<td class="right">2,279</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">5,457</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">13</td>
<td class="left">emas</td>
<td class="left">IGUALA</td>
<td class="left">&#xa0;</td>
<td class="right">-99.524</td>
<td class="right">18.360</td>
<td class="right">766</td>
<td class="right">2004-11-01</td>
<td class="right">2019-09-23</td>
<td class="right">3,836</td>
<td class="left">Human settlements</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">14</td>
<td class="left">emas</td>
<td class="left">INSTITUTO MEXICANO DE TECNOLOGÍA DEL AGUA</td>
<td class="left">Cuernavaca</td>
<td class="right">-99.157</td>
<td class="right">18.882</td>
<td class="right">1,360</td>
<td class="right">2010-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">3,288</td>
<td class="left">Human settlements</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">15</td>
<td class="left">emas</td>
<td class="left">IZUCAR DE MATAMOROS</td>
<td class="left">&#xa0;</td>
<td class="right">-98.452</td>
<td class="right">18.617</td>
<td class="right">1,310</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">4,974</td>
<td class="left">Semi-permanent irrigation agriculture</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">16</td>
<td class="left">emas</td>
<td class="left">LA MALINCHE I</td>
<td class="left">&#xa0;</td>
<td class="right">-98.044</td>
<td class="right">19.298</td>
<td class="right">2,922</td>
<td class="right">2012-11-01</td>
<td class="right">2019-12-30</td>
<td class="right">2,003</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Subhumid semi-cold</td>
</tr>

<tr>
<td class="right">17</td>
<td class="left">emas</td>
<td class="left">LA MALINCHE II</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.032</td>
<td class="right">19.141</td>
<td class="right">2,728</td>
<td class="right">2012-11-03</td>
<td class="right">2019-12-30</td>
<td class="right">2,300</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">18</td>
<td class="left">emas</td>
<td class="left">LAGUNAS DE ZEMPOALA</td>
<td class="left">Cuernavaca</td>
<td class="right">-99.313</td>
<td class="right">19.053</td>
<td class="right">2,846</td>
<td class="right">2012-11-01</td>
<td class="right">2019-12-30</td>
<td class="right">2,099</td>
<td class="left">Pine forest</td>
<td class="left">Cold</td>
</tr>

<tr>
<td class="right">19</td>
<td class="left">emas</td>
<td class="left">MARIPOSA MONARCA I</td>
<td class="left">&#xa0;</td>
<td class="right">-100.278</td>
<td class="right">19.671</td>
<td class="right">3,263</td>
<td class="right">2012-11-03</td>
<td class="right">2019-12-25</td>
<td class="right">2,385</td>
<td class="left">Oyamel-fir forest</td>
<td class="left">Subhumid semi-cold</td>
</tr>

<tr>
<td class="right">20</td>
<td class="left">emas</td>
<td class="left">MARIPOSA MONARCA II</td>
<td class="left">&#xa0;</td>
<td class="right">-100.290</td>
<td class="right">19.539</td>
<td class="right">3,001</td>
<td class="right">2012-11-14</td>
<td class="right">2019-12-30</td>
<td class="right">2,256</td>
<td class="left">Oyamel-fir forest</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">21</td>
<td class="left">emas</td>
<td class="left">NEVADO DE TOLUCA</td>
<td class="left">Toluca</td>
<td class="right">-99.771</td>
<td class="right">19.126</td>
<td class="right">4,084</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">5,855</td>
<td class="left">High mountain meadow</td>
<td class="left">Subhumid semi-cold</td>
</tr>

<tr>
<td class="right">22</td>
<td class="left">emas</td>
<td class="left">PARQUE IZTA-POPO</td>
<td class="left">Valle de México</td>
<td class="right">-98.640</td>
<td class="right">19.096</td>
<td class="right">3,667</td>
<td class="right">2008-02-14</td>
<td class="right">2019-12-30</td>
<td class="right">3,854</td>
<td class="left">High mountain meadow</td>
<td class="left">Subhumid semi-cold</td>
</tr>

<tr>
<td class="right">23</td>
<td class="left">emas</td>
<td class="left">PRESA MADÍN</td>
<td class="left">Valle de México</td>
<td class="right">-99.268</td>
<td class="right">19.524</td>
<td class="right">2,374</td>
<td class="right">2003-01-01</td>
<td class="right">2019-11-29</td>
<td class="right">5,713</td>
<td class="left">Human-induced grassland</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">24</td>
<td class="left">emas</td>
<td class="left">SIERRA DE HUAUTLA</td>
<td class="left">&#xa0;</td>
<td class="right">-98.936</td>
<td class="right">18.541</td>
<td class="right">1,311</td>
<td class="right">2012-11-10</td>
<td class="right">2019-12-30</td>
<td class="right">2,145</td>
<td class="left">Secondary (bushy type) vegetation of dry broadleaf forest</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">25</td>
<td class="left">emas</td>
<td class="left">TEHUACAN</td>
<td class="left">&#xa0;</td>
<td class="right">-97.617</td>
<td class="right">18.314</td>
<td class="right">1,737</td>
<td class="right">2012-11-03</td>
<td class="right">2019-12-30</td>
<td class="right">2,405</td>
<td class="left">Crassicaule shrublands</td>
<td class="left">Semi-dry semi-warm</td>
</tr>

<tr>
<td class="right">26</td>
<td class="left">emas</td>
<td class="left">TEPOZTLAN</td>
<td class="left">Cuernavaca</td>
<td class="right">-99.079</td>
<td class="right">18.951</td>
<td class="right">1,385</td>
<td class="right">2004-10-22</td>
<td class="right">2019-02-07</td>
<td class="right">4,840</td>
<td class="left">Secondary (bushy type) vegetation of dry broadleaf forest</td>
<td class="left">Semi-warm subhumid</td>
</tr>

<tr>
<td class="right">27</td>
<td class="left">emas</td>
<td class="left">TEZONTLE</td>
<td class="left">Valle de México</td>
<td class="right">-99.100</td>
<td class="right">19.385</td>
<td class="right">2,236</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">5,874</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">28</td>
<td class="left">emas</td>
<td class="left">TRES MARIAS</td>
<td class="left">Cuernavaca</td>
<td class="right">-99.249</td>
<td class="right">19.051</td>
<td class="right">2,832</td>
<td class="right">2011-01-02</td>
<td class="right">2019-12-30</td>
<td class="right">2,142</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">29</td>
<td class="left">emas</td>
<td class="left">UNIVERSIDAD TECNOLÓGICA DE TECAMACHALCO</td>
<td class="left">&#xa0;</td>
<td class="right">-97.722</td>
<td class="right">18.866</td>
<td class="right">2,027</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-30</td>
<td class="right">5,351</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">30</td>
<td class="left">emas</td>
<td class="left">VALLE DE BRAVO</td>
<td class="left">&#xa0;</td>
<td class="right">-100.085</td>
<td class="right">19.376</td>
<td class="right">2,514</td>
<td class="right">2012-11-07</td>
<td class="right">2019-12-30</td>
<td class="right">1,859</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">31</td>
<td class="left">esimes</td>
<td class="left">CEMCAS</td>
<td class="left">Valle de México</td>
<td class="right">-98.974</td>
<td class="right">19.480</td>
<td class="right">2,236</td>
<td class="right">2014-05-01</td>
<td class="right">2017-05-22</td>
<td class="right">330</td>
<td class="left">Halophilic grassland</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">32</td>
<td class="left">esimes</td>
<td class="left">CUERNAVACA</td>
<td class="left">Cuernavaca</td>
<td class="right">-99.215</td>
<td class="right">18.943</td>
<td class="right">1,634</td>
<td class="right">2009-09-11</td>
<td class="right">2019-12-30</td>
<td class="right">2,724</td>
<td class="left">Human settlements</td>
<td class="left">Semi-warm subhumid</td>
</tr>

<tr>
<td class="right">33</td>
<td class="left">esimes</td>
<td class="left">PACHUCA</td>
<td class="left">Pachuca</td>
<td class="right">-98.750</td>
<td class="right">20.088</td>
<td class="right">2,365</td>
<td class="right">2013-01-01</td>
<td class="right">2019-10-30</td>
<td class="right">1,840</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">34</td>
<td class="left">esimes</td>
<td class="left">PUEBLA</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.163</td>
<td class="right">19.055</td>
<td class="right">2,190</td>
<td class="right">2013-01-04</td>
<td class="right">2019-12-30</td>
<td class="right">2,133</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">35</td>
<td class="left">esimes</td>
<td class="left">QUERETARO</td>
<td class="left">&#xa0;</td>
<td class="right">-100.369</td>
<td class="right">20.563</td>
<td class="right">1,902</td>
<td class="right">2013-01-10</td>
<td class="right">2018-06-26</td>
<td class="right">1,543</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">36</td>
<td class="left">esimes</td>
<td class="left">TACUBAYA</td>
<td class="left">Valle de México</td>
<td class="right">-99.197</td>
<td class="right">19.404</td>
<td class="right">2,301</td>
<td class="right">2006-01-01</td>
<td class="right">2017-12-22</td>
<td class="right">3,449</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">37</td>
<td class="left">esimes</td>
<td class="left">TLAXCALA</td>
<td class="left">Tlaxcala-Apizaco</td>
<td class="right">-98.247</td>
<td class="right">19.325</td>
<td class="right">2,232</td>
<td class="right">2009-10-09</td>
<td class="right">2019-12-30</td>
<td class="right">3,002</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">38</td>
<td class="left">esimes</td>
<td class="left">TOLUCA</td>
<td class="left">Toluca</td>
<td class="right">-99.714</td>
<td class="right">19.291</td>
<td class="right">2,726</td>
<td class="right">2013-01-08</td>
<td class="right">2019-12-30</td>
<td class="right">2,103</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">39</td>
<td class="left">esimes</td>
<td class="left">TULANCINGO</td>
<td class="left">Tulancingo</td>
<td class="right">-98.357</td>
<td class="right">20.084</td>
<td class="right">2,202</td>
<td class="right">2006-01-01</td>
<td class="right">2015-03-20</td>
<td class="right">3,163</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">40</td>
<td class="left">esimes</td>
<td class="left">ZACATEPEC</td>
<td class="left">&#xa0;</td>
<td class="right">-99.207</td>
<td class="right">18.644</td>
<td class="right">921</td>
<td class="right">2013-03-16</td>
<td class="right">2019-12-20</td>
<td class="right">1,713</td>
<td class="left">Human settlements</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">41</td>
<td class="left">simat</td>
<td class="left">ACO</td>
<td class="left">Valle de México</td>
<td class="right">-98.912</td>
<td class="right">19.636</td>
<td class="right">2,259</td>
<td class="right">2011-07-01</td>
<td class="right">2019-12-31</td>
<td class="right">2,859</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">42</td>
<td class="left">simat</td>
<td class="left">AJM</td>
<td class="left">Valle de México</td>
<td class="right">-99.208</td>
<td class="right">19.272</td>
<td class="right">2,597</td>
<td class="right">2015-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">1,762</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">43</td>
<td class="left">simat</td>
<td class="left">AJU</td>
<td class="left">Valle de México</td>
<td class="right">-99.163</td>
<td class="right">19.154</td>
<td class="right">2,940</td>
<td class="right">2015-04-08</td>
<td class="right">2019-12-31</td>
<td class="right">1,078</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Subhumid semi-cold</td>
</tr>

<tr>
<td class="right">44</td>
<td class="left">simat</td>
<td class="left">BJU</td>
<td class="left">Valle de México</td>
<td class="right">-99.160</td>
<td class="right">19.370</td>
<td class="right">2,249</td>
<td class="right">2015-08-01</td>
<td class="right">2019-12-31</td>
<td class="right">1,579</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">45</td>
<td class="left">simat</td>
<td class="left">CES</td>
<td class="left">Valle de México</td>
<td class="right">-99.075</td>
<td class="right">19.335</td>
<td class="right">2,247</td>
<td class="right">2003-01-01</td>
<td class="right">2010-12-31</td>
<td class="right">2,663</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">46</td>
<td class="left">simat</td>
<td class="left">CHO</td>
<td class="left">Valle de México</td>
<td class="right">-98.886</td>
<td class="right">19.267</td>
<td class="right">2,242</td>
<td class="right">2011-07-01</td>
<td class="right">2019-12-31</td>
<td class="right">2,974</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">47</td>
<td class="left">simat</td>
<td class="left">CUA</td>
<td class="left">Valle de México</td>
<td class="right">-99.292</td>
<td class="right">19.365</td>
<td class="right">2,690</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">5,431</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">48</td>
<td class="left">simat</td>
<td class="left">CUT</td>
<td class="left">Valle de México</td>
<td class="right">-99.199</td>
<td class="right">19.722</td>
<td class="right">2,260</td>
<td class="right">2012-02-21</td>
<td class="right">2019-12-31</td>
<td class="right">2,347</td>
<td class="left">Annual and semi-permanent irrigation agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">49</td>
<td class="left">simat</td>
<td class="left">FAC</td>
<td class="left">Valle de México</td>
<td class="right">-99.244</td>
<td class="right">19.482</td>
<td class="right">2,288</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">5,950</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">50</td>
<td class="left">simat</td>
<td class="left">FAR</td>
<td class="left">Valle de México</td>
<td class="right">-99.046</td>
<td class="right">19.474</td>
<td class="right">2,235</td>
<td class="right">2019-03-01</td>
<td class="right">2019-12-31</td>
<td class="right">205</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">51</td>
<td class="left">simat</td>
<td class="left">GAM</td>
<td class="left">Valle de México</td>
<td class="right">-99.095</td>
<td class="right">19.483</td>
<td class="right">2,239</td>
<td class="right">2015-12-01</td>
<td class="right">2019-12-31</td>
<td class="right">1,443</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">52</td>
<td class="left">simat</td>
<td class="left">HAN</td>
<td class="left">Valle de México</td>
<td class="right">-99.084</td>
<td class="right">19.421</td>
<td class="right">2,234</td>
<td class="right">2003-01-01</td>
<td class="right">2006-05-31</td>
<td class="right">1,200</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">53</td>
<td class="left">simat</td>
<td class="left">HGM</td>
<td class="left">Valle de México</td>
<td class="right">-99.152</td>
<td class="right">19.412</td>
<td class="right">2,241</td>
<td class="right">2012-01-28</td>
<td class="right">2019-12-31</td>
<td class="right">2,093</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">54</td>
<td class="left">simat</td>
<td class="left">IMP</td>
<td class="left">Valle de México</td>
<td class="right">-99.147</td>
<td class="right">19.488</td>
<td class="right">2,241</td>
<td class="right">2008-01-03</td>
<td class="right">2011-03-13</td>
<td class="right">639</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">55</td>
<td class="left">simat</td>
<td class="left">LAA</td>
<td class="left">Valle de México</td>
<td class="right">-99.147</td>
<td class="right">19.484</td>
<td class="right">2,241</td>
<td class="right">2016-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">1,406</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">56</td>
<td class="left">simat</td>
<td class="left">MER</td>
<td class="left">Valle de México</td>
<td class="right">-99.120</td>
<td class="right">19.425</td>
<td class="right">2,238</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">6,054</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">57</td>
<td class="left">simat</td>
<td class="left">MGH</td>
<td class="left">Valle de México</td>
<td class="right">-99.203</td>
<td class="right">19.404</td>
<td class="right">2,327</td>
<td class="right">2015-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">1,815</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">58</td>
<td class="left">simat</td>
<td class="left">MON</td>
<td class="left">Valle de México</td>
<td class="right">-98.903</td>
<td class="right">19.460</td>
<td class="right">2,246</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">4,759</td>
<td class="left">Semi-permanent irrigation agriculture</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">59</td>
<td class="left">simat</td>
<td class="left">MPA</td>
<td class="left">Valle de México</td>
<td class="right">-98.990</td>
<td class="right">19.177</td>
<td class="right">2,582</td>
<td class="right">2016-01-20</td>
<td class="right">2019-12-31</td>
<td class="right">1,317</td>
<td class="left">Annual and permanent rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">60</td>
<td class="left">simat</td>
<td class="left">NEZ</td>
<td class="left">Valle de México</td>
<td class="right">-99.028</td>
<td class="right">19.394</td>
<td class="right">2,234</td>
<td class="right">2011-07-01</td>
<td class="right">2019-12-31</td>
<td class="right">2,495</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">61</td>
<td class="left">simat</td>
<td class="left">PED</td>
<td class="left">Valle de México</td>
<td class="right">-99.204</td>
<td class="right">19.325</td>
<td class="right">2,346</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">5,542</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">62</td>
<td class="left">simat</td>
<td class="left">PLA</td>
<td class="left">Valle de México</td>
<td class="right">-99.200</td>
<td class="right">19.366</td>
<td class="right">2,320</td>
<td class="right">2003-01-01</td>
<td class="right">2010-12-31</td>
<td class="right">2,833</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">63</td>
<td class="left">simat</td>
<td class="left">SAC</td>
<td class="left">Valle de México</td>
<td class="right">-99.009</td>
<td class="right">19.346</td>
<td class="right">2,286</td>
<td class="right">2019-03-01</td>
<td class="right">2019-12-31</td>
<td class="right">304</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">64</td>
<td class="left">simat</td>
<td class="left">SAG</td>
<td class="left">Valle de México</td>
<td class="right">-99.030</td>
<td class="right">19.533</td>
<td class="right">2,236</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">5,431</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">65</td>
<td class="left">simat</td>
<td class="left">SFE</td>
<td class="left">Valle de México</td>
<td class="right">-99.263</td>
<td class="right">19.357</td>
<td class="right">2,589</td>
<td class="right">2012-02-13</td>
<td class="right">2019-12-31</td>
<td class="right">2,747</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">66</td>
<td class="left">simat</td>
<td class="left">SUR</td>
<td class="left">Valle de México</td>
<td class="right">-99.150</td>
<td class="right">19.314</td>
<td class="right">2,266</td>
<td class="right">2008-08-01</td>
<td class="right">2015-06-24</td>
<td class="right">2,475</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">67</td>
<td class="left">simat</td>
<td class="left">TAC</td>
<td class="left">Valle de México</td>
<td class="right">-99.202</td>
<td class="right">19.454</td>
<td class="right">2,261</td>
<td class="right">2003-01-01</td>
<td class="right">2010-12-31</td>
<td class="right">2,344</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">68</td>
<td class="left">simat</td>
<td class="left">TAH</td>
<td class="left">Valle de México</td>
<td class="right">-99.011</td>
<td class="right">19.246</td>
<td class="right">2,287</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">5,215</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">69</td>
<td class="left">simat</td>
<td class="left">TLA</td>
<td class="left">Valle de México</td>
<td class="right">-99.205</td>
<td class="right">19.529</td>
<td class="right">2,285</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">5,551</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">70</td>
<td class="left">simat</td>
<td class="left">TPN</td>
<td class="left">Valle de México</td>
<td class="right">-99.184</td>
<td class="right">19.257</td>
<td class="right">2,506</td>
<td class="right">2003-02-01</td>
<td class="right">2015-02-22</td>
<td class="right">2,970</td>
<td class="left">Oak forest</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">71</td>
<td class="left">simat</td>
<td class="left">UAX</td>
<td class="left">Valle de México</td>
<td class="right">-99.104</td>
<td class="right">19.304</td>
<td class="right">2,238</td>
<td class="right">2015-04-01</td>
<td class="right">2019-12-31</td>
<td class="right">1,680</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">72</td>
<td class="left">simat</td>
<td class="left">UIZ</td>
<td class="left">Valle de México</td>
<td class="right">-99.074</td>
<td class="right">19.361</td>
<td class="right">2,239</td>
<td class="right">2015-04-01</td>
<td class="right">2019-12-31</td>
<td class="right">1,565</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">73</td>
<td class="left">simat</td>
<td class="left">VIF</td>
<td class="left">Valle de México</td>
<td class="right">-99.097</td>
<td class="right">19.658</td>
<td class="right">2,245</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">5,620</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">74</td>
<td class="left">simat</td>
<td class="left">XAL</td>
<td class="left">Valle de México</td>
<td class="right">-99.082</td>
<td class="right">19.526</td>
<td class="right">2,245</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">5,487</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">75</td>
<td class="left">smno</td>
<td class="left">13022</td>
<td class="left">Pachuca</td>
<td class="right">-98.748</td>
<td class="right">20.128</td>
<td class="right">2,437</td>
<td class="right">2006-09-12</td>
<td class="right">2016-12-31</td>
<td class="right">635</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">76</td>
<td class="left">smno</td>
<td class="left">13041</td>
<td class="left">Tulancingo</td>
<td class="right">-98.357</td>
<td class="right">20.084</td>
<td class="right">2,203</td>
<td class="right">2003-02-03</td>
<td class="right">2010-02-28</td>
<td class="right">749</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">77</td>
<td class="left">smno</td>
<td class="left">15126</td>
<td class="left">Toluca</td>
<td class="right">-99.714</td>
<td class="right">19.291</td>
<td class="right">2,726</td>
<td class="right">2003-01-01</td>
<td class="right">2009-07-31</td>
<td class="right">2,114</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">78</td>
<td class="left">smno</td>
<td class="left">17067</td>
<td class="left">Cuernavaca</td>
<td class="right">-99.233</td>
<td class="right">18.892</td>
<td class="right">1,391</td>
<td class="right">2003-01-01</td>
<td class="right">2010-06-30</td>
<td class="right">1,884</td>
<td class="left">Human settlements</td>
<td class="left">Semi-warm subhumid</td>
</tr>

<tr>
<td class="right">79</td>
<td class="left">smno</td>
<td class="left">21065</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.167</td>
<td class="right">19.050</td>
<td class="right">2,178</td>
<td class="right">2003-01-01</td>
<td class="right">2010-01-31</td>
<td class="right">2,455</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">80</td>
<td class="left">smno</td>
<td class="left">21120</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.201</td>
<td class="right">18.996</td>
<td class="right">2,138</td>
<td class="right">2003-01-01</td>
<td class="right">2004-08-31</td>
<td class="right">555</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">81</td>
<td class="left">smno</td>
<td class="left">22013</td>
<td class="left">&#xa0;</td>
<td class="right">-100.400</td>
<td class="right">20.583</td>
<td class="right">1,820</td>
<td class="right">2003-01-01</td>
<td class="right">2010-03-31</td>
<td class="right">1,968</td>
<td class="left">Human settlements</td>
<td class="left">Semi-dry semi-warm</td>
</tr>

<tr>
<td class="right">82</td>
<td class="left">smno</td>
<td class="left">29031</td>
<td class="left">Tlaxcala-Apizaco</td>
<td class="right">-98.244</td>
<td class="right">19.312</td>
<td class="right">2,280</td>
<td class="right">2003-01-01</td>
<td class="right">2010-07-31</td>
<td class="right">1,722</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">83</td>
<td class="left">smno</td>
<td class="left">9048</td>
<td class="left">Valle de México</td>
<td class="right">-99.196</td>
<td class="right">19.404</td>
<td class="right">2,300</td>
<td class="right">2003-01-01</td>
<td class="right">2018-12-31</td>
<td class="right">5,258</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">84</td>
<td class="left">unam</td>
<td class="left">CCA</td>
<td class="left">Valle de México</td>
<td class="right">-99.176</td>
<td class="right">19.326</td>
<td class="right">2,279</td>
<td class="right">2008-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">3,714</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">85</td>
<td class="left">unam</td>
<td class="left">CCHA</td>
<td class="left">Valle de México</td>
<td class="right">-99.204</td>
<td class="right">19.500</td>
<td class="right">2,256</td>
<td class="right">2003-01-28</td>
<td class="right">2019-12-31</td>
<td class="right">5,668</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">86</td>
<td class="left">unam</td>
<td class="left">CCHN</td>
<td class="left">Valle de México</td>
<td class="right">-99.246</td>
<td class="right">19.474</td>
<td class="right">2,337</td>
<td class="right">2004-07-10</td>
<td class="right">2019-12-31</td>
<td class="right">5,480</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">87</td>
<td class="left">unam</td>
<td class="left">CCHO</td>
<td class="left">Valle de México</td>
<td class="right">-99.060</td>
<td class="right">19.384</td>
<td class="right">2,238</td>
<td class="right">2003-01-01</td>
<td class="right">2019-11-10</td>
<td class="right">5,384</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">88</td>
<td class="left">unam</td>
<td class="left">CCHS</td>
<td class="left">Valle de México</td>
<td class="right">-99.199</td>
<td class="right">19.312</td>
<td class="right">2,350</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">5,620</td>
<td class="left">Sarcocaul shrubland</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">89</td>
<td class="left">unam</td>
<td class="left">CCHV</td>
<td class="left">Valle de México</td>
<td class="right">-99.141</td>
<td class="right">19.484</td>
<td class="right">2,241</td>
<td class="right">2004-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">4,817</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">90</td>
<td class="left">unam</td>
<td class="left">ENP1</td>
<td class="left">Valle de México</td>
<td class="right">-99.122</td>
<td class="right">19.271</td>
<td class="right">2,242</td>
<td class="right">2003-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">6,070</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">91</td>
<td class="left">unam</td>
<td class="left">ENP2</td>
<td class="left">Valle de México</td>
<td class="right">-99.100</td>
<td class="right">19.384</td>
<td class="right">2,236</td>
<td class="right">2005-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">3,592</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">92</td>
<td class="left">unam</td>
<td class="left">ENP3</td>
<td class="left">Valle de México</td>
<td class="right">-99.095</td>
<td class="right">19.482</td>
<td class="right">2,239</td>
<td class="right">2003-01-03</td>
<td class="right">2019-12-31</td>
<td class="right">5,866</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">93</td>
<td class="left">unam</td>
<td class="left">ENP5</td>
<td class="left">Valle de México</td>
<td class="right">-99.133</td>
<td class="right">19.307</td>
<td class="right">2,244</td>
<td class="right">2009-01-01</td>
<td class="right">2019-04-30</td>
<td class="right">2,853</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">94</td>
<td class="left">unam</td>
<td class="left">ENP6</td>
<td class="left">Valle de México</td>
<td class="right">-99.156</td>
<td class="right">19.351</td>
<td class="right">2,252</td>
<td class="right">2010-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">2,963</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">95</td>
<td class="left">unam</td>
<td class="left">ENP7</td>
<td class="left">Valle de México</td>
<td class="right">-99.127</td>
<td class="right">19.420</td>
<td class="right">2,236</td>
<td class="right">2003-01-01</td>
<td class="right">2019-09-04</td>
<td class="right">5,797</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">96</td>
<td class="left">unam</td>
<td class="left">ENP8</td>
<td class="left">Valle de México</td>
<td class="right">-99.195</td>
<td class="right">19.366</td>
<td class="right">2,303</td>
<td class="right">2003-01-01</td>
<td class="right">2019-09-30</td>
<td class="right">5,929</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">97</td>
<td class="left">wunderground</td>
<td class="left">012</td>
<td class="left">&#xa0;</td>
<td class="right">-98.336</td>
<td class="right">19.706</td>
<td class="right">2,642</td>
<td class="right">2018-08-27</td>
<td class="right">2019-11-28</td>
<td class="right">341</td>
<td class="left">Annual and permanent rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">98</td>
<td class="left">wunderground</td>
<td class="left">013</td>
<td class="left">&#xa0;</td>
<td class="right">-99.049</td>
<td class="right">20.226</td>
<td class="right">2,000</td>
<td class="right">2018-11-08</td>
<td class="right">2019-08-12</td>
<td class="right">256</td>
<td class="left">Annual and semi-permanent irrigation agriculture</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">99</td>
<td class="left">wunderground</td>
<td class="left">060</td>
<td class="left">Valle de México</td>
<td class="right">-99.253</td>
<td class="right">19.398</td>
<td class="right">2,492</td>
<td class="right">2015-04-20</td>
<td class="right">2019-12-31</td>
<td class="right">1,573</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">100</td>
<td class="left">wunderground</td>
<td class="left">081</td>
<td class="left">Valle de México</td>
<td class="right">-99.237</td>
<td class="right">19.534</td>
<td class="right">2,279</td>
<td class="right">2018-08-16</td>
<td class="right">2019-12-31</td>
<td class="right">492</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">101</td>
<td class="left">wunderground</td>
<td class="left">087</td>
<td class="left">Valle de México</td>
<td class="right">-99.098</td>
<td class="right">19.613</td>
<td class="right">2,362</td>
<td class="right">2018-08-01</td>
<td class="right">2019-12-24</td>
<td class="right">90</td>
<td class="left">Human-induced grassland</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">102</td>
<td class="left">wunderground</td>
<td class="left">089</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.266</td>
<td class="right">19.140</td>
<td class="right">2,190</td>
<td class="right">2019-02-17</td>
<td class="right">2019-12-31</td>
<td class="right">294</td>
<td class="left">Annual irrigation agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">103</td>
<td class="left">wunderground</td>
<td class="left">121</td>
<td class="left">Cuautla</td>
<td class="right">-98.945</td>
<td class="right">18.916</td>
<td class="right">1,449</td>
<td class="right">2018-04-12</td>
<td class="right">2019-09-07</td>
<td class="right">294</td>
<td class="left">Human settlements</td>
<td class="left">Semi-warm subhumid</td>
</tr>

<tr>
<td class="right">104</td>
<td class="left">wunderground</td>
<td class="left">173</td>
<td class="left">&#xa0;</td>
<td class="right">-97.852</td>
<td class="right">18.471</td>
<td class="right">1,862</td>
<td class="right">2017-10-11</td>
<td class="right">2019-12-31</td>
<td class="right">735</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">105</td>
<td class="left">wunderground</td>
<td class="left">201</td>
<td class="left">Toluca</td>
<td class="right">-99.502</td>
<td class="right">19.292</td>
<td class="right">2,574</td>
<td class="right">2018-01-01</td>
<td class="right">2019-11-04</td>
<td class="right">478</td>
<td class="left">Annual humidity based agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">106</td>
<td class="left">wunderground</td>
<td class="left">224</td>
<td class="left">Valle de México</td>
<td class="right">-99.155</td>
<td class="right">19.170</td>
<td class="right">2,861</td>
<td class="right">2019-02-19</td>
<td class="right">2019-12-31</td>
<td class="right">185</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">107</td>
<td class="left">wunderground</td>
<td class="left">226</td>
<td class="left">Valle de México</td>
<td class="right">-99.229</td>
<td class="right">19.324</td>
<td class="right">2,430</td>
<td class="right">2018-11-14</td>
<td class="right">2019-12-31</td>
<td class="right">257</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">108</td>
<td class="left">wunderground</td>
<td class="left">227</td>
<td class="left">Valle de México</td>
<td class="right">-99.123</td>
<td class="right">19.325</td>
<td class="right">2,241</td>
<td class="right">2019-04-21</td>
<td class="right">2019-12-31</td>
<td class="right">226</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">109</td>
<td class="left">wunderground</td>
<td class="left">228</td>
<td class="left">Valle de México</td>
<td class="right">-99.134</td>
<td class="right">19.336</td>
<td class="right">2,242</td>
<td class="right">2018-11-12</td>
<td class="right">2019-12-31</td>
<td class="right">366</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">110</td>
<td class="left">wunderground</td>
<td class="left">229</td>
<td class="left">Valle de México</td>
<td class="right">-99.186</td>
<td class="right">19.289</td>
<td class="right">2,341</td>
<td class="right">2018-01-10</td>
<td class="right">2019-12-01</td>
<td class="right">626</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">111</td>
<td class="left">wunderground</td>
<td class="left">230</td>
<td class="left">Valle de México</td>
<td class="right">-99.133</td>
<td class="right">19.512</td>
<td class="right">2,243</td>
<td class="right">2018-01-13</td>
<td class="right">2019-12-31</td>
<td class="right">703</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">112</td>
<td class="left">wunderground</td>
<td class="left">231</td>
<td class="left">Valle de México</td>
<td class="right">-99.205</td>
<td class="right">19.359</td>
<td class="right">2,343</td>
<td class="right">2018-11-04</td>
<td class="right">2019-12-31</td>
<td class="right">412</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">113</td>
<td class="left">wunderground</td>
<td class="left">232</td>
<td class="left">Valle de México</td>
<td class="right">-99.279</td>
<td class="right">19.334</td>
<td class="right">2,694</td>
<td class="right">2018-05-30</td>
<td class="right">2019-12-31</td>
<td class="right">576</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">114</td>
<td class="left">wunderground</td>
<td class="left">233</td>
<td class="left">Valle de México</td>
<td class="right">-99.197</td>
<td class="right">19.344</td>
<td class="right">2,316</td>
<td class="right">2017-07-26</td>
<td class="right">2019-12-31</td>
<td class="right">739</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">115</td>
<td class="left">wunderground</td>
<td class="left">234</td>
<td class="left">Valle de México</td>
<td class="right">-99.198</td>
<td class="right">19.336</td>
<td class="right">2,312</td>
<td class="right">2018-12-11</td>
<td class="right">2019-12-24</td>
<td class="right">314</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">116</td>
<td class="left">wunderground</td>
<td class="left">236</td>
<td class="left">Valle de México</td>
<td class="right">-99.211</td>
<td class="right">19.420</td>
<td class="right">2,308</td>
<td class="right">2016-10-08</td>
<td class="right">2019-09-17</td>
<td class="right">1,052</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">117</td>
<td class="left">wunderground</td>
<td class="left">253</td>
<td class="left">Valle de México</td>
<td class="right">-99.263</td>
<td class="right">19.422</td>
<td class="right">2,422</td>
<td class="right">2019-01-19</td>
<td class="right">2019-12-31</td>
<td class="right">282</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">118</td>
<td class="left">wunderground</td>
<td class="left">279</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.247</td>
<td class="right">19.068</td>
<td class="right">2,125</td>
<td class="right">2017-04-14</td>
<td class="right">2019-12-31</td>
<td class="right">944</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">119</td>
<td class="left">wunderground</td>
<td class="left">280</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.166</td>
<td class="right">19.080</td>
<td class="right">2,229</td>
<td class="right">2018-05-02</td>
<td class="right">2019-12-31</td>
<td class="right">555</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">120</td>
<td class="left">wunderground</td>
<td class="left">281</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.245</td>
<td class="right">18.993</td>
<td class="right">2,118</td>
<td class="right">2018-05-22</td>
<td class="right">2019-12-31</td>
<td class="right">428</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">121</td>
<td class="left">wunderground</td>
<td class="left">282</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.264</td>
<td class="right">19.065</td>
<td class="right">2,137</td>
<td class="right">2018-06-07</td>
<td class="right">2019-12-31</td>
<td class="right">565</td>
<td class="left">Annual and semi-permanent irrigation agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">122</td>
<td class="left">wunderground</td>
<td class="left">283</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.219</td>
<td class="right">19.085</td>
<td class="right">2,158</td>
<td class="right">2018-06-07</td>
<td class="right">2019-12-31</td>
<td class="right">560</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">123</td>
<td class="left">wunderground</td>
<td class="left">284</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.241</td>
<td class="right">19.044</td>
<td class="right">2,119</td>
<td class="right">2019-04-09</td>
<td class="right">2019-11-14</td>
<td class="right">177</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">124</td>
<td class="left">wunderground</td>
<td class="left">332</td>
<td class="left">&#xa0;</td>
<td class="right">-99.724</td>
<td class="right">20.571</td>
<td class="right">1,791</td>
<td class="right">2018-08-12</td>
<td class="right">2019-12-31</td>
<td class="right">387</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Semi-dry semi-warm</td>
</tr>

<tr>
<td class="right">125</td>
<td class="left">wunderground</td>
<td class="left">333</td>
<td class="left">&#xa0;</td>
<td class="right">-97.919</td>
<td class="right">18.939</td>
<td class="right">2,200</td>
<td class="right">2018-10-28</td>
<td class="right">2019-12-31</td>
<td class="right">290</td>
<td class="left">Annual and semi-permanent irrigation agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">126</td>
<td class="left">wunderground</td>
<td class="left">349</td>
<td class="left">&#xa0;</td>
<td class="right">-100.126</td>
<td class="right">19.152</td>
<td class="right">1,999</td>
<td class="right">2019-01-04</td>
<td class="right">2019-12-18</td>
<td class="right">277</td>
<td class="left">Pine forest</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">127</td>
<td class="left">wunderground</td>
<td class="left">444</td>
<td class="left">Valle de México</td>
<td class="right">-99.187</td>
<td class="right">19.247</td>
<td class="right">2,631</td>
<td class="right">2010-02-19</td>
<td class="right">2019-12-31</td>
<td class="right">3,444</td>
<td class="left">Oak forest</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">128</td>
<td class="left">wunderground</td>
<td class="left">446</td>
<td class="left">Valle de México</td>
<td class="right">-99.200</td>
<td class="right">19.429</td>
<td class="right">2,276</td>
<td class="right">2015-02-16</td>
<td class="right">2019-12-31</td>
<td class="right">1,722</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">129</td>
<td class="left">wunderground</td>
<td class="left">447</td>
<td class="left">Valle de México</td>
<td class="right">-99.206</td>
<td class="right">19.329</td>
<td class="right">2,343</td>
<td class="right">2016-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">1,298</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">130</td>
<td class="left">wunderground</td>
<td class="left">450</td>
<td class="left">Valle de México</td>
<td class="right">-98.741</td>
<td class="right">19.127</td>
<td class="right">2,542</td>
<td class="right">2016-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">913</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">131</td>
<td class="left">wunderground</td>
<td class="left">451</td>
<td class="left">&#xa0;</td>
<td class="right">-100.127</td>
<td class="right">19.184</td>
<td class="right">1,804</td>
<td class="right">2014-09-08</td>
<td class="right">2019-12-31</td>
<td class="right">1,291</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">132</td>
<td class="left">wunderground</td>
<td class="left">452</td>
<td class="left">&#xa0;</td>
<td class="right">-100.127</td>
<td class="right">19.184</td>
<td class="right">1,802</td>
<td class="right">2015-02-27</td>
<td class="right">2019-06-06</td>
<td class="right">1,277</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">133</td>
<td class="left">wunderground</td>
<td class="left">453</td>
<td class="left">Valle de México</td>
<td class="right">-98.847</td>
<td class="right">19.531</td>
<td class="right">2,272</td>
<td class="right">2012-02-24</td>
<td class="right">2019-12-31</td>
<td class="right">2,679</td>
<td class="left">Annual and semi-permanent irrigation agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">134</td>
<td class="left">wunderground</td>
<td class="left">461</td>
<td class="left">&#xa0;</td>
<td class="right">-99.332</td>
<td class="right">18.308</td>
<td class="right">956</td>
<td class="right">2014-09-14</td>
<td class="right">2019-12-20</td>
<td class="right">1,810</td>
<td class="left">Human settlements</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">135</td>
<td class="left">wunderground</td>
<td class="left">462</td>
<td class="left">&#xa0;</td>
<td class="right">-99.538</td>
<td class="right">18.345</td>
<td class="right">744</td>
<td class="right">2008-09-14</td>
<td class="right">2019-12-20</td>
<td class="right">1,752</td>
<td class="left">Human settlements</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">136</td>
<td class="left">wunderground</td>
<td class="left">475</td>
<td class="left">Cuernavaca</td>
<td class="right">-99.243</td>
<td class="right">18.978</td>
<td class="right">1,844</td>
<td class="right">2016-01-01</td>
<td class="right">2019-12-31</td>
<td class="right">1,384</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">137</td>
<td class="left">wunderground</td>
<td class="left">476</td>
<td class="left">Cuernavaca</td>
<td class="right">-99.229</td>
<td class="right">18.952</td>
<td class="right">1,660</td>
<td class="right">2016-06-20</td>
<td class="right">2019-12-31</td>
<td class="right">936</td>
<td class="left">Human settlements</td>
<td class="left">Semi-warm subhumid</td>
</tr>

<tr>
<td class="right">138</td>
<td class="left">wunderground</td>
<td class="left">477</td>
<td class="left">Cuernavaca</td>
<td class="right">-99.234</td>
<td class="right">18.839</td>
<td class="right">1,243</td>
<td class="right">2008-04-05</td>
<td class="right">2019-12-31</td>
<td class="right">1,987</td>
<td class="left">Human settlements</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">139</td>
<td class="left">wunderground</td>
<td class="left">478</td>
<td class="left">&#xa0;</td>
<td class="right">-99.168</td>
<td class="right">18.625</td>
<td class="right">906</td>
<td class="right">2015-07-21</td>
<td class="right">2019-06-26</td>
<td class="right">698</td>
<td class="left">Human settlements</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">140</td>
<td class="left">wunderground</td>
<td class="left">494</td>
<td class="left">&#xa0;</td>
<td class="right">-97.594</td>
<td class="right">19.496</td>
<td class="right">2,352</td>
<td class="right">2015-03-30</td>
<td class="right">2019-12-31</td>
<td class="right">1,646</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">141</td>
<td class="left">wunderground</td>
<td class="left">495</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.221</td>
<td class="right">18.978</td>
<td class="right">2,104</td>
<td class="right">2013-09-05</td>
<td class="right">2019-12-31</td>
<td class="right">1,266</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">142</td>
<td class="left">wunderground</td>
<td class="left">496</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.194</td>
<td class="right">19.013</td>
<td class="right">2,139</td>
<td class="right">2013-04-30</td>
<td class="right">2019-12-31</td>
<td class="right">2,107</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">143</td>
<td class="left">wunderground</td>
<td class="left">497</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.140</td>
<td class="right">19.076</td>
<td class="right">2,255</td>
<td class="right">2013-09-04</td>
<td class="right">2019-12-31</td>
<td class="right">1,486</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">144</td>
<td class="left">wunderground</td>
<td class="left">498</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.184</td>
<td class="right">19.003</td>
<td class="right">2,137</td>
<td class="right">2017-10-05</td>
<td class="right">2019-11-17</td>
<td class="right">668</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">145</td>
<td class="left">wunderground</td>
<td class="left">499</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.196</td>
<td class="right">19.044</td>
<td class="right">2,152</td>
<td class="right">2008-12-04</td>
<td class="right">2019-12-05</td>
<td class="right">3,709</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">146</td>
<td class="left">wunderground</td>
<td class="left">500</td>
<td class="left">&#xa0;</td>
<td class="right">-97.416</td>
<td class="right">18.490</td>
<td class="right">1,674</td>
<td class="right">2012-05-17</td>
<td class="right">2019-12-31</td>
<td class="right">2,502</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Semi-dry semi-warm</td>
</tr>

<tr>
<td class="right">147</td>
<td class="left">wunderground</td>
<td class="left">505</td>
<td class="left">&#xa0;</td>
<td class="right">-100.356</td>
<td class="right">20.479</td>
<td class="right">2,005</td>
<td class="right">2006-08-17</td>
<td class="right">2019-12-31</td>
<td class="right">3,981</td>
<td class="left">Annual irrigation agriculture</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">148</td>
<td class="left">wunderground</td>
<td class="left">506</td>
<td class="left">&#xa0;</td>
<td class="right">-100.270</td>
<td class="right">20.370</td>
<td class="right">2,287</td>
<td class="right">2006-07-21</td>
<td class="right">2019-12-31</td>
<td class="right">4,001</td>
<td class="left">Human-induced grassland</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">149</td>
<td class="left">wunderground</td>
<td class="left">507</td>
<td class="left">&#xa0;</td>
<td class="right">-100.144</td>
<td class="right">20.503</td>
<td class="right">1,920</td>
<td class="right">2006-06-29</td>
<td class="right">2019-12-31</td>
<td class="right">4,483</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">150</td>
<td class="left">wunderground</td>
<td class="left">509</td>
<td class="left">&#xa0;</td>
<td class="right">-100.350</td>
<td class="right">20.578</td>
<td class="right">1,999</td>
<td class="right">2006-07-14</td>
<td class="right">2019-12-31</td>
<td class="right">4,004</td>
<td class="left">Secondary (tree type) vegetation of dry broadleaf forest</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">151</td>
<td class="left">wunderground</td>
<td class="left">510</td>
<td class="left">&#xa0;</td>
<td class="right">-100.212</td>
<td class="right">20.534</td>
<td class="right">1,925</td>
<td class="right">2006-10-04</td>
<td class="right">2019-12-31</td>
<td class="right">3,774</td>
<td class="left">Annual and semi-permanent irrigation agriculture</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">152</td>
<td class="left">wunderground</td>
<td class="left">515</td>
<td class="left">&#xa0;</td>
<td class="right">-99.988</td>
<td class="right">20.384</td>
<td class="right">1,945</td>
<td class="right">2007-04-19</td>
<td class="right">2019-12-31</td>
<td class="right">3,505</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">153</td>
<td class="left">wunderground</td>
<td class="left">516</td>
<td class="left">&#xa0;</td>
<td class="right">-100.002</td>
<td class="right">20.370</td>
<td class="right">1,940</td>
<td class="right">2007-04-19</td>
<td class="right">2019-12-26</td>
<td class="right">3,733</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">154</td>
<td class="left">wunderground</td>
<td class="left">562</td>
<td class="left">Toluca</td>
<td class="right">-99.551</td>
<td class="right">19.227</td>
<td class="right">2,583</td>
<td class="right">2014-07-30</td>
<td class="right">2019-12-26</td>
<td class="right">522</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">155</td>
<td class="left">wunderground</td>
<td class="left">563</td>
<td class="left">&#xa0;</td>
<td class="right">-97.920</td>
<td class="right">19.341</td>
<td class="right">2,465</td>
<td class="right">2012-11-22</td>
<td class="right">2019-12-31</td>
<td class="right">2,330</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">156</td>
<td class="left">wunderground</td>
<td class="left">805</td>
<td class="left">&#xa0;</td>
<td class="right">-97.688</td>
<td class="right">19.464</td>
<td class="right">2,393</td>
<td class="right">2019-06-06</td>
<td class="right">2019-12-26</td>
<td class="right">126</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">157</td>
<td class="left">wunderground</td>
<td class="left">817</td>
<td class="left">&#xa0;</td>
<td class="right">-100.242</td>
<td class="right">20.565</td>
<td class="right">1,915</td>
<td class="right">2019-05-18</td>
<td class="right">2019-12-31</td>
<td class="right">197</td>
<td class="left">Annual and semi-permanent irrigation agriculture</td>
<td class="left">Temperate semi-dry</td>
</tr>

<tr>
<td class="right">158</td>
<td class="left">wunderground</td>
<td class="left">832</td>
<td class="left">&#xa0;</td>
<td class="right">-99.260</td>
<td class="right">18.592</td>
<td class="right">970</td>
<td class="right">2019-06-04</td>
<td class="right">2019-12-31</td>
<td class="right">191</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">159</td>
<td class="left">wunderground</td>
<td class="left">844</td>
<td class="left">Valle de México</td>
<td class="right">-99.201</td>
<td class="right">19.323</td>
<td class="right">2,342</td>
<td class="right">2017-09-25</td>
<td class="right">2019-12-31</td>
<td class="right">711</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">160</td>
<td class="left">wunderground</td>
<td class="left">862</td>
<td class="left">Valle de México</td>
<td class="right">-99.268</td>
<td class="right">19.476</td>
<td class="right">2,419</td>
<td class="right">2017-03-12</td>
<td class="right">2019-12-15</td>
<td class="right">645</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">161</td>
<td class="left">wunderground</td>
<td class="left">864</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.220</td>
<td class="right">19.128</td>
<td class="right">2,188</td>
<td class="right">2019-01-30</td>
<td class="right">2019-07-14</td>
<td class="right">80</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">162</td>
<td class="left">wunderground</td>
<td class="left">876</td>
<td class="left">Valle de México</td>
<td class="right">-99.334</td>
<td class="right">19.405</td>
<td class="right">2,661</td>
<td class="right">2019-05-22</td>
<td class="right">2019-10-25</td>
<td class="right">141</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">163</td>
<td class="left">wunderground</td>
<td class="left">886</td>
<td class="left">&#xa0;</td>
<td class="right">-98.265</td>
<td class="right">18.275</td>
<td class="right">1,133</td>
<td class="right">2019-05-10</td>
<td class="right">2019-12-31</td>
<td class="right">211</td>
<td class="left">Annual rainfed agriculture</td>
<td class="left">Warm subhumid</td>
</tr>

<tr>
<td class="right">164</td>
<td class="left">wunderground</td>
<td class="left">906</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">-98.232</td>
<td class="right">19.022</td>
<td class="right">2,105</td>
<td class="right">2017-07-05</td>
<td class="right">2019-12-02</td>
<td class="right">589</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">165</td>
<td class="left">wunderground</td>
<td class="left">910</td>
<td class="left">&#xa0;</td>
<td class="right">-100.142</td>
<td class="right">20.188</td>
<td class="right">2,623</td>
<td class="right">2006-06-20</td>
<td class="right">2019-12-31</td>
<td class="right">3,929</td>
<td class="left">Human settlements</td>
<td class="left">Temperate subhumid</td>
</tr>

<tr>
<td class="right">166</td>
<td class="left">wunderground</td>
<td class="left">914</td>
<td class="left">&#xa0;</td>
<td class="right">-99.885</td>
<td class="right">20.520</td>
<td class="right">1,878</td>
<td class="right">2006-06-29</td>
<td class="right">2019-12-31</td>
<td class="right">4,456</td>
<td class="left">Human settlements</td>
<td class="left">Temperate semi-dry</td>
</tr>
</tbody>
</table>

<div class="org-src-container">
<pre class="src src-R">db = dbConnect(SQLite(), stpath(<span style="background-color: #ffefd5;">"wunderground-daily-mexico.sqlite"</span>))
dbGetQuery(db, sprintf(
    <span style="background-color: #ffefd5;">"select station_id, surface_type, neighborhood, station_type, software</span>
<span style="background-color: #ffefd5;">        from Stations</span>
<span style="background-color: #ffefd5;">        where stn in (%s)"</span>,
    paste(collapse = <span style="background-color: #ffefd5;">", "</span>, get.ground()$stations[
        network == <span style="background-color: #ffefd5;">"wunderground"</span>,
        stringr::str_extract(name, <span style="background-color: #ffefd5;">"\\d+"</span>)])))
</pre>
</div>

<table>


<colgroup>
<col  class="right">

<col  class="left">

<col  class="left">

<col  class="left">

<col  class="left">

<col  class="left">
</colgroup>
<thead>
<tr>
<th scope="col" class="right">&#xa0;</th>
<th scope="col" class="left">station_id</th>
<th scope="col" class="left">surface_type</th>
<th scope="col" class="left">neighborhood</th>
<th scope="col" class="left">station_type</th>
<th scope="col" class="left">software</th>
</tr>
</thead>
<tbody>
<tr>
<td class="right">1</td>
<td class="left">IALMOLOY3</td>
<td class="left">composite-shingles</td>
<td class="left">Santiago Tetlapayac</td>
<td class="left">Davis Vantage Pro2 (Wireless)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">2</td>
<td class="left">IARAMB3</td>
<td class="left">cement</td>
<td class="left">Francisco I. Madero</td>
<td class="left">AcuRite 5-in-1 Weather Station with Wi-Fi</td>
<td class="left">&#xa0;</td>
</tr>

<tr>
<td class="right">3</td>
<td class="left">ICIUDADD99</td>
<td class="left">shrubbery</td>
<td class="left">Bosques de las Lomas</td>
<td class="left">AcuRite Pro Weather Center</td>
<td class="left">myAcuRite</td>
</tr>

<tr>
<td class="right">4</td>
<td class="left">ICIUDADL14</td>
<td class="left">gravel</td>
<td class="left">Vila Deco, bellavista</td>
<td class="left">Ambient Weather WS-2902</td>
<td class="left">AMBWeatherV4.0.2</td>
</tr>

<tr>
<td class="right">5</td>
<td class="left">ICOACALC3</td>
<td class="left">composite-shingles</td>
<td class="left">Maria Auxiliadora</td>
<td class="left">Ambient Weather WS-1400-IP (Wireless)</td>
<td class="left">Weather logger V3.0.5</td>
</tr>

<tr>
<td class="right">6</td>
<td class="left">ICORONAN2</td>
<td class="left">cement</td>
<td class="left">Kuumkumi VWM</td>
<td class="left">Ambient Weather WS-2902</td>
<td class="left">AMBWeatherV4.0.2</td>
</tr>

<tr>
<td class="right">7</td>
<td class="left">IFRACCIO2</td>
<td class="left">grass</td>
<td class="left">Lomas de Cocoyoc</td>
<td class="left">AcuRite Pro Weather Center</td>
<td class="left">myAcuRite</td>
</tr>

<tr>
<td class="right">8</td>
<td class="left">IIXCAQUI2</td>
<td class="left">composite-shingles</td>
<td class="left">Globalmet - Hortioriente</td>
<td class="left">Davis Vantage Pro2 (Wireless)</td>
<td class="left">weewx-3.5.0</td>
</tr>

<tr>
<td class="right">9</td>
<td class="left">ILERMA2</td>
<td class="left">composite-shingles</td>
<td class="left">UAM LERMA</td>
<td class="left">Davis Vantage Pro2 Plus (Cabled)</td>
<td class="left">weewx-3.8.0</td>
</tr>

<tr>
<td class="right">10</td>
<td class="left">IMEXICOC50</td>
<td class="left">rooftop (wood shingles)</td>
<td class="left">Bosque Residencial los Cedros - Empire of Dirt</td>
<td class="left">AcuRite 5-in-1 Weather Station with AcuRite Access</td>
<td class="left">myAcuRite</td>
</tr>

<tr>
<td class="right">11</td>
<td class="left">IMEXICOC47</td>
<td class="left">composite-shingles</td>
<td class="left">San Jeronimo Lidice</td>
<td class="left">Ambient Weather WS-1001-WiFi (Wireless)</td>
<td class="left">WS-1001 V2.2.9</td>
</tr>

<tr>
<td class="right">12</td>
<td class="left">IMEXIC1</td>
<td class="left">rooftop (composite-shingles)</td>
<td class="left">Coyoacán</td>
<td class="left">AcuRite Pro Weather Center</td>
<td class="left">myAcuRite</td>
</tr>

<tr>
<td class="right">13</td>
<td class="left">IMEXICOC46</td>
<td class="left">composite-shingles</td>
<td class="left">TLALOC MIRAMONTES</td>
<td class="left">Ambient Weather WS-2090 (Wireless)</td>
<td class="left">EasyWeather V8.8.0</td>
</tr>

<tr>
<td class="right">14</td>
<td class="left">IMEXICOC34</td>
<td class="left">spanish-tiles</td>
<td class="left">TLALPAN</td>
<td class="left">Davis Vantage Vue (Wireless)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">15</td>
<td class="left">IMEXICOC35</td>
<td class="left">cement</td>
<td class="left">Ticoman</td>
<td class="left">Netatmo Weather Station</td>
<td class="left"><a class="url" href="http://meteoware.com">http://meteoware.com</a></td>
</tr>

<tr>
<td class="right">16</td>
<td class="left">IMEXICOC44</td>
<td class="left">spanish-tiles</td>
<td class="left">Las Aguilas</td>
<td class="left">Ambient Weather WS-2000</td>
<td class="left">AMBWeatherV4.0.2</td>
</tr>

<tr>
<td class="right">17</td>
<td class="left">IMEXICOC40</td>
<td class="left">spanish-tiles</td>
<td class="left">Hacienda Muitles</td>
<td class="left">Ambient Weather WS-2902</td>
<td class="left">AMBWeatherV3.0.3</td>
</tr>

<tr>
<td class="right">18</td>
<td class="left">IMEXICOC29</td>
<td class="left">composite-shingles</td>
<td class="left">Miguel Hidalgo</td>
<td class="left">Netatmo Weather Station</td>
<td class="left">WeatherApp</td>
</tr>

<tr>
<td class="right">19</td>
<td class="left">IMEXICOC48</td>
<td class="left">other</td>
<td class="left">Tizapan San Angel</td>
<td class="left">AcuRite Atlas Weather Station with AcuRite Access</td>
<td class="left">myAcuRite</td>
</tr>

<tr>
<td class="right">20</td>
<td class="left">IMIGUELH4</td>
<td class="left">composite-shingles</td>
<td class="left">Lomas Virreyes</td>
<td class="left">Davis Vantage Pro2 (Wireless)</td>
<td class="left">meteobridge</td>
</tr>

<tr>
<td class="right">21</td>
<td class="left">INAUCALP34</td>
<td class="left">composite-shingles</td>
<td class="left">Balcones de la Herradura</td>
<td class="left">Ambient Weather WS-2902</td>
<td class="left">AMBWeatherV4.0.3</td>
</tr>

<tr>
<td class="right">22</td>
<td class="left">IPUEBLAC5</td>
<td class="left">spanish-tiles</td>
<td class="left">PROTECCION CIVIL MUNICIPAL PUE02</td>
<td class="left">Ambient Weather WS-900-IP (Wireless)</td>
<td class="left">Weather logger V3.1.2</td>
</tr>

<tr>
<td class="right">23</td>
<td class="left">IPUEBLAC11</td>
<td class="left">composite-shingles</td>
<td class="left">Desarenador</td>
<td class="left">Davis Vantage Pro2 Plus (Cabled)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">24</td>
<td class="left">IPUEBLAC13</td>
<td class="left">composite-shingles</td>
<td class="left">Almacen Castillotla</td>
<td class="left">Davis Vantage Pro2 Plus (Cabled)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">25</td>
<td class="left">IPUEBLAC14</td>
<td class="left">composite-shingles</td>
<td class="left">Planta Quetzalcoatl</td>
<td class="left">Davis Vantage Pro2 Plus (Cabled)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">26</td>
<td class="left">IPUEBLAC15</td>
<td class="left">composite-shingles</td>
<td class="left">Planta Sulfurosa</td>
<td class="left">Davis Vantage Pro2 Plus (Cabled)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">27</td>
<td class="left">IPUEBL5</td>
<td class="left">other</td>
<td class="left">Puebla City</td>
<td class="left">other</td>
<td class="left">WH2600GEN_V2.2.5</td>
</tr>

<tr>
<td class="right">28</td>
<td class="left">ITECOZAU2</td>
<td class="left">composite-shingles</td>
<td class="left">Yextho, Tecozautla</td>
<td class="left">AcuRite Pro Weather Center</td>
<td class="left">myAcuRite</td>
</tr>

<tr>
<td class="right">29</td>
<td class="left">ITEPEACA2</td>
<td class="left">grass</td>
<td class="left">Hacienda Santa Ana</td>
<td class="left">Ambient Weather WS-2090 (Wireless)</td>
<td class="left">AMBWeatherV3.0.3</td>
</tr>

<tr>
<td class="right">30</td>
<td class="left">IVALLEDE36</td>
<td class="left">composite-shingles</td>
<td class="left">Avandaro</td>
<td class="left">Ambient Weather WS-2902</td>
<td class="left">&#xa0;</td>
</tr>

<tr>
<td class="right">31</td>
<td class="left">IDFMEXIC11</td>
<td class="left">composite-shingles</td>
<td class="left">Tlalpuente</td>
<td class="left">Davis</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">32</td>
<td class="left">IDISTRIT45</td>
<td class="left">composite-shingles</td>
<td class="left">POLANCO</td>
<td class="left">Davis Vantage Pro2 (Cabled)</td>
<td class="left">Weather logger V3.1.0</td>
</tr>

<tr>
<td class="right">33</td>
<td class="left">IDISTRIT69</td>
<td class="left">cement</td>
<td class="left">Volcan</td>
<td class="left">Netatmo Weather Station</td>
<td class="left">WeatherApp</td>
</tr>

<tr>
<td class="right">34</td>
<td class="left">IESTADOD44</td>
<td class="left">grass</td>
<td class="left">Coapexco</td>
<td class="left">Ambient Weather WS-1400-IP (Wireless)</td>
<td class="left">WH2602 V4.5.8</td>
</tr>

<tr>
<td class="right">35</td>
<td class="left">IESTADOD4</td>
<td class="left">trees</td>
<td class="left">Sta. Maria Ahuacatlan</td>
<td class="left">Davis Vantage Pro2 (Cabled)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">36</td>
<td class="left">IESTADOD6</td>
<td class="left">spanish-tiles</td>
<td class="left">Sta. Maria Ahuacatlan</td>
<td class="left">Netatmo Weather Station</td>
<td class="left"><a class="url" href="http://meteoware.com">http://meteoware.com</a></td>
</tr>

<tr>
<td class="right">37</td>
<td class="left">IESTADOD2</td>
<td class="left">grass</td>
<td class="left">El Batan, CIMMYT</td>
<td class="left">Davis Vantage Pro2 Plus</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>

<tr>
<td class="right">38</td>
<td class="left">IGUERRER8</td>
<td class="left">spanish-tiles</td>
<td class="left">Huitzuco de los Figueroa</td>
<td class="left">Davis Vantage Vue (Wireless)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">39</td>
<td class="left">IJALISCO24</td>
<td class="left">&#xa0;</td>
<td class="left">Iguala</td>
<td class="left">Davis Vantage Pro 2</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">40</td>
<td class="left">IMORELOS8</td>
<td class="left">composite-shingles</td>
<td class="left">Colonia Universidad, Cuernavaca</td>
<td class="left">AcuRite Pro Weather Center</td>
<td class="left">myAcuRite</td>
</tr>

<tr>
<td class="right">41</td>
<td class="left">IMORELOS9</td>
<td class="left">composite-shingles</td>
<td class="left">Cuernavaca SEP</td>
<td class="left">Davis Vantage Vue (Wireless)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">42</td>
<td class="left">IJALISCO19</td>
<td class="left">cement</td>
<td class="left">Temixco Morelos</td>
<td class="left">Davis Vantage Pro2 Plus (Wireless)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">43</td>
<td class="left">IMORELOS7</td>
<td class="left">cement</td>
<td class="left">Observatorio astronomico Urania</td>
<td class="left">Ambient Weather WS-1001-WiFi (Wireless)</td>
<td class="left">WS-1001 V2.2.2</td>
</tr>

<tr>
<td class="right">44</td>
<td class="left">IPUEBLAP18</td>
<td class="left">trees</td>
<td class="left">LAS INFINITAS</td>
<td class="left">Ambient Weather WS-1001-WiFi (Wireless)</td>
<td class="left">WS-1001 V2.1.9</td>
</tr>

<tr>
<td class="right">45</td>
<td class="left">IPUEBLAP12</td>
<td class="left">composite-shingles</td>
<td class="left">Los Heroes</td>
<td class="left">Davis Vantage Pro2 (Cabled)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">46</td>
<td class="left">IPUEBLAP8</td>
<td class="left">composite-shingles</td>
<td class="left">San Manuel</td>
<td class="left">Davis Vantage Pro Plus</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">47</td>
<td class="left">IPUEBLAP9</td>
<td class="left">composite-shingles</td>
<td class="left">Cerro del Marquez</td>
<td class="left">Davis Vantage Pro2 (Cabled)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">48</td>
<td class="left">IPUEBLAP4</td>
<td class="left">&#xa0;</td>
<td class="left">RAMM13 - Lomas del Marmol</td>
<td class="left">Davis Vantage Pro</td>
<td class="left">weewx-3.9.1</td>
</tr>

<tr>
<td class="right">49</td>
<td class="left">IPUEPUEB2</td>
<td class="left">&#xa0;</td>
<td class="left">RAMM12 - Centro Historico</td>
<td class="left">Davis Vantage Pro</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>

<tr>
<td class="right">50</td>
<td class="left">IPUEBLAT6</td>
<td class="left">composite-shingles</td>
<td class="left">Aeropuerto de Tehuacan</td>
<td class="left">Vantage Pro2</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">51</td>
<td class="left">IHUIMILP1</td>
<td class="left">&#xa0;</td>
<td class="left">CEA EL MILAGRO</td>
<td class="left">VANTAGE PRO 2 PLUS</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>

<tr>
<td class="right">52</td>
<td class="left">IQUERETA19</td>
<td class="left">composite-shingles</td>
<td class="left">CEA-HUIMILPAN</td>
<td class="left">VANTAGE PRO 2 PLUS</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>

<tr>
<td class="right">53</td>
<td class="left">IQUERETA10</td>
<td class="left">&#xa0;</td>
<td class="left">CEA-PEDRO ESCOBEDO</td>
<td class="left">VANTAGE PRO 2 PLUS</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>

<tr>
<td class="right">54</td>
<td class="left">IQUERETA17</td>
<td class="left">composite-shingles</td>
<td class="left">CEA-QCC</td>
<td class="left">VANTAGE PRO 2 PLUS</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">55</td>
<td class="left">IQUERETA23</td>
<td class="left">&#xa0;</td>
<td class="left">CEA-TECMTY</td>
<td class="left">VANTAGE PRO PLUS</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>

<tr>
<td class="right">56</td>
<td class="left">IQUERETA13</td>
<td class="left">&#xa0;</td>
<td class="left">CEA-JAPAM</td>
<td class="left">VANTAGE PRO 2 PLUS</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>

<tr>
<td class="right">57</td>
<td class="left">IQUERETA29</td>
<td class="left">composite-shingles</td>
<td class="left">CEA-UNIVERSIDAD TECNOLOGICA</td>
<td class="left">Davis Vantage Pro2 Plus (Cabled)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">58</td>
<td class="left">ISTATEOF3</td>
<td class="left">grass</td>
<td class="left">CIMMYT, Toluca Station, San Sebastian</td>
<td class="left">Davis Vantage Pro2 Plus (Wireless)</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>

<tr>
<td class="right">59</td>
<td class="left">ITLAXCAL3</td>
<td class="left">&#xa0;</td>
<td class="left">Cd Industrial II</td>
<td class="left">Davis Pro</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">60</td>
<td class="left">ICIUDA6</td>
<td class="left">cement</td>
<td class="left">Ciudad De Libres</td>
<td class="left">AcuRite 5-in-1 Weather Station with AcuRite Access</td>
<td class="left">&#xa0;</td>
</tr>

<tr>
<td class="right">61</td>
<td class="left">IGENER13</td>
<td class="left">cement</td>
<td class="left">General Lázaro Cárdenas</td>
<td class="left">AcuRite 5-in-1 Weather Station with AcuRite Access</td>
<td class="left">myAcuRite</td>
</tr>

<tr>
<td class="right">62</td>
<td class="left">IJOJUT4</td>
<td class="left">rooftop (spanish tiles)</td>
<td class="left">Jojutla</td>
<td class="left">Ambient Weather WS-2902</td>
<td class="left">AMBWeatherV4.2.8</td>
</tr>

<tr>
<td class="right">63</td>
<td class="left">IMEXICOC30</td>
<td class="left">cement</td>
<td class="left">Fuentes del Pedregal</td>
<td class="left">Davis Vantage Pro2 Plus (Wireless)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">64</td>
<td class="left">INAUCALP28</td>
<td class="left">cement</td>
<td class="left">Vista del Valle II</td>
<td class="left">Ambient Weather WS-900-IP (Wireless)</td>
<td class="left">EasyWeather V8.8.0</td>
</tr>

<tr>
<td class="right">65</td>
<td class="left">IPUEBLAC19</td>
<td class="left">composite-shingles</td>
<td class="left">Ecofenix</td>
<td class="left">AcuRite 5-in-1 Weather Station with Wi-Fi</td>
<td class="left">&#xa0;</td>
</tr>

<tr>
<td class="right">66</td>
<td class="left">ISANCR8</td>
<td class="left">rooftop (composite-shingles)</td>
<td class="left">San Cristóbal Texcalucan</td>
<td class="left">AcuRite 5-in-1 Weather Station with AcuRite Access</td>
<td class="left">myAcuRite</td>
</tr>

<tr>
<td class="right">67</td>
<td class="left">ITEHUI2</td>
<td class="left">&#xa0;</td>
<td class="left">Tehuitzingo Municipality</td>
<td class="left">Davis Vantage Pro2 (Wireless)</td>
<td class="left">weewx-3.5.0</td>
</tr>

<tr>
<td class="right">68</td>
<td class="left">IPUEBLAP11</td>
<td class="left">composite-shingles</td>
<td class="left">San Francisco</td>
<td class="left">Davis Vantage Pro2 (Cabled)</td>
<td class="left">weatherlink.com 1.10</td>
</tr>

<tr>
<td class="right">69</td>
<td class="left">IAMEALCO2</td>
<td class="left">&#xa0;</td>
<td class="left">CEA -AMEALCO</td>
<td class="left">VANTAGE PRO 2 PLUS</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>

<tr>
<td class="right">70</td>
<td class="left">IQUERETA11</td>
<td class="left">&#xa0;</td>
<td class="left">CEA-TEQUISQUIAPAN</td>
<td class="left">Vantage Pro 2 Plus</td>
<td class="left">Wunderground v.1.15 PWSDec 27 2007</td>
</tr>
</tbody>
</table>
</div>
</div>

<div class="outline-2">
<h2 id="sec--crossvalidation-results">Cross-validation results</h2>
<div class="outline-text-2">
<div class="org-src-container">
<pre class="src src-R">sr = summarize.cv.results(multi.run.cv(available.years))
</pre>
</div>

<p>
Here are RMSE and <var>R</var><sup>2</sup> by year and DV, as well as the proportion of daily Moran's <var>I</var> statistics of the signed error that are significant at <var>α</var> = .05. <code>N</code> and <code>stn</code> denote the number of observations and stations in the megalopolis, for which predictions were made; stations in the larger region, which were only used for training, aren't counted. <code>sd.s</code> and <code>rmse.s</code> are spatially weighted RMSEs, in which each day and each sixteenth of a lon–lat cell are given total weight 1.
</p>

<div class="org-src-container">
<pre class="src src-R"><span style="color: #006400; font-weight: bold;">library</span>(data.table)
as.data.frame(rd(d = 2, sr$overall))
</pre>
</div>

<table id="tab--cv-overall">


<colgroup>
<col  class="right">

<col  class="right">

<col  class="left">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">
</colgroup>
<thead>
<tr>
<th scope="col" class="right">&#xa0;</th>
<th scope="col" class="right">year</th>
<th scope="col" class="left">dv</th>
<th scope="col" class="right">N</th>
<th scope="col" class="right">stn</th>
<th scope="col" class="right">sd</th>
<th scope="col" class="right">rmse</th>
<th scope="col" class="right">R2</th>
<th scope="col" class="right">sd.s</th>
<th scope="col" class="right">rmse.s</th>
<th scope="col" class="right">R2.spatial</th>
<th scope="col" class="right">R2.temporal</th>
<th scope="col" class="right">Moran ps &lt; .05</th>
</tr>
</thead>
<tbody>
<tr>
<td class="right">1</td>
<td class="right">2003</td>
<td class="left">hi</td>
<td class="right">9622</td>
<td class="right">32</td>
<td class="right">4.64</td>
<td class="right">1.35</td>
<td class="right">0.92</td>
<td class="right">6.04</td>
<td class="right">1.82</td>
<td class="right">0.96</td>
<td class="right">0.88</td>
<td class="right">0.14</td>
</tr>

<tr>
<td class="right">2</td>
<td class="right">2003</td>
<td class="left">lo</td>
<td class="right">9622</td>
<td class="right">32</td>
<td class="right">4.08</td>
<td class="right">1.46</td>
<td class="right">0.87</td>
<td class="right">4.85</td>
<td class="right">1.93</td>
<td class="right">0.90</td>
<td class="right">0.85</td>
<td class="right">0.34</td>
</tr>

<tr>
<td class="right">3</td>
<td class="right">2003</td>
<td class="left">mean</td>
<td class="right">9622</td>
<td class="right">32</td>
<td class="right">3.94</td>
<td class="right">0.92</td>
<td class="right">0.95</td>
<td class="right">5.02</td>
<td class="right">1.21</td>
<td class="right">0.97</td>
<td class="right">0.92</td>
<td class="right">0.32</td>
</tr>

<tr>
<td class="right">4</td>
<td class="right">2004</td>
<td class="left">hi</td>
<td class="right">10453</td>
<td class="right">35</td>
<td class="right">4.58</td>
<td class="right">1.35</td>
<td class="right">0.91</td>
<td class="right">6.33</td>
<td class="right">1.76</td>
<td class="right">0.94</td>
<td class="right">0.86</td>
<td class="right">0.11</td>
</tr>

<tr>
<td class="right">5</td>
<td class="right">2004</td>
<td class="left">lo</td>
<td class="right">10453</td>
<td class="right">35</td>
<td class="right">3.92</td>
<td class="right">1.53</td>
<td class="right">0.85</td>
<td class="right">4.94</td>
<td class="right">2.03</td>
<td class="right">0.82</td>
<td class="right">0.84</td>
<td class="right">0.25</td>
</tr>

<tr>
<td class="right">6</td>
<td class="right">2004</td>
<td class="left">mean</td>
<td class="right">10453</td>
<td class="right">35</td>
<td class="right">3.80</td>
<td class="right">1.04</td>
<td class="right">0.92</td>
<td class="right">5.20</td>
<td class="right">1.37</td>
<td class="right">0.93</td>
<td class="right">0.89</td>
<td class="right">0.23</td>
</tr>

<tr>
<td class="right">7</td>
<td class="right">2005</td>
<td class="left">hi</td>
<td class="right">11489</td>
<td class="right">36</td>
<td class="right">4.96</td>
<td class="right">1.48</td>
<td class="right">0.91</td>
<td class="right">6.67</td>
<td class="right">1.87</td>
<td class="right">0.94</td>
<td class="right">0.87</td>
<td class="right">0.04</td>
</tr>

<tr>
<td class="right">8</td>
<td class="right">2005</td>
<td class="left">lo</td>
<td class="right">11489</td>
<td class="right">36</td>
<td class="right">4.02</td>
<td class="right">1.61</td>
<td class="right">0.84</td>
<td class="right">5.10</td>
<td class="right">2.16</td>
<td class="right">0.87</td>
<td class="right">0.82</td>
<td class="right">0.16</td>
</tr>

<tr>
<td class="right">9</td>
<td class="right">2005</td>
<td class="left">mean</td>
<td class="right">11489</td>
<td class="right">36</td>
<td class="right">4.16</td>
<td class="right">1.09</td>
<td class="right">0.93</td>
<td class="right">5.55</td>
<td class="right">1.40</td>
<td class="right">0.95</td>
<td class="right">0.91</td>
<td class="right">0.07</td>
</tr>

<tr>
<td class="right">10</td>
<td class="right">2006</td>
<td class="left">hi</td>
<td class="right">10882</td>
<td class="right">36</td>
<td class="right">4.76</td>
<td class="right">1.47</td>
<td class="right">0.90</td>
<td class="right">6.29</td>
<td class="right">1.84</td>
<td class="right">0.94</td>
<td class="right">0.84</td>
<td class="right">0.04</td>
</tr>

<tr>
<td class="right">11</td>
<td class="right">2006</td>
<td class="left">lo</td>
<td class="right">10882</td>
<td class="right">36</td>
<td class="right">4.04</td>
<td class="right">1.52</td>
<td class="right">0.86</td>
<td class="right">4.94</td>
<td class="right">1.96</td>
<td class="right">0.87</td>
<td class="right">0.84</td>
<td class="right">0.05</td>
</tr>

<tr>
<td class="right">12</td>
<td class="right">2006</td>
<td class="left">mean</td>
<td class="right">10882</td>
<td class="right">36</td>
<td class="right">3.94</td>
<td class="right">1.11</td>
<td class="right">0.92</td>
<td class="right">5.17</td>
<td class="right">1.40</td>
<td class="right">0.95</td>
<td class="right">0.87</td>
<td class="right">0.04</td>
</tr>

<tr>
<td class="right">13</td>
<td class="right">2007</td>
<td class="left">hi</td>
<td class="right">9854</td>
<td class="right">39</td>
<td class="right">4.82</td>
<td class="right">1.47</td>
<td class="right">0.91</td>
<td class="right">6.39</td>
<td class="right">1.88</td>
<td class="right">0.93</td>
<td class="right">0.84</td>
<td class="right">0.07</td>
</tr>

<tr>
<td class="right">14</td>
<td class="right">2007</td>
<td class="left">lo</td>
<td class="right">9854</td>
<td class="right">39</td>
<td class="right">3.90</td>
<td class="right">1.50</td>
<td class="right">0.85</td>
<td class="right">4.85</td>
<td class="right">1.86</td>
<td class="right">0.88</td>
<td class="right">0.79</td>
<td class="right">0.11</td>
</tr>

<tr>
<td class="right">15</td>
<td class="right">2007</td>
<td class="left">mean</td>
<td class="right">9854</td>
<td class="right">39</td>
<td class="right">3.95</td>
<td class="right">1.04</td>
<td class="right">0.93</td>
<td class="right">5.21</td>
<td class="right">1.29</td>
<td class="right">0.94</td>
<td class="right">0.87</td>
<td class="right">0.18</td>
</tr>

<tr>
<td class="right">16</td>
<td class="right">2008</td>
<td class="left">hi</td>
<td class="right">11430</td>
<td class="right">41</td>
<td class="right">4.85</td>
<td class="right">1.59</td>
<td class="right">0.89</td>
<td class="right">6.78</td>
<td class="right">1.96</td>
<td class="right">0.88</td>
<td class="right">0.85</td>
<td class="right">0.31</td>
</tr>

<tr>
<td class="right">17</td>
<td class="right">2008</td>
<td class="left">lo</td>
<td class="right">11430</td>
<td class="right">41</td>
<td class="right">4.12</td>
<td class="right">1.55</td>
<td class="right">0.86</td>
<td class="right">5.13</td>
<td class="right">2.00</td>
<td class="right">0.94</td>
<td class="right">0.82</td>
<td class="right">0.36</td>
</tr>

<tr>
<td class="right">18</td>
<td class="right">2008</td>
<td class="left">mean</td>
<td class="right">11430</td>
<td class="right">41</td>
<td class="right">4.05</td>
<td class="right">1.11</td>
<td class="right">0.92</td>
<td class="right">5.52</td>
<td class="right">1.44</td>
<td class="right">0.96</td>
<td class="right">0.89</td>
<td class="right">0.43</td>
</tr>

<tr>
<td class="right">19</td>
<td class="right">2009</td>
<td class="left">hi</td>
<td class="right">13114</td>
<td class="right">48</td>
<td class="right">4.99</td>
<td class="right">1.69</td>
<td class="right">0.88</td>
<td class="right">7.20</td>
<td class="right">2.12</td>
<td class="right">0.85</td>
<td class="right">0.87</td>
<td class="right">0.28</td>
</tr>

<tr>
<td class="right">20</td>
<td class="right">2009</td>
<td class="left">lo</td>
<td class="right">13114</td>
<td class="right">48</td>
<td class="right">4.02</td>
<td class="right">1.58</td>
<td class="right">0.85</td>
<td class="right">5.33</td>
<td class="right">2.05</td>
<td class="right">0.88</td>
<td class="right">0.80</td>
<td class="right">0.50</td>
</tr>

<tr>
<td class="right">21</td>
<td class="right">2009</td>
<td class="left">mean</td>
<td class="right">13114</td>
<td class="right">48</td>
<td class="right">4.13</td>
<td class="right">1.21</td>
<td class="right">0.91</td>
<td class="right">5.89</td>
<td class="right">1.48</td>
<td class="right">0.93</td>
<td class="right">0.90</td>
<td class="right">0.54</td>
</tr>

<tr>
<td class="right">22</td>
<td class="right">2010</td>
<td class="left">hi</td>
<td class="right">13980</td>
<td class="right">51</td>
<td class="right">5.36</td>
<td class="right">1.67</td>
<td class="right">0.90</td>
<td class="right">7.73</td>
<td class="right">2.30</td>
<td class="right">0.93</td>
<td class="right">0.88</td>
<td class="right">0.16</td>
</tr>

<tr>
<td class="right">23</td>
<td class="right">2010</td>
<td class="left">lo</td>
<td class="right">13980</td>
<td class="right">51</td>
<td class="right">4.53</td>
<td class="right">1.63</td>
<td class="right">0.87</td>
<td class="right">5.67</td>
<td class="right">2.11</td>
<td class="right">0.90</td>
<td class="right">0.86</td>
<td class="right">0.40</td>
</tr>

<tr>
<td class="right">24</td>
<td class="right">2010</td>
<td class="left">mean</td>
<td class="right">13980</td>
<td class="right">51</td>
<td class="right">4.50</td>
<td class="right">1.26</td>
<td class="right">0.92</td>
<td class="right">6.35</td>
<td class="right">1.71</td>
<td class="right">0.95</td>
<td class="right">0.91</td>
<td class="right">0.28</td>
</tr>

<tr>
<td class="right">25</td>
<td class="right">2011</td>
<td class="left">hi</td>
<td class="right">14036</td>
<td class="right">46</td>
<td class="right">5.03</td>
<td class="right">1.59</td>
<td class="right">0.90</td>
<td class="right">7.16</td>
<td class="right">2.05</td>
<td class="right">0.92</td>
<td class="right">0.87</td>
<td class="right">0.05</td>
</tr>

<tr>
<td class="right">26</td>
<td class="right">2011</td>
<td class="left">lo</td>
<td class="right">14036</td>
<td class="right">46</td>
<td class="right">4.25</td>
<td class="right">1.60</td>
<td class="right">0.86</td>
<td class="right">5.28</td>
<td class="right">1.97</td>
<td class="right">0.90</td>
<td class="right">0.83</td>
<td class="right">0.21</td>
</tr>

<tr>
<td class="right">27</td>
<td class="right">2011</td>
<td class="left">mean</td>
<td class="right">14036</td>
<td class="right">46</td>
<td class="right">4.25</td>
<td class="right">1.16</td>
<td class="right">0.93</td>
<td class="right">5.84</td>
<td class="right">1.46</td>
<td class="right">0.95</td>
<td class="right">0.89</td>
<td class="right">0.22</td>
</tr>

<tr>
<td class="right">28</td>
<td class="right">2012</td>
<td class="left">hi</td>
<td class="right">15161</td>
<td class="right">53</td>
<td class="right">4.71</td>
<td class="right">1.46</td>
<td class="right">0.90</td>
<td class="right">6.57</td>
<td class="right">1.83</td>
<td class="right">0.91</td>
<td class="right">0.87</td>
<td class="right">0.19</td>
</tr>

<tr>
<td class="right">29</td>
<td class="right">2012</td>
<td class="left">lo</td>
<td class="right">15161</td>
<td class="right">53</td>
<td class="right">3.90</td>
<td class="right">1.59</td>
<td class="right">0.83</td>
<td class="right">4.91</td>
<td class="right">1.96</td>
<td class="right">0.91</td>
<td class="right">0.77</td>
<td class="right">0.45</td>
</tr>

<tr>
<td class="right">30</td>
<td class="right">2012</td>
<td class="left">mean</td>
<td class="right">15161</td>
<td class="right">53</td>
<td class="right">3.93</td>
<td class="right">1.06</td>
<td class="right">0.93</td>
<td class="right">5.38</td>
<td class="right">1.35</td>
<td class="right">0.96</td>
<td class="right">0.87</td>
<td class="right">0.50</td>
</tr>

<tr>
<td class="right">31</td>
<td class="right">2013</td>
<td class="left">hi</td>
<td class="right">17317</td>
<td class="right">59</td>
<td class="right">4.97</td>
<td class="right">1.69</td>
<td class="right">0.88</td>
<td class="right">6.35</td>
<td class="right">2.12</td>
<td class="right">0.89</td>
<td class="right">0.85</td>
<td class="right">0.22</td>
</tr>

<tr>
<td class="right">32</td>
<td class="right">2013</td>
<td class="left">lo</td>
<td class="right">17317</td>
<td class="right">59</td>
<td class="right">4.16</td>
<td class="right">1.71</td>
<td class="right">0.83</td>
<td class="right">4.89</td>
<td class="right">1.98</td>
<td class="right">0.90</td>
<td class="right">0.75</td>
<td class="right">0.58</td>
</tr>

<tr>
<td class="right">33</td>
<td class="right">2013</td>
<td class="left">mean</td>
<td class="right">17317</td>
<td class="right">59</td>
<td class="right">4.21</td>
<td class="right">1.14</td>
<td class="right">0.93</td>
<td class="right">5.23</td>
<td class="right">1.32</td>
<td class="right">0.96</td>
<td class="right">0.86</td>
<td class="right">0.61</td>
</tr>

<tr>
<td class="right">34</td>
<td class="right">2014</td>
<td class="left">hi</td>
<td class="right">18685</td>
<td class="right">62</td>
<td class="right">4.66</td>
<td class="right">1.65</td>
<td class="right">0.87</td>
<td class="right">6.13</td>
<td class="right">2.03</td>
<td class="right">0.89</td>
<td class="right">0.83</td>
<td class="right">0.28</td>
</tr>

<tr>
<td class="right">35</td>
<td class="right">2014</td>
<td class="left">lo</td>
<td class="right">18685</td>
<td class="right">62</td>
<td class="right">4.21</td>
<td class="right">1.63</td>
<td class="right">0.85</td>
<td class="right">5.14</td>
<td class="right">1.97</td>
<td class="right">0.90</td>
<td class="right">0.79</td>
<td class="right">0.48</td>
</tr>

<tr>
<td class="right">36</td>
<td class="right">2014</td>
<td class="left">mean</td>
<td class="right">18685</td>
<td class="right">62</td>
<td class="right">4.02</td>
<td class="right">1.10</td>
<td class="right">0.92</td>
<td class="right">5.21</td>
<td class="right">1.29</td>
<td class="right">0.96</td>
<td class="right">0.86</td>
<td class="right">0.58</td>
</tr>

<tr>
<td class="right">37</td>
<td class="right">2015</td>
<td class="left">hi</td>
<td class="right">20712</td>
<td class="right">69</td>
<td class="right">4.60</td>
<td class="right">1.60</td>
<td class="right">0.88</td>
<td class="right">6.28</td>
<td class="right">2.01</td>
<td class="right">0.89</td>
<td class="right">0.82</td>
<td class="right">0.25</td>
</tr>

<tr>
<td class="right">38</td>
<td class="right">2015</td>
<td class="left">lo</td>
<td class="right">20712</td>
<td class="right">69</td>
<td class="right">3.96</td>
<td class="right">1.63</td>
<td class="right">0.83</td>
<td class="right">5.16</td>
<td class="right">1.84</td>
<td class="right">0.87</td>
<td class="right">0.75</td>
<td class="right">0.47</td>
</tr>

<tr>
<td class="right">39</td>
<td class="right">2015</td>
<td class="left">mean</td>
<td class="right">20712</td>
<td class="right">69</td>
<td class="right">3.92</td>
<td class="right">1.09</td>
<td class="right">0.92</td>
<td class="right">5.38</td>
<td class="right">1.23</td>
<td class="right">0.95</td>
<td class="right">0.84</td>
<td class="right">0.62</td>
</tr>

<tr>
<td class="right">40</td>
<td class="right">2016</td>
<td class="left">hi</td>
<td class="right">23716</td>
<td class="right">74</td>
<td class="right">4.82</td>
<td class="right">1.64</td>
<td class="right">0.88</td>
<td class="right">6.15</td>
<td class="right">2.05</td>
<td class="right">0.89</td>
<td class="right">0.87</td>
<td class="right">0.28</td>
</tr>

<tr>
<td class="right">41</td>
<td class="right">2016</td>
<td class="left">lo</td>
<td class="right">23716</td>
<td class="right">74</td>
<td class="right">4.25</td>
<td class="right">1.87</td>
<td class="right">0.81</td>
<td class="right">5.26</td>
<td class="right">2.05</td>
<td class="right">0.85</td>
<td class="right">0.78</td>
<td class="right">0.62</td>
</tr>

<tr>
<td class="right">42</td>
<td class="right">2016</td>
<td class="left">mean</td>
<td class="right">23716</td>
<td class="right">74</td>
<td class="right">4.18</td>
<td class="right">1.24</td>
<td class="right">0.91</td>
<td class="right">5.37</td>
<td class="right">1.33</td>
<td class="right">0.94</td>
<td class="right">0.88</td>
<td class="right">0.56</td>
</tr>

<tr>
<td class="right">43</td>
<td class="right">2017</td>
<td class="left">hi</td>
<td class="right">23915</td>
<td class="right">80</td>
<td class="right">4.47</td>
<td class="right">1.66</td>
<td class="right">0.86</td>
<td class="right">6.00</td>
<td class="right">2.04</td>
<td class="right">0.87</td>
<td class="right">0.82</td>
<td class="right">0.31</td>
</tr>

<tr>
<td class="right">44</td>
<td class="right">2017</td>
<td class="left">lo</td>
<td class="right">23915</td>
<td class="right">80</td>
<td class="right">4.54</td>
<td class="right">1.92</td>
<td class="right">0.82</td>
<td class="right">5.60</td>
<td class="right">2.26</td>
<td class="right">0.84</td>
<td class="right">0.82</td>
<td class="right">0.58</td>
</tr>

<tr>
<td class="right">45</td>
<td class="right">2017</td>
<td class="left">mean</td>
<td class="right">23915</td>
<td class="right">80</td>
<td class="right">4.15</td>
<td class="right">1.30</td>
<td class="right">0.90</td>
<td class="right">5.45</td>
<td class="right">1.47</td>
<td class="right">0.91</td>
<td class="right">0.87</td>
<td class="right">0.51</td>
</tr>

<tr>
<td class="right">46</td>
<td class="right">2018</td>
<td class="left">hi</td>
<td class="right">23558</td>
<td class="right">91</td>
<td class="right">4.18</td>
<td class="right">1.58</td>
<td class="right">0.86</td>
<td class="right">5.66</td>
<td class="right">1.82</td>
<td class="right">0.85</td>
<td class="right">0.86</td>
<td class="right">0.39</td>
</tr>

<tr>
<td class="right">47</td>
<td class="right">2018</td>
<td class="left">lo</td>
<td class="right">23558</td>
<td class="right">91</td>
<td class="right">3.96</td>
<td class="right">1.77</td>
<td class="right">0.80</td>
<td class="right">5.01</td>
<td class="right">1.90</td>
<td class="right">0.85</td>
<td class="right">0.80</td>
<td class="right">0.62</td>
</tr>

<tr>
<td class="right">48</td>
<td class="right">2018</td>
<td class="left">mean</td>
<td class="right">23558</td>
<td class="right">91</td>
<td class="right">3.77</td>
<td class="right">1.26</td>
<td class="right">0.89</td>
<td class="right">5.09</td>
<td class="right">1.29</td>
<td class="right">0.90</td>
<td class="right">0.88</td>
<td class="right">0.56</td>
</tr>

<tr>
<td class="right">49</td>
<td class="right">2019</td>
<td class="left">hi</td>
<td class="right">29093</td>
<td class="right">99</td>
<td class="right">4.17</td>
<td class="right">1.86</td>
<td class="right">0.80</td>
<td class="right">5.96</td>
<td class="right">2.18</td>
<td class="right">0.78</td>
<td class="right">0.81</td>
<td class="right">0.42</td>
</tr>

<tr>
<td class="right">50</td>
<td class="right">2019</td>
<td class="left">lo</td>
<td class="right">29093</td>
<td class="right">99</td>
<td class="right">3.88</td>
<td class="right">1.83</td>
<td class="right">0.78</td>
<td class="right">5.15</td>
<td class="right">2.02</td>
<td class="right">0.81</td>
<td class="right">0.76</td>
<td class="right">0.70</td>
</tr>

<tr>
<td class="right">51</td>
<td class="right">2019</td>
<td class="left">mean</td>
<td class="right">29093</td>
<td class="right">99</td>
<td class="right">3.68</td>
<td class="right">1.22</td>
<td class="right">0.89</td>
<td class="right">5.27</td>
<td class="right">1.31</td>
<td class="right">0.92</td>
<td class="right">0.85</td>
<td class="right">0.72</td>
</tr>
</tbody>
</table>

<p>
Here are the RMSEs (and Moran's I <var>p</var>-values for the per-station mean signed error) by meteorological season:
</p>

<div class="org-src-container">
<pre class="src src-R">as.data.frame(rd(d = 2, sr$by.season))
</pre>
</div>

<table id="tab--cv-by-season">


<colgroup>
<col  class="right">

<col  class="right">

<col  class="left">

<col  class="left">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">
</colgroup>
<thead>
<tr>
<th scope="col" class="right">&#xa0;</th>
<th scope="col" class="right">year</th>
<th scope="col" class="left">dv</th>
<th scope="col" class="left">season</th>
<th scope="col" class="right">N</th>
<th scope="col" class="right">stn</th>
<th scope="col" class="right">sd</th>
<th scope="col" class="right">rmse</th>
<th scope="col" class="right">sd - rmse</th>
<th scope="col" class="right">Moran p</th>
</tr>
</thead>
<tbody>
<tr>
<td class="right">1</td>
<td class="right">2003</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">3215</td>
<td class="right">32</td>
<td class="right">4.29</td>
<td class="right">1.34</td>
<td class="right">2.96</td>
<td class="right">0.35</td>
</tr>

<tr>
<td class="right">2</td>
<td class="right">2003</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">4788</td>
<td class="right">32</td>
<td class="right">4.53</td>
<td class="right">1.37</td>
<td class="right">3.15</td>
<td class="right">0.58</td>
</tr>

<tr>
<td class="right">3</td>
<td class="right">2003</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">1619</td>
<td class="right">32</td>
<td class="right">4.21</td>
<td class="right">1.32</td>
<td class="right">2.90</td>
<td class="right">0.34</td>
</tr>

<tr>
<td class="right">4</td>
<td class="right">2003</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">3215</td>
<td class="right">32</td>
<td class="right">3.49</td>
<td class="right">1.69</td>
<td class="right">1.80</td>
<td class="right">0.68</td>
</tr>

<tr>
<td class="right">5</td>
<td class="right">2003</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">4788</td>
<td class="right">32</td>
<td class="right">3.07</td>
<td class="right">1.21</td>
<td class="right">1.87</td>
<td class="right">0.01</td>
</tr>

<tr>
<td class="right">6</td>
<td class="right">2003</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">1619</td>
<td class="right">32</td>
<td class="right">3.50</td>
<td class="right">1.66</td>
<td class="right">1.84</td>
<td class="right">0.37</td>
</tr>

<tr>
<td class="right">7</td>
<td class="right">2003</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">3215</td>
<td class="right">32</td>
<td class="right">3.49</td>
<td class="right">0.96</td>
<td class="right">2.53</td>
<td class="right">0.33</td>
</tr>

<tr>
<td class="right">8</td>
<td class="right">2003</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">4788</td>
<td class="right">32</td>
<td class="right">3.57</td>
<td class="right">0.89</td>
<td class="right">2.67</td>
<td class="right">0.34</td>
</tr>

<tr>
<td class="right">9</td>
<td class="right">2003</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">1619</td>
<td class="right">32</td>
<td class="right">3.70</td>
<td class="right">0.91</td>
<td class="right">2.79</td>
<td class="right">0.28</td>
</tr>

<tr>
<td class="right">10</td>
<td class="right">2004</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">3518</td>
<td class="right">35</td>
<td class="right">4.74</td>
<td class="right">1.35</td>
<td class="right">3.39</td>
<td class="right">0.93</td>
</tr>

<tr>
<td class="right">11</td>
<td class="right">2004</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">5286</td>
<td class="right">34</td>
<td class="right">4.13</td>
<td class="right">1.38</td>
<td class="right">2.75</td>
<td class="right">0.78</td>
</tr>

<tr>
<td class="right">12</td>
<td class="right">2004</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">1649</td>
<td class="right">29</td>
<td class="right">4.86</td>
<td class="right">1.25</td>
<td class="right">3.61</td>
<td class="right">0.33</td>
</tr>

<tr>
<td class="right">13</td>
<td class="right">2004</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">3518</td>
<td class="right">35</td>
<td class="right">3.58</td>
<td class="right">1.78</td>
<td class="right">1.80</td>
<td class="right">0.50</td>
</tr>

<tr>
<td class="right">14</td>
<td class="right">2004</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">5286</td>
<td class="right">34</td>
<td class="right">2.83</td>
<td class="right">1.33</td>
<td class="right">1.50</td>
<td class="right">0.51</td>
</tr>

<tr>
<td class="right">15</td>
<td class="right">2004</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">1649</td>
<td class="right">29</td>
<td class="right">3.53</td>
<td class="right">1.54</td>
<td class="right">1.99</td>
<td class="right">0.12</td>
</tr>

<tr>
<td class="right">16</td>
<td class="right">2004</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">3518</td>
<td class="right">35</td>
<td class="right">3.70</td>
<td class="right">1.12</td>
<td class="right">2.57</td>
<td class="right">0.24</td>
</tr>

<tr>
<td class="right">17</td>
<td class="right">2004</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">5286</td>
<td class="right">34</td>
<td class="right">3.23</td>
<td class="right">1.01</td>
<td class="right">2.22</td>
<td class="right">0.78</td>
</tr>

<tr>
<td class="right">18</td>
<td class="right">2004</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">1649</td>
<td class="right">29</td>
<td class="right">4.03</td>
<td class="right">0.95</td>
<td class="right">3.08</td>
<td class="right">0.22</td>
</tr>

<tr>
<td class="right">19</td>
<td class="right">2005</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">3773</td>
<td class="right">36</td>
<td class="right">4.42</td>
<td class="right">1.46</td>
<td class="right">2.96</td>
<td class="right">0.65</td>
</tr>

<tr>
<td class="right">20</td>
<td class="right">2005</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">5673</td>
<td class="right">36</td>
<td class="right">4.83</td>
<td class="right">1.50</td>
<td class="right">3.33</td>
<td class="right">0.82</td>
</tr>

<tr>
<td class="right">21</td>
<td class="right">2005</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">2043</td>
<td class="right">35</td>
<td class="right">4.85</td>
<td class="right">1.47</td>
<td class="right">3.38</td>
<td class="right">0.85</td>
</tr>

<tr>
<td class="right">22</td>
<td class="right">2005</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">3773</td>
<td class="right">36</td>
<td class="right">3.35</td>
<td class="right">1.70</td>
<td class="right">1.65</td>
<td class="right">0.74</td>
</tr>

<tr>
<td class="right">23</td>
<td class="right">2005</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">5673</td>
<td class="right">36</td>
<td class="right">3.34</td>
<td class="right">1.52</td>
<td class="right">1.82</td>
<td class="right">0.14</td>
</tr>

<tr>
<td class="right">24</td>
<td class="right">2005</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">2043</td>
<td class="right">35</td>
<td class="right">3.73</td>
<td class="right">1.72</td>
<td class="right">2.01</td>
<td class="right">0.66</td>
</tr>

<tr>
<td class="right">25</td>
<td class="right">2005</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">3773</td>
<td class="right">36</td>
<td class="right">3.55</td>
<td class="right">1.07</td>
<td class="right">2.48</td>
<td class="right">0.56</td>
</tr>

<tr>
<td class="right">26</td>
<td class="right">2005</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">5673</td>
<td class="right">36</td>
<td class="right">3.85</td>
<td class="right">1.14</td>
<td class="right">2.72</td>
<td class="right">0.55</td>
</tr>

<tr>
<td class="right">27</td>
<td class="right">2005</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">2043</td>
<td class="right">35</td>
<td class="right">4.00</td>
<td class="right">1.02</td>
<td class="right">2.98</td>
<td class="right">0.46</td>
</tr>

<tr>
<td class="right">28</td>
<td class="right">2006</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">3598</td>
<td class="right">36</td>
<td class="right">4.51</td>
<td class="right">1.53</td>
<td class="right">2.98</td>
<td class="right">0.39</td>
</tr>

<tr>
<td class="right">29</td>
<td class="right">2006</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">5207</td>
<td class="right">35</td>
<td class="right">4.50</td>
<td class="right">1.49</td>
<td class="right">3.01</td>
<td class="right">0.76</td>
</tr>

<tr>
<td class="right">30</td>
<td class="right">2006</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">2077</td>
<td class="right">35</td>
<td class="right">4.59</td>
<td class="right">1.33</td>
<td class="right">3.25</td>
<td class="right">0.69</td>
</tr>

<tr>
<td class="right">31</td>
<td class="right">2006</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">3598</td>
<td class="right">36</td>
<td class="right">3.67</td>
<td class="right">1.63</td>
<td class="right">2.03</td>
<td class="right">0.89</td>
</tr>

<tr>
<td class="right">32</td>
<td class="right">2006</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">5207</td>
<td class="right">35</td>
<td class="right">3.15</td>
<td class="right">1.35</td>
<td class="right">1.80</td>
<td class="right">0.40</td>
</tr>

<tr>
<td class="right">33</td>
<td class="right">2006</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">2077</td>
<td class="right">35</td>
<td class="right">3.59</td>
<td class="right">1.68</td>
<td class="right">1.91</td>
<td class="right">0.95</td>
</tr>

<tr>
<td class="right">34</td>
<td class="right">2006</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">3598</td>
<td class="right">36</td>
<td class="right">3.65</td>
<td class="right">1.11</td>
<td class="right">2.54</td>
<td class="right">0.81</td>
</tr>

<tr>
<td class="right">35</td>
<td class="right">2006</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">5207</td>
<td class="right">35</td>
<td class="right">3.54</td>
<td class="right">1.13</td>
<td class="right">2.40</td>
<td class="right">0.73</td>
</tr>

<tr>
<td class="right">36</td>
<td class="right">2006</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">2077</td>
<td class="right">35</td>
<td class="right">3.84</td>
<td class="right">1.03</td>
<td class="right">2.80</td>
<td class="right">0.75</td>
</tr>

<tr>
<td class="right">37</td>
<td class="right">2007</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">3566</td>
<td class="right">39</td>
<td class="right">4.52</td>
<td class="right">1.52</td>
<td class="right">3.00</td>
<td class="right">0.38</td>
</tr>

<tr>
<td class="right">38</td>
<td class="right">2007</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">4737</td>
<td class="right">37</td>
<td class="right">4.81</td>
<td class="right">1.49</td>
<td class="right">3.33</td>
<td class="right">0.15</td>
</tr>

<tr>
<td class="right">39</td>
<td class="right">2007</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">1551</td>
<td class="right">30</td>
<td class="right">4.96</td>
<td class="right">1.32</td>
<td class="right">3.63</td>
<td class="right">0.73</td>
</tr>

<tr>
<td class="right">40</td>
<td class="right">2007</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">3566</td>
<td class="right">39</td>
<td class="right">3.28</td>
<td class="right">1.64</td>
<td class="right">1.64</td>
<td class="right">0.63</td>
</tr>

<tr>
<td class="right">41</td>
<td class="right">2007</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">4737</td>
<td class="right">37</td>
<td class="right">3.46</td>
<td class="right">1.37</td>
<td class="right">2.09</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">42</td>
<td class="right">2007</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">1551</td>
<td class="right">30</td>
<td class="right">3.87</td>
<td class="right">1.54</td>
<td class="right">2.34</td>
<td class="right">0.22</td>
</tr>

<tr>
<td class="right">43</td>
<td class="right">2007</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">3566</td>
<td class="right">39</td>
<td class="right">3.53</td>
<td class="right">1.09</td>
<td class="right">2.44</td>
<td class="right">0.03</td>
</tr>

<tr>
<td class="right">44</td>
<td class="right">2007</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">4737</td>
<td class="right">37</td>
<td class="right">3.79</td>
<td class="right">1.01</td>
<td class="right">2.78</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">45</td>
<td class="right">2007</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">1551</td>
<td class="right">30</td>
<td class="right">4.23</td>
<td class="right">1.04</td>
<td class="right">3.20</td>
<td class="right">0.33</td>
</tr>

<tr>
<td class="right">46</td>
<td class="right">2008</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">3873</td>
<td class="right">41</td>
<td class="right">4.67</td>
<td class="right">1.56</td>
<td class="right">3.12</td>
<td class="right">0.33</td>
</tr>

<tr>
<td class="right">47</td>
<td class="right">2008</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">5699</td>
<td class="right">39</td>
<td class="right">4.88</td>
<td class="right">1.67</td>
<td class="right">3.22</td>
<td class="right">0.08</td>
</tr>

<tr>
<td class="right">48</td>
<td class="right">2008</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">1858</td>
<td class="right">39</td>
<td class="right">4.16</td>
<td class="right">1.39</td>
<td class="right">2.76</td>
<td class="right">0.87</td>
</tr>

<tr>
<td class="right">49</td>
<td class="right">2008</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">3873</td>
<td class="right">41</td>
<td class="right">3.31</td>
<td class="right">1.70</td>
<td class="right">1.60</td>
<td class="right">0.38</td>
</tr>

<tr>
<td class="right">50</td>
<td class="right">2008</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">5699</td>
<td class="right">39</td>
<td class="right">3.48</td>
<td class="right">1.44</td>
<td class="right">2.04</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">51</td>
<td class="right">2008</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">1858</td>
<td class="right">39</td>
<td class="right">3.41</td>
<td class="right">1.54</td>
<td class="right">1.87</td>
<td class="right">0.74</td>
</tr>

<tr>
<td class="right">52</td>
<td class="right">2008</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">3873</td>
<td class="right">41</td>
<td class="right">3.76</td>
<td class="right">1.14</td>
<td class="right">2.62</td>
<td class="right">0.01</td>
</tr>

<tr>
<td class="right">53</td>
<td class="right">2008</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">5699</td>
<td class="right">39</td>
<td class="right">3.81</td>
<td class="right">1.14</td>
<td class="right">2.67</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">54</td>
<td class="right">2008</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">1858</td>
<td class="right">39</td>
<td class="right">3.58</td>
<td class="right">0.97</td>
<td class="right">2.61</td>
<td class="right">0.04</td>
</tr>

<tr>
<td class="right">55</td>
<td class="right">2009</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">4114</td>
<td class="right">47</td>
<td class="right">4.90</td>
<td class="right">1.69</td>
<td class="right">3.22</td>
<td class="right">0.83</td>
</tr>

<tr>
<td class="right">56</td>
<td class="right">2009</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">6689</td>
<td class="right">46</td>
<td class="right">4.77</td>
<td class="right">1.73</td>
<td class="right">3.05</td>
<td class="right">0.49</td>
</tr>

<tr>
<td class="right">57</td>
<td class="right">2009</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">2311</td>
<td class="right">43</td>
<td class="right">4.40</td>
<td class="right">1.62</td>
<td class="right">2.79</td>
<td class="right">0.51</td>
</tr>

<tr>
<td class="right">58</td>
<td class="right">2009</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">4114</td>
<td class="right">47</td>
<td class="right">3.50</td>
<td class="right">1.73</td>
<td class="right">1.77</td>
<td class="right">0.05</td>
</tr>

<tr>
<td class="right">59</td>
<td class="right">2009</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">6689</td>
<td class="right">46</td>
<td class="right">3.24</td>
<td class="right">1.45</td>
<td class="right">1.79</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">60</td>
<td class="right">2009</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">2311</td>
<td class="right">43</td>
<td class="right">3.59</td>
<td class="right">1.62</td>
<td class="right">1.97</td>
<td class="right">0.69</td>
</tr>

<tr>
<td class="right">61</td>
<td class="right">2009</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">4114</td>
<td class="right">47</td>
<td class="right">3.74</td>
<td class="right">1.17</td>
<td class="right">2.58</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">62</td>
<td class="right">2009</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">6689</td>
<td class="right">46</td>
<td class="right">3.79</td>
<td class="right">1.25</td>
<td class="right">2.53</td>
<td class="right">0.03</td>
</tr>

<tr>
<td class="right">63</td>
<td class="right">2009</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">2311</td>
<td class="right">43</td>
<td class="right">3.81</td>
<td class="right">1.15</td>
<td class="right">2.66</td>
<td class="right">0.08</td>
</tr>

<tr>
<td class="right">64</td>
<td class="right">2010</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">4429</td>
<td class="right">50</td>
<td class="right">5.07</td>
<td class="right">1.55</td>
<td class="right">3.53</td>
<td class="right">0.81</td>
</tr>

<tr>
<td class="right">65</td>
<td class="right">2010</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">7219</td>
<td class="right">48</td>
<td class="right">5.03</td>
<td class="right">1.66</td>
<td class="right">3.38</td>
<td class="right">0.76</td>
</tr>

<tr>
<td class="right">66</td>
<td class="right">2010</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">2332</td>
<td class="right">45</td>
<td class="right">4.98</td>
<td class="right">1.91</td>
<td class="right">3.07</td>
<td class="right">0.60</td>
</tr>

<tr>
<td class="right">67</td>
<td class="right">2010</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">4429</td>
<td class="right">50</td>
<td class="right">3.74</td>
<td class="right">1.80</td>
<td class="right">1.93</td>
<td class="right">0.18</td>
</tr>

<tr>
<td class="right">68</td>
<td class="right">2010</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">7219</td>
<td class="right">48</td>
<td class="right">3.58</td>
<td class="right">1.51</td>
<td class="right">2.07</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">69</td>
<td class="right">2010</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">2332</td>
<td class="right">45</td>
<td class="right">3.71</td>
<td class="right">1.63</td>
<td class="right">2.08</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">70</td>
<td class="right">2010</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">4429</td>
<td class="right">50</td>
<td class="right">3.89</td>
<td class="right">1.21</td>
<td class="right">2.69</td>
<td class="right">0.02</td>
</tr>

<tr>
<td class="right">71</td>
<td class="right">2010</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">7219</td>
<td class="right">48</td>
<td class="right">3.96</td>
<td class="right">1.26</td>
<td class="right">2.70</td>
<td class="right">0.25</td>
</tr>

<tr>
<td class="right">72</td>
<td class="right">2010</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">2332</td>
<td class="right">45</td>
<td class="right">4.19</td>
<td class="right">1.35</td>
<td class="right">2.84</td>
<td class="right">0.07</td>
</tr>

<tr>
<td class="right">73</td>
<td class="right">2011</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">4557</td>
<td class="right">46</td>
<td class="right">4.61</td>
<td class="right">1.45</td>
<td class="right">3.16</td>
<td class="right">0.88</td>
</tr>

<tr>
<td class="right">74</td>
<td class="right">2011</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">7196</td>
<td class="right">45</td>
<td class="right">4.98</td>
<td class="right">1.68</td>
<td class="right">3.30</td>
<td class="right">0.67</td>
</tr>

<tr>
<td class="right">75</td>
<td class="right">2011</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">2283</td>
<td class="right">41</td>
<td class="right">5.01</td>
<td class="right">1.59</td>
<td class="right">3.42</td>
<td class="right">0.82</td>
</tr>

<tr>
<td class="right">76</td>
<td class="right">2011</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">4557</td>
<td class="right">46</td>
<td class="right">3.63</td>
<td class="right">1.69</td>
<td class="right">1.94</td>
<td class="right">0.87</td>
</tr>

<tr>
<td class="right">77</td>
<td class="right">2011</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">7196</td>
<td class="right">45</td>
<td class="right">3.80</td>
<td class="right">1.56</td>
<td class="right">2.25</td>
<td class="right">0.09</td>
</tr>

<tr>
<td class="right">78</td>
<td class="right">2011</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">2283</td>
<td class="right">41</td>
<td class="right">4.09</td>
<td class="right">1.59</td>
<td class="right">2.50</td>
<td class="right">0.67</td>
</tr>

<tr>
<td class="right">79</td>
<td class="right">2011</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">4557</td>
<td class="right">46</td>
<td class="right">3.80</td>
<td class="right">1.07</td>
<td class="right">2.73</td>
<td class="right">0.60</td>
</tr>

<tr>
<td class="right">80</td>
<td class="right">2011</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">7196</td>
<td class="right">45</td>
<td class="right">4.03</td>
<td class="right">1.23</td>
<td class="right">2.80</td>
<td class="right">0.10</td>
</tr>

<tr>
<td class="right">81</td>
<td class="right">2011</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">2283</td>
<td class="right">41</td>
<td class="right">4.39</td>
<td class="right">1.07</td>
<td class="right">3.32</td>
<td class="right">0.39</td>
</tr>

<tr>
<td class="right">82</td>
<td class="right">2012</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">4810</td>
<td class="right">52</td>
<td class="right">4.54</td>
<td class="right">1.50</td>
<td class="right">3.04</td>
<td class="right">0.48</td>
</tr>

<tr>
<td class="right">83</td>
<td class="right">2012</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">7685</td>
<td class="right">50</td>
<td class="right">4.51</td>
<td class="right">1.47</td>
<td class="right">3.04</td>
<td class="right">0.34</td>
</tr>

<tr>
<td class="right">84</td>
<td class="right">2012</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">2666</td>
<td class="right">49</td>
<td class="right">4.34</td>
<td class="right">1.36</td>
<td class="right">2.98</td>
<td class="right">0.62</td>
</tr>

<tr>
<td class="right">85</td>
<td class="right">2012</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">4810</td>
<td class="right">52</td>
<td class="right">3.53</td>
<td class="right">1.72</td>
<td class="right">1.81</td>
<td class="right">0.08</td>
</tr>

<tr>
<td class="right">86</td>
<td class="right">2012</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">7685</td>
<td class="right">50</td>
<td class="right">3.30</td>
<td class="right">1.48</td>
<td class="right">1.82</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">87</td>
<td class="right">2012</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">2666</td>
<td class="right">49</td>
<td class="right">3.55</td>
<td class="right">1.66</td>
<td class="right">1.89</td>
<td class="right">0.44</td>
</tr>

<tr>
<td class="right">88</td>
<td class="right">2012</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">4810</td>
<td class="right">52</td>
<td class="right">3.70</td>
<td class="right">1.13</td>
<td class="right">2.57</td>
<td class="right">0.01</td>
</tr>

<tr>
<td class="right">89</td>
<td class="right">2012</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">7685</td>
<td class="right">50</td>
<td class="right">3.57</td>
<td class="right">1.03</td>
<td class="right">2.54</td>
<td class="right">0.01</td>
</tr>

<tr>
<td class="right">90</td>
<td class="right">2012</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">2666</td>
<td class="right">49</td>
<td class="right">3.69</td>
<td class="right">0.99</td>
<td class="right">2.70</td>
<td class="right">0.15</td>
</tr>

<tr>
<td class="right">91</td>
<td class="right">2013</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">5853</td>
<td class="right">58</td>
<td class="right">4.66</td>
<td class="right">1.67</td>
<td class="right">2.99</td>
<td class="right">0.21</td>
</tr>

<tr>
<td class="right">92</td>
<td class="right">2013</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">8739</td>
<td class="right">59</td>
<td class="right">4.81</td>
<td class="right">1.68</td>
<td class="right">3.13</td>
<td class="right">0.35</td>
</tr>

<tr>
<td class="right">93</td>
<td class="right">2013</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">2725</td>
<td class="right">53</td>
<td class="right">5.57</td>
<td class="right">1.76</td>
<td class="right">3.81</td>
<td class="right">0.51</td>
</tr>

<tr>
<td class="right">94</td>
<td class="right">2013</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">5853</td>
<td class="right">58</td>
<td class="right">3.70</td>
<td class="right">1.89</td>
<td class="right">1.81</td>
<td class="right">0.15</td>
</tr>

<tr>
<td class="right">95</td>
<td class="right">2013</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">8739</td>
<td class="right">59</td>
<td class="right">3.58</td>
<td class="right">1.47</td>
<td class="right">2.12</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">96</td>
<td class="right">2013</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">2725</td>
<td class="right">53</td>
<td class="right">4.68</td>
<td class="right">1.97</td>
<td class="right">2.71</td>
<td class="right">0.22</td>
</tr>

<tr>
<td class="right">97</td>
<td class="right">2013</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">5853</td>
<td class="right">58</td>
<td class="right">3.85</td>
<td class="right">1.21</td>
<td class="right">2.64</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">98</td>
<td class="right">2013</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">8739</td>
<td class="right">59</td>
<td class="right">3.91</td>
<td class="right">1.06</td>
<td class="right">2.85</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">99</td>
<td class="right">2013</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">2725</td>
<td class="right">53</td>
<td class="right">4.94</td>
<td class="right">1.21</td>
<td class="right">3.72</td>
<td class="right">0.03</td>
</tr>

<tr>
<td class="right">100</td>
<td class="right">2014</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">6194</td>
<td class="right">62</td>
<td class="right">4.54</td>
<td class="right">1.57</td>
<td class="right">2.97</td>
<td class="right">0.41</td>
</tr>

<tr>
<td class="right">101</td>
<td class="right">2014</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">9305</td>
<td class="right">59</td>
<td class="right">4.33</td>
<td class="right">1.69</td>
<td class="right">2.64</td>
<td class="right">0.85</td>
</tr>

<tr>
<td class="right">102</td>
<td class="right">2014</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">3186</td>
<td class="right">59</td>
<td class="right">4.70</td>
<td class="right">1.72</td>
<td class="right">2.98</td>
<td class="right">0.33</td>
</tr>

<tr>
<td class="right">103</td>
<td class="right">2014</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">6194</td>
<td class="right">62</td>
<td class="right">3.99</td>
<td class="right">1.83</td>
<td class="right">2.17</td>
<td class="right">0.36</td>
</tr>

<tr>
<td class="right">104</td>
<td class="right">2014</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">9305</td>
<td class="right">59</td>
<td class="right">3.49</td>
<td class="right">1.41</td>
<td class="right">2.08</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">105</td>
<td class="right">2014</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">3186</td>
<td class="right">59</td>
<td class="right">4.02</td>
<td class="right">1.82</td>
<td class="right">2.20</td>
<td class="right">0.29</td>
</tr>

<tr>
<td class="right">106</td>
<td class="right">2014</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">6194</td>
<td class="right">62</td>
<td class="right">3.93</td>
<td class="right">1.17</td>
<td class="right">2.76</td>
<td class="right">0.01</td>
</tr>

<tr>
<td class="right">107</td>
<td class="right">2014</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">9305</td>
<td class="right">59</td>
<td class="right">3.55</td>
<td class="right">1.05</td>
<td class="right">2.49</td>
<td class="right">0.11</td>
</tr>

<tr>
<td class="right">108</td>
<td class="right">2014</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">3186</td>
<td class="right">59</td>
<td class="right">4.16</td>
<td class="right">1.11</td>
<td class="right">3.04</td>
<td class="right">0.01</td>
</tr>

<tr>
<td class="right">109</td>
<td class="right">2015</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">6527</td>
<td class="right">68</td>
<td class="right">4.40</td>
<td class="right">1.56</td>
<td class="right">2.84</td>
<td class="right">0.70</td>
</tr>

<tr>
<td class="right">110</td>
<td class="right">2015</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">10971</td>
<td class="right">66</td>
<td class="right">4.42</td>
<td class="right">1.63</td>
<td class="right">2.79</td>
<td class="right">0.45</td>
</tr>

<tr>
<td class="right">111</td>
<td class="right">2015</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">3214</td>
<td class="right">64</td>
<td class="right">5.14</td>
<td class="right">1.59</td>
<td class="right">3.55</td>
<td class="right">0.89</td>
</tr>

<tr>
<td class="right">112</td>
<td class="right">2015</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">6527</td>
<td class="right">68</td>
<td class="right">3.86</td>
<td class="right">1.83</td>
<td class="right">2.04</td>
<td class="right">0.01</td>
</tr>

<tr>
<td class="right">113</td>
<td class="right">2015</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">10971</td>
<td class="right">66</td>
<td class="right">3.36</td>
<td class="right">1.42</td>
<td class="right">1.94</td>
<td class="right">0.02</td>
</tr>

<tr>
<td class="right">114</td>
<td class="right">2015</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">3214</td>
<td class="right">64</td>
<td class="right">4.10</td>
<td class="right">1.84</td>
<td class="right">2.27</td>
<td class="right">0.13</td>
</tr>

<tr>
<td class="right">115</td>
<td class="right">2015</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">6527</td>
<td class="right">68</td>
<td class="right">3.80</td>
<td class="right">1.14</td>
<td class="right">2.66</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">116</td>
<td class="right">2015</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">10971</td>
<td class="right">66</td>
<td class="right">3.55</td>
<td class="right">1.03</td>
<td class="right">2.52</td>
<td class="right">0.03</td>
</tr>

<tr>
<td class="right">117</td>
<td class="right">2015</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">3214</td>
<td class="right">64</td>
<td class="right">4.46</td>
<td class="right">1.19</td>
<td class="right">3.27</td>
<td class="right">0.03</td>
</tr>

<tr>
<td class="right">118</td>
<td class="right">2016</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">7816</td>
<td class="right">74</td>
<td class="right">4.56</td>
<td class="right">1.61</td>
<td class="right">2.95</td>
<td class="right">0.82</td>
</tr>

<tr>
<td class="right">119</td>
<td class="right">2016</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">11895</td>
<td class="right">74</td>
<td class="right">4.37</td>
<td class="right">1.64</td>
<td class="right">2.73</td>
<td class="right">0.97</td>
</tr>

<tr>
<td class="right">120</td>
<td class="right">2016</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">4005</td>
<td class="right">71</td>
<td class="right">5.55</td>
<td class="right">1.70</td>
<td class="right">3.85</td>
<td class="right">0.49</td>
</tr>

<tr>
<td class="right">121</td>
<td class="right">2016</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">7816</td>
<td class="right">74</td>
<td class="right">3.97</td>
<td class="right">2.11</td>
<td class="right">1.86</td>
<td class="right">0.04</td>
</tr>

<tr>
<td class="right">122</td>
<td class="right">2016</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">11895</td>
<td class="right">74</td>
<td class="right">3.51</td>
<td class="right">1.57</td>
<td class="right">1.95</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">123</td>
<td class="right">2016</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">4005</td>
<td class="right">71</td>
<td class="right">4.28</td>
<td class="right">2.16</td>
<td class="right">2.12</td>
<td class="right">0.03</td>
</tr>

<tr>
<td class="right">124</td>
<td class="right">2016</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">7816</td>
<td class="right">74</td>
<td class="right">3.81</td>
<td class="right">1.29</td>
<td class="right">2.51</td>
<td class="right">0.01</td>
</tr>

<tr>
<td class="right">125</td>
<td class="right">2016</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">11895</td>
<td class="right">74</td>
<td class="right">3.72</td>
<td class="right">1.16</td>
<td class="right">2.55</td>
<td class="right">0.11</td>
</tr>

<tr>
<td class="right">126</td>
<td class="right">2016</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">4005</td>
<td class="right">71</td>
<td class="right">4.63</td>
<td class="right">1.35</td>
<td class="right">3.28</td>
<td class="right">0.02</td>
</tr>

<tr>
<td class="right">127</td>
<td class="right">2017</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">7907</td>
<td class="right">79</td>
<td class="right">4.08</td>
<td class="right">1.57</td>
<td class="right">2.51</td>
<td class="right">0.79</td>
</tr>

<tr>
<td class="right">128</td>
<td class="right">2017</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">11890</td>
<td class="right">79</td>
<td class="right">4.48</td>
<td class="right">1.72</td>
<td class="right">2.76</td>
<td class="right">0.98</td>
</tr>

<tr>
<td class="right">129</td>
<td class="right">2017</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">4118</td>
<td class="right">74</td>
<td class="right">4.70</td>
<td class="right">1.68</td>
<td class="right">3.02</td>
<td class="right">0.85</td>
</tr>

<tr>
<td class="right">130</td>
<td class="right">2017</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">7907</td>
<td class="right">79</td>
<td class="right">4.02</td>
<td class="right">2.20</td>
<td class="right">1.82</td>
<td class="right">0.95</td>
</tr>

<tr>
<td class="right">131</td>
<td class="right">2017</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">11890</td>
<td class="right">79</td>
<td class="right">3.54</td>
<td class="right">1.62</td>
<td class="right">1.93</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">132</td>
<td class="right">2017</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">4118</td>
<td class="right">74</td>
<td class="right">4.21</td>
<td class="right">2.12</td>
<td class="right">2.08</td>
<td class="right">0.34</td>
</tr>

<tr>
<td class="right">133</td>
<td class="right">2017</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">7907</td>
<td class="right">79</td>
<td class="right">3.79</td>
<td class="right">1.35</td>
<td class="right">2.44</td>
<td class="right">0.17</td>
</tr>

<tr>
<td class="right">134</td>
<td class="right">2017</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">11890</td>
<td class="right">79</td>
<td class="right">3.77</td>
<td class="right">1.25</td>
<td class="right">2.52</td>
<td class="right">0.30</td>
</tr>

<tr>
<td class="right">135</td>
<td class="right">2017</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">4118</td>
<td class="right">74</td>
<td class="right">4.35</td>
<td class="right">1.36</td>
<td class="right">2.99</td>
<td class="right">0.30</td>
</tr>

<tr>
<td class="right">136</td>
<td class="right">2018</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">7124</td>
<td class="right">88</td>
<td class="right">3.88</td>
<td class="right">1.59</td>
<td class="right">2.29</td>
<td class="right">0.23</td>
</tr>

<tr>
<td class="right">137</td>
<td class="right">2018</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">12402</td>
<td class="right">87</td>
<td class="right">3.85</td>
<td class="right">1.53</td>
<td class="right">2.32</td>
<td class="right">0.09</td>
</tr>

<tr>
<td class="right">138</td>
<td class="right">2018</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">4032</td>
<td class="right">77</td>
<td class="right">4.09</td>
<td class="right">1.72</td>
<td class="right">2.37</td>
<td class="right">0.89</td>
</tr>

<tr>
<td class="right">139</td>
<td class="right">2018</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">7124</td>
<td class="right">88</td>
<td class="right">3.87</td>
<td class="right">2.00</td>
<td class="right">1.87</td>
<td class="right">0.59</td>
</tr>

<tr>
<td class="right">140</td>
<td class="right">2018</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">12402</td>
<td class="right">87</td>
<td class="right">3.00</td>
<td class="right">1.61</td>
<td class="right">1.40</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">141</td>
<td class="right">2018</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">4032</td>
<td class="right">77</td>
<td class="right">3.65</td>
<td class="right">1.82</td>
<td class="right">1.82</td>
<td class="right">0.43</td>
</tr>

<tr>
<td class="right">142</td>
<td class="right">2018</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">7124</td>
<td class="right">88</td>
<td class="right">3.52</td>
<td class="right">1.29</td>
<td class="right">2.23</td>
<td class="right">0.01</td>
</tr>

<tr>
<td class="right">143</td>
<td class="right">2018</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">12402</td>
<td class="right">87</td>
<td class="right">3.23</td>
<td class="right">1.24</td>
<td class="right">1.99</td>
<td class="right">0.14</td>
</tr>

<tr>
<td class="right">144</td>
<td class="right">2018</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">4032</td>
<td class="right">77</td>
<td class="right">3.69</td>
<td class="right">1.26</td>
<td class="right">2.42</td>
<td class="right">0.30</td>
</tr>

<tr>
<td class="right">145</td>
<td class="right">2019</td>
<td class="left">hi</td>
<td class="left">ColdDry</td>
<td class="right">9278</td>
<td class="right">96</td>
<td class="right">3.88</td>
<td class="right">1.72</td>
<td class="right">2.17</td>
<td class="right">1.00</td>
</tr>

<tr>
<td class="right">146</td>
<td class="right">2019</td>
<td class="left">hi</td>
<td class="left">Rainy</td>
<td class="right">14924</td>
<td class="right">95</td>
<td class="right">4.07</td>
<td class="right">1.95</td>
<td class="right">2.12</td>
<td class="right">0.29</td>
</tr>

<tr>
<td class="right">147</td>
<td class="right">2019</td>
<td class="left">hi</td>
<td class="left">WarmDry</td>
<td class="right">4891</td>
<td class="right">92</td>
<td class="right">3.93</td>
<td class="right">1.87</td>
<td class="right">2.06</td>
<td class="right">0.41</td>
</tr>

<tr>
<td class="right">148</td>
<td class="right">2019</td>
<td class="left">lo</td>
<td class="left">ColdDry</td>
<td class="right">9278</td>
<td class="right">96</td>
<td class="right">3.64</td>
<td class="right">1.97</td>
<td class="right">1.66</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">149</td>
<td class="right">2019</td>
<td class="left">lo</td>
<td class="left">Rainy</td>
<td class="right">14924</td>
<td class="right">95</td>
<td class="right">3.15</td>
<td class="right">1.67</td>
<td class="right">1.48</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">150</td>
<td class="right">2019</td>
<td class="left">lo</td>
<td class="left">WarmDry</td>
<td class="right">4891</td>
<td class="right">92</td>
<td class="right">3.88</td>
<td class="right">1.99</td>
<td class="right">1.89</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">151</td>
<td class="right">2019</td>
<td class="left">mean</td>
<td class="left">ColdDry</td>
<td class="right">9278</td>
<td class="right">96</td>
<td class="right">3.46</td>
<td class="right">1.21</td>
<td class="right">2.25</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">152</td>
<td class="right">2019</td>
<td class="left">mean</td>
<td class="left">Rainy</td>
<td class="right">14924</td>
<td class="right">95</td>
<td class="right">3.34</td>
<td class="right">1.22</td>
<td class="right">2.13</td>
<td class="right">0.00</td>
</tr>

<tr>
<td class="right">153</td>
<td class="right">2019</td>
<td class="left">mean</td>
<td class="left">WarmDry</td>
<td class="right">4891</td>
<td class="right">92</td>
<td class="right">3.60</td>
<td class="right">1.22</td>
<td class="right">2.38</td>
<td class="right">0.00</td>
</tr>
</tbody>
</table>

<div class="org-src-container">
<pre class="src src-R">as.data.frame(rd(d = 2, sr$by.region))
</pre>
</div>

<table id="tab--cv-by-region">


<colgroup>
<col  class="right">

<col  class="left">

<col  class="left">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">
</colgroup>
<thead>
<tr>
<th scope="col" class="right">&#xa0;</th>
<th scope="col" class="left">dv</th>
<th scope="col" class="left">region</th>
<th scope="col" class="right">N</th>
<th scope="col" class="right">stn</th>
<th scope="col" class="right">sd</th>
<th scope="col" class="right">rmse</th>
<th scope="col" class="right">sd - rmse</th>
</tr>
</thead>
<tbody>
<tr>
<td class="right">1</td>
<td class="left">hi</td>
<td class="left">Cuautla</td>
<td class="right">196</td>
<td class="right">1</td>
<td class="right">2.54</td>
<td class="right">1.22</td>
<td class="right">1.32</td>
</tr>

<tr>
<td class="right">2</td>
<td class="left">hi</td>
<td class="left">Cuernavaca</td>
<td class="right">1717</td>
<td class="right">7</td>
<td class="right">4.41</td>
<td class="right">1.93</td>
<td class="right">2.47</td>
</tr>

<tr>
<td class="right">3</td>
<td class="left">hi</td>
<td class="left">Pachuca</td>
<td class="right">132</td>
<td class="right">1</td>
<td class="right">4.29</td>
<td class="right">3.42</td>
<td class="right">0.87</td>
</tr>

<tr>
<td class="right">4</td>
<td class="left">hi</td>
<td class="left">Tlaxcala-Apizaco</td>
<td class="right">224</td>
<td class="right">1</td>
<td class="right">2.71</td>
<td class="right">1.73</td>
<td class="right">0.98</td>
</tr>

<tr>
<td class="right">5</td>
<td class="left">hi</td>
<td class="left">Toluca</td>
<td class="right">1015</td>
<td class="right">4</td>
<td class="right">6.50</td>
<td class="right">2.49</td>
<td class="right">4.01</td>
</tr>

<tr>
<td class="right">6</td>
<td class="left">hi</td>
<td class="left">Valle de México</td>
<td class="right">16859</td>
<td class="right">64</td>
<td class="right">3.60</td>
<td class="right">1.45</td>
<td class="right">2.14</td>
</tr>

<tr>
<td class="right">7</td>
<td class="left">hi</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">3415</td>
<td class="right">13</td>
<td class="right">2.99</td>
<td class="right">1.57</td>
<td class="right">1.42</td>
</tr>

<tr>
<td class="right">8</td>
<td class="left">lo</td>
<td class="left">Cuautla</td>
<td class="right">196</td>
<td class="right">1</td>
<td class="right">1.41</td>
<td class="right">1.51</td>
<td class="right">-0.09</td>
</tr>

<tr>
<td class="right">9</td>
<td class="left">lo</td>
<td class="left">Cuernavaca</td>
<td class="right">1717</td>
<td class="right">7</td>
<td class="right">4.21</td>
<td class="right">1.70</td>
<td class="right">2.51</td>
</tr>

<tr>
<td class="right">10</td>
<td class="left">lo</td>
<td class="left">Pachuca</td>
<td class="right">132</td>
<td class="right">1</td>
<td class="right">3.48</td>
<td class="right">2.14</td>
<td class="right">1.34</td>
</tr>

<tr>
<td class="right">11</td>
<td class="left">lo</td>
<td class="left">Tlaxcala-Apizaco</td>
<td class="right">224</td>
<td class="right">1</td>
<td class="right">2.94</td>
<td class="right">2.54</td>
<td class="right">0.40</td>
</tr>

<tr>
<td class="right">12</td>
<td class="left">lo</td>
<td class="left">Toluca</td>
<td class="right">1015</td>
<td class="right">4</td>
<td class="right">3.44</td>
<td class="right">2.34</td>
<td class="right">1.10</td>
</tr>

<tr>
<td class="right">13</td>
<td class="left">lo</td>
<td class="left">Valle de México</td>
<td class="right">16859</td>
<td class="right">64</td>
<td class="right">3.56</td>
<td class="right">1.76</td>
<td class="right">1.80</td>
</tr>

<tr>
<td class="right">14</td>
<td class="left">lo</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">3415</td>
<td class="right">13</td>
<td class="right">3.22</td>
<td class="right">1.61</td>
<td class="right">1.61</td>
</tr>

<tr>
<td class="right">15</td>
<td class="left">mean</td>
<td class="left">Cuautla</td>
<td class="right">196</td>
<td class="right">1</td>
<td class="right">1.59</td>
<td class="right">1.04</td>
<td class="right">0.55</td>
</tr>

<tr>
<td class="right">16</td>
<td class="left">mean</td>
<td class="left">Cuernavaca</td>
<td class="right">1717</td>
<td class="right">7</td>
<td class="right">4.31</td>
<td class="right">1.22</td>
<td class="right">3.09</td>
</tr>

<tr>
<td class="right">17</td>
<td class="left">mean</td>
<td class="left">Pachuca</td>
<td class="right">132</td>
<td class="right">1</td>
<td class="right">3.28</td>
<td class="right">1.10</td>
<td class="right">2.18</td>
</tr>

<tr>
<td class="right">18</td>
<td class="left">mean</td>
<td class="left">Tlaxcala-Apizaco</td>
<td class="right">224</td>
<td class="right">1</td>
<td class="right">2.16</td>
<td class="right">1.11</td>
<td class="right">1.06</td>
</tr>

<tr>
<td class="right">19</td>
<td class="left">mean</td>
<td class="left">Toluca</td>
<td class="right">1015</td>
<td class="right">4</td>
<td class="right">4.59</td>
<td class="right">1.59</td>
<td class="right">2.99</td>
</tr>

<tr>
<td class="right">20</td>
<td class="left">mean</td>
<td class="left">Valle de México</td>
<td class="right">16859</td>
<td class="right">64</td>
<td class="right">3.18</td>
<td class="right">1.27</td>
<td class="right">1.91</td>
</tr>

<tr>
<td class="right">21</td>
<td class="left">mean</td>
<td class="left">Puebla-Tlaxcala</td>
<td class="right">3415</td>
<td class="right">13</td>
<td class="right">2.75</td>
<td class="right">1.15</td>
<td class="right">1.61</td>
</tr>
</tbody>
</table>

<p>
These by-region results are only for 2018.
</p>

<div class="org-src-container">
<pre class="src src-R">as.data.frame(rd(d = 2, sr$by.network))
</pre>
</div>

<table id="tab--cv-by-network">


<colgroup>
<col  class="right">

<col  class="left">

<col  class="left">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">
</colgroup>
<thead>
<tr>
<th scope="col" class="right">&#xa0;</th>
<th scope="col" class="left">dv</th>
<th scope="col" class="left">network</th>
<th scope="col" class="right">N</th>
<th scope="col" class="right">stn</th>
<th scope="col" class="right">sd</th>
<th scope="col" class="right">rmse</th>
<th scope="col" class="right">sd - rmse</th>
</tr>
</thead>
<tbody>
<tr>
<td class="right">1</td>
<td class="left">hi</td>
<td class="left">emas</td>
<td class="right">2740</td>
<td class="right">14</td>
<td class="right">7.50</td>
<td class="right">2.10</td>
<td class="right">5.40</td>
</tr>

<tr>
<td class="right">2</td>
<td class="left">hi</td>
<td class="left">esimes</td>
<td class="right">823</td>
<td class="right">4</td>
<td class="right">3.31</td>
<td class="right">2.15</td>
<td class="right">1.16</td>
</tr>

<tr>
<td class="right">3</td>
<td class="left">hi</td>
<td class="left">simat</td>
<td class="right">7787</td>
<td class="right">25</td>
<td class="right">3.22</td>
<td class="right">1.26</td>
<td class="right">1.96</td>
</tr>

<tr>
<td class="right">4</td>
<td class="left">hi</td>
<td class="left">smno</td>
<td class="right">285</td>
<td class="right">1</td>
<td class="right">2.85</td>
<td class="right">1.45</td>
<td class="right">1.39</td>
</tr>

<tr>
<td class="right">5</td>
<td class="left">hi</td>
<td class="left">unam</td>
<td class="right">3187</td>
<td class="right">12</td>
<td class="right">2.82</td>
<td class="right">0.94</td>
<td class="right">1.88</td>
</tr>

<tr>
<td class="right">6</td>
<td class="left">hi</td>
<td class="left">wunderground</td>
<td class="right">8736</td>
<td class="right">35</td>
<td class="right">3.54</td>
<td class="right">1.77</td>
<td class="right">1.76</td>
</tr>

<tr>
<td class="right">7</td>
<td class="left">lo</td>
<td class="left">emas</td>
<td class="right">2740</td>
<td class="right">14</td>
<td class="right">5.66</td>
<td class="right">1.92</td>
<td class="right">3.75</td>
</tr>

<tr>
<td class="right">8</td>
<td class="left">lo</td>
<td class="left">esimes</td>
<td class="right">823</td>
<td class="right">4</td>
<td class="right">3.72</td>
<td class="right">2.47</td>
<td class="right">1.25</td>
</tr>

<tr>
<td class="right">9</td>
<td class="left">lo</td>
<td class="left">simat</td>
<td class="right">7787</td>
<td class="right">25</td>
<td class="right">3.34</td>
<td class="right">1.75</td>
<td class="right">1.59</td>
</tr>

<tr>
<td class="right">10</td>
<td class="left">lo</td>
<td class="left">smno</td>
<td class="right">285</td>
<td class="right">1</td>
<td class="right">2.72</td>
<td class="right">1.07</td>
<td class="right">1.65</td>
</tr>

<tr>
<td class="right">11</td>
<td class="left">lo</td>
<td class="left">unam</td>
<td class="right">3187</td>
<td class="right">12</td>
<td class="right">2.76</td>
<td class="right">1.04</td>
<td class="right">1.72</td>
</tr>

<tr>
<td class="right">12</td>
<td class="left">lo</td>
<td class="left">wunderground</td>
<td class="right">8736</td>
<td class="right">35</td>
<td class="right">3.66</td>
<td class="right">1.89</td>
<td class="right">1.77</td>
</tr>

<tr>
<td class="right">13</td>
<td class="left">mean</td>
<td class="left">emas</td>
<td class="right">2740</td>
<td class="right">14</td>
<td class="right">6.53</td>
<td class="right">1.50</td>
<td class="right">5.02</td>
</tr>

<tr>
<td class="right">14</td>
<td class="left">mean</td>
<td class="left">esimes</td>
<td class="right">823</td>
<td class="right">4</td>
<td class="right">2.93</td>
<td class="right">1.10</td>
<td class="right">1.82</td>
</tr>

<tr>
<td class="right">15</td>
<td class="left">mean</td>
<td class="left">simat</td>
<td class="right">7787</td>
<td class="right">25</td>
<td class="right">2.77</td>
<td class="right">1.04</td>
<td class="right">1.73</td>
</tr>

<tr>
<td class="right">16</td>
<td class="left">mean</td>
<td class="left">smno</td>
<td class="right">285</td>
<td class="right">1</td>
<td class="right">2.41</td>
<td class="right">0.74</td>
<td class="right">1.67</td>
</tr>

<tr>
<td class="right">17</td>
<td class="left">mean</td>
<td class="left">unam</td>
<td class="right">3187</td>
<td class="right">12</td>
<td class="right">2.47</td>
<td class="right">0.72</td>
<td class="right">1.74</td>
</tr>

<tr>
<td class="right">18</td>
<td class="left">mean</td>
<td class="left">wunderground</td>
<td class="right">8736</td>
<td class="right">35</td>
<td class="right">3.20</td>
<td class="right">1.51</td>
<td class="right">1.68</td>
</tr>
</tbody>
</table>

<p>
These by-network results are only for 2018.
</p>

<div class="org-src-container">
<pre class="src src-R">time.series.plot()
</pre>
</div>


<figure id="fig--g/cv-time-series"><div class="figure-label"><a class="internal figure-label-text" href="#fig--g/cv-time-series">g/cv-time-series</a></div>
<img 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" alt="cv-time-series.png">

</figure>

<div class="org-src-container">
<pre class="src src-R">pred.error.plot()
</pre>
</div>


<figure id="fig--g/error-density"><div class="figure-label"><a class="internal figure-label-text" href="#fig--g/error-density">g/error-density</a></div>
<img src="data:image/png;base64,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" alt="error-density.png">

</figure>
</div>

<div class="outline-3">
<h3 id="sec--with-vs-without-training">With vs. without training Wunderground</h3>
<div class="outline-text-3">
<div class="org-src-container">
<pre class="src src-R">x = lapply(c(<span style="color: #00688b; font-weight: bold;">F</span>, <span style="color: #00688b; font-weight: bold;">T</span>), <span style="color: #cd0000; font-weight: bold;">function</span>(train.wunder)
    summarize.cv.results(multi.run.cv(year(earliest.date) : latest.year,
        train.wunder = train.wunder), test.wunder = <span style="color: #00688b; font-weight: bold;">F</span>)$overall)
x = cbind(x[[1]][, .(year, dv, N, rmseF = rmse.s)],
    rmseT = x[[2]]$rmse.s)
x[, diff := rmseF - rmseT]
</pre>
</div>

<div class="org-src-container">
<pre class="src src-R">rd(as.data.frame(x))
</pre>
</div>

<table id="tab--with-vs-without-wunderground">


<colgroup>
<col  class="right">

<col  class="right">

<col  class="left">

<col  class="right">

<col  class="right">

<col  class="right">

<col  class="right">
</colgroup>
<thead>
<tr>
<th scope="col" class="right">&#xa0;</th>
<th scope="col" class="right">year</th>
<th scope="col" class="left">dv</th>
<th scope="col" class="right">N</th>
<th scope="col" class="right">rmseF</th>
<th scope="col" class="right">rmseT</th>
<th scope="col" class="right">diff</th>
</tr>
</thead>
<tbody>
<tr>
<td class="right">1</td>
<td class="right">2003</td>
<td class="left">hi</td>
<td class="right">9622</td>
<td class="right">1.816</td>
<td class="right">1.816</td>
<td class="right">0.000</td>
</tr>

<tr>
<td class="right">2</td>
<td class="right">2003</td>
<td class="left">lo</td>
<td class="right">9622</td>
<td class="right">1.932</td>
<td class="right">1.932</td>
<td class="right">0.000</td>
</tr>

<tr>
<td class="right">3</td>
<td class="right">2003</td>
<td class="left">mean</td>
<td class="right">9622</td>
<td class="right">1.205</td>
<td class="right">1.205</td>
<td class="right">0.000</td>
</tr>

<tr>
<td class="right">4</td>
<td class="right">2004</td>
<td class="left">hi</td>
<td class="right">10453</td>
<td class="right">1.765</td>
<td class="right">1.765</td>
<td class="right">0.000</td>
</tr>

<tr>
<td class="right">5</td>
<td class="right">2004</td>
<td class="left">lo</td>
<td class="right">10453</td>
<td class="right">2.027</td>
<td class="right">2.027</td>
<td class="right">0.000</td>
</tr>

<tr>
<td class="right">6</td>
<td class="right">2004</td>
<td class="left">mean</td>
<td class="right">10453</td>
<td class="right">1.368</td>
<td class="right">1.368</td>
<td class="right">0.000</td>
</tr>

<tr>
<td class="right">7</td>
<td class="right">2005</td>
<td class="left">hi</td>
<td class="right">11489</td>
<td class="right">1.875</td>
<td class="right">1.875</td>
<td class="right">0.000</td>
</tr>

<tr>
<td class="right">8</td>
<td class="right">2005</td>
<td class="left">lo</td>
<td class="right">11489</td>
<td class="right">2.158</td>
<td class="right">2.158</td>
<td class="right">0.000</td>
</tr>

<tr>
<td class="right">9</td>
<td class="right">2005</td>
<td class="left">mean</td>
<td class="right">11489</td>
<td class="right">1.403</td>
<td class="right">1.403</td>
<td class="right">0.000</td>
</tr>

<tr>
<td class="right">10</td>
<td class="right">2006</td>
<td class="left">hi</td>
<td class="right">10882</td>
<td class="right">1.839</td>
<td class="right">1.836</td>
<td class="right">0.003</td>
</tr>

<tr>
<td class="right">11</td>
<td class="right">2006</td>
<td class="left">lo</td>
<td class="right">10882</td>
<td class="right">1.968</td>
<td class="right">1.959</td>
<td class="right">0.009</td>
</tr>

<tr>
<td class="right">12</td>
<td class="right">2006</td>
<td class="left">mean</td>
<td class="right">10882</td>
<td class="right">1.400</td>
<td class="right">1.397</td>
<td class="right">0.003</td>
</tr>

<tr>
<td class="right">13</td>
<td class="right">2007</td>
<td class="left">hi</td>
<td class="right">9854</td>
<td class="right">1.874</td>
<td class="right">1.880</td>
<td class="right">-0.006</td>
</tr>

<tr>
<td class="right">14</td>
<td class="right">2007</td>
<td class="left">lo</td>
<td class="right">9854</td>
<td class="right">1.850</td>
<td class="right">1.858</td>
<td class="right">-0.007</td>
</tr>

<tr>
<td class="right">15</td>
<td class="right">2007</td>
<td class="left">mean</td>
<td class="right">9854</td>
<td class="right">1.295</td>
<td class="right">1.285</td>
<td class="right">0.010</td>
</tr>

<tr>
<td class="right">16</td>
<td class="right">2008</td>
<td class="left">hi</td>
<td class="right">11378</td>
<td class="right">1.799</td>
<td class="right">1.942</td>
<td class="right">-0.142</td>
</tr>

<tr>
<td class="right">17</td>
<td class="right">2008</td>
<td class="left">lo</td>
<td class="right">11378</td>
<td class="right">1.982</td>
<td class="right">1.996</td>
<td class="right">-0.013</td>
</tr>

<tr>
<td class="right">18</td>
<td class="right">2008</td>
<td class="left">mean</td>
<td class="right">11378</td>
<td class="right">1.387</td>
<td class="right">1.440</td>
<td class="right">-0.053</td>
</tr>

<tr>
<td class="right">19</td>
<td class="right">2009</td>
<td class="left">hi</td>
<td class="right">12743</td>
<td class="right">2.027</td>
<td class="right">2.098</td>
<td class="right">-0.071</td>
</tr>

<tr>
<td class="right">20</td>
<td class="right">2009</td>
<td class="left">lo</td>
<td class="right">12743</td>
<td class="right">2.068</td>
<td class="right">2.071</td>
<td class="right">-0.003</td>
</tr>

<tr>
<td class="right">21</td>
<td class="right">2009</td>
<td class="left">mean</td>
<td class="right">12743</td>
<td class="right">1.462</td>
<td class="right">1.480</td>
<td class="right">-0.018</td>
</tr>

<tr>
<td class="right">22</td>
<td class="right">2010</td>
<td class="left">hi</td>
<td class="right">13357</td>
<td class="right">1.788</td>
<td class="right">2.302</td>
<td class="right">-0.514</td>
</tr>

<tr>
<td class="right">23</td>
<td class="right">2010</td>
<td class="left">lo</td>
<td class="right">13357</td>
<td class="right">2.070</td>
<td class="right">2.157</td>
<td class="right">-0.087</td>
</tr>

<tr>
<td class="right">24</td>
<td class="right">2010</td>
<td class="left">mean</td>
<td class="right">13357</td>
<td class="right">1.411</td>
<td class="right">1.741</td>
<td class="right">-0.330</td>
</tr>

<tr>
<td class="right">25</td>
<td class="right">2011</td>
<td class="left">hi</td>
<td class="right">13376</td>
<td class="right">1.974</td>
<td class="right">2.030</td>
<td class="right">-0.056</td>
</tr>

<tr>
<td class="right">26</td>
<td class="right">2011</td>
<td class="left">lo</td>
<td class="right">13376</td>
<td class="right">2.001</td>
<td class="right">1.984</td>
<td class="right">0.017</td>
</tr>

<tr>
<td class="right">27</td>
<td class="right">2011</td>
<td class="left">mean</td>
<td class="right">13376</td>
<td class="right">1.473</td>
<td class="right">1.464</td>
<td class="right">0.009</td>
</tr>

<tr>
<td class="right">28</td>
<td class="right">2012</td>
<td class="left">hi</td>
<td class="right">14074</td>
<td class="right">1.813</td>
<td class="right">1.828</td>
<td class="right">-0.015</td>
</tr>

<tr>
<td class="right">29</td>
<td class="right">2012</td>
<td class="left">lo</td>
<td class="right">14074</td>
<td class="right">1.969</td>
<td class="right">1.937</td>
<td class="right">0.031</td>
</tr>

<tr>
<td class="right">30</td>
<td class="right">2012</td>
<td class="left">mean</td>
<td class="right">14074</td>
<td class="right">1.392</td>
<td class="right">1.376</td>
<td class="right">0.015</td>
</tr>

<tr>
<td class="right">31</td>
<td class="right">2013</td>
<td class="left">hi</td>
<td class="right">15622</td>
<td class="right">2.102</td>
<td class="right">2.145</td>
<td class="right">-0.044</td>
</tr>

<tr>
<td class="right">32</td>
<td class="right">2013</td>
<td class="left">lo</td>
<td class="right">15622</td>
<td class="right">1.979</td>
<td class="right">1.957</td>
<td class="right">0.022</td>
</tr>

<tr>
<td class="right">33</td>
<td class="right">2013</td>
<td class="left">mean</td>
<td class="right">15622</td>
<td class="right">1.342</td>
<td class="right">1.335</td>
<td class="right">0.006</td>
</tr>

<tr>
<td class="right">34</td>
<td class="right">2014</td>
<td class="left">hi</td>
<td class="right">16907</td>
<td class="right">2.049</td>
<td class="right">2.066</td>
<td class="right">-0.017</td>
</tr>

<tr>
<td class="right">35</td>
<td class="right">2014</td>
<td class="left">lo</td>
<td class="right">16907</td>
<td class="right">1.992</td>
<td class="right">1.965</td>
<td class="right">0.027</td>
</tr>

<tr>
<td class="right">36</td>
<td class="right">2014</td>
<td class="left">mean</td>
<td class="right">16907</td>
<td class="right">1.300</td>
<td class="right">1.291</td>
<td class="right">0.009</td>
</tr>

<tr>
<td class="right">37</td>
<td class="right">2015</td>
<td class="left">hi</td>
<td class="right">18598</td>
<td class="right">1.990</td>
<td class="right">2.002</td>
<td class="right">-0.012</td>
</tr>

<tr>
<td class="right">38</td>
<td class="right">2015</td>
<td class="left">lo</td>
<td class="right">18598</td>
<td class="right">1.845</td>
<td class="right">1.841</td>
<td class="right">0.004</td>
</tr>

<tr>
<td class="right">39</td>
<td class="right">2015</td>
<td class="left">mean</td>
<td class="right">18598</td>
<td class="right">1.192</td>
<td class="right">1.208</td>
<td class="right">-0.016</td>
</tr>

<tr>
<td class="right">40</td>
<td class="right">2016</td>
<td class="left">hi</td>
<td class="right">19899</td>
<td class="right">2.011</td>
<td class="right">2.028</td>
<td class="right">-0.017</td>
</tr>

<tr>
<td class="right">41</td>
<td class="right">2016</td>
<td class="left">lo</td>
<td class="right">19899</td>
<td class="right">2.090</td>
<td class="right">2.075</td>
<td class="right">0.015</td>
</tr>

<tr>
<td class="right">42</td>
<td class="right">2016</td>
<td class="left">mean</td>
<td class="right">19899</td>
<td class="right">1.260</td>
<td class="right">1.294</td>
<td class="right">-0.034</td>
</tr>

<tr>
<td class="right">43</td>
<td class="right">2017</td>
<td class="left">hi</td>
<td class="right">18459</td>
<td class="right">1.977</td>
<td class="right">2.012</td>
<td class="right">-0.035</td>
</tr>

<tr>
<td class="right">44</td>
<td class="right">2017</td>
<td class="left">lo</td>
<td class="right">18459</td>
<td class="right">2.284</td>
<td class="right">2.310</td>
<td class="right">-0.026</td>
</tr>

<tr>
<td class="right">45</td>
<td class="right">2017</td>
<td class="left">mean</td>
<td class="right">18459</td>
<td class="right">1.365</td>
<td class="right">1.425</td>
<td class="right">-0.060</td>
</tr>

<tr>
<td class="right">46</td>
<td class="right">2018</td>
<td class="left">hi</td>
<td class="right">14822</td>
<td class="right">1.731</td>
<td class="right">1.808</td>
<td class="right">-0.077</td>
</tr>

<tr>
<td class="right">47</td>
<td class="right">2018</td>
<td class="left">lo</td>
<td class="right">14822</td>
<td class="right">1.904</td>
<td class="right">1.946</td>
<td class="right">-0.041</td>
</tr>

<tr>
<td class="right">48</td>
<td class="right">2018</td>
<td class="left">mean</td>
<td class="right">14822</td>
<td class="right">1.100</td>
<td class="right">1.241</td>
<td class="right">-0.141</td>
</tr>

<tr>
<td class="right">49</td>
<td class="right">2019</td>
<td class="left">hi</td>
<td class="right">17058</td>
<td class="right">1.954</td>
<td class="right">2.128</td>
<td class="right">-0.174</td>
</tr>

<tr>
<td class="right">50</td>
<td class="right">2019</td>
<td class="left">lo</td>
<td class="right">17058</td>
<td class="right">2.040</td>
<td class="right">2.054</td>
<td class="right">-0.014</td>
</tr>

<tr>
<td class="right">51</td>
<td class="right">2019</td>
<td class="left">mean</td>
<td class="right">17058</td>
<td class="right">1.127</td>
<td class="right">1.285</td>
<td class="right">-0.158</td>
</tr>
</tbody>
</table>

<p>
<code>rmseF</code> is the spatial RMSE obtained from a CV that tests on non-Wunderground stations and trains on non-Wunderground stations. <code>rmseT</code> is similar except Wunderground stations are allowed in training. <code>diff</code> is <code>rmseF - rmseT</code>, so positive <code>diff</code> means an improvement in RMSE when Wunderground is included in training.
</p>

<div class="org-src-container">
<pre class="src src-R">round(d = 5, mean(x[year &gt; 2005, diff]))
</pre>
</div>

<table>


<colgroup>
<col  class="left">

<col  class="right">
</colgroup>
<thead>
<tr>
<th scope="col" class="left">&#xa0;</th>
<th scope="col" class="right">value</th>
</tr>
</thead>
<tbody>
<tr>
<td class="left">&#xa0;</td>
<td class="right">-0.04761</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>

<div class="outline-2">
<h2 id="sec--learning-curve">Learning curve</h2>
<div class="outline-text-2">
<div class="org-src-container">
<pre class="src src-R">learning.curve.plot()
</pre>
</div>


<figure id="fig--g/learning-curve"><div class="figure-label"><a class="internal figure-label-text" href="#fig--g/learning-curve">g/learning-curve</a></div>
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAHgCAIAAADytinCAAAACXBIWXMAABJ0AAASdAHeZh94AAAgAElEQVR4nO3deVwUR/o/8BpOOQYREaJ4oCIuHiteKF6YgAfejoRdJd9FsoiCJDEYYxJ0owaJrgoYFUUUjFdiNBoMRoxiSEzURF0RwWDEK0qMoiAg18Awvz/6Z+8sR8PAzHSV/Xm//KOmp+l5uqd9uqaqulqmVqsJAADQx0jsAAAAoGFI0AAAlEKCBgCgFBI0AAClTAzzMU+fPk1OTs7MzFQqlb179547d66zs3OddVQq1aeffnr27NmamhoPD4958+aZmpoaJjwAAArJDDOKY/ny5SUlJcHBwebm5keOHMnKytq8eXO7du0010lMTDx79mxoaKiJicnWrVv79Onz9ttvGyA2AAA6GaKJ48mTJ1euXAkNDe3fv7+rq+s777xDCPnll18016moqDh58mRwcLCHh8egQYMWLFhw5syZ4uJiA4QHAEAnQzRx1NbWzp49u2fPntzLmpoapVJZW1uruc7du3crKyvd3d25lwMGDFCpVLdu3Ro4cCC3JDk5+cKFC1zZ2tr63//+twEiBwAQkSESdIcOHWbPns2Vq6qq4uLi5HL5qFGjNNcpKioyMTGxsrL6/2GZmFhbWxcWFvIr3Lx5k69029raTp8+3QCRAwDolaOj4/bt2xt710CdhIQQtVr93Xff7d2719HRMTY2Vi6X13lXJpPV+ROVSsWXo6KioqKiuHJRUVF4ePi+ffv0HTMAgP5UV1f7+fkJrGCgBF1cXLx27dqHDx8GBgaOGTOmfi62s7Orrq6uqKiwsLAghKhUqmfPntnb2xsmPAAAChmik1CtVq9cudLS0nLTpk1eXl71szMhpGvXrubm5levXuVeXrt2zcjIqHv37gYIDwCAToaoQWdlZd28eXP69Ok3btzgFzo5Odnb26enpyuVSl9fX0tLSx8fn+Tk5Pbt28tksh07dnh5edUZhwcAICmGSNC3b99Wq9UbNmzQXDh//vzJkydnZGSUlZX5+voSQoKDg5OSklavXl1bWzts2LDg4GADxAYAQC0D3aiiW+gkBIAXANdJmJKS0tgKmIsDAIBSSNAAAJRCggYAoBQSNAAApZCgAQAohQQNAEApJGgAAEohQQMAUAoJGgCAUkjQAACUQoIGAKAUEjQAAKWQoAEAKIUEDQBAKSRoAABKIUEDAFAKCRoAgFJI0AAAlEKCBgCgFBI0AAClkKABACiFBA0AQCkkaAAASiFBAwBQCgkaAIBSUknQJ06ckD23evVqscMBAGiaidgBGMjo0aPz8vK4sp2dnbjBAAA0h1QStKWlZc+ePcWOAgBAC1Jp4gAAYA4SNAAApZCgAQAohQQNAEApJGgAAEoZdBRHTU1NYGDgtm3b5HJ5/XcLCgqSk5OzsrLMzMzc3d2Dg4MtLS0NGR4AAFUMVINWKpVZWVkxMTGlpaUNrlBZWRkZGVlVVbV8+fK33377/v37H3/8sWFiAwCgk4ESdGpqalxc3NWrVxtb4fLly4WFhe+++27v3r379+//7rvvXrly5e7du7oKAHcSAgBzDNTEoVAoFApFXl5eREREgyuUlZWZmJiYmZlxL62trWUy2d27d7t168YtuXDhwr1797hyTU2NtgHgTkIAYA4tdxL+9a9/ValUe/bs8fPzq6ys3LVrl1qtfvr0Kb9CSkpKWloaV7a1tXV0dNRq+7iTEACYQ0uCdnBwWLp0aXx8/KFDh0xNTRUKhbW1tY2NDb/C9OnTBw0axJVrampSUlJEihQAwEBoSdCEkCFDhiQlJRUVFcnlcpVK9cUXX7Rv355/d+jQoUOHDuXKRUVFSNAA8MKjZRx0cXHxunXr7t+/365dOxMTk/Pnz9vY2Li5uYkdFwCAaERO0Onp6cePHyeEtG3bNj8/f9OmTVevXj179mxiYuLMmTNNTCiq4AMAGJjICTojI+PkyZNc+YMPPrCwsIiKivrss8/8/f0VCoW4sQEAiEumVqvFjkFrRUVF4eHh+/btEzsQAICWq66u9vPzE+hRo6UNGgAA6kCCBgCgFBI0AAClkKABACiFBA0AQCkkaAAASiFBAwBQCgkaAIBSSNAAAJRCggYAoBQSNAAApZCgAQAohQQNAEApJGgAAEohQQMAUAoJGgCAUkjQAACUQoIGAKAUEjQAAKWQoAEAKIUEDQBAKSRoAABKIUEDAFAKCRoAgFJI0AAAlEKCBgCgFBI0AAClkKABACiFBA0AQCkkaAAASiFBAwBQCgkaAIBSSNAAAJQyMeSH1dTUBAYGbtu2TS6X13/36dOnycnJly9fVqlUAwYMeP311+3t7Q0ZHgAAVQyUoJVKZW5ublpaWmlpaWPrrF27VqVShYWFGRsbf/XVVx999NHGjRsNEx4AAIUMlKBTU1NTU1Orq6sbW0GpVF67dm3lypXu7u6EELlc/u677z59+tTW1tYwEQIA0MZACVqhUCgUiry8vIiIiAZXMDMz69Onz7ffftuhQwdjY+Pjx487OztrZueKigo+vz979swQQQMAiMqgbdDC3nvvvbCwsB9//JEQYmlpuXnzZs13V69enZaWxpVtbW0dHR1FCBEAwIBoSdCVlZXLli0bPHjwrFmzjIyMjh49unz58nXr1llbW3Mr9OzZ08PDgyubmZkVFBSIFywAgCHQkqAvXbr06NGjuLg4Y2NjQkhYWFhQUNAvv/zyyiuvcCsEBQUFBQVx5aKiovDwcNFiBQAwCFrGQdfU1KjVarVazb1Uq9W1tbUCnYoAAC88kRN0enr68ePHCSGDBg2ytLRct27db7/99ttvv8XFxdXW1vJtGq134sQJ2XOrV6/W1WYBAPRH5CaOjIyMsrIyX19fuVy+evXq3bt3f/TRR7W1tb179169enW7du109UGjR4/Oy8vjynZ2drraLACA/sj4VgWGcG3Q+/btEzsQAICWq66u9vPzS0lJaWwFWtqgAQCgDiRoAABKIUEDAFAKCRoAgFJI0AAAlEKCBgCgFBI0AAClkKABACiFBA0AQCkkaAAASiFBAwBQCgkaAIBSSNAAAJRCggYAoBQSNAAApZCgAQAohQQNAEApJGgAAEohQQMAUAoJGgCAUkjQAACUQoIGAKAUEjQAAKWQoAEAKIUEDQBAKSRoAABKIUEDAFAKCRoAgFJI0AAAlEKCBgCgFBI0AAClkKABACiFBA0AQCkkaAAASpkY8sNqamoCAwO3bdsml8vrvHX27Nk1a9bUWejt7f3WW28ZKjoAALoYKEErlcrc3Ny0tLTS0tIGV+jTp8+KFSs019+4caOHh4dhwgMAoJCBEnRqampqamp1dXVjK9ja2g4aNIh/uXXr1ldeecXT09Mg0QEA0MhACVqhUCgUiry8vIiIiCZXzszMvHz58pYtWzQXJicnX7hwgSubmZnpJUoAAJpQ10lYW1u7c+fOwMBAU1NTzeU3b9785bns7GxtN3vixAnZc6tXr9ZdvAAA+mLQTsLm+O6774yMjEaOHFlneWRk5LvvvsuVi4uL33vvPa02O3r06Ly8PK5sZ2fX+jgBAPSNugT99ddfT5w4sf5yCwsLCwsLrqxSqbTdrKWlZc+ePVsbHACAAdHVxJGbm3vv3j0vLy+xAwEAEJ/ICTo9Pf348eP8y7Nnz7q6ulpaWooYEgAAJURO0BkZGSdPnuRfXrp0qV+/fvr4IHQSAgBzZGq1WuwYtFZUVBQeHr5v377m/0l5efmDBw+4sp2dXbt27fQTGgBAc1VXV/v5+aWkpDS2AnWdhHqCTkIAYA5dnYQAAMBDggYAoBQSNAAApZCgAQAohQQNAEApJGgAAEohQQMAUEoqCRp3EgIAc6RyowqmGwUA5kglQeNOQgBgjlSaOKKjo/kmjnHjxokdDgBA06RSgw4JCeEfQevs7CxqLAAAzSKVGvSlS5deeW7//v1ihwMA0DSp1KDRSQgAzJFKgkYnIQAwRypNHBgHDQDMkUoNGk0cAMAcqSRoNHEAAHOk0sQBAMCc5tag1Wp1dnb2999/f+fOnYcPH6rV6pdeesnZ2dnLy6tfv34ymUyvUQIASFDTCfrWrVsJCQm7du169OiRiYlJ+/bt27dvTwgpLCx8/PhxTU1Nhw4d5s6du2DBgh49eug/YAAAqRBq4njy5Mm8efNcXV3Pnj37/vvvnz9//tmzZ3/++WdOTk5OTs6DBw+ePXv2888/R0ZGnj9/3tXVdd68eU+ePDFY6FrBKA4AYI5QDXrAgAGvvvrqzZs3u3Xr1uAK5ubmHh4eHh4eb7311u+//x4bG+vu7n7v3j39hNoqGMUBAMwRqkFfvHgxNja2sexcR9euXWNjY3/55RcdBaZjZ86ccXkuPj5e7HAAAJomlKA3bdrU5N/fv39/0qRJ/MuOHTvqICg94GrQnLCwMLHDAQBomlATR3R0dFlZWVxcXGMrJCUlRUREFBcX6yEwHcM4aABgjlAN2t/ff+PGjSEhIbW1tXXe+v333ydMmPDPf/7T2to6NTVVnxECAEiUUA36s88+s7GxSUxMLC8v//TTT42NjbnliYmJixcvLi0tDQ4OXr9+fdu2bQ0SKgCAtAglaCMjo8TExLZt227YsKGiouKzzz77448/goOD09PTnZ2dDx8+7OPjY7BAAQCkpukbVdavX29ra7t8+fLRo0dfu3atrKwsPDx8zZo1VlZWBogPAECymjUXx7JlyzZu3HjhwgWlUnn69OlNmzYxl51xowoAMKe5kyW9+eabycnJKpVqy5Yt1dXVeo1JH6qqqvhyRUWFiJEAADSTUBPHzZs3NV+OGjXq448/Xrp0qUKhiImJMTL6b3Jv5gi2mpqawMDAbdu2yeXyBldIT08/duxYfn6+q6vrggULnJycmrPZ5vDx8cGdhADAFqEE7eLi0uDy1NTUOkPr1Gq18Mcolcrc3Ny0tLTS0tLG1klPT09ISAgJCXFwcDh48OBHH30UHx+veRloDXbHQZ84cWLixIlcOSoqKjIyUtx4AMBghBL0okWLdPUxXE4XaBtRq9WHDh0KDAzkRoZ06tRp586djx8/dnBw0EkA7KY5zCICIFlCCTo2NlZXH6NQKBQKRV5eXkRERIMr3L9/Pz8/39PTU61Wl5SU2NvbL126VHOFCxcu8NMw1dTUaBsAu2mO3bo/ALSSUILOz8/XthW4BX/CefLkibGxcUZGxoEDByoqKuzs7EJCQkaMGMGvkJKSkpaWxpVtbW0dHR212v6ZM2cYrUEDgGQJJejhw4fPmjVr0aJFzs7OTW7o9u3bsbGxR44cadl0oyUlJSqVKjc3d9OmTdbW1t9888369es3btzYpUsXboXp06cPGjSIK9fU1KSkpGi1fXZr0AAgWUJdcFeuXKmoqHBxcRk1alRMTMy5c+cqKys1V6isrDx37lxMTMzIkSN79epVVVV15cqVlsXB3S8eGhrq4OBgaWnp5+dnZ2d3+fJlfoWhQ4cqnhs3bpy22+caCjjt2rVrWZAAAIYkVIO2s7NLSEh47733EhIS1q1b9+effxobG9vZ2XE10MLCwsLCQpVK9dJLLwUGBu7du7d79+4tjsPJyUkmkz179ozLniqVqqqqirnbYQAAdKjpW727d+++Zs2aNWvW5OTk/PDDD7dv33748KFMJnNwcOjevfuYMWP69u3b4o9PT09XKpW+vr729vYjR46MiYmZO3eulZVVSkqKsbGxh4dHi7cMAMC65j7VmxDSt2/f1uTiBmVkZJSVlfn6+hJCFi1atGPHjo0bN1ZVVbm5uUVHRzd2PwsAgBRokaBbz8XF5ejRo5pLPvroI75sZmamv2edsDsOGgAkSzf36dGP3UdeYZonAMkyaA1aROyOg8YAQQDJkkqCZjfN4U5CAMmSSoJGmgMA5rS2DTo/P//nn3/WSSh6hZZcAGCOUILu0qXLunXrNJcEBQUdPnxYc8mOHTuGDx+ul9B0ChP2AwBzhJo47t+/X1JSorlk165dnTt3VigUeo5K9zBhPwAwB23QAACUkso4aAAA5iBBAwBQCgkaAIBSUknQGGYHAMxpopMwJyfnwIEDAkuys7P1EpeusXsnIQBIVhMJ+siRI0eOHBFewgSM4gAA5ggl6L179xosDn1jd7pRdiMHgFYSStABAQEGi0Pf2G3iYDdyAGilltyoUlpa+tNPPxkbGw8dOtTW1lbnMekDu00c7EYOAK3UxCiOkpKSt99+e+jQoXwl7vz58y4uLr6+vuPHj3dycvrss8/0HyQAgBQJ1aBLS0sHDx6cl5fXt2/fNm3aEEKqq6v9/PwKCwvff//9bt26JSQkBAQE/PWvf9X5swoBAECoBh0TE3Pz5s0jR45kZ2d37tyZEPL111/n5+fPnTs3Ojp6/vz533//va2tbZ0Z7wAAQCeEEvTRo0enTJkyY8YMfklaWhohJCIignspl8snTZr0n//8R68hAgBIk1CCvnXr1uDBgzWXpKenu7m5ubm58UucnJxu376tr+gAACRMKEEbGxur1Wr+5a1bt27duuXt7a25TmFhoZWVlb6iAwCQMKEE3atXr4yMDP7lzp07CSF1EvSFCxd69Oihn9gAACRNKEH/4x//+P7771etWlVcXJydnb1161Zra2sfHx9+ha1bt165cmXWrFn6jxMAQHKEEvS8efMmTJjw4Ycf2tra9u/fv6io6N1337W2tiaE7NmzZ9y4cWFhYb169QoLCzNUtAAAEiI0DtrExOT48eO7d+8+c+ZMWVnZpEmTXnvtNe6to0ePZmVlzZ07d+PGjRYWFgYJFQBAWpq41VsmkwUGBgYGBtZZvmvXLvQNAgDoVQsn7GcuO2PCfsOLjo7mj/m4cePEDgeAPUI16Hbt2jVzK0VFRboIRo8wJ5zhhYSEeHp6cmVnZ2dRYwFgklCCfvr0KSHEwcFhxIgRJiYtmfeOHpgTzvDs7e1ffvllsaMAYJhQ2l24cOGRI0f++OOPn376afr06QqFwtvb28zMzGDBAQBImVAb9ObNm+/fv3/u3LmgoKCMjIxJkyZ16NAhICDg8OHD5eXlBgsRAECamugklMlkw4cPX7t27Y0bN7KyshYvXpyTkzNr1ix7e3uFQrF3716uGaSZampqAgICSktLG3z30KFD0zTMnDlTi/1oCjoJAYA5WrQs9+/fv3///v/6179u3bp15MiRw4cPBwYGGhsbv/LKK9wsdwKUSmVubm5aWlpj2ZkQkp+fP2TIkClTpnAvZTJZ82NrEjoJAYA5Len669Gjx+LFi2fMmLFly5ZPPvnkxIkTTf5JampqampqdXW1wDr5+fmjR48eNGhQC0JqEjoJAYA5Wo+D/vXXX6OiogYOHOji4rJ58+Zx48Zt3769yb9SKBRJSUkffvihwDr5+fmZmZlBQUFz5sxZtWpVfn6+trEJYLeJg93IAaCVmluDzszM/PLLL7/88stff/3VwsJiwoQJixcvnjJliq4eGltSUlJaWiqTyd555x2VSnXgwIFly5Zt2bLF0tKSW2HZsmV8Q4qtra2jo6NW22e3iYPdyAGglYQStFqt/uWXX7i8fOvWLRsbm8mTJ69atcrX11fndxJaWVklJyfb2dlxTc89e/YMDAy8cOGCl5cXt0LPnj09PDy4spmZWUFBgVbbZ7eJg93IT5w4MXHiRK4cFRUVGRkpbjwAzBFK0F26dMnPz2/fvv20adM++eQTHx8fc3NzPcVhbGzcvn17/qWVlZWjo+Pjx4/5JUFBQUFBQVy5qKgoPDxcT5GArqDuD9BKQgmaawUuKiras2fPnj17BNYU7v1rjgsXLuzevTs6OloulxNCKisrCwoKuCfVAqPYrfsDUEIoQfOTi+pPenq6Uqn09fXt27dvaWnphg0bZsyYYWZm9sUXXzg6Og4ZMkTfAdAPDQUAkiWUoIVrzTqRkZFRVlbm6+traWm5cuXKnTt3rlmzxtzc3N3dfdGiRcbGxvoOgH5VVVV8uaKiQsRItIVLC0AryTQfC9syhw8fVigUOommmbg26H379jX/T9hNFuXl5Q8ePODKdnZ2zZ9iUHTsRg5gGNXV1X5+fikpKY2t0MQwux9++GHt2rW//vprmzZtpkyZsnLlSgsLi1OnTqWnpz9+/LigoODu3buZmZmtz/L6dv78eb588uRJhhL0mTNnGL20oA0aoJWEEvTp06d9fHzUarWdnV1xcfG6detycnImTZqkOYKic+fO48eP13+craXZjalSqUSMRFsYCwEgWUIJOioqytTU9NixY9yTvDMyMiZOnHjy5MkpU6bExsY6OzsbGRkZGbXwmSwGtmjRIm9vb67M1uTxqIcCSJZQes3Ozp45cyaXnQkhY8eO9fPzq66ujo+Pd3FxMTExYSU7k+eTx3O6d+8udjhawK3eAJIllGELCgrq5DLuZZcuXfQblB6wm+a4Jg5OWFiY2OFogd1jDkCJJjoJ6zzpit0HX7HbkstuEwe7xxyAEqwmXG2xm+bYhWMO0ErMNCIDAEhNEzXoS5cuJSQk8C8vXrxICNFcwpk/f77OIwNOdHQ0P/bZx8fn5MmT4sYDAAYjdCdh8x86ZeAbVVpwJyG7Hj9+fPXqVa7s7OzM1hAUABDQqjsJU1NT9RASaOfSpUuM3kkIAK0klKAnT55ssDigMeyOhWB3/hMASkhlFAe72B0Lwe6lBYASSNCgL+xeWgAoIZVhduPHj+fvatN8thZAfbgHEighlRr05s2b+T5PPKgFhKFxBighlQR96NAhjCaGZkLjDFBCKgk6JCTE09OTK7M13SgASJZU2qAvXbr0ynP79+8XOxygWnR0NN8GPW7cOLHDAemSSoJm99Gr7GK3q61Xr158ecCAASJGAhInlSYOHx8fRrt92L3dg92utsmTJzMaObxgpFKDPnPmjMtz8fHxYoejBXbr/uwec66TkIOHkYOIpFKDZrc2x27dn91jDkAJqSRodgdOsRv5mTNnGG2cAaCEVJo42O2wYhe7T1MEoIRUatDs/txmd8J+duv+AJSQSoJmN1lkZ2fz5Rs3bogYCQAYmFQSNLuD1dauXctPzN23b19xgwEAQ5JKgmZ3sFqXLl0CAgLEjgIARPACJeiBAwXenFJbW+nmxpVNjh4lx44JbWrLFjJihA5DAwBogRcoQWdmCrxpRIh58zdVWtrqaHSG3cYZRA7QSi/QMDu1WuDfibQ0GSHcv9VRUcIrkwkTxN6Z/2K3cYbdYXbsHnOCmZ5eLC9QDVoQu8Ps2MXuyBmmYWbdF4lUEjTuaoPmY/f2ekKIvb39yy+/LHYUoBsGbeKoqakJCAgobaqFNycnZ/r06U2uphV2f25zyYKzePFiscPRArt3b2KyJKCEgRK0UqnMysqKiYlpMu2Wl5fHxsaq1WrdBoCZ1QyP6ZZcdrF7XYT6DJSgU1NT4+Lirl692uSa8fHxbdu21XkA7Nag2e3zuXz5Ml/+8ccfRYxEUtg91XFpqc9AbdAKhUKhUOTl5UVERAislpGRkZeXFx4e/sEHH9R56/PPP898PpDOxETrsNltg2a3z2fhwoVjxozhymxFzvQwO3b7ZtGTXx9FnYQPHz5MTExcsWKFTCar/252dvapU6e4sq2traOjo1YbZ/fnNrt9PuxGjkwhCnYvLfpDyzjo2tramJiY6dOnaz4OTlNkZOTp55KTk7XdPrtdbew2cQQFBfGRu7i4iB2OFtht9ydoKHix0FKDPnr0aElJyfDhw/Pz8x89ekQI+eOPPxwcHPj/HhYWFhYWFlxZpVJpu312L87sPsB01apVPj4+XBnTPBkMqv8vEloS9IMHD/Lz88PDw/klS5Ys8fb2fuutt0SMigbm5v+9R71NmzYiRqKta9euvfbaa1w5KirK3d1d3Hgkgt26CNNN/3oicoJOT09XKpW+vr6hoaGhoaHcQq4vcd++fXK5XNzwaMDuTROoyoFW2D1h9HdpEbkNOiMjwzCPCGG3YY7d9tC4uDh+7Lm/v7/Y4WiB3bOFaeye6vob2mjQGrSLi8vRo0c1l3z00UfNWa312B3Fwa7y8nK+XFlZKWIk2mK3KkfQUCAG/TUr0TKKQ98OHjzIl/fv3y9iJNpitzan2aU5bNgwESPRFrv3nRKWb1SB+qSSoCdozCA6adIkESPRFrv/3yZPnsxHzlY9junfW+w2FLBbF9Ff5LSM4tC3mzdv8uWcnBwRI9EWu/dAsjucAETBbsuS/iKXSoJm97Zjds9adrE7coZp7F7R0QbdWtu3b3/luZCQELHD0QK7v1jZxXQbNLsNBVCfVGrQ7E45hE55w2P6V8v58+f58qlTp3DCME0qCRoT90DzsdvuT1huzYuOjuYPtY+Pj2HukKCcVJo42MXuz212p3lid+QMIeTSpUt8ax5bI0rZnXZGf6SSoNlNFpq/WNmqU4SEhPATEG7fvl3scLTA7kWRsHx1YXfaGf2RSoJm9+Lcp08fvjxkyBARI9EW16zE6d69u9jhaKFOM66IkbQAu3fYs0t/9T+ptEFzN01wZbZactmNnF3sNuMSlvvD2aXHY65mUGFh4Zw5c7T6k7S0NH6Xo6Ki9BQYaNIc4+Xj4yN2OEC7srIyvnGmsLBQ7HC00OL0olQqp02bJrCCVGrQGAtheOw2K2E4gSjYvVFFf3MDSKUNmt3bPdi974DdPh92uzcJyycMu5Hr7wH2UqlBs4vpiXsYdenSJXbHQbN7wrAbuf46LaSSoHE/nuGxO6MFu5kCRKG/++CkkqDxX87wFi5cuGvXLq7cs2dPPlnTr87s4VFRUSIGoy12r4vsRq6/+p9UEjS73z27kbP7VO8JEybs3buXK7M1ezhh+T71uLg4Rvtm9Vf/k0qCZreDmN3/b126dAkICBA7ipY4ceIEX/7mm29EjKQF2B2wxO4z0vRHKqM42MXurd7s3l7fqVMnvuzg4CBiJC3A7oAlqE8qNWh2OwnZvdWb3VvaNI/zqFGjRIxEUhYtWuTt7c2V2Tph9EcqNWi+SZEQsv7P7iEAACAASURBVHPnThEj0Ra7o4nZnYuD3WNOWB5NzO48fFxHEWfx4sU63LJUatDs/mhlt5MQRMFuGzS7keuvi0sqCXrJkiV8Ewdbv57Y7d5kl/5uDDMAdnuV2R2XqT9SaeJg9+c2u9jtJKyurubLKpVKxEhagN25UvkGaEIIf42ROKnUoKk2cKDAm6WlpTdv3uTKHTt2dHR0FNrUli1kxAgdhtYamqOm2L05SK1Wix2CdtidK1WhUPC9ymw1ceBGldaiehRHZqbAm3JC3PkXDx6QBw+ENlVaqqugWu/WrVt8+f79+yJGoi2mhxMsWbKE0YYCdps49Nd6LpUETXX/g2AdbeXKlStWrODKXl5eGRkZBohIJ9auXTt58mSuzNadhOw+YpiwfAOnZu99hw4dRIxEW/q7tEglQbPb1Xb9+nW+fOfOHfEC0dqePXsYvXOXadeuXXvttde4clRUlLu7u/D69GC3J59vhCSEFBQU6HDLUknQ7GK3HsrunbtBQUGM/tYm9Yb809WaJ2j79u2MXtF37NiRmprKlXV7NxkSNO3YrRBpPkVl2LBhIkaiLXZ/axOWZ3pi9xE8hw4d0tOlRSoJmupOQkFUt54Ljj/xuHuXH05stmkTSU8X2hRN40/Y/a1NWJ7p6dq1a3z50qVLIkaiLf1dWqSSoKlOc4Kobj0XHH/SjZBu/AulUnhlqsafsPtbmxASHR3NX13YahPTnHZm8ODBIkaiLf1dWgyaoGtqagIDA7dt2yaXy+u/e//+/aSkpNzcXGNj4/79+7/++uv29va6+mh2b6+imuD4k8ePH1+9epUrOzs7M3R/EB82IeTGjRsiRtIC7LaJTZ48mdFalOadTZrl1jPQnYRKpTIrKysmJqa0kYpSdXX1qlWrzM3NV61a9cYbbzx+/HjNmjU6DICrQXPCwsJ0uGV9Y/d+PHbnvuF7ZQkhU6ZMETGSFmD34UHsTpSqvyH/BqpBp6ampqamClxbbt++/eeff8bExFhbWxNC2rRps2zZssrKSl3NJUZ1Q4EgdntO2M0U7N7SRlieXYvdwTNdunThyx07dtThlg2UoBUKhUKhyMvLi4iIaHAFFxeXL774ok2bNpWVlQ8ePPjpp5969eqlmZ2zs7P//PNPrqxUKg0RNB2YnvqSUew+e4mwXBfRHDyjw+ZNA/j555/5cm5urg63TEsnoZGREZd9VqxYce3aNWtr67Vr12qu8Pnnn6elpXFlW1vbJqakeIFozqz2008/iRiJtqiuygmOPwl7+pQfnuaQlSW8MlXjT5jG7nMS2rdvz5etrKx0uGVaEjQvMjKyoqLi22+/ff/99xMTEy0sLLjl48eP53/s19bWajtNF7vD7Njt2qa6Y1ZwSImt5vwnjx6RR4+ENkXT+BNCyPjx4/kqv52d3ZMnT8SNp/k0x0JcvHhRxEi0pXn34LNnz3S4ZVoS9N27d588eTJo0CC5XC6XywMCAlJSUq5everh4cGtMGbMGH6OrqKiIm0TNLvD7Njt2qb6mLM2R13z/f3vf+cTtEKhEDeYugR/i/z9+vXpz8tGZ84w9MNF826mF7MGffv27Z07d+7atcvY2JgQUl5erlQqTUx0Fh67DXNU10MFsRs5u7+3yP+23lLXEij4w6W35ovaWoYGzs+cOfPw4cN8WYdbFnnC/vT09OPHjxNCBg0aVFtbu2nTpry8vF9//fXf//53x44ddTjMnt0HtbE7FoLdoY3sHnPaqdUC/2YpFDJCuH9OnToJr0wmTBB7Z/5Lf3dvilyDzsjIKCsr8/X1tbGx+fDDD5OTk5ctW2Zubt6vX7+FCxdqDmBoJap/bguiuqtNENNjIdi1ceNGvhwfHx8VFSViMFrRXz1UBwTbWz66c4d/Uqzp/fs6bJwxaIJ2cXE5evSo5pKPPvqIL7u6un788cd6+mg0cRgeuyO42R05QwixtbXly5aWliJGoi0bGxu+rLkXVBBsb3HWfFFdrcPGGVraoPUtOjqa0docuz+3NS/Ghw8fXr9+vYjBaIX2kTOCFbTPq6ufdfv/k6C0adOGoa62gwcP8uX9+/fTVfcX7FX29/fng+/Spcvvv/+uq4+VSoJmtzZH9VkriN1Ze2gfOSNYQTMlRIu7pGnqamN3otRp06bx/0+nTZumwy1LJUHT/l+uceymOXZn7aG99VywNrds2TK+G3zUqFFnzpwxSEw6wO4d9vprnJFKgma3DbpLly4BAQFiR9ES58+f58unTp1iqPU8JCSEzxTMzQfN7hNv2X1orB578tUMKiwsnDNnjlZ/wt8mTgiJiorSU2D6oDko0MfHR+xwtFBQUHD6uVu3bokdjhbmzp3LH/OePXuKHY522D1h+vXrx0fepk0bscPRQovTi1KpnDZtmsAKUqlBszvMjt3a3KVLlxgdf8Lug7EJy90tmtPEm5qaihiJtvT3HEipJGh2mzjYTXPsjj9ht/WcsDyjxfDhw8+dO8eVe/fuLbwyVfTXUSTynYQGw+609//+97/58oYNG0SMRDrYvbQQQq5fv86X79y5I14gWuN/KRJCvLy8RIxEW3v27HntuSVLluhwy1KpQbPbULB161Y9PdFd39i9B5Jp+ps8Xt/w0Nj6pJKg7e3tX375ZbGjaAlXV9fGnnJAOdoHqzWO6UsLu48k13ziUk1NjYiRaGvbtm18OSkpSYf3ZEklQbOL3ecAZWdn82W2Hr3K7qWFELJkyRJGTxh2BwgGBAScPn2aK8+aNUuHW5ZKgmb3Vm92RxRMmjTps88+48psPXqV3QYx8r8PjtKcp5h+7F5a7t27x5d1WxeRSoJm978cuzeqUD33jSB2MwX53wFqOpxR3QC8vb35w8630jBBc/IW3XYUsfT9tQa7bdDsYrcll91fLXWomXpwDLtXdP3NCyaVYXbsTtjPbuRnzpxxeS4+Pl7scLTA/WrhsDUImvzvKILhw4eLGIl0dOrUiS9rNjG1nlRq0OyObGX3Hkh2I2f6kVf6G1EAjdHf88ilUoNmF7v10Li4OD5yf39/scPRguY0Twz1J3M0eyx0O6JA3+rMrCtiJNrS3whuqdSg2W0PZbceyu6kEAsXLuQfIc9WlzKh/KGxgtidWVd/T3iQSoJmdy4OdiNndw5uduc/ISw/r2vPnj2MjoXFjSqtxXSrIqPYvbSw22NB6H9eV+PYHQs7YMAA/kYV3Z7zUknQ7DYUsHuLDbvYbRAj+OEihg8++GDq1KlcWbeXFqkkaHZrc+xWK9j91cLuk9QJy6c6uz9c9HdpkUqCZhe7t9iw+/+tzigOthI0u9dFdn+46O9UR4IGfWH3eeT6u3PXANhtzWO37q+/SwsSNO3YrRCxO2qK3WZcwnj7DKP0d2nBjSq04ypEnLCwMLHD0cK//vUv/jETfn5+YoejBXZvDiKM32XDKP3Nx4AaNO3YfRY9u1MOsdtKQBi/y4ZR+ntoLGrQtOOnMCeszcHIPXqVc+zYMbHD0QLTNeg5c+a88hxbDejszgs2YcIEvjxp0iQdbhk1aNqxOwcju/VQdsefEEI2b97M6EMs2T1hFAoFPxYWnYTSwu7YI3Y75Zl26NAhRu9sYvdJY/o71aWSoNkdC8HuWcsudi+KhOU7m9idXUt/pJKg2f31xO5Zy+5FkemRauzeMM306EY9kUonIfcbhNOuXTuxw9GCubk5X27Tpo2IkWhLc7zXqVOnRIxEW+xGTlhuQGe6b1ZPDFqDrqmpCQwM3LZtm1wur//u06dPk5OTMzMzlUpl7969586dq8MfaOzW5tj9uc3ueC92J4RjGrs/c/XHQAlaqVTm5uampaWVlpY2ts6GDRtKSkreeecdc3PzI0eOREZGbt68WVe1XXa/e3a72rZv385o6zm7v1oIy3fYs3uq64+BmjhSU1Pj4uKuXr3a2ApPnjy5cuVKaGho//79XV1d33nnHULIL7/8oqsA2H38EruDQ0NCQk4/t337drHDkQr9jcnVN3ZPdf0xUA1aoVAoFIq8vLyIiIgGV6itrZ09ezZ//aypqVEqlbW1tboKgN2uNnbr/uzOw8e0mzdv8uWcnBwRI9EWu63n+kPLKI4OHTrMnj2bK1dVVcXFxcnlcs3n4y5btiwtLY0r29raavuwNXY7iNkdUYB2f1Gw2/TP9GHXE1oSNEetVn/33Xd79+51dHSMjY3V7Evs1KmTm5sbV27Tpk15eblWW2Z3NDG7NWh2K0TsXhQJyz9c0AZdH0UJuri4eO3atQ8fPgwMDBwzZoxMJtN8NywsjJ/LraioKDw8XKuNszt6H2et4bF7USQs/3CB+mhJ0Gq1euXKlXZ2dps2bbK0tNT59tmtVrCL3V+sTF8U2f3hAvWJnKDT09OVSqWvr29WVtbNmzenT59+48YN/l0nJyd7e3sRw4PWYDfNMV0JZfe6CPWJnKAzMjLKysp8fX1v376tVqs3bNig+e78+fMnT54sVmyUwFO9DY/pSii710WoT6ZWq8WOQWtcG/S+ffvEDsQQHj9+zI8fd3Z27t69u7jxSEF5efmDBw+4sp2dHVtzAwBDqqur/fz8UlJSGltBKnNxsOvSpUv8/Ov79+8XOxxJYHfmFhAFHnklXUyPKACQAv39J0UNmnaY4guAcvr7yYUaNO1QgwaQLKnUoNmdhwXtoYbH7tkCLxip1KBRDzU8dkcT42wBSkglQWNwqOGxm+bYnbkFXjBSSdBgeOxeFNmdnBZeMEjQAHWxOzktvGCk0knILnRYGR46ZoESqEHTjt2WXABoJdSgacfu0xQBoJWQoGmHDisAyUITB+3QYQUgWUjQtGN3sBoAtBKaOAAAKCWVBI3BagDAHKk0cWCwGgAwRyoJGi25AMAcqTRxgOGhWQmglaRSgwbDQ7MSQCshQYO+oFkJoJXQxAH6giYOgFZCDRr0BU0cAK2EBA36giYOgFZCEwcAAKWQoAEAKIUEDQBAKSRoAABKIUEDAFAKCRoAgFJI0AAAlEKCBgCgFBI0AAClDJqga2pqAgICSktLW7kOAIAUGChBK5XKrKysmJgYgczbnHVaDBP3AABzDDQXR2pqampqanV1dSvXaTFM3AMAzJGp1WqDfVheXl5ERMS+ffvkcrm263z++eeZmZlc2cTE5Keffho2bJh+wwUA0Kfa2trr16+npKQ0tgIzs9llZ2efOnWKK7dr127t2rXabuHGjRvHjx8fM2aMu7u7rqPTrwcPHhw8eHDgwIGjR48WOxbtlJaWJicnu7i4TJo0SexYtPbJJ584Ojr+7W9/EzsQrSUnJ6vV6tdff13sQLR28ODBBw8ehIeHGxkxNn4hLS3tt99+CwoKEqh9Nsjc3FzgXWYSdERERGhoKFc2MjLq2LGjtlt4+vRpbm7upEmTPDw8dB2dfmVlZeXm5g4ePJi5yB8+fJibm9u5c2fmIieEXL9+3cTEhMXIV61apVKpWIw8ISEhNzfXw8ODuQT91Vdf5ebmurm5derUSYebZSZBo+EYAKSGscsUAIB0iFyDTk9PVyqVvr6+Bvistm3burm5sVgTt7CwcHNzc3R0FDsQrZmamrq5uTk5OYkdSEu4ubk5OzuLHUVLuLi41NbWih1FS3Tr1k2pVIodRUs4OTm5ubmZmprqdrMij+JYvnx5WVlZTEyMwDoAANJk0AQNAADNhzZoAABKIUEDAFCKmWF2LVZTUxMYGLht2za+UVulUn366adnz56tqanx8PCYN2+ezpv2W+np06fJycmZmZlKpbJ3795z587leqvoj/z+/ftJSUm5ubnGxsb9+/d//fXX7e3tCQuR83Jycj744IO9e/dyJwz9kR86dGj37t38S2Nj4yNHjhAWIieEpKenHzt2LD8/39XVdcGCBVx/MuWRnz17ds2aNXUWent7v/XWWzqP/EVug1Yqlbm5uWlpaT/++KNmr2NiYuLZs2dDQ0NNTEy2bt3ap0+ft99+W9xQ61i+fHlJSUlwcLC5ufmRI0eysrI2b97crl07yiOvrq5euHBhz549Z82aVVhYeOjQodra2vXr1xMWjjmnvLz8zTfffPToEX/C0B/5xo0bi4uLp0yZwr2UyWQDBw4kLESenp6ekJAQEhLi4OBw8ODBgoKC+Ph4IyMjyiN/+vTprVu3+JdKpXLjxo1vvvmmp6en7iNXv7i+/PLLoKCg1157berUqSUlJdzC8vLyV1999ccff+ReXrx4cebMmU+fPhUvzLoeP348derUX3/9lXtZU1MzZ86ctLQ0+iO/fv361KlTS0tLuZdXrlyZOnVqRUUF/ZHz1q1bFxERwZ8wTES+ZMmSo0eP1llIf+S1tbULFixITU3lXhYUFKxZs+bhw4f0R15HfHz89u3b1fo55i9yG7RCoUhKSvrwww81F969e7eyspKfjmPAgAEqlUrzeii62tra2bNn9+zZk3tZU1OjVCpra2vpj9zFxeWLL76wtraurKy8ffv2Tz/91KtXrzZt2tAfOScjIyMvLy8oKIhfwkTk+fn5mZmZQUFBc+bMWbVqVX5+PmEh8vv37+fn53t6eqrV6uLiYnt7+6VLlzo4ONAfuabMzMzLly/PnTuX6OeYv/ht0HUUFRWZmJhYWVlxL01MTKytrQsLC8WNSlOHDh1mz57NlauqquLi4uRy+ahRo7KzsymP3MjIqE2bNoSQFStWXLt2zdrampvTiv5jTgh5+PBhYmLiihUrZDIZv5D+yEtKSkpLS2Uy2TvvvKNSqQ4cOLBs2bItW7bQH/mTJ0+MjY0zMjIOHDhQUVFhZ2cXEhIyYsQI+iPn1dbW7ty5MzAwkGto1kfkkkvQarVa838gR6VSiRKMALVa/d133+3du9fR0TE2NlYul7MSOSEkMjKyoqLi22+/ff/99xMTE+mPvLa2NiYmZvr06b169eLnDScsnC1WVlbJycl2dnZcnD179gwMDLxw4YKpqSnlkZeUlKhUqtzc3E2bNllbW3/zzTfr16/fuHEj/cec99133xkZGY0cOZJ7qY/IX+QmjgbZ2dlVV1dXVFRwL1Uq1bNnz7iRBvQoLi6OjIzct29fYGBgdHR027ZtCQuR37179z//+Q8hRC6XOzg4BAQEVFVVXb16lf7Ijx49WlJSMnz48Pz8/EePHhFC/vjjj6KiIvojNzY2bt++PZ8XrKysHB0dHz9+TH/k3FkdGhrq4OBgaWnp5+dnZ2d3+fJl+iPnff311xMnTuRf6iNyySXorl27mpubX716lXt57do1IyOj7t27ixuVJrVavXLlSktLy02bNnl5efH/9+iP/Pbt27GxsXyVoby8XKlUmpiY0B/5gwcP8vPzw8PDQ0NDuRFUS5Ys2b17N/2RX7hw4Y033uCfEldZWVlQUNC5c2f6I3dycpLJZM+ePeNeqlSqqqoqKysr+iPn5Obm3rt3z8vLi1+ij8gl18RhaWnp4+OTnJzM1Tt27Njh5eXVrl07seP6r6ysrJs3b06fPv3GjRv8QicnJ3t7e8ojHzRoUGJi4qZNm6ZMmVJdXf3555937Nixb9++5ubmlEceGhrKzzZeZzYYyiPv27dvaWnphg0bZsyYYWZm9sUXXzg6Og4ZMsTY2JjyyO3t7UeOHBkTEzN37lwrK6uUlBRjY2MPDw/6/4dyzp496+rqamlpyS/RR+Qv8jhoTv3Zl1QqVVJS0rlz52pra4cNGxYcHEzVMPivvvoqKSmpzsL58+dPnjyZ8sgJIb/99ltycvLt27fNzc379esXGBjo4OBAqD/mmuqcMPRHfvfu3Z07d/7222/m5ubu7u5BQUG2traEhciVSuWOHTsuXrxYVVXl5ub2+uuvc7Pd0x85IWThwoUjRowICAjQXKjzyF/8BA0AwCjJtUEDALACCRoAgFJI0AAAlEKCBgCgFBI0AACl2B4H3di8yZq0nS3XMHPRNhaVJm0jNEDkAjPhNmfvxIq8ZXOC03D860fOyjlfP3JWzvk6kYt7zrM9zK6xeZM119F2tlzDzEXbWFSatI3QAJELzITbnL0zfOStmRNc3OPfWOT0n/ONRU7/Od9g5CKf862Zq1Rcjc2bXGc1rWbLNdhctA1G1ZoIRZlFl58Jtw56jnmL5wQX/fg3GDkT53yDkTcWVWsiNFjkmgx8zjPcxNHYvMl1VuNmyz18+HBVVdVf/vKXf/7zn05OTo3N3GphYdHg8vqX+lZqMCrNFbSN0GCR87iZcLds2dLMvRPlmCsUCoVCwd0cyC9sLBLNTxT9+DcYORPnfIORNxaV5gp0HnNNhj/nGe4k5OZN5hpuNOdN1lxHc7bc9957r6qqatmyZeXl5Y3N3GqYuWgbi0pzHW0jNPAsunVmwm3O3lESOWnevL10Hn+c84aPnCfKOc9wDZqjrjdvsua72s6WqzbIXLSNRaU5M1ZjkWi7XLeR8+rMhKuJzmOuqTmfSPPxxzlvyMh5opzzbCfo4uLitWvXPnz4MDAwcMyYMfX3mZstl3/Jz5bbt29fbuZWCwsLojFzq6WlZYPLdRt2Y1FprsPPLdvMCA0TOa/OTLjN2Ttxj7mmxo5tc9YR/fjjnJfUOc9wE4e6kXmTNWk7W65h5qJtLCrNdbSN0JCz6NafCbc5e0dD5JzmfCKdxx/nvOEj54h1zjNcgxaYNzk9Pb2ysnLy5MktmC3XAHPRNhYVIYSPXGBuWREj59SfCVczcjqPuSaBY0v58cc5L7VznuFx0ALzJi9fvry0tDQuLo5oP1uuYeaibSwqzci1jdBgs+g2OBMuzce8+XOC03b860TO0Dlf/5izcs7Xj5yIeM43c4Qgc2JjY8UOoYUQubjY3QtEbnj6jpzhNmgBaWlpvXr1EjuKlkDk4mJ3LxC54RkgcoabOASUlpbWGXvECkQuLnb3ApEbngEifzETNADAC+DFbOIAAHgBIEEDAFAKCRoAgFJI0AAAlEKCBgCglIQS9Lx582Qy2dKlS+u/5enp2b9/f91+nEqlkslkK1eu1O1mm+/NN9+0tbWdNWuWWAEwZ8OGDTKZrLi4WKu/Ev2LhheYhBI0JzY2NicnR+wo9C4jI2PTpk3e3t7h4eFa/SGXpJ48edKClTt27Njg9D1AA9G/rBZ/olbnZGs+l8ITmOHJklrGxMQkLCzs+++/FzsQ/eKeovbxxx+7uroa7EM7dOhgsM+CVjL8lyXW6dH8z6XwBJZcDfqDDz744Ycf9uzZI3YgzVVRUXHx4kVt/4q7/8jc3FwPETUqKyvrwYMHhvxEiWjZOSDM8F+Wzj+xmYel+Z9L4wms15k+qBIcHEwIqaysdHV1dXBwKCoq4t8aPnx4v379uLK7u/uUKVM0/3DKlCn8u1OmTJkxY8bFixfHjRtna2s7ePDgr776SqlUvv322y4uLjY2NpMnT75//75ara6pqSGErFixYt++fZ6enjY2NkOHDo2Pj9fc8q1bt/z9/bt162ZjYzNmzJhjx47xb02cONHPzy81NVUulzs7Oze4RxcuXPD19XV0dHzppZd8fX0vXrzILffz8+O/34kTJ9b/w5KSkvfff9/FxcXCwqJHjx7vvPPOs2fP1Gr12LFj+T987bXXuJX37dvn4eFha2srl8sHDhyYmJjILa+/8sSJE4cMGdJkeNyaM2bMuHfv3vjx462srF566aV58+YVFxcLh9egxsJr8lPUavX+/ftHjBhhY2MzePDgLVu2rF+/nhDS2FNHG9udJr9o4d3R6hzw9/c3NTUtLCzk1ykrK7OysuK/ZZq/LM1PbPKr4TUYef3/GsKnQTM/V6sIjx8/7uXl1bZtWw8Pj4SEhHXr1llbWze4460huQStVqu//fZbQkhYWBj/llYJunfv3q+88sq5c+dycnLGjBljZmY2dOjQFStW5OXlHThwwNTU9NVXX1U//3/br18/uVz+xhtvREZG9unThxDy7rvvcpvKzMy0sbHp1KnT0qVLV6xY0a9fP25mWO7diRMnDho0qF27dv7+/lu2bKm/O99++62pqWnXrl3fe++9999/v1u3bqampt9++61arc7JyVmyZAkh5PPPP8/Kyqr/tzNmzDAxMZk1a9aqVasmT55MCAkODuZCCg0NJYSkpKRwj47+8ssvCSHDhg2Ljo5esmQJ15V68ODBBlfWPL8FwuPWHDFixJgxYw4dOnT79u34+HiZTPb6668Lh1efQHhNfgqXjt3c3D744IMFCxZYWlpyM6Y3mKAFdqfJL1pgd7Q9B7j93b17Nx/YwYMHCSH79++n/8uqk/4EtqmpwcgbPCwCp0EzP7f5a37++edGRkYDBgxYuXLlggULzM3NnZyckKBbhU/QarX6b3/7m5GR0YULF7iXWiVoY2PjO3fucC8zMjIIIf7+/vzK06dP79Kli/r5/1uZTHb+/HnurfLyck9PTzMzM+7Pvby8unbt+uTJE+5dpVI5duxYuVxeWlqqVqu5h+skJSU1uC8qlapfv35OTk4FBQXcksePH3fq1GnAgAG1tbVqtXrHjh2EED5OTcXFxTKZ7K233uKX+Pv7u7q6cmUubT1+/Jh7OXPmzM6dO1dVVXEvKysrbWxsQkJCGlyZP7+bDI/bu5MnT/IxTJw4sWvXrk2GV4dweAKfUlBQIJfLhwwZUlZWxr119uxZroOofoIW3h3hL1p4d7Q9B8rLy62trWfOnKm5NRsbm/Lycvq/rDrpr7Ft1lc/8jqHpcnToJmf28w1q6qqunbtOnTo0IqKCu6to0ePEkL0kaAl1wbNiYmJsbKyCg0Nrf/I+ib16NGjW7duXNnR0ZEQ4u3tzb/70ksvVVRU8C+9vb2HDRvGlS0sLD788EOlUvndd98VFRV9//33ISEhdnZ23Lumpqbh4eGlpaU///wzt8TW1jYwMLDBGO7cuZOdnR0aGso/x6x9+/YLFiy4cuXK77//Lhw/l4bOnDmTn5/PLTlw4MD169cbXDkxMTErK8vMzIx7WVpaqlKp6jyMuWXh2dnZ+fj4AbIpyQAAByxJREFU8H/i5OTEbVa34TX2Kd9//31paWlkZCT/jAxPT09fX98W705jX7TA7rTgHLCwsJg2bdqJEye4c6yiouLYsWOvvvoq92g7yr+sOhrbZnPUOSxa7XjzP7exNc+fP//7779HRES0adOGe2vq1Kl/+ctfmhm8ViSaoDt16rRy5cqLFy9u27ZN27/lH5ZOnp+g9Zfw+vXrp/ly0KBBhJC8vDzuJF62bJlMA9d2XFBQwK3s5ORkZNTwF5SXl1d/49xL7i0Bcrl85cqVmZmZ3bp1Gzt2bGRk5Pnz5xtbuX379k+ePNmzZ8/ixYvHjh3buXPnsrIy4e03M7yuXbtqvssfN92G19incI+Mcnd313x3wIABLd6dxr5ogd1p2Tng7+9fXl5+4sQJQsg333xTVlb2j3/8o5lHo8V7p5Mvq47GttkcdQ6LVjve/M9tbE3usHANWbw6L3VFogmaEPLGG2/89a9/jYyMfPjwofCalZWVuvpQ9fPBFdzV/r333suoh+8V4apFAtupgztluV/cwpYvX56VlbVs2TKVSrVhwwZPT89p06Y1+NT3TZs29enTZ9GiRY8ePZo9e/a5c+e6dOnSzN0UDs/EpNEhnjoMr7FPaXC5sbFxi3enwT/hRtE0tjstOwcmTpxoY2Nz+PBhQsjBgwednZ1Hjx7dzKPR4r3TyZdVh8A2m1TnsGi1483/3MbWVCqV9Rc2dvK0knQTtImJSXx8fHFxMdefpqlOu0eTdVIBWVlZmi8vXbpECOnVq5eLiwshxMjIyEsDN2aZe5SZsJ49exJCfv31V82F3A04TQ58Li4uvn79evfu3VesWHHmzJk///wzODj466+/Pn78eJ01y8rKlixZMmfOnEePHu3Zs2f+/PkDBw6sqqpiPTxCSI8ePQghV65c0VyYnZ3d4t1p7IsW2J2WnQPm5ubTp09PTU0tKSlJTU39xz/+wdXsKP+y9Kc1p0HLcE9Ryc3N1VzYzIYdbUk3QRNCRo4cGRQUtGfPHs2z08LCIjc3l68CfPPNN3fu3GnxR5w+ffqHH37gyhUVFatWrWrbtu2ECRNsbGy8vb23b9/O/5itra0NDAz8+9//3pynXvbo0cPNzS0+Pr6oqIhbUlhYuHXr1j59+vDt4425ePHiX/7yl4SEBO6lra3ttGnTyP9elrjy7du3q6qquIcTc8tPnDjx6NGjOhew+u34+g6P08zwGjR27FgbG5vo6Gi+wyAzM/Prr79ucOXm7E5jX7TA7rT4HPD39y8qKlq6dGlZWdn//d//aXU0xPqyWq+xbbbmNGiZYcOGdejQIS4ujq9Kp6en17lC64rk7iSsY+3atV999VVhYSH/m8jb2zsqKmrGjBmzZs3Ky8vbsWPH6NGj+XNXWx4eHr6+vkFBQfb29l9++WV2dvYnn3zCPV993bp1Y8aMGTBgQFBQkLGx8bFjx/7zn//s2bOnOb+VjIyMYmJipk6dOmTIEG5w6N69ex8+fJiUlNRYszVv+PDh3bt3X7Zs2ZUrV/r27Xv9+vWvvvqqe/fu3M9qLjXExsZOmjTJw8Ojc+fO0dHRBQUFPXr0+OWXX7788svOnTufOnVq165dc+fO1Vx51KhRBghPk6urq3B4Ap9iZ2f34YcfLl68eOjQoX5+fsXFxUlJSZ6enj/++KO2R5u7ljf2RQvvTsvOgfHjx9va2iYkJIwcOZKrhjfnaIj7ZbVGY5E3c8d1GAnH2tp6zZo1//znP0eOHDlz5sxHjx59+umnXl5ejf0CaxWdjwuhluYwO03bt28nhPAD6SorK99++20nJydbW9vx48f//PPPCQkJ/NDOKVOmuLu783/L/czZu3cvvyQsLKxXr15qtVqlUvn4+Jw6dWrr1q1DhgyxsbEZOXIkPzaTc/36dW6EUNu2bUeOHJmamsq/Vec+ggb9/PPPEyZMcHR0dHR0nDhxoubNBQLD7LjP9ff379Spk7m5ubOzc3Bw8N27d7m37ty58/LLL1taWi5cuFCtVmdlZfn4+NjY2HTt2nX27Nl37tw5d+7cmDFjuANSZ+U6MQuEV3/v5s+fzx034fDqEA5P+FPUavX+/fs9PT25Wxs++eST8+fP+/j4NHafRWO70+QXLbw7LTsHuLyTkJDQ/KMh+pdVZxCb8FejSTjyJne8+Z+rVYSHDh0aNmyYjY3N2LFjT58+zY1/bzD+1sAzCQEAtKBSqZ4+fWplZcUPsyOEzJkz588//zx9+rRuP0vSbdAAANqqrKzs1KnTokWL+CUPHz5MSUnRHDStK1JvgwYA0IqVldXcuXO3b99eU1PzyiuvFBUVbdiwwcTEZN68eTr/LDRxAABoR6lUrlu3bvfu3ffu3evQoYO7u3tsbCw3dlO3kKABACiFNmgAAEohQQMAUAoJGgCAUkjQAACUQoIGAKAUEjQAAKWQoAEAKIUEDQBAKSRoAABK/T9xPrtH4RSp8AAAAABJRU5ErkJggg==" alt="learning-curve.png">

</figure>
</div>
</div>

<div class="outline-2">
<h2 id="sec--new-predictions">New predictions</h2>
<div class="outline-text-2">
<div class="org-src-container">
<pre class="src src-R"><span style="color: #1874cd;"># </span><span style="color: #0000ff;">d = predict.temps("~/Jdrive/PM/Just_Lab/projects/PROGRESS_physical_activity/data/intermediate/allvar_aug8.rds")</span>
</pre>
</div>

<div class="org-src-container">
<pre class="src src-R">temp.quantiles.map(2018L)
</pre>
</div>


<figure id="fig--g/map-temp-quantiles"><div class="figure-label"><a class="internal figure-label-text" href="#fig--g/map-temp-quantiles">g/map-temp-quantiles</a></div>
<img 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" alt="map-temp-quantiles.png">

</figure>

<p>
Above are the .95 quantiles of the lows and highs, respectively, in 2018.
</p>

<div class="org-src-container">
<pre class="src src-R">pop.map(<span style="background-color: #ffefd5;">"POB65_MAS"</span>)
</pre>
</div>


<figure id="fig--g/map-population"><div class="figure-label"><a class="internal figure-label-text" href="#fig--g/map-population">g/map-population</a></div>
<img src="data:image/png;base64,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" alt="map-population.png">

</figure>

<p>
Above is the gridded population density in 2010 for the whole area (counting only people ages 65 and up).
</p>

<div class="org-src-container">
<pre class="src src-R">pop.map(<span style="background-color: #ffefd5;">"POB65_MAS"</span>, thresholds.tempC = c(5, 30))
</pre>
</div>


<figure id="fig--g/map-extreme-person-days"><div class="figure-label"><a class="internal figure-label-text" href="#fig--g/map-extreme-person-days">g/map-extreme-person-days</a></div>
<img 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" alt="map-extreme-person-days.png">

</figure>

<p>
Above is the person-days of exposure to extreme lows or highs, respectively, in 2010. The total exposure, summed across all pixels, is:
</p>

<table>


<colgroup>
<col  class="right">

<col  class="left">

<col  class="right">
</colgroup>
<thead>
<tr>
<th scope="col" class="right">&#xa0;</th>
<th scope="col" class="left">kind</th>
<th scope="col" class="right">total person-days</th>
</tr>
</thead>
<tbody>
<tr>
<td class="right">1</td>
<td class="left">≤ 5 °C</td>
<td class="right">51,842,088</td>
</tr>

<tr>
<td class="right">2</td>
<td class="left">≥ 30 °C</td>
<td class="right">18,242,720</td>
</tr>
</tbody>
</table>
</div>
</div>

<div class="outline-2">
<h2 id="bibliography">References</h2>
<div class="outline-text-2" id="text-bibliography">
<p id="bibref--hubmbw_2014">
Hu, L., Brunsell, N. A., Monaghan, A. J., Barlage, M., &amp; Wilhelmi, O. V. (2014). How can we use MODIS land surface temperature to validate long-term urban model simulations? <i>Journal of Geophysical Research, 119</i>(6), 3185–3201. <a class='doi' href='http://dx.doi.org/10.1002/2013JD021101'>doi:10.1002/2013JD021101</a> <span class="Z3988" title="rft.au=Hu%2C%20Leiqiu&amp;rft.au=Brunsell%2C%20Nathaniel%20A.&amp;rft.au=Monaghan%2C%20Andrew%20J.&amp;rft.au=Barlage%2C%20Michael&amp;rft.au=Wilhelmi%2C%20Olga%20V.&amp;ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi/fmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft_id=info%3Adoi/10.1002/2013JD021101&amp;rft.atitle=How%20can%20we%20use%20MODIS%20land%20surface%20temperature%20to%20validate%20long-term%20urban%20model%20simulations%3F&amp;rft.jtitle=Journal%20of%20Geophysical%20Research&amp;rft.date=2014&amp;rft.volume=119&amp;rft.issue=6"></span>
</p>

<p id="bibref--rosenfelddsnj_2017">
Rosenfeld, A., Dorman, M., Schwartz, J., Novack, V., Just, A. C., &amp; Kloog, I. (2017). Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel. <i>Environmental Research, 159</i>, 297–312. <a class='doi' href='http://dx.doi.org/10.1016/j.envres.2017.08.017'>doi:10.1016/j.envres.2017.08.017</a> <span class="Z3988" title="rft.au=Rosenfeld%2C%20Adar&amp;rft.au=Dorman%2C%20Michael&amp;rft.au=Schwartz%2C%20Joel&amp;rft.au=Novack%2C%20Victor&amp;rft.au=Just%2C%20Allan%20C.&amp;rft.au=Kloog%2C%20Itai&amp;ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi/fmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft_id=info%3Adoi/10.1016/j.envres.2017.08.017&amp;rft.atitle=Estimating%20daily%20minimum%2C%20maximum%2C%20and%20mean%20near%20surface%20air%20temperature%20using%20hybrid%20satellite%20models%20across%20Israel&amp;rft.jtitle=Environmental%20Research&amp;rft.date=2017&amp;rft.volume=159"></span>
</p>

<p id="bibref--williamsonhgkk_2013">
Williamson, S. N., Hik, D. S., Gamon, J. A., Kavanaugh, J. L., &amp; Koh, S. (2013). Evaluating cloud contamination in clear-sky MODIS Terra daytime land surface temperatures using ground-based meteorology station observations. <i>Journal of Climate, 26</i>(5), 1551–1560. <a class='doi' href='http://dx.doi.org/10.1175/JCLI-D-12-00250.1'>doi:10.1175/JCLI-D-12-00250.1</a> <span class="Z3988" title="rft.au=Williamson%2C%20Scott%20N.&amp;rft.au=Hik%2C%20David%20S.&amp;rft.au=Gamon%2C%20John%20A.&amp;rft.au=Kavanaugh%2C%20Jeffrey%20L.&amp;rft.au=Koh%2C%20Saewan&amp;ctx_ver=Z39.88-2004&amp;rft_val_fmt=info%3Aofi/fmt%3Akev%3Amtx%3Ajournal&amp;rft.genre=article&amp;rft_id=info%3Adoi/10.1175/JCLI-D-12-00250.1&amp;rft.atitle=Evaluating%20cloud%20contamination%20in%20clear-sky%20MODIS%20Terra%20daytime%20land%20surface%20temperatures%20using%20ground-based%20meteorology%20station%20observations&amp;rft.jtitle=Journal%20of%20Climate&amp;rft.date=2013&amp;rft.volume=26&amp;rft.issue=5"></span>
</p>
</div>
</div>
</div>
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