Mexico temperature notebook

Stages:

1. Mixed effects for grid cells with both satellite and ground data
2. Predict temperature in cells with satellite data but no ground data, using the mixed model(s) fit at stage 1
3. Predict temperature in cells with no satellite data using a spatial smoother

Rosenfeld et al. (2017) describes the method as applied to Israel.

Hu, Brunsell, Monaghan, Barlage, and Wilhelmi (2014): "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 (Williamson, Hik, Gamon, Kavanaugh, & Koh, 2013)."

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 < 0 or - 24 if local solar time >= 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."

Are there any days on which one of the ground-station meterological variables is missing from every station?

sapply(
    subset(select = -date, ground[,
        by = date,
        .SDcols = c(temp.ground.vars, nontemp.ground.vars),
        lapply(.SD, function(v) all(is.na(v)))]),
    any)

Fortunately, no.

sr = summarize.cv.results(multi.run.cv(available.years))

Here are RMSE and R² by year and DV, as well as the proportion of daily Moran's I statistics of the signed error that are significant at α = .05. N and stn 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. sd.s and rmse.s are spatially weighted RMSEs, in which each day and each sixteenth of a lon–lat cell are given total weight 1.

library(data.table)
as.data.frame(rd(d = 2, sr$overall))

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

as.data.frame(rd(d = 2, sr$by.season))

as.data.frame(rd(d = 2, sr$by.region))

These by-region results are only for 2018.

as.data.frame(rd(d = 2, sr$by.network))

These by-network results are only for 2018.

time.series.plot()

g/cv-time-series

pred.error.plot()

g/error-density

# d = predict.temps("~/Jdrive/PM/Just_Lab/projects/PROGRESS_physical_activity/data/intermediate/allvar_aug8.rds")

temp.quantiles.map(2018L)

g/map-temp-quantiles

Above are the .95 quantiles of the lows and highs, respectively, in 2018.

pop.map("POB65_MAS")

g/map-population

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

pop.map("POB65_MAS", thresholds.tempC = c(5, 30))

g/map-extreme-person-days

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

Hu, L., Brunsell, N. A., Monaghan, A. J., Barlage, M., & Wilhelmi, O. V. (2014). How can we use MODIS land surface temperature to validate long-term urban model simulations? Journal of Geophysical Research, 119(6), 3185–3201. doi:10.1002/2013JD021101

Rosenfeld, A., Dorman, M., Schwartz, J., Novack, V., Just, A. C., & Kloog, I. (2017). Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel. Environmental Research, 159, 297–312. doi:10.1016/j.envres.2017.08.017

Williamson, S. N., Hik, D. S., Gamon, J. A., Kavanaugh, J. L., & Koh, S. (2013). Evaluating cloud contamination in clear-sky MODIS Terra daytime land surface temperatures using ground-based meteorology station observations. Journal of Climate, 26(5), 1551–1560. doi:10.1175/JCLI-D-12-00250.1