Urban Meteorological Observations from the UHIARC Campaign across Cities in the North-Eurasian Arctic
Authors/Creators
Description
Urban meteorological observations collected in the cities of North-Eurasian Arctic during the UHIARC (Urban Heat Island Arctic Research Campaign) campaign using automatic weather stations (AWSs) and temperature loggers (TLs), associated with the manuscript:
Urban Heat Islands in the Arctic: Long-Term Observations from the UHIARC Campaign in North-Eurasian Cities
Data can be downloaded from the bottom of this page. Use of any data must give credit through citation of the above manuscript.
Overview
The UHIARC (Urban Heat Island Arctic Research Campaign) dataset contains harmonized in situ meteorological observations from the cities in the North-Eurasian Arctic collected between 2015 and 2024. The primary measured parameter is near‑surface air temperature, which is the focus of the urban heat island (UHI) analysis. Ancillary variables include relative humidity, wind speed and direction, atmospheric pressure, and soil temperature. Observations were collected using automatic weather stations (AWSs) Davis Vantage Pro 2, iButton temperature loggers (TLs) and HOBO MX2303 temperature loggers, and also include routine meteorological observations from synoptic weather stations derived from open‑access sources. The campaign combines long‑term monitoring (multiple years at several sites) with short‑term intensive observation periods (IOPs) employing dense logger networks. All data have undergone quality control, timestamp harmonization, gap‑filling, and integration into a common format. The dataset is designed for urban heat island (UHI) research, validation of numerical weather prediction models, development of urban canopy parameterizations, and machine‑learning applications. For full details, see the associated manuscript.
Cities
The study region includes seven medium-sized Russian cities located near the Arctic Circle: Murmansk (MUR) and Apatity (APT) on the Kola Peninsula; Vorkuta (VOR) in the northeastern part of the Russian Plain, west of the Polar Urals; Salekhard (SAL), Nadym (NAD), and Novy Urengoy (NUR) in Western Siberia; and Norilsk (NSK) in northern Eastern Siberia.
| City name | Acronym | Coordinates (lat, lon) | AWS observation period | IOPs with TLs (periods) |
|---|---|---|---|---|
| Murmansk | MUR | 68.955°N, 33.043°E | – | IOP‑1 (2020–2024) |
| Apatity | APT | 67.566°N, 33.402°E | AWSu1 (2015–2022) AWSr2 (2016–2024) |
IOP‑1 (winter 2015/2016) IOP‑2 (winter 2017/2018) IOP‑3 (2020–2024) |
| Vorkuta | VOR | 67.498°N, 64.063°E | AWSu1 (Feb–Mar 2017) | IOP‑1 (winter 2016-2017) |
| Salekhard | SAL | 66.532°N, 66.628°E | AWSu1 (2017–2022) | IOP‑1 (winter 2016-2017) |
| Nadym | NAD | 65.534°N, 72.527°E | AWSu1 (2016–2022) AWSu2 (2020–2023) AWSu3 (2020–2021) |
IOP‑1 (winter 2016/2017) IOP‑2 (2020–2021) |
| Novy Urengoy | NUR | 66.083°N, 76.668°E | – | IOP‑1 (winter 2016/2017) |
| Norilsk | NSK | 69.357°N, 88.187°E | AWSu1 (2019–2021) | IOP‑1 (winter 2019/2020) |
Data
The UHIARC dataset is provided as city-specific subsets, available in separate ZIP files for each city: Murmansk (MUR), Apatity (APT), Vorkuta (VOR), Salekhard (SAL), Nadym (NAD), Novy Urengoy (NUR), Norilsk (NSK). Each subset has the following structure::
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L0/– raw data, unified format, original timestamps:-
SWSr1.csv– observations of the city’s local synoptic weather station operated by Roshydromet. -
AWS*.csv– observations from a specific Davis Vantage Pro 2 automatic weather station (e.g.,AWSu1.csv,AWSu2.csv). -
HOBO_TLs/&{basic_site}.csv– folder with observations from HOBO MX2303 TLs attached to AWSs or installed at SWS sites. Each file inside contains observations from a specific logger with two temperature sensors (1.5 m and 3 m); thebasic_sitein its filename corresponds to the AWS or SWS that this TL supplemented. -
TLs_IOP{i}*/– folder with observations from iButton TLs installed during the i‑th Intensive Observation Period (IOP) in this city, e.g.,TLs_IOP1_2015-2016. The folder includes files named asTL{i}.{j}.csvwith observations from a specific logger, where{i}indicates the IOP number and{j}the logger number, and filecrd.kmlwith locations of TLs deployed during this specific IOP in Keyhole Markup Language. Unless otherwise stated, the logger measures air temperature at 2 m height. If the logger measured soil temperature, the filename contains_soiland the measurement depth in centimetres (e.g.,TL3.1_soil7cm.csv).
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L1/– same as L0, but timestamps are rounded and corrected for time shifts (e.g., due to logger malfunctions, power outages, or sensor inertia). For details on the correction procedure, see the associated manuscript. -
L2/– same as L1, but with gap‑filled and integrated data. Missing temperature records in AWS or SWS are filled using nearby TLs. For sites with multiple sensors, data from auxiliary sensors are merged into a single file corresponding to the basic one (AWS or SWS). Filled temperatures are marked by ata2m_is_filledflag. -
L3/– merged wide‑table files with data from all sites in the city:-
data_WSs_3h.csv– data from all weather stations (SWS and AWSs) with 3‑hour timestep. -
data_WSs_30min.csv– same with 30‑minute timestep. -
data_IOP{i}*.csv– observations of all available variables and from all available sites (SWS, AWSs, TLs) for a specific i‑th IOP, with timestamps corresponding to the frequency of iButton observations (1 or 3 hours) -
data_IOP{i}*_ta2m.csv– a reduced version containing only air temperature columns from all sites, facilitating mapping and analysis of air temperature anomalies.
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L4.csv– tabular dataset designed specifically for quantifying canopy‑layer urban heat island (UHI) intensity, derived from the 3‑hourly L3 data by supplementing it with temperature differences between selected urban and rural sites (ΔT_{i-j} = T_i – T_j). -
crd.kml– locations (lat, lon) of all observational sites for the city (SWS, AWSs, TLs deployed during all IOPs) in Keyhole Markup Language; can be opened immediately in Google Earth. -
crd.csv– locations of all observational sites in tabular format. LCZ_map.tiff– map of Local Climate Zones (LCZs) in this city and its surroundings built using LCZ-Generator (GeoTIFF format).
All files containing observed time series are standardized, utilizing UTC timestamps and consistent variable names. The variable names in the dataset correspond to meteorological variables as detailed below, along with measurement heights specified in meters for air and centimeters for soil. For instance, the variables ta2m and tso07cm represent air temperature measured at a height of 2 meters above the ground and soil temperature measured at a depth of 7 centimeters below the ground, respectively.
| Variable name | Description | Unit | Availability |
| ta | air temperature | °C | AWSs, SWSs, TLs |
| tso | soil temperature | °C | Some AWSs and TLs |
| rh | air relative humidity | % | AWSs, SWSs |
| vel | wind speed | m/s | AWSs, SWSs |
| velmax | maximum wind speed | m/s | AWSs |
| ps | surface pressure | hPa | AWSs, SWSs |
| tcc | total cloud cover* | tenths | SWSs |
| lcc | low cloud cover* | tenths | SWSs |
*measured in tenths on a scale from 0 (clear sky) to 10 (overcast).
Files
Additional details
Software
- Development Status
- Active