Contains measurement data of air temperature for the manuscript “Real-time measurements of micro-climatic temperature and relative humidity in the Finnish cities of Tampere and Helsinki” by Kühn et al. (in preparation) of 23 measurement stations in Tampere. Technical Info: NB: Update on 2.6.2025: data file Tampere_202505.dat added and file Tampere_Table1_New.txt added. The measurements were conducted at a height of 3 m in the locations listed in Tampere_Table1_New.txt during 07/2023-05/2025. Each measurement station consisted of three parts: a temperature and humidity sensor, a solar radiation shield, and an Internet of Things (IoT) device, which collected the measurement data and communicated them to a server via Long Range Wide Area Network (LoRaWAN). Temperature and relative humidity (RH) were measured by one integrated sensor, the Digital Matter I2C Temperature and Humidity Sensor [https://www.digitalmatter.com/wp-content/uploads/2020/09/I2C-Temperature-and-Humidity-Sensor-Datasheet.pdf]. Within the sensor, the temperature and RH were measured using the Silicon Labs Si7021-A20 I2C Humidity and Temperature Sensor chip. The chip is factory calibrated and has maximum operating ranges of 0% to 100% RH and -40°C to +125°C temperature. The measurement accuracy for temperature is maximum ±0.4°C if the ambient temperature is between -10°C and 85°C. The measurement accuracy of the chip is maximum ±3% RH if the ambient RH is between 0% and 80%. The Temperature and Humidity Sensor was protected by a radiation shield to minimize the influence of direct sunlight and thermal radiation on the measurements. The radiation shield (height 11.5 cm, radius 14 cm) was made of white plastic and consisted of 9 ventilated plates stacked in a cylindrical design allowing for adequate airflow while shielding the sensor from external radiation. Quality check The temperature data was quality checked using a multi-step procedure. First, values were screened based on long-term climatological daily minimum and maximum temperatures derived from 10 km × 10 km resolution gridded temperature data for the Tampere region (Aalto et al., 2016). Measurements falling clearly outside the climatological range were removed. Subsequently, remaining values were filtered based on statistical properties of the measurements, using median and median absolute deviation (MAD) over short time intervals to identify and remove outliers. A final threshold based on deviations from the local median was applied to exclude any remaining extreme values. The Local Climate Zones (LCZs) in Table 1. have been defined for each measurement station following the Global LCZ data (Demuzere 2022a, Demuzere, et al. 2022b) based on the Local Climate Zone (LCZ) Classification system by Stewart and Oke (2012). Table Of Contents: The descriptions of the measurement stations are in Tampere_Table1_New.txt. Columns 1. Station_code 2. Station_id 3. latitude 4. longitude 5. elevation above mean se alevel (m) 6. LCZ_global_point (LCZ at the grid point nearest to the measurement station) 7. LCZ_global_r200 (Mode of the LCZs within a 200-meter radius around the measurement station) The data (hourly Temperature) of each the measurements are in ASCII (tabulator as separator) files Tampere_YEAR.dat, with Celsius as Unit and missing value -999. Columns: 1. Station_code 2. Timestamp(YMDHH24) UTC 3. Mean temperature of the previous hour 4. Minimum temperature of the previous hour 5. Maximum temperature of the previous hour 6. Standard deviation of the temperature measurements during the previous hour 7. Number of measurements during the previous hour (usually 12) References: Aalto, J., Pirinen, P., & Jylhä, K. (2016). New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate. Journal of Geophysical Research: Atmospheres, 121(8), 3807–3823. https://doi.org/10.1002/2015JD024651 Stewart ID, Oke TR. Local Climate Zones for Urban Temperature Studies. Bull Am Meteorol Soc. 2012;93(12):1879-1900. doi:10.1175/BAMS-D-11-00019.1 Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022a): A global map of local climate zones to support earth system modelling and urban-scale environmental science, Earth Syst. Sci. Data, 14, 3835-3873, https://doi.org/10.5194/essd-14-3835-2022. Demuzere, M., Kittner, J., Martilli, A., Mills, G., Moede, C., Stewart, I. D., van Vliet, J., and Bechtel, B. (2022b): Global map of Local Climate Zones. Zenodo. https://doi.org/10.5281/zenodo.6364593.

This National Oceanic and Atmospheric Administration (NOAA) map service displays Quality Controlled Local Climatological Data (QCLCD) contains summaries from major airport weather stations. The summaries include a daily account of temperature extremes, degree days, precipitation amounts and winds. Also included are the hourly precipitation amounts and abbreviated 3-hourly weather observations. The source data is Global Hourly (DSI 3505) which includes a number of quality control checks. The local climatological data annual file is produced from the National Weather Service (NWS) first and second order stations.The monthly summaries include maximum, minimum, and average temperature, temperature departure from normal, dew point temperature, average station pressure, ceiling, visibility, weather type, wet bulb temperature, relative humidity, degree days (heating and cooling), daily precipitation, average wind speed, fastest wind speed/direction, sky cover, and occurrences of sunshine, snowfall and snow depth.The annual summary with comparative data contains monthly and annual averages of the above basic climatological data in the meteorological data for the current year section, a table of the normals, means, and extremes of these same data, and sequential table of monthly and annual values of average temperature, total precipitation, total snowfall, and total degree days. Also included is a station location table showing in detail a history of, and relative information about, changes in the locations and exposure of instruments.Data currency: Current NOAA map service.For additional information, please contact the National Climatic Data Center (NCDC) at ncdc.orders@noaa.gov._Other Health Datapalooza focused content that may interest you: Health Datapalooza Health Datapalooza

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

Carbon Dioxide (Difference from Global Mean, Best Available, OCO-2) from NASA GIBSTemporal coverage: 2002 SEP - 2012 FEBThe Carbon Dioxide (L3, Free Troposphere, Monthly) layer displays monthly Carbon Dioxide in the free troposphere. It is created from the AIRX3C2M data product which is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 Monthly Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is monthly gridded data at 2.5x2 degreee (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This quantity is not a total column quantity because the sensitivity function of the AIRS mid-tropospheric CO2 retrieval system peaks over the altitude range 6-10 km. The quantity is what results when the true atmospheric CO2 profile is weighted, level-by-level, by the AIRS sensitivity function.The Atmospheric Infrared Sounder (AIRS), in conjunction with the Advanced Microwave Sounding Unit (AMSU), senses emitted infrared and microwave radiation from Earth to provide a three-dimensional look at Earth's weather and climate. Working in tandem, the two instruments make simultaneous observations down to Earth's surface. With more than 2,000 channels sensing different regions of the atmosphere, the system creates a global, three-dimensional map of atmospheric temperature and humidity, cloud amounts and heights, greenhouse gas concentrations and many other atmospheric phenomena. Launched into Earth orbit in 2002, the AIRS and AMSU instruments fly onboard NASA's Aqua spacecraft and are managed by NASA's Jet Propulsion Laboratory in Pasadena, California. More information about AIRS can be found at https://airs.jpl.nasa.gov.References: AIRX3C2M doi:10.5067/Aqua/AIRS/DATA339ABOUT NASA GIBSThe Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.The Global Imagery Browse Services (GIBS) system is a core EOSDIS component which provides a scalable, responsive, highly available, and community standards based set of imagery services. These services are designed with the goal of advancing user interactions with EOSDIS’ inter-disciplinary data through enhanced visual representation and discovery.MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (originally known as EOS AM-1) and Aqua (originally known as EOS PM-1) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.GIBS Available Imagery ProductsThe GIBS imagery archive includes approximately 1000 imagery products representing visualized science data from the NASA Earth Observing System Data and Information System (EOSDIS). Each imagery product is generated at the native resolution of the source data to provide "full resolution" visualizations of a science parameter. GIBS works closely with the science teams to identify the appropriate data range and color mappings, where appropriate, to provide the best quality imagery to the Earth science community. Many GIBS imagery products are generated by the EOSDIS LANCE near real-time processing system resulting in imagery available in GIBS within 3.5 hours of observation. These products and others may also extend from present to the beginning of the satellite mission. In addition, GIBS makes available supporting imagery layers such as data/no-data, water masks, orbit tracks, and graticules to improve imagery usage.The GIBS team is actively engaging the NASA EOSDIS Distributed Active Archive Centers (DAACs) to add more imagery products and to extend their coverage throughout the life of the mission. The remainder of this page provides a structured view of the layers currently available within GIBS grouped by science discipline and science observation. For information regarding how to access these products, see the GIBS API section of this wiki. For information regarding how to access these products through an existing client, refer to the Map Library and GIS Client sections of this wiki. If you are aware of a science parameter that you would like to see visualized, please contact us at support@earthdata.nasa.gov. https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products#expand-AerosolOpticalDepth29ProductsNASA GIS API for Developers https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers

Ice core data collected by Yi-Fang (Yvonne) Hsieh and collaborators for her PhD project, “Modeling Ice Cover and Water Temperature of Lake Mendota.†Part of the project was the development of a 3D hydrodynamic-ice model that simulated both temporal and spatial distributions of ice cover on Lake Mendota for the winter 2009-2010. The parameters from these ice core data were used as model inputs to run model simulations. Parameters measured include: blue ice, white ice, snow depth, and total ice. On February 13, 2009, ice cores were taken on Lake Mendota at four different stations.  From January 14, 2010 through March 3, 2010 ice cores were taken on Lake Mendota at 31 different stations. In addition, ice cores were taken on other Yahara Lakes during February of 2009: Lake Kegonsa (4 stations_February 6), Lake Waubesa (4 stations_February 7), Lake Wingra (2 stations_February 8), and Lake Monona (4 stations_February 8). Only total ice measurements are reported for 2009. Included in this data set are the ice core data, and geospatial information for ice coring stations. Documentation: Hsieh, Y.-F., 2012a. Modeling ice cover and water temperature of Lake Mendota. ProQuest Dissertations and Theses. The University of Wisconsin - Madison, United States -- Wisconsin, p. 157. Â

Melting snowpack is a key part of the spring water budget in many parts of the world. Like a natural reservoir, snowpack stores winter precipitation and releases it as runoff over the course of many months. Where summer rains are scarce snowpack provides crucial base flow without which rivers might go dry. Where summer rains are torrential, this exacerbates the flooding and can lead to the loss of lives. This map contains a historical record showing the water stored in snowpack during each month from March 2000 to the present. It is not a map of snow depth, but of snow water equivalent, which is the amount of water that would be produced if all the snow melted. For fresh snow, this can be anywhere from 5% to 20% the depth of the snow, depending on temperature (snow tends to be fluffier at lower temperatures). As the snow settles and melts, it becomes more dense, up to 40% or 50% in the spring. Temperature, albedo (the reflective property of the snow), density, and volume all affect the melting rate of the snowpack. Additionally, melting rate is influenced by wind, relative humidity, air temperature and solar radiation.Dataset SummaryThe GLDAS Snowpack layer is a time-enabled image service that shows average monthly snowpack from 2000 to present, measured in millimeters of snow water equivalent. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-2.1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!Phenomenon Mapped: SnowpackUnits: MillimetersTime Interval: MonthlyTime Extent: 2000/01/01 to presentCell Size: 28 kmSource Type: ScientificPixel Type: Signed IntegerData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global Land SurfaceSource: NASAUpdate Cycle: SporadicWhat can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Is useful for scientific modeling, but only at global scales. The GLDAS snowpack data is useful for modeling, but only at global scales. By applying the "Calculate Anomaly" processing template, it is also possible to view these data in terms of deviation from the mean, instead of total snowpack. Mean snowpack for a given month is calculated over the entire period of record - 2000 to present.Time: This is a time-enabled layer. By default, it will show the first month from the map's time extent. Or, if time animation is disabled, a time range can be set using the layer's multidimensional settings. If you wish to calculate the average, sum, or min/max over the time extent, change the mosaic operator used to resolve overlapping pixels. In ArcGIS Online, you do this in the "Image Display Order" tab. In ArcGIS Pro, use the "Data" ribbon. In ArcMap, it is in the 'Mosaic' tab of the layer properties window. The minimum time extent is one month, and the maximum is 8 years. Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.

Data repository for measurements from 6 meteorological stations in Zambia. Data contains 1 minute average values for solar radiation levels, air temperature, relative humidity, wind speed, atmospheric pressure and precipitation. Delivered files: Solar-Measurements_Zambia_sitename_WB-ESMAP_Header metadata for ground measurements files. Solar-Measurements_Zambia_sitename_WB-ESMAP_Raw raw ground measurements from datalogger. Do not use for further development. Solar-Measurements_Zambia_sitename_WB-ESMAP_QC quality checked ground measurements from dataloger. Solar-Measurements_Zambia_WB-ESMAP_SatelliteTS site adapted time series of satellite data. Solar-Measurements_Zambia_WB-ESMAP_SatelliteTMY Typical meteorological year data file (P50) based on site adapted time series of satellite data. For more information and additional outputs, please visit: https://www.esmap.org/ For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Zambia-Solar Radiation Measurement Data, 2017,

M2IMNXLFO (or instM_2d_lfo_Nx) is an instantaneous 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of land surface forcing parameters, such as height, specific humidity, wind, and air temperature of the model surface layer. The collection also includes variance of certain parameters.

MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month.

Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.

MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.

Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

M2TMNXFLX (or tavgM_2d_flx_Nx) is a time-averaged 2-dimensional monthly mean data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilated surface flux diagnostics, such as total precipitation, bias corrected total precipitation, surface air temperature, surface specific humidity, surface wind speed, and evaporation from turbulence. The “surface” in this data collection is the model surface layer. The heights of the model surface layer (HLML) vary with time and location, with the value of ~60 meter above ground. The collection also includes variance of certain parameters.

MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month.

Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.

MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.

Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

Data repository for ground measurements and satellite data from 4 meteorological stations in Maldives. Data contains 1 minute average values for solar radiation levels, air temperature, relative humidity, wind speed and atmospheric pressure. Delivered files: Solar-Measurements_Maldives_sitename_WB-ESMAP_Header metadata for ground measurements files. Solar-Measurements_Maldives_sitename_WB-ESMAP_Raw raw ground measurements from datalogger. Do not use for further development. Solar-Measurements_Maldives_sitename_WB-ESMAP_QC quality checked ground measurements from dataloger. _ Solar-Measurements_Maldives_WB-ESMAP_SatelliteTS_ site adapted time series of satellite data. Solar-Measurements_Maldives_WB-ESMAP_SatelliteTMY Typical meteorological year data file (P50) based on site adapted time series of satellite data. For more information and additional outputs, please visit: http://esmap.org/re_mapping_madlvies For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Maldives-Solar Radiation Measurement Data, 2018,

This data set is a three-level classification map of Eurasian grassland remote sensing in 2009. The data is in TIF grid format, with a spatial resolution of 1km. The three-level grassland is classified as: temperate meadow grassland, temperate typical grassland, temperate desertification grassland, temperate grassland desertification, and temperate desert. The data is processed according to the ESA global cover 2009 Product global cover map, combined with the historical meteorological data (precipitation, annual accumulated temperature, humidity coefficient, evaporation) and DEM data of ECMWF website. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.

Data repository for ground measurements and satellite data from 3 meteorological stations in Malawi. Data contains 1 minute average values for solar radiation levels, air temperature, relative humidity, precipitation, wind speed and atmospheric pressure. Delivered files: Solar-Measurements_Malawi_sitename_WB-ESMAP_Header metadata for ground measurements files. Solar-Measurements_Malawi_sitename_WB-ESMAP_Raw raw ground measurements from datalogger. Do not use for further development. Solar-Measurements_Malawi_sitename_WB-ESMAP_QC quality checked ground measurements from dataloger. _ Solar-Measurements_Malawi_WB-ESMAP_SatelliteTS_ site adapted time series of satellite data. Solar-Measurements_Malawi_WB-ESMAP_SatelliteTMY Typical meteorological year data file (P50) based on site adapted time series of satellite data. For more information and additional outputs, please visit: http://esmap.org/re_mapping_malawi For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/ Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Malawi-Solar Radiation Measurement Data, 2017,

This dataset provides a complete historical reconstruction for a set of indices representing human thermal stress and discomfort in outdoor conditions. This dataset, also known as ERA5-HEAT (Human thErmAl comforT) represents the current state-of-the-art for bioclimatology data record production. The dataset is organised around two main variables:

the mean radiant temperature (MRT) the universal thermal climate index (UTCI)

These variables describe how the human body experiences atmospheric conditions, specifically air temperature, humidity, ventilation and radiation. The dataset is computed using the ERA5 reanalysis from the European Centre for Medium-Range Forecasts (ECMWF). ERA5 combines model data with observations from across the world to provide a globally complete and consistent description of the Earth’s climate and its evolution in recent decades. ERA5 is regarded as a good proxy for observed atmospheric conditions. The dataset currently covers 01/01/1940 to near real time and is regularly extended as ERA5 data become available. The dataset is produced by the European Centre for Medium-range Weather Forecasts.

The Global Energy and Water Cycle Experiment (GEWEX) Continental-scale International Project (GCIP) Enhanced Annual Observing Period - 1998 (EAOP-98) takes place in the Ohio-Tennessee River basins as a data collection effort in a Large Scale Area (LSA) of the entire Mississippi River basin. The Ohio-Tennessee River basins provide a number of watershed areas that are potentionally useful for focused hydrologic studies. The EAOP-98 dataset constitutes the fourth GCIP ESOP dataset during the GCIP five year Enhanced Observing Period (EOP) and the first in the Ohio-Tennessee River basins.

The database includes hourly data of the main meteorological parameters measured in the area of Rome (Italy) by 17 of the weather stations belonging to the ASTI-Network. The dataset also includes hourly data of Urban Heat Island (UHI) intensity, calculated using the imperviousness method [1], covering the time period of two summers (June, July, August, JJA) 2019 and 2020.

The data are sampled by the sensors as 5-minute averages (except for precipitation, which is cumulative) and are subjected to a filtering process before being averaged to hourly resolution. The filtering process includes the following sequential steps: interference filter, climatic filter, temporal variation filter (of which spikes are a particular case), and spatial filter. Details on the implemented algorithms are provided in [1].

The final hourly data are calculated taking the right edge, and the time zone adopted is Central European Time (UTC+1).

The parameters available in the dataset include atmospheric pressure (relative and absolute), air temperature, relative humidity, wind speed, wind direction, wind gusts, rain rate, cumulative precipitation, dew point, and, where available, global radiation and UV index. More details about the variables and dataset formatting are provided in the file ‘README.txt’.

The file ‘Hourly_UHI.csv’ contains the UHI intensity, calculated using a linear fit between the measured temperatures (T) and the associated imperviousness (IMP, %) [1], for each time step, resulting in hourly resolution. Assuming the linear relationship T(IMP) = mIMP + q, the UHI intensity is given by 100m, and the corresponding column in the dataset is named ‘DT=100m’. Additional columns provide statistical parameters and the average values across all stations for temperature, wind speed, rain rate, and cumulative precipitation. Further details are available in the file ‘README.txt’.

The file ‘metadata.csv’ contains metadata for each weather station, such as ID, coordinates, altitude, and associated imperviousness. Figure 'asti_network_sat.png' shows their spatial distribution on 2d map.

This dataset forms the basis of the paper “Measuring the urban heat island of Rome through a dense weather station network and remote sensing imperviousness data” published in December 2022 in the journal Urban Climate (doi.org/10.1016/j.uclim.2022.101355). It was also used for validating the numerical model developed within the LIFE-ASTI project.

The ASTI-Network measurement network consists of rooftop weather stations and distributed throughout the city of Rome. It includes amateur stations from the Meteo Lazio network (meteoregionelazio.it) and eight stations funded by the EU LIFE-ASTI project "Implementation of a forecasting system for urban heat island effect for the development of urban adaptation strategy" (LIFE17 CCA/GR/000108) and installed by the CNR-ISAC research team based in Rome.

Bibliography

1. Cecilia, A., Casasanta, G., Petenko, I., Conidi, A., Argentini, S. Measuring the urban heat island of Rome through a dense weather station network and remote sensing imperviousness data, Urban Climate 47 (01 2022).

The Global Energy and Water Cycle Experiment (GEWEX) Continental-scale International Project (GCIP) Enhanced Annual Observing Period - 1999 (EAOP-99) takes place in the Ohio-Tennessee River basins as a data collection effort in a Large Scale Area (LSA) of the entire Mississippi River basin. The Ohio-Tennessee River basins provide a number of watershed areas that are potentionally useful for focused hydrologic studies. The EAOP-99 dataset constitutes the fifth GCIP ESOP dataset during the GCIP five year Enhanced Observing Period (EOP) and the first in the Ohio-Tennessee River basins.