The dataset provides official temperature data measured from 513 weather stations in Germany from 1990 to 2021.

The original data are provided by the German Meteorological Service (DWD, Deutscher Wetterdienst) via the OpenData area of the Climate Data Center (CDC). These data are provided in 1611 files, resulting in > 500 million rows of measurement information (or missing values), a format that is poorly suited for further analysis.

Therefore, the data are converted from "long format" to "wide format". The result is a time series with 10 minute frequency containing one column per weather station. The exact columns in the file are: - MESS_DATUM: the datetime values of the time series, representing the index of the time series - list of weather station ids: one column per weather station, represented by the weather station id

From the five numerical measurement values of the original data, only "air temperature at 2m height in °C" was kept.

In addition to the extracted temperature data, a notebook is provided which can be used to extract the other four types of measurements in the same format.

The following files are provided in this dataset: - german_temperature_data_1990_2021.csv, containing the extracted original data (download and transformation, see this notebook). - german_temperature_data_1996_2021_from_selected_weather_stations.csv, containing a selection of the original data from 55 weather stations that have continuously provided a high amount of measurements from 1996-2021 (and thus no change in distribution over time). For the selection process, see this notebook. - zehn_min_tu_Beschreibung_Stationen.txt, additional information about the weather stations. - DESCRIPTION_obsgermany_climate_10min_tu_historical_en.pdf, the official data set description.

The terms of use are described by https://opendata.dwd.de/climate_environment/CDC/Nutzungsbedingungen_German.pdf and https://gdz.bkg.bund.de.

This dataset replaces the previous Time Bias Corrected Divisional Temperature-Precipitation Drought Index. The new divisional data set (NClimDiv) is based on the Global Historical Climatological Network-Daily (GHCN-D) and makes use of several improvements to the previous data set. For the input data, improvements include additional station networks, quality assurance reviews and temperature bias adjustments. Perhaps the most extensive improvement is to the computational approach, which now employs climatologically aided interpolation. This 5km grid based calculation nCLIMGRID helps to address topographic and network variability. This data set is primarily used by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC) to issue State of the Climate Reports on a monthly basis. These reports summarize recent temperature and precipitation conditions and long-term trends at a variety of spatial scales, the smallest being the climate division level. Data at the climate division level are aggregated to compute statewide, regional and national snapshots of climate conditions. For CONUS, the period of record is from 1895-present. Derived quantities such as Standardized precipitation Index (SPI), Palmer Drought Indices (PDSI, PHDI, PMDI, and ZNDX) and degree days are also available for the CONUS sites. In March 2015, data for thirteen Alaskan climate divisions were added to the NClimDiv data set. Data for the new Alaskan climate divisions begin in 1925 through the present and are included in all monthly updates. Alaskan climate data include the following elements for divisional and statewide coverage: average temperature, maximum temperature (highs), minimum temperature (lows), and precipitation. The Alaska NClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the NClimGrid data set. As of November 2018, NClimDiv includes county data and additional inventory files.

The monthly mean temperature data presented in this dataset was obtained from the Climate Prediction Center (CPC) Global Land Surface Air Temperature Analysis, which was loaded into Python using xarray. The data was then filtered to include only the latitude and longitude coordinates corresponding to each city in the dataset. In order to select the nearest location to each city, the 'select' method with the nearest point was used, resulting in temperature data that may not be exactly at the city location. The data is presented on a 0.5x0.5 degree grid across the globe.

The temperature data provides a valuable resource for time series analysis, and if you are interested in obtaining temperature data for additional cities, please let me know. I will also be sharing the source code on GitHub for anyone who would like to reproduce the data or analysis.

This version has been superseded by a newer version. It is highly recommended for users to access the current version. Users should only access this superseded version for special cases, such as reproducing studies. If necessary, this version can be accessed by contacting NCEI. The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is derived from two independent analyses: the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the land surface temperature (LST) analysis using the Global Historical Climatology Network (GHCN) temperature database. The data is merged into a monthly global surface temperature dataset dating back from 1880 to the present, updated monthly, in gridded (5 degree x 5 degree) and time series formats. This data set is used in climate monitoring assessments of near-surface temperatures on a global scale. The changes from version 3.5.4 to version 4.0.0 include an update to the primary input dataset (ERSST) now at version 4.0.0 and GHCN-Monthly now at version 3.3.0. This dataset is formerly known as Merged Land-Ocean Surface Temperature (MLOST).

This dataset provides high-resolution gridded temperature and precipitation observations from a selection of sources. Additionally the dataset contains daily global average near-surface temperature anomalies. All fields are defined on either daily or monthly frequency. The datasets are regularly updated to incorporate recent observations. The included data sources are commonly known as GISTEMP, Berkeley Earth, CPC and CPC-CONUS, CHIRPS, IMERG, CMORPH, GPCC and CRU, where the abbreviations are explained below. These data have been constructed from high-quality analyses of meteorological station series and rain gauges around the world, and as such provide a reliable source for the analysis of weather extremes and climate trends. The regular update cycle makes these data suitable for a rapid study of recently occurred phenomena or events. The NASA Goddard Institute for Space Studies temperature analysis dataset (GISTEMP-v4) combines station data of the Global Historical Climatology Network (GHCN) with the Extended Reconstructed Sea Surface Temperature (ERSST) to construct a global temperature change estimate. The Berkeley Earth Foundation dataset (BERKEARTH) merges temperature records from 16 archives into a single coherent dataset. The NOAA Climate Prediction Center datasets (CPC and CPC-CONUS) define a suite of unified precipitation products with consistent quantity and improved quality by combining all information sources available at CPC and by taking advantage of the optimal interpolation (OI) objective analysis technique. The Climate Hazards Group InfraRed Precipitation with Station dataset (CHIRPS-v2) incorporates 0.05° resolution satellite imagery and in-situ station data to create gridded rainfall time series over the African continent, suitable for trend analysis and seasonal drought monitoring. The Integrated Multi-satellitE Retrievals dataset (IMERG) by NASA uses an algorithm to intercalibrate, merge, and interpolate “all'' satellite microwave precipitation estimates, together with microwave-calibrated infrared (IR) satellite estimates, precipitation gauge analyses, and potentially other precipitation estimators over the entire globe at fine time and space scales for the Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM) satellite-based precipitation products. The Climate Prediction Center morphing technique dataset (CMORPH) by NOAA has been created using precipitation estimates that have been derived from low orbiter satellite microwave observations exclusively. Then, geostationary IR data are used as a means to transport the microwave-derived precipitation features during periods when microwave data are not available at a location. The Global Precipitation Climatology Centre dataset (GPCC) is a centennial product of monthly global land-surface precipitation based on the ~80,000 stations world-wide that feature record durations of 10 years or longer. The data coverage per month varies from ~6,000 (before 1900) to more than 50,000 stations. The Climatic Research Unit dataset (CRU v4) features an improved interpolation process, which delivers full traceability back to station measurements. The station measurements of temperature and precipitation are public, as well as the gridded dataset and national averages for each country. Cross-validation was performed at a station level, and the results have been published as a guide to the accuracy of the interpolation. This catalogue entry complements the E-OBS record in many aspects, as it intends to provide high-resolution gridded meteorological observations at a global rather than continental scale. These data may be suitable as a baseline for model comparisons or extreme event analysis in the CMIP5 and CMIP6 dataset.

This data set includes daily, monthly, and yearly mean surface air temperatures for four interior West Antarctic sites between 1978 and 1997. Data include air surface temperatures measured at the Byrd, Lettau, Lynn, and Siple Station automatic weather stations. In addition, because weather stations in Antarctica are difficult to maintain, and resulting multi-decade records are often incomplete, the investigators also calculated surface temperatures from satellite passive microwave brightness temperatures. Calibration of 37-GHz vertically polarized brightness temperature data during periods of known air temperature, using emissivity modeling, allowed the investigators to replace data gaps with calibrated brightness temperatures.

MS Excel data files and GIF images derived from the data are available via ftp from the National Snow and Ice Data Center.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

This dataset contains Russian Historical Soil Temperature Data. This data set is a collection of monthly and annual average soil temperatures measured at Russian meteorological stations. Data were recovered from many sources and compiled by staff at the University of Colorado, USA, and the Russian Academy of Sciences in Puschino, Russia. Soil temperatures were measured at depths of 0.02 to 3.2 m using bent stem thermometers, extraction thermometers, and electrical resistance thermistors. Data coverage extends from the 1800s through 1990, but is not continuous. Data are not available for all stations for the entire period of coverage. For example, data collection began at many stations in the 1930s and 1950s, and not all stations continued taking measurements through 1990. This research was supported by the National Science Foundation (NSF) Office of Polar Programs (OPP) awards OPP-9614557, OPP-9907541, and OPP-0229766. Data are available as tar.gz files.

This resource references a large temperature database developed for the western U.S. that contains records for >23,000 unique stream and river sites and consists of data contributed by hundreds of professionals working for dozens of natural resource agencies. All the records have been QA/QC’d and linked to the National Hydrography Dataset and fully documented with metadata for easy use. The website describing the NorWeST project and serving the data is here (https://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.html). The publication, The NorWeST Summer Stream Temperature Model and Scenarios for the Western U.S.: A Crowd‐Sourced Database and New Geospatial Tools Foster a User Community and Predict Broad Climate Warming of Rivers and Streams can be found here: https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2017WR020969

The NOAA Global Surface Temperature Dataset (NOAAGlobalTemp) is a monthly global merged land-ocean surface temperature analysis product that is derived from two independent analyses. The first is the Extended Reconstructed Sea Surface Temperature (ERSST) analysis and the second is a land surface air temperature (LSAT) analysis that uses the Global Historical Climatology Network - Monthly (GHCN-M) temperature database. The NOAAGlobalTemp data set contains global surface temperatures in gridded (5° × 5°) and monthly resolution time series (from 1850 to present time) data files. The product is used in climate monitoring assessments of near-surface temperatures on a global scale. This version, v6.0, an updated version to the current operational release v5.1, is implemented by an Artificial Neural Network method to improve the surface temperature reconstruction over the land.

This dataset compiles heat flow and temperature gradient data from over 44,000 wells across the United States, along with more than 6,000 related geothermal exploration resources. Originally assembled prior to 2014 for the now-retired National Geothermal Data System (NGDS), the collection includes curated well data, scanned field notes, temperature-depth curves, publications, maps, and other supporting documents. SMU Geothermal Laboratory contributed two different nationwide heat flow databases to the project. One is based on equilibrium temperature measurements (over 14,000 sites) and the other is based on corrected bottom hole temperature (BHT) data from oil and gas industry wells (over 30,000 sites). In addition, scanned field notes and temperature-depth curves were associated with approximately 6,000 specific sites in the heat flow database. Records were corrected and overlapping sites in the equilibrium heat flow database were linked between the original SMU National database and the UND Global Heat Flow database. New or related sites, which were not previously published because they lacked full heat flow content, are now included as gradient only information along with their detailed temperature data to fill in data gaps. Finally, SMU submitted over 920 scanned publications, reports, and maps suitable for full text searching. The dataset is provided in two flat-structured zip archives: one containing the curated well data and another containing related resources. An Excel index file is provided for each archive, allowing filtering by well name, location, and description. Data files are labeled with state or institutional origin where available.

The Ocean Data Inventory database is an inventory of all of the oceanographic time series data held by the Ocean Science Division at the Bedford Institute of Oceanography. The data archive includes about 5800 current meter and acoustic doppler time series, 4500 coastal temperature time series from thermographs, as well as a small number (200) of tide gauges. Many of the current meters also have temperature and salinity sensors. The area for which there are data is roughly defined as the North Atlantic and Arctic from 30° - 82° N, although there are some minor amounts of data from other parts of the world. The time period is from 1960 to present. The database is updated on a regular basis.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Lake. The data include parameters of paleolimnology (reconstruction) with a geographic location of Arctic. The time period coverage is from Unavailable begin date to Unavailable end date in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Open-access database of englacial temperature measurements compiled from data submissions and published literature. It is developed on GitHub and published to Zenodo. The dataset is described in the following publication:

Mylène Jacquemart, Ethan Welty, Marcus Gastaldello, and Guillem Carcanade (2025). glenglat: A database of global englacial temperatures. Earth System Science Data Discussions. https://doi.org/10.5194/essd-2024-249

Dataset structure

The dataset adheres to the Frictionless Data Tabular Data Package specification. The metadata in datapackage.json describes, in detail, the contents of the tabular data files in the data folder:

source.csv: Description of each data source (either a personal communication or the reference to a published study).

borehole.csv: Description of each borehole (location, elevation, etc), linked to source.csv via source_id and less formally via source identifiers in notes.

profile.csv: Description of each profile (date, etc), linked to borehole.csv via borehole_id and to source.csv via source_id and less formally via source identifiers in notes.

measurement.csv: Description of each measurement (depth and temperature), linked to profile.csv via borehole_id and profile_id.

For boreholes with many profiles (e.g. from automated loggers), pairs of profile.csv and measurement.csv are stored separately in subfolders of data named {source.id}-{glacier}, where glacier is a simplified and kebab-cased version of the glacier name (e.g. flowers2022-little-kluane).

Supporting information

The folder sources, available on GitHub but omitted from dataset releases on Zenodo, contains subfolders (with names matching column source.id) with files that document how and from where the data was extracted.

Tables

Jump to: source · borehole · profile · measurement

source

Sources of information considered in the compilation of this database. Column names and categorical values closely follow the Citation Style Language (CSL) 1.0.2 specification. Names of people in non-Latin scripts are followed by a latinization in square brackets (e.g. В. С. Загороднов [V. S. Zagorodnov]) and non-English titles are followed by a translation in square brackets. The family name of Latin-script names is wrapped in curly braces when it is not the last word of the name (e.g. Emmanuel {Le Meur}, e.g. {Duan} Keqin) or the name ends in two or more unabbreviated words (e.g. Jon Ove {Hagen}). The family name of a Chinese name (and of the latinization) is wrapped in curly braces when it is not the first character.

name type description

id (required) string Unique identifier constructed from the first author's lowercase, latinized, family name and the publication year, followed as needed by a lowercase letter to ensure uniqueness (e.g. Загороднов 1981 → zagorodnov1981a).

author string Author names (optionally followed by their ORCID or contact email in parentheses) as a pipe-delimited list.

year (required) year Year of publication.

type (required) string Item type.- article-journal: Journal article- book: Book (if the entire book is relevant)- chapter: Book section- document: Document not fitting into any other category- dataset: Collection of data- map: Geographic map- paper-conference: Paper published in conference proceedings- personal-communication: Personal communication between individuals- speech: Presentation (talk, poster) at a conference- report: Report distributed by an institution- thesis-phd: Doctor of Philosophy (PhD) thesis- thesis-msc: Master of Science (MSc) thesis- webpage: Website or page on a website

title (required) string Item title.

url string URL (DOI if available).

language (required) string Language as ISO 639-1 two-letter language code.- da: Danish- de: German- en: English- es: Spanish- fr: French- ja: Japanese- ko: Korean- ru: Russian- sv: Swedish- zh: Chinese

container_title string Title of the container (e.g. journal, book).

volume integer Volume number of the item or container.

issue string Issue number (e.g. 1) or range (e.g. 1-2) of the item or container, with an optional letter prefix (e.g. F1) or part number (e.g. 75pt2).

page string Page number (e.g. 1) or range (e.g. 1-2) of the item in the container, with an optional letter prefix (e.g. S1).

version string Version number (e.g. 1.0) of the item.

editor string Editor names (e.g. of the containing book) as a pipe-delimited list.

collection_title string Title of the collection (e.g. book series).

collection_number string Number (e.g. 1) or range (e.g. 1-2) in the collection (e.g. book series volume).

publisher string Publisher name.

borehole

Metadata about each borehole.

name type description

id (required) integer Unique identifier.

source_id (required) string Identifier of the source of the earliest temperature measurements. This is also the source of the borehole attributes unless otherwise stated in notes.

glacier_name (required) string Glacier or ice cap name (as reported).

glims_id string Global Land Ice Measurements from Space (GLIMS) glacier identifier.

location_origin (required) string Origin of location (latitude, longitude).- submitted: Provided in data submission- published: Reported as coordinates in original publication- digitized: Digitized from published map with complete axes- estimated: Estimated from published plot by comparing to a map (e.g. Google Maps, CalTopo)- guessed: Estimated with difficulty, for example by comparing elevation to a map (e.g. Google Maps, CalTopo)

latitude (required) number [degree] Latitude (EPSG 4326).

longitude (required) number [degree] Longitude (EPSG 4326).

elevation_origin (required) string Origin of elevation (elevation).- submitted: Provided in data submission- published: Reported as number in original publication- digitized: Digitized from published plot with complete axes- estimated: Estimated from elevation contours in published map- guessed: Estimated with difficulty, for example by comparing location (latitude, longitude) to a map of contemporary elevations (e.g. CalTopo, Google Maps)

elevation (required) number [m] Elevation above sea level.

mass_balance_area string Mass balance area.- ablation: Ablation area- equilibrium: Near the equilibrium line- accumulation: Accumulation area

label string Borehole name (e.g. as labeled on a plot).

date_min date (%Y-%m-%d) Begin date of drilling, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).

date_max date (%Y-%m-%d) End date of drilling, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).

drill_method string Drilling method.- mechanical: Push, percussion, rotary- thermal: Hot point, electrothermal, steam- combined: Mechanical and thermal

ice_depth number [m] Starting depth of continuous ice. Infinity (INF) indicates that only snow, firn, or intermittent ice was reached.

depth number [m] Total borehole depth (not including drilling in the underlying bed).

to_bed boolean Whether the borehole reached the glacier bed.

temperature_uncertainty number [°C] Estimated temperature uncertainty (as reported).

notes string Additional remarks about the study site, the borehole, or the measurements therein as a pipe-delimited list. Sources are referenced by source.id. Quality concerns are prefixed with '[flag]'.

curator string Names of people who added the data to the database, as a pipe-delimited list.

investigators string Names of people and/or agencies who performed the work, as a pipe-delimited list. Each entry is in the format 'person (agency; ...) {notes}', where only person or one agency is required. Person and agency may contain a latinized form in square brackets.

funding string Funding sources as a pipe-delimited list. Each entry is in the format 'funder [rorid] > award [number] url', where only funder is required and rorid is the funder's ROR (https://ror.org) ID (e.g. 01jtrvx49).

profile

Date and time of each measurement profile.

name type description

borehole_id (required) integer Borehole identifier.

id (required) integer Borehole profile identifier (starting from 1 for each borehole).

source_id (required) string Source identifier.

measurement_origin (required) string Origin of measurements (measurement.depth, measurement.temperature).- submitted: Provided as numbers in data submission- published: Numbers read from original publication- digitized-discrete: Digitized with Plot Digitizer from discrete points of depth versus temperature- digitized-continuous: Digitized with Plot Digitizer from a continuous data source (e.g. line plot of depth versus temperature)

date_min date (%Y-%m-%d) Measurement date, or if not known precisely, the first possible date (e.g. 2019 → 2019-01-01).

date_max (required) date (%Y-%m-%d) Measurement date, or if not known precisely, the last possible date (e.g. 2019 → 2019-12-31).

time time (%H:%M:%S) Measurement time.

utc_offset number [h] Time offset relative to Coordinated Universal Time (UTC).

equilibrium string Whether and how reported temperatures equilibrated following drilling.- true: Equilibrium was measured- estimated: Equilibrium was estimated (typically by extrapolation)- false: Equilibrium was not reached

notes string Additional remarks about the profile or the measurements therein as a pipe-delimited list. Sources are referenced by source.id. Quality concerns are prefixed with '[flag]'.

measurement

Temperature measurements with depth.

name type description

borehole_id (required) integer Borehole identifier.

profile_id (required) integer Borehole profile identifier.

depth (required) number [m] Depth below the glacier surface.

temperature (required) number [°C] Temperature.

The synthetic sensor dataset contains more than 3000 samples, each representing a set of sensor readings. It consists of six columns: Temperature, Sensor1, Sensor2, Sensor3, Sensor4, and Sensor5.

The dataset is designed to mimic a scenario where temperature readings are influenced by multiple independent sensor measurements. The values of the independent variables and the added noise introduce variability in the temperature readings.

This dataset contains in-situ soil moisture profile and soil temperature data collected at 30-minute intervals at SoilSCAPE (Soil moisture Sensing Controller and oPtimal Estimator) project sites since 2021 in the United States and New Zealand. The SoilSCAPE network has used wireless sensor technology to acquire high temporal resolution soil moisture and temperature data over varying durations since 2011. Since 2021, the SoilSCAPE has upgraded the two previously active sites in Arizona and added several new sites in the United States and New Zealand. These new sites typically use the METER Teros-12 soil moisture sensor. At its maximum, the new network consisted of 57 wireless sensor installations (nodes), with a range of 6 to 8 nodes per site. Each SoilSCAPE site contains multiple wireless end-devices (EDs). Each ED supports up to five soil moisture probes typically installed at 5, 10, 20, and 30 cm below the surface. Sites in Arizona have soil moisture probes installed at up to 75 cm below the surface. Soil conditions (e.g., hard soil or rocks) may have limited sensor placement. The data enables estimation of local-scale soil moisture at high temporal resolution and validation of remote sensing estimates of soil moisture at regional and national (e.g. NASA's Cyclone Global Navigation Satellite System - CYGNSS and Soil Moisture Active Passive - SMAP) scales. The data are provided in NetCDF format.

Temperature records on experimental and control plots.

Water temperature in the river and streams are important for water quality aspects altering the ecology and affecting the survival and habitat of different freshwater flora and fauna. Higher temperatures are hence undesirable in such an environment. This data provides documentation of temperature data of the Logan River, UT using a database structure and a jupyter notebook for extracting and visualizing those data. The visualization of maximum temperature variation for each site for the data period and the month of July shows higher temperatures in Sites 2 (near Mendon Road), 13 (near Blacksmiths fork), and 3 (near Main street) which is not suitable for coldwater fish. The distribution of average temperature variations however shows the temperature within the range of requirement ( less than 20oC).

Data content: temperature data of Nukus irrigation area from January 2021 to December 2021, unit: 0.1 ℃. Data source and processing method: this data is collected from the automatic groundwater monitoring station in Nukus irrigation area. Data quality description: this data is site data with a time resolution of 3 hours. Data application achievements and prospects: in the context of climate change, it can be used to analyze the correlation between meteorological elements and groundwater characteristics, and can also be combined with other hydrometeorological data to analyze the temporal and spatial distribution and change characteristics of groundwater. At the same time, it can also be used as basic data for research in related fields such as extreme climate, food production reduction and human health.

This data release (DR) is the update of the U.S. Geological Survey - ScienceBase data release by Bera (2024), with the processed data through September 30, 2024. The primary data for water year 2024 (a water year is the 12-month period, October 1 through September 30, in which it ends) is downloaded from ANL website (Argonne National Laboratory, 2025) and is processed following the guidelines documented in Over and others (2010). Processed WY24 data are appended to update the Watershed Data Management (WDM) database file ARGN23.WDM (Bera, 2024), which was then renamed as ARGN24.WDM. Daily potential evapotranspiration (PET) was computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation and disaggregated to hourly PET, in thousandths of an inch, using the Fortran program LXPET (Murphy, 2005) for the period 10/01/2023 – 09/30/2024. This DR also describes the Watershed Data Management (WDM) database file ARGN24.WDM. ARGN24.WDM file contains nine data series: air temperature, in degrees Fahrenheit (dsn 400), dewpoint temperature, in degrees Fahrenheit (dsn 500), wind speed, in miles per hour (dsn 300), solar radiation, in Langleys (dsn 600), computed potential evapotranspiration, in thousandths of an inch (dsn 200), and four data-source flag series: for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310), and solar radiation (dsn 610), respectively, from January 1,1948, to September 30, 2024. Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup.” The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2025) station at St. Charles, Illinois, was used as "backup" for the hourly air temperature, solar radiation, and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2025) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service from the station at O'Hare International Airport and used as "backup." Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). This DR provides WDM file ARGN24.WDM and the following tab-delimited text files, each with data from January 1, 1948, to September 30, 2024: "Air_temperature.txt" contains hourly air temperature data in degrees Fahrenheit and associated data-source flags. "Dewpoint_temperature.txt" contains hourly dewpoint temperature data in degrees Fahrenheit and associated data-source flags. "Solar_radiation.txt" contains hourly solar radiation data in Langleys and associated data-source flags. "Wind_speed.txt" contains hourly wind speed data in miles per hour and associated data-source flags. "PET.txt" contains hourly PET data, in thousandths of an inch. Tab-delimited text files can be opened with any text editor or Microsoft Excel. To open the WDM file user needs to use the SARA Timeseries utility executable file attached in this page. References Cited: Argonne National Laboratory, 2025, Meteorological data, accessed on February 18, 2025, at https://www.atmos.anl.gov/ANLMET/numeric/. Bera, M., 2024, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2023: U.S. Geological Survey data release, https://doi.org/10.5066/P146RBHK. Midwestern Regional Climate Center, 2025, Meteorological data, accessed on February 19, 2025, at https://mrcc.purdue.edu/CLIMATE/. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program, Illinois Climate Network, 2025, Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on January 9, 2025, at http://dx.doi.org/10.13012/J8MW2F2Q.