Global and high-resolution regional atmospheric models from Météo-France.

• ARPEGE World covers the entire world at a base horizontal resolution of 0.5° (~55km) between grid points, it predicts weather out up to 114 hours in the future.
• ARPEGE Europe covers Europe and North-Africa at a base horizontal resolution of 0.1° (~11km) between grid points, it predicts weather out up to 114 hours in the future.
• AROME France covers France at a base horizontal resolution of 0.025° (~2.5km) between grid points, it predicts weather out up to 42 hours in the future.
• AROME France HD covers France and neighborhood at a base horizontal resolution of 0.01° (~1.5km) between grid points, it predicts weather out up to 42 hours in the future.

Result of a course project in the context of the Master's Degree in Data Science at Università Degli Studi di Milano-Bicocca. The dataset was built in hopes of finding ways to tackle the bad air quality for which Milan is becoming renown for, and to make the training of ML models possible. The data was collected through Open-Meteo's APIs, who in turn got it from "Reanalyses Models" of Europea initiative, used for weather and air quality forecast. The data used was validated by the owners of the reanalyses datasets from which the data comes from, and through the construction of this specific dataset it's data quality was assessed across accuracy, completeness and consistency dimensions. We aggregated the data from hourly to daily, it is possible to consult the entire Data Management process in the attached pdf.

File descriptions: - weatheraqDataset.csv : contains data on weather and air quality for the city of Milan in comma separateda values (csv) format. - weatheraqDataset_Report.pdf : report built to illustrate and explicit the process followed in order to build the final dataset starting from the original data sources; it also explains any processing and aggregation/integration operations carried out.

GitHub repo of the project: https://github.com/edmos7/weather-aqMilan

Column descriptions:

• date: refers to day in calendar year which other values are relative to. YYYY-MM-DD format, in Milan local time.
• avg_nitrogen_dioxide : the average of the hourly instant(10 meters above ground in μg/m3) nitrogen dioxide values for a particular day.
• max_nitrogen_dioxide : the maximum value among the hourly instant(10 meters above ground in μg/m3) nitrogen dioxide values for a particular day
• min_nitrogen_dioxide : the minimum value among the hourly instant (10 meters above ground in μg/m3) nitrogen dioxidevalues for a particular day
• max_time_nitrogen_dioxide : hour at which hourly nitrogen dioxide values reached their maximum, HH:MM:SS
• min_time_nitrogen_dioxide : hour at which hourly nitrogen dioxide values reached their minimum, HH:MM:SS NOTE: all other "pollutant" columns (pm10, pm2_5, sulphur_dioxide, ozone) follow same structure as the above unless specified below.
• pm2_5_avgdRolls : the average of the 24hr rolling averages for particulate matter with diameter below μg/m (pm2.5), in a particular day. Rolling averages are used to compute the European Air Quality Index(EAQI) in a given moment, so in the computation of our Daily EAQI, averages of rolling averages were used. NOTE: the above goes also for the 'pm10_avgdRolls' field.
• eaqi : the computed air quality level according to European Environment Agency thresholds, considering daily averages for ozone, sulphur dioxide and nitrogen dioxide, and average of daily rolling averages for pm10 and pm2.5. The value corresponds to the highest level among single pollutant levels.
• nitrogen_dioxide_eaqi : the air quality level computed through EAQI thresholds for nitrogen dioxide individually, all other [pollutant]_eaqi fields follow same reasoning.
• avg_temperature_2m: average of hourly air temperatures recorded at 2 meters above ground level for the day(°C);
• max_temperature_2m: maximum among hourly air temperatures recorded at 2 meters above ground level for the day(°C);
• min_temperature_2m: minimum among hourly air temperatures recorded at 2 meters above ground level for the day(°C);
• avg_relative_humidity_2m: average of hourly humidity recorded at 2 meters above ground level for the day(%);
• avg_dew_point_2m: average of hourly dew point temperatures recorded at 2 meters above ground for the day(°C);
• avg_apparent_temperature: average of hourly apparent (feels-like temperature) temperatures for the day (°C);
• avg_pressure_msl or avg_surface_pressure: average of atmospheric air pressure reduced to mean sea level(msl) or pressure at surface (hPa) for the day;
• sum_ precipitation: sum of hourly total precipitation(rain,snow) sums relating to the previous hour for the day (mm);
• sum_rain: sum of hourly liquid precipitations recorded as sums of the previous hour for the day(mm);
• avg_cloud_cover: average of total cloud cover as area fractions (%) at each hour of a day;
• avg_shortwave_radiation: average of hourly shortwave solar radiations computed as averages of the preceding hour (W/m2) for the day;
• avg_direct_radiation: average of hourly Direct solar radiations on the horizontal plane computed as average of the previous hour(W/m2) for the day;
• avg_direct_normal_irradiance : average of hourly Direct solar radiations on the normal plane computed as average of the previous hour(W/m2) for the day;
• avg_diffuse_radiation: average of hourly averages of the previous hour (W/m2);
• avg_global_tilted_irradiance: Average of hourly Total radiation received on a tilted pane computed as averages of the preceding hour(W/m2);
• max_wind_speed_10m: maximum daily wind speed at 10 meters above ground (km/h);
• max_wind_speed_100m: maximum daily wind speed at 100 meters above ground (km/h);
• avg_wind_direction_10m: average of daily wind directions at 10 meters above ground (°);
• avg_wind_direction_100m: average of daily wind directions at 100 meters above ground (°);
• max_wind_gusts_10m: gusts at 10 meters above ground of the indicated hour;
• sum_et0_fao_evapotranspiration: evapotranspiration (ET0) of a well-watered grass field, expressed in millimeters(mm). This value represents the potential evaporation from the soil surface, considering the energy available and weather conditions;
• avg_soil_temperature_0_to_7cm or avg_soil_temperature_7_to_28cm : average of hourly measurements of average temperature in different soil levels below ground (C°) for a day; avg_soil_moisture_0_to_7cm or avg_soil_moisture_7_to_28cm : average of hourly instant soil water content as volumetric mixing ratio at 0-7, 7-28 cm depths in m³/m³;
• avg_vapour_pressure_deficit : average of hourly instant Vapor Pressure Deficit (VPD) in kilopascal (kPa). For high VPD (>1.6), water transpiration of plants increases. For low VPD (<0.4), transpiration decreases

Currently looking for accurate terrestrial radiation definition. For more information on the data, please check https://open-meteo.com/en/docs/air-quality-api and https://open-meteo.com/en/docs/historical-weather-api information (columns are aggregated to daily from hourly variables from these APIs). If you find any missing definitions or other, please comment to inform.

Zippenfenig, P. (2023). Open-Meteo.com Weather API [Computer software]. Zenodo. https://doi.org/10.5281/ZENODO.7970649

Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2023). ERA5 hourly data on single levels from 1940 to present [Data set]. ECMWF. https://doi.org/10.24381/cds.adbb2d47

Muñoz Sabater, J. (2019). ERA5-Land hourly data from 2001 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.E2161BAC

Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q. (2021). CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present [Data set]. ECMWF. https://doi.org/10.24381/CDS.622A565A

METEO FRANCE, Institut national de l'environnement industriel et des risques (Ineris), Aarhus University, Norwegian Meteorological Institute (MET Norway), Jülich Institut für Energie- und Klimaforschung (IEK), Institute of Environmental Protection – National Research Institute (IEP-NRI), Koninklijk Nederlands Meteorologisch Instituut (KNMI), Nederlandse Organisatie voor toegepast-natuurwetenschappelijk onderzoek (TNO), Swedish Meteorological and Hydrological Institute (SMHI) and Finnish Meteorological Institute (FMI) (2020): CAMS European air quality forecasts, ENSEMBLE data. Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS). (Accessed on <04-FEB-2025>), \href{https://ads.atmosphere.copernicus.eu/datasets/cams-europe-air-quality-forecasts?tab=overview}{https://ads.atmosphere.copernicus.eu/datasets/cams-europe-air-quality-forecasts?tab=overview}

METEO FRANCE, Institut national de l'environnement industriel et des risques (Ineris), Aarhus University, Norwegian Meteorological Institute (MET Norway), Jülich Institut für Energie- und Klimaforschung (IEK), Institute of Environmental Protection – National Research Institute (IEP-NRI), Koninklijk Nederlands Meteorologisch Instituut (KNMI), Nederlandse Organisatie voor toegepast-natuurwetenschappelijk onderzoek (TNO), Swedish Meteorological and Hydrological Institute (SMHI), Finnish Meteorological Institute (FMI), Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA) and Barcelona Supercomputing Center (BSC) (2022): CAMS European air quality forecasts, ENSEMBLE data. Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS). (Accessed on <04-FEB-2025>), \href{https://ads.atmosphere.copernicus.eu/datasets/cams-europe-air-quality-forecasts?tab=overview}{https://ads.atmosphere.copernicus.eu/datasets/cams-europe-air-quality-forecasts?tab=overview}

In the framework of a long-term joint co-operation between Japan and KNMI aimed at climate reconstruction of Japan in its pre-instrumental era, we now explored the availability of the mostly visual weather data in the daily Diary of the Chief of the Dutch trading post on the island Dejima near Nagasaki. A Pilot project extracted the Januaries of the years 1700-1860; the Follow-up project extracted all months during the period 1817-1823, the term of office of the Chief Jan Cock Blomhoff. Together with the subsequently extracted Von Siebold data 1825-1828 (a supplementary project), the Cock Blomhoff series provides a detailed picture of the Kyushu daily weather in the early 19th century. With this report all data are made systematically accessible and available for further analysis.

WILLIAM Meteo Database

Introduction of project WILLIAM:

Wide-field imaging is a popular way of monitoring the night sky to get a real-time view of current weather conditions. The project WILLIAM (Wide-field all-sky image analyzing monitoring system) was created on demand to provide autonomous control of the telescope and observatory dome. The main goal of this project was to develop a low-cost wide-field and high resolution camera system, whose image data is can be archived for later analysis. One of the options of evaluating current weather conditions from the captured image data is to count visible stellar objects. To work properly, the system must be calibrated to a minimum number of visible stellar objects. If actual image data includes less detected stellar objects than it is calibrated for, the system evaluates the possible occurrence of clouds or rain. Such conditions are then interpreted as inappropriate for using a telescope. Thus the observatory dome stays closed or is going to be closed. The detection of clouds can also be carried out directly in the vicinity to mid-IR. The advantage of IR-based systems is the possibility to detect clouds under any conditions. However, these systems require very complicated and expensive optics and detectors.

Dataset

Original and support test image data for the research letter "Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images".

Data for this database are provided from the WILLIAM system located in Jarošov nad Nežárkou (South Bohemia, GPS 49.185N, 15.072E).

Images are stored in the original raw NIKON format NEF in the separate WMD_NEF.zip file. Support images with clustered data in LAB color space and XYZ color space are located in the LAB_clusters.zip and XYZ_clusters.zip files. Cloud annotation (attributes) for LAB and XYZ clustering is present within the WMD.xlsx file.

attributes

• Image Number
• ID (name of image file)
• Day image number (number of image in a day of capturing)
• Date (date of image capturing)
• Time (time of capturing)
• Cluster with sun (index of the cluster from the support cluster image files that includes the sun - only if the sun was present)
• Clear sky (index of one cluster or several clusters from the support cluster image files that include clear sky part of image)
• Cumulus (index of one cluster or several clusters from the support cluster image files that include cumulus cloud part of image)
• Stratus (index of one cluster or several clusters from the support cluster image files that include stratus cloud part of image)
• Stratocumulus (index of one cluster or several clusters from the support cluster image files that stratocumulus cloud part of image)
• Nimbostratus (index of one cluster or several clusters from the support cluster image files that include nimbostratus cloud part of image)
• Altocumulus (index of one cluster or several clusters from the support cluster image files that include altocumulus cloud part of image)
• Altostratus (index of one cluster or several clusters from the support cluster image files that include altostratus cloud part of image)
• Cumulonimbus (index of one cluster or several clusters from the support cluster image files that include cumulonimbus cloud part of image)
• cirrocumulus (index of one cluster or several clusters from the support cluster image files that include Cirrocumulus cloud part of image)
• Cirrostratus (index of one cluster or several clusters from the support cluster image files that include cirrostratus cloud part of image)
• Cirrus (index of one cluster or several clusters from the support cluster image files that include cirrus cloud part of image)
• Edges (index of the cluster that includes masked edges of the image)
• Rain (values 0 or 1 if the rain was present)
• Cloud groups (main cloud classification groups)
  • 1. high-level clouds (index of one cluster or several clusters from the support cluster image files that include high-level clouds in the image)
  • 2. low-level (cumulus type) clouds (index of one cluster or several clusters from the support cluster image files that include low-level clouds in the image)
  • 3. rain clouds (index of one cluster or several clusters from the support cluster image files that include rainy clouds in the image)
  • 4. clear sky (index of one cluster or several clusters from the support cluster image files that include clear sky in the image)
• Time distance from solar noon (in hours)
• Time distance from Sunset or Sunrise (in hours)
• Sun elevation (in degrees)

Note: The classification of the exact cloud class within the all-sky image is mainly tentative. The cloud group division served for classification purposes.

The WMD.xlsx file consists of two separate clustering annotations in LAB and XYZ colour spaces. The file also includes the EXIF data infromation of each image.

This dataset expones meteorological data managed by Province of Trento.

This dataset contains Raleigh Durham International Airport weather data pulled from the NOAA web service described at Climate Data Online: Web Services Documentation. We have pulled this data and converted it to commonly used units. This dataset is an archive - it is not being updated.

KNMI operates automatic weather stations on land (incl. airports). These weather stations measures meteorological parameters such as temperature, precipitation, wind, air pressure and global radiation. On a daily basis all real-time collected observations and measurements (hourly) are validated on correctness and completeness. The validated data is archived in the Klimatologisch Informatie Systeem (KIS) of KNMI. The daily data is composed from hourly data and each day reference evaporation is calculated using the Makkink method. After the data has been processed and archived in KIS, changes are no longer possible. This assures data integrity.

World Weather Records (WWR) is an archived publication and digital data set. WWR is meteorological data from locations around the world. Through most of its history, WWR has been a publication, first published in 1927. Data includes monthly mean values of pressure, temperature, precipitation, and where available, station metadata notes documenting observation practices and station configurations. In recent years, data were supplied by National Meteorological Services of various countries, many of which became members of the World Meteorological Organization (WMO). The First Issue included data from earliest records available at that time up to 1920. Data have been collected for periods 1921-30 (2nd Series), 1931-40 (3rd Series), 1941-50 (4th Series), 1951-60 (5th Series), 1961-70 (6th Series), 1971-80 (7th Series), 1981-90 (8th Series), 1991-2000 (9th Series), and 2001-2011 (10th Series). The most recent Series 11 continues, insofar as possible, the record of monthly mean values of station pressure, sea-level pressure, temperature, and monthly total precipitation for stations listed in previous volumes. In addition to these parameters, mean monthly maximum and minimum temperatures have been collected for many stations and are archived in digital files by NCEI. New stations have also been included. In contrast to previous series, the 11th Series is available for the partial decade, so as to limit waiting period for new records. It begins in 2010 and is updated yearly, extending into the entire decade.

KNMI collects observations from the automatic weather stations situated in the Netherlands and BES islands on locations such as aerodromes and North Sea platforms. In addition, wind data from KNMI wind poles are included. The weather stations report every 10 minutes meteorological parameters such as temperature, relative humidity, wind, air pressure, visibility, precipitation, and cloud cover. The number of parameters differs per station. The file for the past 10 minutes is available a few minutes later and contains a timestamp denoting the end of the observation period in UTC. It is possible that a station's observations may not be immediately available. Files are updated with missing data up to 4 hours later. For more technical documentation, you can go to https://english.knmidata.nl/open-data/actuele10mindataknmistations For archived 10-min data, the data is split per variable https://dataplatform.knmi.nl/dataset/?tags=Archive For validated history of climatological time series, you can go to https://www.knmi.nl/nederland-nu/klimatologie-metingen-en-waarnemingen

This dataset contains historical weather data of 2 different places , the data features parameters like temperature, humidity, dew point, precipitation, pressure, cloud cover, vapor pressure deficit, wind speed, and wind direction.

As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY) methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) files that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains fTMY files for 18 cities in the continental United States. The locations are representative cities for each climate zone. The data for each city is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6- ACCESS-CM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2059 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O’Neill et al. (2020).

More information about the six selected CMIP6 GCMs:

ACCESS-CM2 - http://dx.doi.org/10.1071/ES19040

BCC-CSM2-MR - https://doi.org/10.5194/gmd-14-2977-2021

CNRM-ESM2-1- https://doi.org/10.1029/2019MS001791

MPI-ESM1-2-HR - https://doi.org/10.5194/gmd-12-3241-2019

MRI-ESM2-0 - https://doi.org/10.2151/jmsj.2019-051

NorESM2-MM - https://doi.org/10.5194/gmd-13-6165-2020

Additional references:

O’Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework. Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0

Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734

Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.

Current METAR weather stations and associated weather conditions based on Meteorological Terminal Aviation Routine Weather Report (METAR) data collected globally from either airports or permanent weather observation stations by NOAA’s NWS Aviation Weather Center (http://www.aviationweather.gov/metar). IGEMS reads this source data and updates the layer every 10 minutes.

This layer is a component of Interior Geospatial Emergency Management System (IGEMS) General Data.

This map presents the geospatial locations and additional information for global tide monitoring stations, and U.S. stream gages, weather stations and DOI managed lands. This map is part of the Interior Geospatial Emergency Management System (IGEMS) and is supported by the DOI Office of Emergency Management. This map contains data from a variety of public data sources, including non-DOI data, and information about each of these data providers, including specific data source and update frequency is available at: http://igems.doi.gov.

© DOI Office of Emergency Management

Gspatial historical weather data is an accurate and reliable dataset that helps professionals gather, simplify and analyze processes to promote sustainability, awareness, or tackle specific climate action initiatives.

This historical weather dataset gives access to historical local weather updates for temperature, pressure, humidity, wind, cloud coverage, visibility, and dew point. These parameters have connections with various weather-sensitive sectors and play a key role in weather-based services. The dataset is easy to read, user-friendly, and adds value to multiple segments of industries when it comes to application.

Some features of Gsptial historical weather data are

Available for all locations across the globe More than 30 years of data are available on the platform It covers all major parameters, of which some are: - Air temperature and Apparent (“feels like”) temperature
- Dew Point - Relative humidity - Sea-level air pressure - Wind speed and wind gust speed - Wind bearing (Direction that the wind is coming from) - Percentage of the sky covered by clouds - UV Index - Average visibility in miles - up to 10 miles - Columnar density of total atmospheric ozone at the given time

Downloadable formats are available too.

Fast Track Level 1(.5) meteorological data with added manual quality control. The data starts from the last respective L2 release. Kubistin, D., Plaß-Dülmer, C., Kneuer, T., Lindauer, M., Müller-Williams, J. (2025). ICOS ATC FastTrack NRT Meteo data product from Lindenberg (10.0 m), 2024-04-01–2024-09-30, ICOS RI, https://hdl.handle.net/11676/kHeE_1HRCz5mExtbx2kej0Yc

If you're looking for weather datasets, there are several reputable sources where you can access comprehensive weather data for various applications, including research, machine learning, and more. Here are some popular options:

1. National Centers for Environmental Information (NCEI):

   • The NCEI, part of NOAA, offers a wide range of climate and weather data. You can find historical weather data, global climate data, and more.
   • NCEI Weather Data

2. OpenWeatherMap:

   • Provides current weather data, forecasts, and historical data. They offer free and paid plans depending on the level of access and detail needed.
   • OpenWeatherMap API

3. Weather Underground:

   • Offers a rich set of weather data including current conditions, forecasts, and historical weather data.
   • Weather Underground API

4. European Centre for Medium-Range Weather Forecasts (ECMWF):

   • ECMWF provides datasets including ERA-Interim, ERA5, and seasonal forecasts. They focus on global weather and climate data.
   • ECMWF Data

5. The Weather Company (IBM):

   • Offers a range of weather data services, including historical weather data, forecasts, and more through their APIs.
   • The Weather Company API

6. NASA Earth Observing System Data and Information System (EOSDIS):

   • Provides access to a vast array of global climate data, satellite imagery, and other environmental data.
   • NASA EOSDIS

7. Global Surface Summary of the Day (GSOD):

   • A dataset that includes daily weather summaries from global stations, available through the National Centers for Environmental Information.
   • GSOD Data

8. Climate Data Online (CDO):

   • Another resource from NOAA, offering access to a variety of climate data, including daily and monthly summaries, storm data, and more.
   • NOAA CDO

9. Meteostat:

   • Provides free access to historical weather and climate data, focusing on quality-controlled and easy-to-use datasets.
   • Meteostat

The provided dataset appears to contain weather-related information for New Delhi Safdarjung, India, spanning from January 1, 2023, to July 21, 2023. The dataset includes the following columns: Station ID, Station Name, Date, Precipitation (PRCP), Average Temperature (TAVG), Maximum Temperature (TMThe dataset includes daily observations with information on precipitation and temperature. It seems that some values are missing (NULL values), and there are variations in the units used for precipitation AX), and Minimum Temperature (TMIN).

Fast Track Level 1(.5) meteorological data with added manual quality control. The data starts from the last respective L2 release. Kubistin, D., Plaß-Dülmer, C., Kneuer, T., Lindauer, M., Müller-Williams, J. (2025). ICOS ATC FastTrack NRT Meteo data product from Lindenberg (40.0 m), 2024-04-01–2024-09-30, ICOS RI, https://hdl.handle.net/11676/1F-Rkg87wl7sLtOTnGixzI28

Weather is recorded every 10 minutes for the 2020 whole year, which contains 21 meteorological indicators, such as air temperature, humidity, etc. The dataset in CSV format can be downloaded at https://drive.google.com/file/d/1Tc7GeVN7DLEl-RAs-JVwG9yFMf--S8dy/view?usp=share_link.

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.