The Defense Meteorological Satellite Program (DMSP) satellites collect visible and infrared cloud imagery as well as monitoring the atmospheric, oceanographic, hydrologic, cryospheric and near-Earth space environments. The DMSP program maintains a constellation of sun-synchronous, near-polar orbiting satellites. The orbital period is 101 minutes and inclination is 99 degrees. The atmospheric and oceanographic sensors record radiances at visible, infrared and microwave wavelengths. The solar geophysical sensors measure ionospheric plasma fluxes, densities, temperatures and velocities. DMSP visible and infrared imagery of clouds covers a 3,000 km swath, thus each satellite provides global coverage of both day night time conditions each day. The field view of the microwave imagers and sounders is only 1,500 km thus approximately 3 days data are required for one instrument to provide global coverage at equatorial latitudes. The solar geophysical instruments make in-situ measurements of ionospheric parameters, some of which vary very rapidly. The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive the complete DMSP data stream from the Air Force Weather Agency (AFWA), Offutt Air Force Base, Omaha, Nebraska. Data are currently transmitted in near realtime from AFWA directly to the archive via a designated T1 line. Archive processing prepares orbital data sets of calibrated, quality assessed data organized as a time-series, restores data lost during transmission,and accurately computes satellite positions. NCEI maintains an archive of all data recorded on DMSP satellites as relayed to The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) by the Air Force Weather Agency. Data from March 1992 to March 1994, are considered to be experimental. After March 1994, the system was fully operational. NCEI archives contain data that are post process reconstructed, positioned and geolocated using the same software.

The SBU Meteorological Station IMPACTS dataset consists of weather station data collected at two Stony Brook University (SBU) weather stations (1 mobile radar truck and 1 stationary site in Manhattan, New York City, New York) during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to advance prediction capabilities significantly. The surface meteorological data variables include temperature, dew point, relative humidity, absolute humidity, mixing ratio, air pressure, windspeed, and wind direction. The dataset files are available from January 1, 2020, through January 25, 2023, in netCDF-4 and ASCII-CSV formats.

The BOREAS HYD-03 team collected several data sets related to the hydrology of forested areas. This data set includes measurements of wind speed and direction; air temperature; relative humidity; and canopy, trunk, and snow surface temperatures within three forest types. The data were collected in the SSA-OJP (1994) and SSA-OBS and SSA-OA (1996). Measurements were taken for 3 days in 1994 and 4 days at each site in 1996. These measurements were intended to be short term to allow the relationship between subcanopy measurements and those collected above the forest canopy to be determined. The subcanopy estimates of wind speed were used in a snow melt model to help predict the timing of snow ablation.

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) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.

This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, 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 2015–2100 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).

Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.

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/10.1029/2022EF002734Thornton, 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.

Please cite the following if this data is used in any research or project:

Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: 10.1145/3600100.3626637

Cross-Model Version:

Representative Cities Version:

Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a

Ambee’s global weather dataset provides a continuum of data that flows seamlessly from the year 1995 to present and into the forecast with forward forecast views ranging from one hour all the way out to 46 days. All data is available in both hourly and daily format and daily data includes min. and max. values, and daily averages.

The Ambee global weather dataset was developed using direct observations, sensors, satellite imagery, reanalysis model outputs, and more, to deliver a global dataset that is harmonized and calibrated for use across any analytics or modeling workload.

Parameters include core atmospheric variables such as temperature, feels like temperature, humidity, dew point, wind speed and direction, precipitation, both rain and snow, cloud cover, visibility, solar radiation, UV index, and pressure. Additional derived fields are available upon request.

Gaps in data are intelligently filled using companion datasets and climatological data. cross-station substitution, and meteorological context modeling. All values are passed through Ambee’s multi-stage quality pipeline to ensure long-term temporal integrity and cross-region comparability.

This dataset is ideal for machine learning training, demand forecasting, infrastructure planning, ESG reporting, and climate-related risk modeling. Clients across pharmaceuticals, retail, agriculture, and finance use Ambee’s global weather data to optimize operations, mitigate weather risk, and create predictive and prescriptive models to improve overall business performance.

The dataset with 2.5 arcminutes (~4 km) was spatially interpolated based on 699 weather stations in China using thin-plate spline and random forest methods with six covariates (e.g., location, terrain, and reanalysis data), including daily 2m maximum temperature (maxtmp), maximum temperature (mintmp), mean temperature (meantmp), total precipitation (pre), skin temperature (gst), 10m wind speed (win), relative humidity (rhu), surface pressure (prs), and sunshine duration (ssd). The dataset covers the mainland area of China and ranges from 1 January 2000 to 31 December 2020. The dataset was well evaluated by independent weather stations and reliable for the study related to climate change across China.

Zhang Jielin, Liu Bo, Ren Siqing, Han Wenqi, Ding Yongxia, Peng Shouzhang. A 4 km daily gridded meteorological dataset for China from 2000 to 2020. Scientific Data, 2024, 11, 1230. https://doi.org/10.1038/s41597-024-04029-x

KNMI collects in-situ meteorological observations in the Netherlands aggregated into hourly, daily, monthly, and annual datasets. These data are derived from the 10-minute automatic measurements since the early 1990s, hourly observations from 1951 and daily observations from 1901. The earliest data starts in 1901 with one station and gradually increases to the current measurement network of more than 50 automatic weather stations. The weather stations measure essential climate variables (ECVs), surface precipitation, pressure, global radiation, temperature, water vapor, wind speed and direction, clouds, and visibility. Sunshine duration and potential evapotranspiration are calculated from the ECVs. When applicable, minima, maxima, means, and sums are defined or calculated. This dataset contains meteorological observations since 1901-01-01 at a daily interval. To form this dataset, hourly observations have been aggregated to daily intervals. When applicable, hourly intervals are calculated in which the minimum or maximum occurred during the day. Before aggregation to daily intervals, the hourly data has undergone manual validation by KNMI experts, filling gaps and correcting measurement errors, after which it becomes available. The expert validation can take 1-10 working days to complete. Validated data can be modified at any moment in the future if further issues are found with the data. Supplemental information The following supplemental information is available:

The metadata record summarizes information for time series of yearly weather measurements from the 18 meteo stations of AGE. The dataset goes back 50 years in time, starting at the foundation of each station. The kind of measurements differ from station to station. This dataset is available via the WMS Time (https://wms.inspire.geoportail.lu/geoserver/mf/wms?service=WMS&version=1.3.0&request=GetCapabilities) and WFS (https://wms.inspire.geoportail.lu/geoserver/mf/wfs?service=WFS&version=2.0.0&request=GetCapabilities) API protocols. See for example the following sample requests: WFS: https://wms.inspire.geoportail.lu/geoserver/mf/wfs?SERVICE=wfs&VERSION=2.0.0&REQUEST=GetFeature&TRANSPARENT=true&TYPENAME=MF.PointTimeSeriesObservation_Yearly_AGE_sum_nn050&srsName=EPSG:3857&OUTPUTFORMAT=application/json&CQL_FILTER=datetime%20BEFORE%202020-01-01T00:00:00Z%20%20AND%20datetime%20AFTER%202018-12-31T00:00:00Z%20AND%20name_descr=%20%27Bigonville%27 WMS Time: https://wms.inspire.geoportail.lu/geoserver/mf/wms?SERVICE=WMS&VERSION=1.3.0&REQUEST=GetMap&FORMAT=image%2Fpng&TRANSPARENT=true&STYLES&LAYERS=mf%3AMF.PointTimeSeriesObservation_Yearly_AGE_sum_nn050&CRS=EPSG%3A3035&WIDTH=474&HEIGHT=769&BBOX=2914640.6691353433%2C4006619.263203916%2C3031916.5131074684%2C4079000.448155462&TIME=2019-01-01T00:00:00.000Z Data is transformed into INSPIRE data model. Description copied from catalog.inspire.geoportail.lu.

2023

The Southwest Watershed Research Center (SWRC) operates the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona as an outdoor laboratory for studying semiarid rangeland hydrologic, ecosystem, climate, and erosion processes. Resources in this dataset:Resource Title: GeoData catalog record. File Name: Web Page, url: https://geodata.nal.usda.gov/geonetwork/srv/eng/catalog.search#/metadata/2f6d09cc-4097-494a-b57a-19bc9ecfba66

Purpose: Describes the current weather and, over time, the climate. Basis for forecasts and climate studies.

Unit: degrees

Characteristics: Wind direction measured at measuring stations. Average over 10 minutes each and every 3 hours.

Network information: - SMHI's station network: Data is collected and stored in SMHI's databases. Data quality is checked, which means that incorrect data is corrected and that data loss is supplemented based on expert judgement where possible. Most of the stations are continuously monitored, inspected and maintained by SMHI. - Other stations: Data is collected and stored in SMHI's databases. The data quality is unknown to SMHI as SMHI does not perform quality control on data or inspections at the stations.

Quality codes: Green = Verified and accepted values. Yellow = Suspected values, aggregated values, roughly controlled archive data and uncontrolled real-time data (last 2 hours).

Boundaries: 0 to 360 degrees (When the wind speed is 0 m/s, the wind direction is set to 0 degrees. 360 degrees represents north, 90 degrees east, etc.)

For more info see: https://www.smhi.se/data/meteorology/wind

A selection of weather model parameters, including aviation parameters. It concerns near surface and boundary layer parameters (up to ~3000m) of the UWC-West HARMONIE-AROME Cy43 model, for the Dutch domain. For this weather forecast model, KNMI works closely with Iceland, Denmark and Ireland on local short-term weather forecasts under the name "United Weather Centres-West" (UWC-West). An international project team is working on a joint Numerical Weather Prediction model (NWP), procurement and management of the HPC (supercomputer) and infrastructure. Every hour 18 new files will be available, each with one parameter. The parameters are: wind gusts at 10m, wind speed at 10m and multiple levels up to ~3000m, wind as a vector at 10m (both speed and direction) and multiple levels up to ~3000m, air temperature at 2m, air pressure at MSL (mean sea level), relative humidity, precipitation parameters, cloud base altitude, visibility, mixed layer depth, absolute updraft helicity, thermal velocity, W/U (vertical velocity scale of thermals normalized with a horizontal turbulent velocity scale), wet bulb globe temperature (note that this output parameter is experimental, please be aware that it may undergo changes or be discontinued or removed at any time without prior notice). The forecast time resolution is 1 hour, with a forecast horizon of 60 hours.

The LTAR network maintains stations for standard meteorological measurements including, generally, air temperature and humidity, shortwave (solar) irradiance, longwave (thermal) radiation, wind speed and direction, barometric pressure, and precipitation. Many sites also have extensive comparable legacy datasets. The LTAR scientific community decided that these needed to be made available to the public using a single web source in a consistent manner. To that purpose, each site sent data on a regular schedule, as frequently as hourly, to the National Agricultural Library, which has developed a web service to provide the data to the public in tabular or graphical form. This archive of the LTAR legacy database exports contains meteorological data through April 30, 2021. For current meteorological data, visit the GeoEvent Meteorology Resources page, which provides tools and dashboards to view and access data from the 18 LTAR sites across the United States. Resources in this dataset:Resource Title: Meteorological data. File Name: ltar_archive_DB.zipResource Description: This is an export of the meteorological data collected by LTAR sites and ingested by the NAL LTAR application. This export consists of an SQL schema definition file for creating database tables and the data itself. The data is provided in two formats: SQL insert statements (.sql) and CSV files (.csv). Please use the format most convenient for you. Note that the SQL insert statements take much longer to run since each row is an individual insert. Description of zip files The ltararchive*.zip files contain database exports. The schema is a .sql file; the data is exported as both SQL inserts and CSV for convenience. There is a README in markdown and PDF in the zips. Contains the database export of the schema and data for the site, site_station, and met tables as SQL insert statements. ltar_archive_db_sql_export_20201231.zip --> has data until 2020-12-31 ltar_archive_db_sql_export_20210430.zip --> has data until 2021-04-30 Contains the database export of the schema and data for the site, site_station, and met tables as CSV. ltar_archive_db_csv_export_20201231.zip --> has data until 2020-12-31 ltar_archive_db_csv_export_20210430.zip --> has data until 2021-04-30 Contains the raw CSV files that were sent to NAL from the LTAR sites/stations. ltar_rawcsv_archive.zip --> has data until 2021-04-30

The High-resolution Urban Meteorology for Impacts Dataset, HUMID, will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology. We have explicitly included representation of spatial meteorological variability over urban areas in the contiguous United States (CONUS) as compared to other observation-only gridded meteorology products by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. Further, we include in situ meteorological observations such as local mesonets to bias correct the HRLDAS output, creating a model-observation fusion product. The data spans 1 January 1981 to 31 December 2018, covering all of CONUS at 1 km grid spacing. The dataset includes daily maximum, minimum, and mean values for a variety of temperature estimates such as 2 m temperature, skin temperature, urban temperatures, as well as specific humidity and surface energy budget terms. The full variable list with corresponding file and variable metadata is in this file [https://rda.ucar.edu/OS/web/datasets/d314008/docs/humid_dataset_readme.pdf].

These raw data have not been subjected to the National Ocean Service's quality control or quality assurance procedures and do not meet the criteria and standards of official National Ocean Service data. They are released for limited public use as preliminary data to be used only with appropriate caution.

The BOREAS AFM-06 team from the National Oceanic and Atmospheric Administration Environment Technology Laboratory (NOAA/ETL) collected surface meteorological data from 21-May to 20-Sep-1994 near the SSA-OJP tower site.

Automatic weather station data from locations within the distributed Swedish research infrastructure SITES. Check preview or file for the specific parameters included at this location. Data has been quality controlled and cleaned from outliers and other events producing unrealistic data. Gaps have not been filled. Tarfala Research Station (2025). Meteorological data from Alesjaure, 2014-01-01–2014-12-31 [Data set]. Swedish Infrastructure for Ecosystem Science (SITES). https://hdl.handle.net/11676.1/jBOqX-FFMMEE70wR22KAj6wI

This data set contains the surface meteorological data from the Argonne National Laboratory ABLE (Atmospheric Boundary Layer Experiments) Automated Weather Systems (AWS) that were located in Whitewater, Kansas and Smileyberg, Kansas. Data were collected during the Cooperative Atmospheric Surface Exchange Study 1999 (CASES-99).

The global weather forecasting market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of approximately 7% from 2025 to 2033, reaching a value exceeding $25 billion by the end of the forecast period. This expansion is fueled by several key factors. Firstly, the rising reliance on accurate weather information for effective decision-making in sectors like aviation, energy, and agriculture is a major catalyst. Secondly, advancements in technology, including the use of sophisticated meteorological models, satellite imagery, and artificial intelligence, are enhancing forecast accuracy and providing more granular data. This leads to improved risk management and operational efficiency across industries. Thirdly, the growing awareness of climate change and its associated risks is increasing the demand for advanced weather forecasting solutions for disaster preparedness and mitigation efforts. The increasing adoption of IoT devices and the growth of big data analytics further contributes to this market growth. Market segmentation reveals strong performance across various applications. Aviation and energy sectors are significant contributors, driven by the need for precise weather information to ensure safe operations and efficient resource management. The agricultural segment is witnessing growth due to precision farming techniques reliant on weather data for optimizing crop yields and minimizing losses. Furthermore, the increasing penetration of short-range forecasting solutions is driving market expansion, particularly in areas requiring immediate weather updates. However, challenges such as the inherent limitations of weather forecasting accuracy and the need for substantial investments in advanced technologies pose restraints. Despite these challenges, the continuous advancements in meteorological science and technology, coupled with increasing awareness of the economic benefits of accurate weather predictions, are expected to propel the market’s growth trajectory in the coming years. Key players are investing heavily in research and development, strategic partnerships, and mergers and acquisitions to strengthen their market position and expand their service offerings.

Bangladesh Weather Dataset
This dataset falls under the category Environmental Data Climate Data.
It contains the following data: This dataset contains the monthly average value of Bangladesh temperature and rain from 1901 to 2015
This dataset was scouted on 2022-03-01 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.kaggle.com/yakinrubaiat/bangladesh-weather-dataset