Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,121
datasets available to search
ShareScore release 0.9.0
Dataset results
1,121 results for “weathering”
GPM Ground Validation Weather Research and Forecasting (WRF) Images MC3E V1
The GPM Ground Validation Weather Research and Forecasting (WRF) Images MC3E dataset consists of browse only images showing radar reflectivity, radar echo top, convective available potential energy (CAPE), temperature, geopotential height, wind speed, relative humidity, rain water, snow, cloud water, cloud ice, and graupel. These data were simulated by the Weather Research and Forecasting (WRF) for the period of the GPM Ground Validation Mid-latitude Continental Convective Cloud Experiment (MC3E) field campaign. The overarching goal of the MC3E field campaign was to provide the most complete characterization of convective cloud systems, precipitation, and the environment ever obtained and to provide new constraints for model cumulus parameterizations and space-based rainfall retrieval algorithms over land. Browse imagery files in PNG and GIF formats are available for April 19, 2011 through June 6, 2011.
CAMEX-4 MOBILE X-BAND POLARIMETRIC WEATHER RADAR V1
The CAMEX-4 Mobile X-Band Polarimetric Weather Radar dataset was collected by the Mobile X-band Polarimetric Weather Radar on Wheels (X-POW), which is a Doppler scanning radar operating at 9.3 GHz with horizontal and vertical polarization. The X-POW was used for detection and detailing of surface rainfall rate and precipitation classification fields, as well as for 3D precipitation microphysical retrievals including water/frozen hydrometeor contents and drop size distribution profiles. The X-POW was located in the Florida Keys during the CAMEX-4 field experiment.
Weather Research and Forecasting (WRF) North American Mountain Snow Data, Version 1
This data set consists of modeled snow water equivalent (SWE) data for 10 mountain ranges in North America, simulated by the Weather Research and Forecasting (WRF) regional climate model.
Dendrometer, Soil, and Weather Observations, Arctic Tree Line, AK and NWT, 2016-2019
This dataset provides in situ measurements of radial tree growth of selected white spruce (Picea glauca) and black spruce (Picea mariana) trees, as well as simultaneous in situ measurements of environmental variables (air temperature, air pressure, relative humidity, soil temperature, volumetric water content, and solar irradiance) at two Arctic treeline sites: one in the Brooks Range of Alaska (AK), USA, and the other near Inuvik, Northwest Territories (NWT), Canada. In AK, 36 trees were monitored from June 7, 2016 to September 13, 2019, and in NWT, 24 trees were monitored from July 5, 2017 to July 25, 2019 with a sampling interval of 5- or 20-minutes for radial tree growth and 5-minutes for all environmental variables. The dendrometer data included in this dataset are only those gathered from 2016-2017. Dendrometer data from 2018-2019 are available from a related dataset. The data were collected to better understand the influence of environmental variables on radial tree growth dynamics. The data are provided in comma-separated values (CSV) format.
GPM GROUND VALIDATION IOWA X-BAND POLARIMETRIC MOBILE DOPPLER WEATHER RADARS IFLOODS V1
The GPM Ground Validation Iowa X-band Polarimetric Mobile Doppler Weather Radars IFloodS dataset was gathered during the IFloodS campaign from April to June 2013 throughout central and northeastern Iowa. The Iowa Flood Studies (IFloodS) was a ground measurement campaign that took place throughout Iowa from May 1 to June 15, 2013. The main goal of IFloodS was to evaluate how well the GPM satellite rainfall data can be used for flood forecasting. Four X-band Polarimetric (XPOL) Mobile Doppler Weather Radars were used to collected high-resolution observations of precipitation. The data consists of reflectivity, Doppler velocity, spectrum width, differential reflectivity, differential phase, copolar correlation coefficient, and sound-to-noise ratios. These data are available in netCDF, and browse image files are available in .png format.
Wakasa Bay Weather Forecast Maps, Version 1
The AMSR-E Wakasa Bay Field Campaign was conducted over Wakasa Bay, Japan. The Wakasa Bay Field Campaign includes joint research observations, such as precipitation amount, pressure thickness, seal level pressure, and surface winds by the Japan Aerospace Exploration Agency (JAXA), the AMSR precipitation validation team, and the NASA AMSR-E team.
CMS: Fire Weather Indices for Interior Alaska, 2001-2010
This dataset provides daily fire weather indices for interior Alaska during the active fire seasons from 2001 to 2010. Data are gridded at 60-m resolution. The active fire season is defined as May 24-September 18 (days of the year 144-261) in this dataset. Fire weather is the use of meteorological parameters such as relative humidity, wind speed and direction, cloud cover, mixing heights, and soil moisture to determine whether conditions are favorable for fire growth and smoke dispersion. The six indices provided in this dataset are defined and produced following the methodology of the Canadian Forest Fire Weather Index System: Fine Fuel Moisture Code, Duff Moisture Code, Drought Code, Initial Spread Index, Buildup Index, Fire Weather Index. The dataset was developed following point source data interpolation from weather station observations.
First ISCCP Regional Experiment (FIRE) Cirrus Phase II National Weather Service(NWS) Inner-Network Rawinsonde Data
The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between ISCCP data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.The FIRE_CI2_NWS_IN_SND data set was collected for the period Nov. 13, 1991 to Dec. 7, 1991. Each granule has multiple ASCII data files. Data were collected from 17 different National Weather Service (NWS) sites. These sites are: (ABQ) Albuquerque, NM; (AMA) Amarillo, TX; (DDC) Dodge City, KS; (DEN) Denver, CO; (DRT) Del Rio, TX; (ELP) El Paso, TX; (GGG) Longview, TX; (LBF) North Platte, NE; (LIT) North Little Rock, AR; (MAF) Midland, TX; (OMA) Omaha, NE; (OUN) Norman, OK; (PAH) Paducah, KY; (PIA) Peoria, IL; (SEP) Stephenville, TX; (TOP)Topeka, KS; and (UMN) Monett, MO.
Evaluation of a customized variable-resolution global model and its application for high-resolution weather forecasts in East Asia
<p>The data for shallow water test for the paper "Evaluation of a customized variable-resolution global model and its application for high-resolution weather forecasts in East Asia" are in the two sw[2,5]_GlobalIntegrals.tar.bz2 files.</p> <p>The data of MPAS model output is stored as Demo data of the <a href="https://cpas.earth/">CPAS cloud platform</a> with Jupyter visualization tools and user interface. Please see "How-to-visualize-MPAS-model-data.pdf" on how to use it.</p>
Verification datasets for publication "Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP over the United Arab Emirates" - UAE weather station data
<p>These are excel files containing measurement data for 2015 in the UAE, used for the submission to GMD journal, "Seasonal and diurnal performance of daily forecasts with WRF-NOAHMP over the United Arab Emirates". All data is courtesy of NCM, UAE.</p> <p>Also included is a station_ids.csv file. </p>
Data for "GFDL SHiELD: A Unified System for Weather-to-Seasonal Prediction" by Harris et al.
<p>This archive contains data relevant to this article.</p>
Data, Code, Models, and Results for 'Empirical estimation of yield variability due to weather shocks using biophysical characteristics for U.S. rainfed and irrigated maize, soybeans, and winter wheat'
<p>Data, Code, Models, and Results for 'Empirical estimation of yield variability due to weather shocks using biophysical characteristics for U.S. rainfed and irrigated maize, soybeans, and winter wheat'</p>
Tourism Forecast with Weather, Event, and Cross-Industry Data
<p><strong>Introduction</strong></p> <p>Substantial short-term demand fluctuations are common in the tourism industry. Therefore, tourism companies such as accommodation, transportation, catering, and leisure facilities have a vital interest in precise forecasts of the number of customers.</p> <p>We present a novel forecasting dataset for tourism, consisting of four Swiss companies, one accommodation, two transportation, and one indoor leisure businesses, all located in the same touristic region. It covers a total of ten years starting in 2007 and ending in 2016. The dataset allows using cross-series information and includes explanatory variables, such as calendar effects, event data, and weather forecast information.</p> <p>Machine learning (ML) practitioners, statisticians, and experts of tourism sectors are invited to investigate our dataset for new insights on short-term forecasting for industries in tourism.</p> <p>The Algorithmic Business Research Lab (ABIZ) of the Lucerne University of Applied Sciences and Arts, Switzerland, researches ML algorithms for businesses to support industry partners in developing business models and services based on complex algorithms as well as in the induced digital transformation. In a joint effort with Institute of Tourism (ITW) of the School of Business of the Lucerne University of Applied Sciences and Arts, Switzerland, we provide the present tourism dataset as part of a publication.</p> <p>Contacts and further information can be found at <a href="http://www.abiz.ch/.">http://www.abiz.ch/.</a></p> <p><strong>The dataset</strong></p> <p>The dataset comprises 3653 days of customer numbers of four Swiss companies in the tourism sector. There are 556 feature columns, four target columns, and two mask columns, 562 columns in total.</p> <p><strong>Target variables</strong></p> <p>The customer volume data has daily resolution and features at worst minor interruptions of a few days over a common period of ten years, starting in 2007 and ending in 2016. The missing values for one transportation and the indoor leisure company are masked, and the masks are available as indicator variables.</p> <p><strong>Feature Variables</strong></p> <p>The dataset contains both numerical and categorical,</p> <ul> <li>calendar effects, such as day of the week, weekend, and month features,</li> <li>event data, e.g., public and school holidays, free-time regional events, promotions or revisions for the facilities under exam,</li> <li>weather forecast features provided by the Federal Office of Meteorology and Climatology (MeteoSwiss), which encodes information about conditions in the locations of the four businesses and neighboring regions.</li> </ul> <p>The weather forecast data consist of information about temperature, sunshine, precipitation, and wind, forecasted up to 3 days in advance. Note that the weather forecast model is updated regularly, and therefore many features do not cover the entire period. We want to point out that there are categorical weather summary annotations created by meteorologists, which are only provided for the last year.</p> <p><strong>Dataset Download</strong></p> <p>In the published version of the dataset, feature names are replaced with pseudonyms, but descriptions are given to identify feature groups with similar meaning. The content available for download consists of </p> <ul> <li>data.csv,<br> CSV format, 11.3 MB, 3654 rows, and 562 columns with the time series data;<br> </li> <li>data-description.csv,<br> CSV format, 36 KB, 563 rows and 12 columns with feature name and short description, minimal statistics for cross-checking, and indicator variables that specify which single-company datasets a feature belongs.</li> </ul> <p> </p>
Daily Weather Data - Continental USA
Open the record for dataset details and reuse information.
[VISIR-2 ship weather routing model] raw data
<h2>VISIR-2 ship weather routing model raw data</h2> <p>This repository contains the raw data necessary to reproduce the results from scratch as presented in the Geosci. Model Dev. Discussions manuscript titled "<em>VISIR-2: ship weather routing in Python</em>". The paper will be linked here as soon as available online. </p> <p> </p> <h3>How to Use the Data</h3> <p>To reproduce the results from the manuscript, please follow these steps:</p> <p>1. download this repository to your local machine.</p> <p>2. download the Zenodo repository containing VISIR-2 source code from this <a href="../doi/10.5281/zenodo.8305526">link</a></p> <p>3. run VISIR-2 jobs following the instructions included in the manual that can be found inside VISIR-2 source code at <em>VISIR-2/Docs/Manual/</em>VISIR-2_userManual.pdf</p> <p> </p> <h3>Contact</h3> <p>If you have any questions or need further assistance, please feel free to contact us:</p> <p>- Mario Leonardo Salinas</p> <p>- Email: mario.salinas@cmcc.it</p> <p> </p>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)
<p>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.</p> <p>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 1 and the representative concentration pathway (RCP) used was RCP 2.6. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>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.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>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.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>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: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>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 href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</p>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SSP2-RCP4.5)
<p>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.</p> <p>This dataset contains the individual-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 2 and the representative concentration pathway (RCP) used was RCP 4.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>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.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>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.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>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: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>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 href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</p>
Data from Automatic Weather Station (AWS) measurements at Berlin–Mobile deployment (BEMOBI) from 2022-03-22 to 2022-08-13 [RAW]
<p>Original data files from AWS measurements.</p>
Weathering data of LV55-38-3 in the Sea of Okhotsk and simulated rainfall data acorss different latitudes
<p>This dataset includes chemical weathering proxies(CIA,CIX, K/Al ratio, and smectite/illite) and provenance proxy (La/Yb, Nd/Gd, and Sr-Nd isotopic))of Site LV55-38-3 and some surface sediments. They are all analyzed based on the clay-sized fractions. It also includes the data of simulated rainfall changes in the Northeast Russia(NER), Northeast China (NEC), South Asia (SA) as well as in Central West Africa (CWA) during past 240 ka.</p>
Weather dataset (Typical Downscaled Year, Extreme Cold Year, Extreme Warm Year) for building energy simulations (.epw format) in future climate (2069-2098, RCP 8.5), Antwerp Berchem, Belgium
<p>Typical Downscaled Year (TDY), Extreme Cold Year and Extreme Warm Year based on the methodology of Nik (2016), is extracted for the location of Antwerp Berchem (51°12'00"N 4°26'24" E) from the EC-Earth driven convection-permitting climate model COSMO-CLM for the Belgian domain extended with land-surface scheme TERRA_URB(v2.0) making use of the SURY (Semi-empirical URban canopY) parameterization ( Wouters et al. 2016). The integrations are identical to the ones which are described in Vanden Broucke et al. (2019). The climate model has a spatial resolution of 2.8 km and an hourly temporal resolution and is available for the recent past (1976-2004) and future (2070-2098, RCP 8.5 climate change scenario) as 30-year datasets. For this dataset, the TDY, ECY, and EWY are extracted for the future period. A bias correction is applied for the following variables: temperature (as described in Ramon et al. 2020), solar radiation and relative humidity as described in Ramon et al. (202X).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.