license: mit
Dataset Card for WINDSET
WINDSET is the Weather Insights and Novel Data for Systematic Evaluation and Testing dataset. WINDSET's goal is to provide a simple standard for evaluating the performance of Atmospheric and Earth Science AI over a range of tasks.
Dataset Details
WINDSET contains data for 6 tasks:
- Nonlocal paramterization of gravity wave momentum flux
- Prediction of aviation turbulence
- Identifying weather analogs
- Generating natural language forecasts
- Long-term precipitation forecasting
- Hurricane track and intensity prediction
Dataset Description
Nonlocal parameterization of gravity wave momentum flux
The input variables contain three dynamical variables concatenated along the vertical dimension: zonal and meridional winds and potential temperature, and the output variables comprise of the zonal and meridional components of the vertical momentum fluxes due to gravity waves.
- Curated by: Aman Gupta
Nonlocal parameterization of gravity wave momentum flux
The dataset was prepared using four years of ERA5 reanalysis data on model pressure levels. The top 15 levels (above 1 hPa) were discarded due to artificial damping by the mesospheric sponge. The input variables were obtained by conservatively coarsegraining the winds and temperature from the 0.3 deg uniform grid. The output variables
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Long-term precipitation forecast
The daily precipitation accumulations are derived from the PERSIANN CDR dataset up until June 2020 and from the IMERG final daily product.
- Sorooshian, Soroosh; Hsu, Kuolin; Braithwaite, Dan; Ashouri, Hamed; and NOAA CDR Program (2014): NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Version 1 Revision 1. NOAA National Centers for Environmental Information. doi:10.7289/V51V5BWQ, Accessed: 2023/12/01.
- Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06, Edited by Andrey Savtchenko, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: 2023/12/01, 10.5067/GPM/IMERGDF/DAY/06
The satellite observations are derived from the PATMOS-x, GridSat-B1, and the SSMI(S) brightness temperatures CDRs.
- Foster, Michael J.; Phillips, Coda; Heidinger, Andrew K.; and NOAA CDR Program (2021): NOAA Climate Data Record (CDR) of Advanced Very High Resolution Radiometer (AVHRR) and High-resolution Infra-Red Sounder (HIRS) Reflectance, Brightness Temperature, and Cloud Products from Pathfinder Atmospheres - Extended (PATMOS-x), Version 6.0. NOAA National Centers for Environmental Information. https://doi.org/10.7289/V5X9287S, Accessed: 2023/12/01.
- Kummerow, Christian D., Wesley K. Berg, Mathew R. P. Sapiano, and NOAA CDR Program (2013): NOAA Climate Data Record (CDR) of SSM/I and SSMIS Microwave Brightness Temperatures, CSU Version 1. NOAA National Climatic Data Center. doi:10.7289/V5CC0XMJ, Accessed 2023/12/01. Knapp, Kenneth R.; NOAA CDR Program; (2014): NOAA Climate Data Record (CDR) of Gridded Satellite Data from ISCCP B1 (GridSat-B1) Infrared Channel Brightness Temperature, Version 2. NOAA National Centers for Environmental Information. doi:10.7289/V59P2ZKR, Accessed: 2023/12/01.
Finally, baseline forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the UK Met Office (UKMO) were downloaded from the S2S database.
- Vitart et al.,The Sub-seasonal to Seasonal (S2S) Prediction Project Database. Bull. Amer. Meteor. Soc., 98(1), 163-176. doi: http://dx.doi.org/10.1175/BAMS-D-16-0017.1.
Dataset Structure
Refer to individual directories for a corresponding downstream task.
| WINDSET
- | aviation_turbulence
- | nonlocal_parameterization
- | weather_analogs
- | hurricane
- | precipitation_forecast
- | weather_forecast_discussion
- | long_term_precipitation_forecast
Dataset Creation
[More Information Needed]
Citation [optional]
BibTeX:
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Dataset Card Authors
- Rajat Shinde,
- Christopher E. Phillips,
- Sujit Roy,
- Ankur Kumar,
- Aman Gupta,
- Simon Pfreundschuh,
- Sheyenne Kirkland,
- Vishal Gaur,
- Amy Lin,
- Aditi Sheshadri,
- Manil Maskey, and
- Rahul Ramachandran
Dataset Card Contact
- Nonlocal paramterization of gravity wave momentum flux - Aman Gupta
- Prediction of aviation turbulence - Christopher E. Phillips
- Identifying weather analogs - [Christopher E. Phillips], Rajat Shinde
- Generating natural language forecasts - Rajat Shinde, Sujit Roy
- Long-term precipitation forecasting - Simon Pfreundschuh
- Hurricane track and intensity prediction - Ankur Kumar