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license: mit
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# 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

<!-- Provide a longer summary of what this dataset is. -->
#### 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](github.com/amangupta2)
<!--- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]-->
- **License:** MIT License

#### Generation of Natural Language-based Weather Forecast Reports
Input contains HRRR re-analysis dataset with corresponding NOAA Storm Prediction Center Reports on a daily basis for January 2017 (1 month). 

- **Curated by:** [NASA IMPACT](github.com/nasa-impact)
<!--- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]-->
- **License:** MIT License

#### Long-term precipitation forecast

The long-term precipitation forecast datasets consists of global daily rainfall accumulations and corresponding global
satellite observations derived from multiple sensors. The aim of the task is to predict the daily rainfall accumulations 
up to 28 days into the future.


- **Curated by:** [Simon Pfreundschuh](github.com/simonpf) (Colorado State University)

#### Aviation Turbulence Detection

- **Curated by:** [NASA IMPACT](github.com/nasa-impact)
<!--- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]-->
- **License:** MIT License

#### Hurricane Prediction and Intensity Estimation 

- **Curated by:** [NASA IMPACT](github.com/nasa-impact)
<!--- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]-->
- **License:** MIT License

#### Weather Analog Search

- **Curated by:** [NASA IMPACT](github.com/nasa-impact)
<!--- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]-->
- **License:** MIT License


### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

#### 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.
  

<!-- ## Uses -->

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## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

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

<!-- ### Curation Rationale -->

<!-- Motivation for the creation of this dataset. -->

<!-- [More Information Needed] -->

<!-- ### Source Data -->

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

<!-- #### Data Collection and Processing -->

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

<!-- #### Who are the source data producers? -->

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<!-- ### Annotations [optional] -->

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

<!-- #### Annotation process -->

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

<!-- #### Who are the annotators? -->

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<!-- #### Personal and Sensitive Information -->

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<!-- ## Bias, Risks, and Limitations (check inside folders) -->

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[More Information Needed]

<!-- ### Recommendations -->

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## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

## 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](https://www.github.com/amangupta2)
- Prediction of aviation turbulence - [Christopher E. Phillips](https://www.github.com/sodoesaburningbus)
- Identifying weather analogs - [Christopher E. Phillips], Rajat Shinde
- Generating natural language forecasts - [Rajat Shinde](https://www.github.com/omshinde), Sujit Roy
- Long-term precipitation forecasting - [Simon Pfreundschuh](https://www.github.com/simonpf)
- Hurricane track and intensity prediction - [Ankur Kumar](https://www.github.com/ankurk017)