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 license: mit
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 # Dataset Card for WxC-Bench
 
-WxC-Bench is the Weather Insights and Novel Data for Systematic Evaluation and Testing dataset.
-WxC-Bench's goal is to provide a simple standard for evaluating the performance of Atmospheric and Earth Science AI over a range of tasks.
+**WxC-Bench** primary goal is to provide a standardized benchmark for evaluating the performance of AI models in Atmospheric and Earth Sciences across various tasks.
 
 ## Dataset Details
-WxC-Bench 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
+WxC-Bench contains datasets for six key tasks:
+1. **Nonlocal Parameterization of Gravity Wave Momentum Flux**
+2. **Prediction of Aviation Turbulence**
+3. **Identifying Weather Analogs**
+4. **Generation of Natural Language Weather Forecasts**
+5. **Long-Term Precipitation Forecasting**
+6. **Hurricane Track and Intensity Prediction**
 
-- **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
+### Dataset Description
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+#### 1. Nonlocal Parameterization of Gravity Wave Momentum Flux
+The input variables consist of three dynamic atmospheric variables (zonal and meridional winds and potential temperature), concatenated along the vertical dimension. The output variables are the zonal and meridional components of vertical momentum flux due to gravity waves.
 
-#### Long-term precipitation forecast
+- **Curated by:** [Aman Gupta](https://www.github.com/amangupta2)
+<!-- - **License:** MIT License -->
 
-The daily precipitation accumulations are derived from the PERSIANN CDR dataset up until June 2020 and from the
-IMERG final daily product.
+#### 2. Generation of Natural Language Weather Forecasts
+The dataset includes the HRRR re-analysis data paired with NOAA Storm Prediction Center daily reports for January 2017. This task aims to generate human-readable weather forecasts.
 
-- 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
+- **Curated by:** [NASA IMPACT](https://www.github.com/nasa-impact)
+<!-- - **License:** MIT License -->
 
-The satellite observations are derived from the PATMOS-x, GridSat-B1, and the SSMI(S) brightness temperatures CDRs.
+#### 3. Long-Term Precipitation Forecasting
+This dataset contains daily global rainfall accumulation records and corresponding satellite observations. The goal is to predict rainfall up to 28 days in advance.
 
-- 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.
+- **Curated by:** [Simon Pfreundschuh](https://www.github.com/simonpf) (Colorado State University)
 
-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.
+#### 4. Aviation Turbulence Prediction
+Aimed at detecting turbulence conditions that impact aviation safety.
 
-- 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.
-  
+- **Curated by:** [NASA IMPACT](https://www.github.com/nasa-impact)
+<!-- - **License:** MIT License -->
 
-<!-- ## Uses -->
+#### 5. Hurricane Track and Intensity Prediction
+Provides HURDAT2 data for predicting hurricane paths and intensity changes.
 
-<!-- Address questions around how the dataset is intended to be used. -->
+- **Curated by:** [NASA IMPACT](https://www.github.com/nasa-impact)
+<!-- - **License:** MIT License -->
 
-<!-- ### Direct Use -->
+#### 6. Weather Analog Search
+Data to identify analog weather patterns for improved forecasting.
 
-<!-- This section describes suitable use cases for the dataset. -->
+- **Curated by:** [NASA IMPACT](https://www.github.com/nasa-impact)
+<!-- - **License:** MIT License -->
 
-<!-- ### Out-of-Scope Use -->
+### Dataset Sources
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+#### Nonlocal Parameterization of Gravity Wave Momentum Flux
+Developed using ERA5 reanalysis data (top 15 pressure levels above 1 hPa are excluded). Inputs were coarsely grained from winds and temperatures on a 0.3° grid.
 
+#### Long-Term Precipitation Forecasting
+Precipitation data sources include the PERSIANN CDR dataset (until June 2020) and IMERG final daily product. Satellite observations are sourced from PATMOS-x, GridSat-B1, and SSMI(S) brightness temperatures CDRs, with baseline forecasts from ECMWF and the UK Met Office S2S database.
 
 ## 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. -->
+WxC-Bench datasets are organized by task directories:
+| WxC-Bench           |
+|---------------------|
+| aviation_turbulence |
+| nonlocal_parameterization |
+| weather_analogs |
+| hurricane |
+| precipitation_forecast |
+| weather_forecast_discussion |
+| long_term_precipitation_forecast |
 
-Refer to individual directories for a corresponding downstream task.
-
-| WxC-Bench
-- | aviation_turbulence
-- | nonlocal_parameterization
-- | weather_analogs
-- | hurricane
-- | precipitation_forecast
-- | weather_forecast_discussion
-- | long_term_precipitation_forecast
+Each directory contains datasets specific to the respective downstream tasks.
 
 ## Dataset Creation
 
-<!-- ### Curation Rationale -->
-
-<!-- Motivation for the creation of this dataset. -->
+### Curation Rationale
+The WxC-Bench dataset aims to create a unified standard for assessing AI models applied to complex meteorological and atmospheric science tasks.
 
-<!-- [More Information Needed] -->
+### Source Data
 
-<!-- ### Source Data -->
+The datasets were created using multiple authoritative data sources, such as ERA5 reanalysis data, NOAA Storm Prediction Center reports, PERSIANN CDR, and IMERG products. Data processing involved spatial and temporal alignment, quality control, and variable normalization.
 
-<!-- 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? -->
-
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-
-
-<!-- ### 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? -->
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-<!-- #### Personal and Sensitive Information -->
-
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
-
-
-<!-- ## Bias, Risks, and Limitations (check inside folders) -->
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-<!-- ### Recommendations -->
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-<!-- Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. -->
-
-## 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. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+*To be provided upon publication of the dataset.*
 
 ## 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 
+- Rajat Shinde
+- Christopher E. Phillips
+- Sujit Roy
+- Ankur Kumar
+- Aman Gupta
+- Simon Pfreundschuh
+- Sheyenne Kirkland
+- Vishal Gaur
+- Amy Lin
+- Aditi Sheshadri
+- Manil Maskey
 - Rahul Ramachandran
 
-## Dataset Card Contact 
+## Dataset Card Contact
+
+For each task, please 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)
+- **Nonlocal Parameterization of Gravity Wave Momentum Flux:** [Aman Gupta](https://www.github.com/amangupta2)
+- **Aviation Turbulence Prediction:** [Christopher E. Phillips](https://www.github.com/sodoesaburningbus)
+- **Identifying Weather Analogs:** Christopher E. Phillips, Rajat Shinde
+- **Natural Language Weather 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)