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  1. README.md +14 -8
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@@ -53,8 +53,8 @@ Here are the descriptions of the 31 weather variables with their units:
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  | Aerosol Optical Depth 55 | AOD_55 | 0 to 1 |
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  | Reference evapotranspiration | ET0 | mm/day |
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  | Reference evapotranspiration | ET0 | mm/day |
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- | Vapor Pressure | VAP | Pa |
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- | Vapor Pressure Deficit | VAD | Pa |
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  ### Grid coordinates for the regions
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@@ -229,14 +229,20 @@ The `raw` data is in the JSON format and unprocessed. The `csvs` and the `pytorc
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  - Data was pivoted. So each measurement has x columns where x is either 365, 52, or 12.
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  - `pytorch` data was standardized using the mean and std of the weather over the continental United States.
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- ## Citation [optional]
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  <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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  **BibTeX:**
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- [More Information Needed]
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-
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- **APA:**
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-
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- [More Information Needed]
 
 
 
 
 
 
 
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  | Aerosol Optical Depth 55 | AOD_55 | 0 to 1 |
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  | Reference evapotranspiration | ET0 | mm/day |
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  | Reference evapotranspiration | ET0 | mm/day |
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+ | Vapor Pressure | VAP | kPa |
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+ | Vapor Pressure Deficit | VAD | kPa |
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  ### Grid coordinates for the regions
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  - Data was pivoted. So each measurement has x columns where x is either 365, 52, or 12.
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  - `pytorch` data was standardized using the mean and std of the weather over the continental United States.
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+ ## Citation
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  <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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  **BibTeX:**
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+ ```
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+ @misc{hasan2024weatherformerpretrainedencodermodel,
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+ title={WeatherFormer: A Pretrained Encoder Model for Learning Robust Weather Representations from Small Datasets},
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+ author={Adib Hasan and Mardavij Roozbehani and Munther Dahleh},
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+ year={2024},
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+ eprint={2405.17455},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV},
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+ url={https://arxiv.org/abs/2405.17455},
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+ }
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+ ```