ORBIT-2 Dataset for Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling

Lu, Dan; Wang, Xiao; Choi, Jong Youl; Tsaris, Aristeidis (aris); Ashfaq, Moetasim

doi:10.13139/OLCF/2589526

Title: ORBIT-2 Dataset for Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling

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Abstract

We release the dataset used in ORBIT-2, an exascale vision foundation model for weather and climate downscaling. The dataset was constructed by preprocessing raw data into paired low-resolution and high-resolution samples for model training and evaluation. Downscaling was performed using bilinear regridding following the WeatherBench2 methodology (https://weatherbench2.readthedocs.io). The dataset integrates four publicly available, open-source sources: ERA5, PRISM, DAYMET, and IMERG, obtained respectively from: • ERA5: https://weatherbench2.readthedocs.io • PRISM: https://prism.oregonstate.edu • DAYMET: https://daymet.ornl.gov • IMERG: https://gpm.nasa.gov/data/imerg Further details on the dataset construction and usage are provided in: Wang, Xiao, et al. “ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling.” arXiv preprint arXiv:2505.04802 (2025).

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;

ORNL

Publication Date:: Mon Sep 01 04:00:00 UTC 2025

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 3002026

DOI:: https://doi.org/10.13139/OLCF/2589526

Citation Formats


                    Lu, Dan, Wang, Xiao, Choi, Jong Youl, Tsaris, Aristeidis, and Ashfaq, Moetasim. ORBIT-2 Dataset for Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling.  United States: N. p., 2025. 
        Web.  doi:10.13139/OLCF/2589526.

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                    Lu, Dan, Wang, Xiao, Choi, Jong Youl, Tsaris, Aristeidis, & Ashfaq, Moetasim. ORBIT-2 Dataset for Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling.  United States.  doi:https://doi.org/10.13139/OLCF/2589526

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                    Lu, Dan, Wang, Xiao, Choi, Jong Youl, Tsaris, Aristeidis, and Ashfaq, Moetasim. 2025.  
"ORBIT-2 Dataset for Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling".  United States.  doi:https://doi.org/10.13139/OLCF/2589526.  https://www.osti.gov/servlets/purl/3002026. Pub date:Mon Sep 01 04:00:00 UTC 2025

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@article{osti_3002026,

  title        = {ORBIT-2 Dataset for Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling},

  author       = {Lu, Dan and Wang, Xiao and Choi, Jong Youl and Tsaris, Aristeidis and Ashfaq, Moetasim},

  abstractNote = {We release the dataset used in ORBIT-2, an exascale vision foundation model for weather and climate downscaling. The dataset was constructed by preprocessing raw data into paired low-resolution and high-resolution samples for model training and evaluation. Downscaling was performed using bilinear regridding following the WeatherBench2 methodology (https://weatherbench2.readthedocs.io). The dataset integrates four publicly available, open-source sources: ERA5, PRISM, DAYMET, and IMERG, obtained respectively from: • ERA5: https://weatherbench2.readthedocs.io • PRISM: https://prism.oregonstate.edu • DAYMET: https://daymet.ornl.gov • IMERG: https://gpm.nasa.gov/data/imerg Further details on the dataset construction and usage are provided in: Wang, Xiao, et al. “ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling.” arXiv preprint arXiv:2505.04802 (2025).},

  doi          = {10.13139/OLCF/2589526},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Sep 01 04:00:00 UTC 2025},

  month        = {Mon Sep 01 04:00:00 UTC 2025}

}

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DOI: https://doi.org/10.13139/OLCF/2589526

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