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Data from: A Regional High Resolution AI Weather Model for the Prediction of Atmospheric Rivers and Extreme Precipitation

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Data from: A Regional High Resolution AI Weather Model for the Prediction of Atmospheric Rivers and Extreme Precipitation

About this collection

Extent

1 digital object.

Description

The files contain total precipitation forecasts generated by two AI models, provided in NetCDF format. Each file includes forecasts initialized on a specific date by a particular model. The files are organized by model, resolution, and year, covering three winter seasons: 2020–2021, 2021–2022, and 2022–2023. Data for each year are stored in separate zip files. In the naming convention of the zip files, “AI-31km” and “AI-6km” refer to 6-hour accumulated precipitation forecasts produced by the global and stretched-grid AI models, respectively. The precipitation regridded to a 4km grid refers to 24-hour accumulated data interpolated to PRISM’s 4-km resolution over the western United States.

Creation Date
  • 2025-03 to 2025-05
Date Issued
  • 2025
Authors
Funding

This research is supported by the Office of Naval Research (ONR) (Award Number N000142412731), the California Department of Water Resources Atmospheric River Program Phase V (Grant 4600015671), and U.S. Army Corps of Engineers (USACE) Forecast Informed Reservoir Operations Phase 3 (USACE W912HZ-24-2-0001). The authors gratefully acknowledge the computational resources provided by the National Artificial Intelligence Research Resource Pilot (NAIRR Award 240367) and the DeltaAI advanced computing and data resource which is supported by the National Science Foundation (award NSF-OAC 2320345).

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Format

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Language
  • English
Identifier

Identifier: Agniv Sengupta: https://orcid.org/0000-0003-3687-5549

Identifier: Dan Steinhoff: https://orcid.org/0000-0002-4233-8750

Identifier: Jorge Baño-Medina: https://orcid.org/0000-0003-3380-1579

Identifier: Luca Delle Monache: https://orcid.org/0000-0003-4953-0881

Identifier: Mario Santa-Cruz: https://orcid.org/0000-0003-4446-2648

Identifier: Patrick Mulrooney: https://orcid.org/0000-0002-3315-0940

Identifier: Thomas Nipen: https://orcid.org/0000-0003-0481-9489

Identifier: Yanbo Nie: https://orcid.org/0000-0001-5166-7751

Related Resources

    Primary associated publication:

    • Jorge Baño-Medina, Agniv Sengupta, Daniel Steinhoff et al. A Regional High Resolution AI Weather Model for the Prediction of Atmospheric Rivers and Extreme Precipitation, 17 July 2025, PREPRINT (Version 1) available at Research Square. https://doi.org/10.21203/rs.3.rs-7087242/v1

    • Baño-Medina, Jorge; Sengupta, Agniv; Steinhoff, Dan; Mulrooney, Patrick; Nipen, Thomas; Santa-Cruz, Mario; Nie, Yanbo; Delle Monache, Luca (in review). A Regional High Resolution AI Weather Model for the Prediction of Atmospheric Rivers and Extreme Precipitation.

    Software

    Collection image

    • Image credit: Jorge Baño-Medina. "Schematic to train the global and stretched-grid AI-driven weather models."

Data from: A Regional High Resolution AI Weather Model for the Prediction of Atmospheric Rivers and Extreme Precipitation
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