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Data from: Are AI weather models learning atmospheric physics? A sensitivity analysis of cyclone Xynthia

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Data from: Are AI weather models learning atmospheric physics? A sensitivity analysis of cyclone Xynthia

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1 digital object.

Cite This Work

Baño-Medina, Jorge; Sengupta, Agniv; Doyle, James D.; Reynolds, Carolyn A.; Watson-Parris, Duncan; Delle Monache, Luca (2025). Data from: Are AI weather models learning atmospheric physics? A sensitivity analysis of cyclone Xynthia. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0QV3MWT

Description

This collection contains the data for the manuscript titled "Are AI Weather Models Learning Atmospheric Physics? A Sensitivity Analysis of Cyclone Xynthia", which is necessary to reproduce the results presented. The data is provided in NetCDF format and organized into three directories: gradients, perturbations, and predictions. The gradients directory contains the sensitivity fields of kinetic energy over the Bay of Biscay for Cyclone Xynthia, relative to the input variables at the initial time. The perturbations directory includes sensitivity-based initial condition perturbations at a 36-hour forecast lead time for Cyclone Xynthia. Finally, the predictions directory holds the control and perturbed forecasts for Cyclone Xynthia.

Creation Date
  • 2024
Date Issued
  • 2025
Authors
Funding

This work is supported by the Office of Naval Research (ONR) Award number N000142412731, the California Department of Water Resources Atmospheric River Program Phase IV (Grant 4600014942), and U.S. Army Corps of Engineers (USACE) Forecast Informed Reservoir Operations Phase 2 Award (USACE W912HZ192). A.S. was partly supported by the National Aeronautics and Space Administration (Grant 80NSSC22K0926). J.D.D. and C.A.R. gratefully acknowledge the support of the ONR Study of Air-Sea Fluxes and Atmospheric River Intensity (SAFARI) initiative, program element 0602435N.

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Language
  • English
Identifier

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

Identifier: Carolyn A. Reynolds: https://orcid.org/0000-0003-4690-4171

Identifier: Duncan Watson-Parris: https://orcid.org/0000-0002-5312-4950

Identifier: James D. Doyle: https://orcid.org/0000-0003-2941-2132

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

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

Related Resources

    Primary associated publication

    • J. Baño-Medina, A. Sengupta, J. D. Doyle, C. A. Reynolds, D. Watson-Parris, L. Delle Monache. Are AI weather models learning atmospheric physics? A sensitivity analysis of cyclone Xynthia. npj Climate and Atmospheric Science. https://doi.org/10.1038/s41612-025-00949-6

    Software

    Collection image

    • Image credit: Jorge Baño-Medina. "AI-based sensitivity field for cyclone Xynthia."

Data from: Are AI weather models learning atmospheric physics? A sensitivity analysis of cyclone Xynthia
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