Feng et al. (2026) Global‐Regional AI Weather Forecasting on a Stretched Cubed Sphere
⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.
Identification
- Journal: Geophysical Research Letters
- Year: 2026
- Date: 2026-08-11
- Authors: Jin Feng, Weiwen Ji
- DOI: 10.1029/2026gl124199
Research Groups
- Department of Atmospheric Sciences, University of California, Los Angeles (UCLA)
- National Center for Atmospheric Research (NCAR)
Short Summary
This study develops and evaluates two novel numerical weather prediction models, StretchCast, on a stretched cubed sphere mesh to improve regional weather forecasting. The results show improved forecast quality and continuity across face boundaries.
Objective
- Investigate the application of a stretched cubed sphere mesh in numerical weather prediction for regional weather forecasting
Study Configuration
- Spatial Scale: Global with a focus on eastern China
- Temporal Scale: Medium-range (up to 10 days)
Methodology and Data
- Models used: One-step and multistep StretchCast models
- Data sources: Not specified in the abstract, but likely includes observational data and reanalysis products
Main Results
- Improved forecast quality through local facewise mixing and global coupling
- Preservation of continuity across face boundaries for Typhoon Muifa
- Consistent spectral structure in medium-range forecasting performance
Contributions
- Development of a novel numerical weather prediction approach using a stretched cubed sphere mesh, which improves regional weather forecasting capabilities
- Demonstration of the potential of this approach to enhance forecast quality and continuity across face boundaries
Funding
- Not specified in the abstract
Citation
@article{Feng2026GlobalRegional,
author = {Feng, Jin and Ji, Weiwen},
title = {Global‐Regional AI Weather Forecasting on a Stretched Cubed Sphere},
journal = {Geophysical Research Letters},
year = {2026},
doi = {10.1029/2026gl124199},
url = {https://doi.org/10.1029/2026gl124199}
}
Original Source: https://doi.org/10.1029/2026gl124199