Investigating the Use of Terrain-Following Coordinates in AI-Driven Precipitation Forecasts
Sha, Y., Schreck, J. S., Chapman, W., Gagne, D. J.. (2025). Investigating the Use of Terrain-Following Coordinates in AI-Driven Precipitation Forecasts. Geophysical Research Letters, doi:https://doi.org/10.1029/2025gl118478
| Title | Investigating the Use of Terrain-Following Coordinates in AI-Driven Precipitation Forecasts |
|---|---|
| Genre | Article |
| Author(s) | Yingkai Sha, John S. Schreck, William Chapman, David John Gagne |
| Abstract | Artificial Intelligence (AI) weather prediction (AIWP) models often produce "blurry" precipitation forecasts. This study presents a novel solution to tackle this problem—integrating terrain-following coordinates into AIWP models. Forecast experiments are conducted to evaluate the effectiveness of terrain-following coordinates using FuXi, an example AIWP model, adapted to 1.0 (Formula presented.) grid spacing data. Verification results show a largely improved estimation of extreme events and precipitation intensity spectra. Terrain-following coordinates are also found to collaborate well with global mass and energy conservation constraints, with a clear reduction of drizzle bias. Case studies reveal that terrain-following coordinates can represent near-surface winds better, which helps AIWP models in learning the relationships between precipitation and other prognostic variables. The result of this study suggests that terrain-following coordinates are worth considering for AIWP models in producing more accurate precipitation forecasts. |
| Publication Title | Geophysical Research Letters |
| Publication Date | Oct 28, 2025 |
| Publisher's Version of Record | https://doi.org/10.1029/2025gl118478 |
| OpenSky Citable URL | https://n2t.net/ark:/85065/d7t1584x |
| OpenSky Listing | View on OpenSky |
| RAL Affiliations | HAP |