West-WRF AI model achieves detailed 2-km weather forecasts for western US
West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation
Artificial Intelligence
Summary
Predicting weather accurately in the western U.S. is tough because of mountains and special weather patterns called atmospheric rivers. The authors created an AI model called West-WRF AI 2-km that makes very detailed forecasts of rain and moisture flow every six hours. They trained it on big sets of past weather data and then improved it using high-resolution regional data. Testing showed the model does a better job capturing localized, intense rain and moisture than less detailed models. This improvement can help with understanding and preparing for heavy rain and flooding events.
What this means in practice
- •For regional weather forecasters: Create more precise forecasts of rain and moisture transport in complex terrain to improve flood warnings and water resource management.
- •For emergency response planners: Use high-resolution 6-hour weather forecasts to better anticipate extreme precipitation events and coordinate disaster preparedness locally.
Authors
Nazak Rouzegari, Vesta Afzali Gorooh, Agniv Sengupta, Phu Nguyen, Kuo-Lin Hsu, Amir AghaKouchak, Soroosh Sorooshian, F. Martin Ralph, Luca Delle Monache
Abstract
We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.