Investigating the use of terrain-following coordinates in AI-driven precipitation forecasts
This addresses the issue of inaccurate precipitation forecasts for meteorology and climate applications, but it is incremental as it adapts an existing method to a specific domain.
The study tackled the problem of blurry precipitation forecasts in AI weather prediction models by integrating terrain-following coordinates, resulting in improved estimation of extreme events and precipitation intensity spectra, with a clear reduction of drizzle bias.
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 degree 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.