Investigating the Use of Terrain‐Following Coordinates in AI‐Driven Precipitation Forecasts

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 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.