Physics-guided Expert Selection for Weather Forecasting

Heterogeneous AI weather models excel under different atmospheric states and locations, creating complementary strengths that forecast fusion is expected to exploit. Yet conventional fusion blends expert predictions and may dilute the locally better forecast. To better exploit this complementarity, we develop Physics-guided Expert Selection (PES), a lightweight plug-and-play layer for spatial expert selection, using three physically interpretable diagnostics: advection, expert disagreement, and their joint magnitude. These diagnostics reveal where expert selection is beneficial, turning black-box routing into gray-box selection. PES outperforms the task-wise stronger constituent expert across all 40 tasks, reducing RMSE by up to 6.3% with only 110K task-specific routing parameters and about 50 ms per variable-lead field. Across two additional heterogeneous expert pairs, PES again improves upon the task-wise stronger constituent expert across all 40 tasks, showing that the selection framework transfers across different expert combinations.