An adaptive and evolvable deep reinforcement learning framework for weather prediction
No single AI weather model excels at all variables, pressure levels, and lead times. Rather than building yet another architecture, we reframe the forecasting problem as one of coordination. Here we present Feitian Adaptive Ensemble Weather (FTAE-Weather), a lightweight framework that learns, through deep reinforcement learning, when and where to trust each member of an open pool of pretrained forecasters. A tactical Weight-Agent reads the current atmospheric state and assigns variable- and horizon-specific fusion weights, while a strategic Evolve-Agent periodically prunes underperforming models and absorbs newly released ones. Asynchronous prediction caching keeps training cost independe...
| Authors | Qiang Wu, Han Li, Jianping Huang |
|---|---|
| Primary category | physics.ao-ph |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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