BlendCast: Teaching Vision-Language Model to Anticipate Member Skill in Weather Ensembles

AI weather models are fast and skillful, yet their errors are strongly conditional because the best member changes with lead time, variable, initialization state, and atmospheric regime. Operational deterministic multi-model forecasting therefore requires more than averaging strong forecasters. It requires anticipating which member deserves trust before future analyses exist. BlendCast turns this anticipation problem into a vision-language forecasting task. Given meteorological maps and structured ensemble diagnostics, the model reads the current weather situation, reasons about prospective member reliability, and converts that judgment into deterministic per-variable ensemble weights. To teach this behavior, BlendCast first gives the model demonstrations of what member-skill reasoning and weight decisions should look like, then refines the resulting policy with multi-variable reinforcement learning so that its own analyses are rewarded when they lead to better, physically consistent forecasts. On held-out 2025 forecasts, BlendCast reduces WRMSE by 2.6\% over equal weighting and by 2.0% over conventional ensemble post-processing, closing 63.2% of the gap to oracle blending. These results show that, with multimodal weather evidence and reward-aligned training, VLMs can act as real-time forecasters of forecasters for deterministic AI weather ensembles. Project and demo:https://anonymous.4open.science/status/BlendCast-2567