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   MIT-led AI post-processing doubles subseasonal forecast accuracy, tops ECMWF contest

   A team including MIT and Microsoft researchers published a Nature paper describing a machine-learning "post-processing" wrapper that learns a forecasting system's systemic errors from historical data and corrects its output, doubling the predictive skill of a gold-standard ECMWF model in the two-to-six-week subseasonal range and, for the first time, beating climatological averages there. In ECMWF's 2025 real-time forecasting competition the approach produced the most accurate global forecasts across all variables and lead times, outperforming numerical models from six operational centers, ECMWF's own AI model and systems from 34 competing teams, while also showing a statistically significant improvement in extreme-weather prediction. The researchers describe the tool as model-agnostic, open source and low cost, and say it can be layered onto other models, including ones already bias-corrected by AI; Kenya's meteorological office has already asked about using it for monsoon forecasting.

   MIT News · AI · 🔥 0

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