MIT's AI Post-Processing Doubles 2-6 Week Weather Forecast Skill, Wins ECMWF Contest
MIT News AI · rss · 2026-10-14
MIT, Microsoft, and collaborators published a Nature paper on a machine-learning "post-processing" layer that corrects errors in existing forecast models, for the first time beating climatological averages in the 2-6 week subseasonal range.
- The wrapper learns systematic forecast errors from historical data and adapts as more data arrives; open source, low cost, runs without a supercomputer
- Combined with ECMWF's dynamical model, it doubled predictive skill and won first place in ECMWF's seasonal forecasting competition
- Statistically significant gains on extreme weather prediction — notably where ECMWF's own non-AI bias correction made extreme-event forecasts worse
- Motivated by disasters like 2021's Texas winter storms (246 deaths); Kenya's met office is already in talks to apply it to monsoon forecasting
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