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Machine-learning correction improves week-three and week-four weather forecasts in tests

Researchers writing in Nature report that MicroDuet topped a 2025 forecasting contest across weather variables and lead times.

10 Sources, 2h ago, first seen 2h ago

TLDR

Researchers writing in Nature say their lightweight probabilistic bias correction method learns from past forecasts and observations to correct systematic errors in physics-based and AI weather models. They report improved week-three and week-four forecasts, including for extreme weather. Their MicroDuet entry combined corrected forecasts and ranked first for every variable and lead time in the 2025 AI Weather Quest’s September–November season. The researchers say the method could aid farming and disaster planning.

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10 Sources

Lester Mackey@LesterMackeyWeather forecasts lose reliability after ~10 days, but farmers, resource managers, and communities make critical decisions weeks in advance. New in @Nature: a lightweight ML method that corrects predictable errors in physics-based and AI weather forecasts at extended lead times.2h
Judah Cohen@judah47For those that asked me about more details on our contest winning AI subseasonal forecast model that I posted last winter, I'm excited to share that our paper in @Nature is now open access online including for the more ambitious where to download the model https://www.nature.com/articles/s41586-026-11126-x2h
Nicolò Fusi@nfusiExciting milestone from @LesterMackey, @alexijielu and colleagues! Advancing the state of the art in ML-driven forecasting is crucial for building resilience and addressing global climate challenges. Wonderful to see this deployed and broadly available.1h
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    10 Sources

    Lester Mackey@LesterMackeyWeather forecasts lose reliability after ~10 days, but farmers, resource managers, and communities make critical decisions weeks in advance. New in @Nature: a lightweight ML method that corrects predictable errors in physics-based and AI weather forecasts at extended lead times.2h
    Judah Cohen@judah47For those that asked me about more details on our contest winning AI subseasonal forecast model that I posted last winter, I'm excited to share that our paper in @Nature is now open access online including for the more ambitious where to download the model https://www.nature.com/articles/s41586-026-11126-x2h
    Nicolò Fusi@nfusiExciting milestone from @LesterMackey, @alexijielu and colleagues! Advancing the state of the art in ML-driven forecasting is crucial for building resilience and addressing global climate challenges. Wonderful to see this deployed and broadly available.1h
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