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Adaptive Bias Correction for Improved Subseasonal Forecast
Soukayna Mouatadid · Paulo Orenstein · Genevieve Flaspohler · Judah Cohen · Miruna Oprescu · Ernest Fraenkel · Lester Mackey

Fri Dec 09 04:00 PM -- 04:11 PM (PST) @
Event URL: https://www.climatechange.ai/papers/neurips2022/79 »

Subseasonal forecasting — predicting temperature and precipitation 2 to 6 weeks ahead — is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remains poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. To counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. When applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60-90% and precipitation forecasting skill by 40-69% in the contiguous U.S. We couple these performance improvements with a practical workflow, based on Cohort Shapley, for explaining ABC skill gains and identifying higher-skill windows of opportunity based on specific climate conditions.

Author Information

Soukayna Mouatadid (University of Toronto)
Paulo Orenstein (Instituto de Matematica Pura e Aplicada)
Genevieve Flaspohler (Massachusetts Institute of Technology)
Judah Cohen (AER)
Miruna Oprescu (Cornell University)
Ernest Fraenkel (MIT)
Lester Mackey (Microsoft Research)

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