Adaptive bias correction for improved subseasonal forecasting

Soukayna Mouatadid (), Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel and Lester Mackey ()
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Soukayna Mouatadid: University of Toronto
Paulo Orenstein: Instituto de Matemática Pura e Aplicada
Genevieve Flaspohler: nLine Inc.
Judah Cohen: Atmospheric and Environmental Research
Miruna Oprescu: Cornell University
Ernest Fraenkel: Massachusetts Institute of Technology
Lester Mackey: Microsoft Research New England

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract 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 remain poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. Here, 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. We show that, 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% (over baseline skills of 0.18–0.25) and precipitation forecasting skill by 40–69% (over baseline skills of 0.11–0.15) in the contiguous U.S. We couple these performance improvements with a practical workflow to explain ABC skill gains and identify higher-skill windows of opportunity based on specific climate conditions.

Date: 2023
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DOI: 10.1038/s41467-023-38874-y

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