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- Title
Adaptive bias correction for improved subseasonal forecasting.
- Authors
Mouatadid, Soukayna; Orenstein, Paulo; Flaspohler, Genevieve; Cohen, Judah; Oprescu, Miruna; Fraenkel, Ernest; Mackey, Lester
- 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. This paper proposes a low-cost machine learning correction for physics-based dynamical models that improves subseasonal forecasting of temperature and precipitation two to six weeks ahead.
- Subjects
EUROPEAN Centre for Medium-Range Weather Forecasts (Organization); DROUGHT forecasting; DROUGHT management; ATMOSPHERIC physics; ATMOSPHERIC circulation; PRECIPITATION forecasting; FORECASTING; WATER rights
- Publication
Nature Communications, 2023, Vol 14, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-023-38874-y