Abstract:
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Subseasonal forecasting—predicting temperature and precipitation 2-6 weeks ahead—is critical for effective water allocation, wildfire management, and drought and flood mitigation. However, accurate forecasts for the subseasonal regime are lacking due to the chaotic nature of weather and the complex dependence on both local weather variables and global climate variables. To address this need, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We evaluate our adaptive bias correction method in the contiguous U.S. over the years 2011-2020 and demonstrate consistent improvement over standard meteorological baselines, state-of-the-art learning models, and the leading subseasonal dynamical models, as measured by root mean squared error and uncentered anomaly correlation skill. To facilitate future subseasonal benchmarking and development, we release our model code through the subseasonal_toolkit Python package and our routinely updated SubseasonalClimateUSA dataset through the subseasonal_data Python package.
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