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Learned Benchmarks for Subseasonal Forecasting
Soukayna Mouatadid · Paulo Orenstein · Genevieve Flaspohler · Miruna Oprescu · Judah Cohen · Franklyn Wang · Sean Knight · Maria Geogdzhayeva · Sam Levang · Ernest Fraenkel · Lester Mackey
Event URL: https://www.climatechange.ai/papers/neurips2021/30 »

We develop a subseasonal forecasting toolkit of simple learned benchmark models that outperform both operational practice and state-of-the-art machine learning and deep learning methods. Our new models include (a) Climatology++, an adaptive alternative to climatology that, for precipitation, is 9% more accurate and 250\% more skillful than the United States operational Climate Forecasting System (CFSv2); (b) CFSv2++, a learned CFSv2 correction that improves temperature and precipitation accuracy by 7-8% and skill by 50-275%; and (c) Persistence++, an augmented persistence model that combines CFSv2 forecasts with lagged measurements to improve temperature and precipitation accuracy by 6-9% and skill by 40-130%. Across the contiguous U.S., these models consistently outperform standard meteorological baselines, state-of-the-art learning methods, and the European Centre for Medium-Range Weather Forecasts ensemble. Overall, we find that augmenting traditional forecasting approaches with learned enhancements yields an effective and computationally inexpensive strategy for building the next generation of subseasonal forecasting benchmarks.

Author Information

Soukayna Mouatadid (University of Toronto)
Paulo Orenstein (Instituto de Matematica Pura e Aplicada)
Genevieve Flaspohler (Massachusetts Institute of Technology)
Miruna Oprescu (Microsoft Research)
Judah Cohen (AER)
Franklyn Wang (Harvard University)
Sean Knight (MIT)
Maria Geogdzhayeva (MIT)
Sam Levang (Salient Predictions)
Ernest Fraenkel (MIT)
Lester Mackey (Microsoft Research)

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