An Accurate and Scalable Subseasonal Forecasting Toolkit for the United States (Papers Track) Spotlight
Soukayna Mouatadid (University of Toronto); Paulo Orenstein (IMPA); Genevieve E Flaspohler (MIT); Miruna Oprescu (Microsoft Research); Judah Cohen (AER); Franklyn Wang (Harvard); Sean Knight (MIT); Ernest Fraenkel (MIT); Lester Mackey (Microsoft Research)
We develop a subseasonal forecasting toolkit of accurate and highly scalable benchmarks that outperform both the United States operational Climate Forecasting System (CFSv2) and state-of-the-art learning methods from the literature. Our new learned benchmarks include (a) Climatology++, an enhanced form of climatology using knowledge of only the day of the year; (b) CFSv2++, a learned correction for CFSv2; and (c) Persistence++, an augmented persistence model that combines lagged measurements with CFSv2forecasts. These methods alone improve upon CFSv2 accuracy by 9% for US precipitation and 6% for US temperature over 2011-2020. Ensembling our benchmarks with diverse forecasting methods leads to even further gains. Overall, we find that augmenting classical forecasting approaches with learned corrections yields an effective, low-cost strategy for building next-generation subseasonal forecasting models.