@unpublished{mouatadid2021learned,
author = {Mouatadid, Soukayna and Orenstein, Paulo and Flaspohler, Genevieve and Oprescu, Miruna and Cohen, Judah and Wang, Franklyn and Knight, Sean and Geogdzhayeva, Maria and Levang, Sam and Fraenkel, Ernest and Mackey, Lester},
title = {Learned Benchmarks for Subseasonal Forecasting},
year = {2021},
month = {September},
abstract = {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., our Climatology++, CFSv2++, and Persistence++ toolkit consistently outperforms standard meteorological baselines, state-of-the-art machine and deep 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.},
url = {http://approjects.co.za/?big=en-us/research/publication/learned-benchmarks-for-subseasonal-forecasting/},
note = {Preprint},
}