July 1, 2022, 1:10 a.m. | Taehyeon Kim, Namgyu Ho, Donggyu Kim, Se-Young Yun

cs.LG updates on arXiv.org arxiv.org

Precipitation forecasting is an important scientific challenge that has
wide-reaching impacts on society. Historically, this challenge has been tackled
using numerical weather prediction (NWP) models, grounded on physics-based
simulations. Recently, many works have proposed an alternative approach, using
end-to-end deep learning (DL) models to replace physics-based NWP. While these
DL methods show improved performance and computational efficiency, they exhibit
limitations in long-term forecasting and lack the explainability of NWP models.
In this work, we present a hybrid NWP-DL workflow to …

arxiv benchmark dataset forecasting lg numerical precipitation prediction processing weather

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