May 13, 2024, 4:42 a.m. | Mengxuan Chen, Ziqi Yuan, Jinxiao Zhang, Runmin Dong, Haohuan Fu

cs.LG updates on

arXiv:2405.06590v1 Announce Type: cross
Abstract: Operational weather forecasting models have advanced for decades on both the explicit numerical solvers and the empirical physical parameterization schemes. However, the involved high computational costs and uncertainties in these existing schemes are requiring potential improvements through alternative machine learning methods. Previous works use a unified model to learn the dynamics and physics of the atmospheric model. Contrarily, we propose a simple yet effective machine learning model that learns the horizontal movement in the dynamical …

abstract advanced alternative arxiv computational costs cs.lg forecasting however improvements machine machine learning networks neural networks numerical through type weather weather forecasting

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