MetMamba：基于时空Mamba模型的区域天气预报
MetMamba: Regional Weather Forecasting with Spatial-Temporal Mamba Model
AI总结：
针对当前DLWP领域有限区域建模和骨干网络研究不足的问题，提出基于Mamba状态空间模型的MetMamba模型，验证其性能优于传统注意力、神经算子骨干网络，并证实与全局模型耦合训练的有限区域深度学习建模可行。
摘要：
近年来，基于深度学习的天气预报（DLWP）模型发展迅速，其表现已大幅超越最先进的数值天气预报方法。目前多数优化工作聚焦于通过训练课程拓展全球场景下的预报范围，而有限区域建模与更优的天气预报骨干网络这两个方向仍鲜有探索。本文提出的MetMamba是一款基于顶尖状态空间模型Mamba构建的DLWP模型，实验证明，相较于采用传统注意力机制与神经算子的其他主流骨干网络，该模型能带来显著的性能提升与独特优势。此外，本文还验证了通过与全局主模型耦合训练，实现基于深度学习的有限区域建模的可行性。
英文摘要：
Deep Learning based Weather Prediction (DLWP) models have been improving rapidly over the last few years, surpassing state of the art numerical weather forecasts by significant margins. While much of the optimization effort is focused on training curriculum to extend forecast range in the global context, two aspects remains less explored: limited area modeling and better backbones for weather forecasting. We show in this paper that MetMamba, a DLWP model built on a state-of-the-art state-space model, Mamba, offers notable performance gains and unique advantages over other popular backbones using traditional attention mechanisms and neural operators. We also demonstrate the feasibility of deep learning based limited area modeling via coupled training with a global host model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
