This paper demonstrates that pre-trained language models (PLMs) are strong
foundation models for on-device meteorological variables modeling. We present
LM-Weather, a generic approach to taming PLMs, that have learned massive
sequential knowledge from the universe of natural language databases, to
acquire an immediate capability to obtain highly customized models for
heterogeneous meteorological data on devices while keeping high efficiency.
Concretely, we introduce a lightweight personalized adapter into PLMs and
endows it with weather pattern awareness. During communication between clients
and the server, low-rank-based transmission is performed to effectively fuse
the global knowledge among devices while maintaining high communication
efficiency and ensuring privacy. Experiments on real-wold dataset show that
LM-Weather outperforms the state-of-the-art results by a large margin across
various tasks (e.g., forecasting and imputation at different scales). We
provide extensive and in-depth analyses experiments, which verify that
LM-Weather can (1) indeed leverage sequential knowledge from natural language
to accurately handle meteorological sequence, (2) allows each devices obtain
highly customized models under significant heterogeneity, and (3) generalize
under data-limited and out-of-distribution (OOD) scenarios.

基于预训练语言模型的天气模型 LM-Weather 通过引入个性化轻量级适配器，使得设备能够高效地获取高度定制的气象模型，进一步提高了通信效率并确保了隐私，广泛的实验证明了其在天气预测和插值等任务上相较于现有方法具有更好的性能。