Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data

• URL: http://arxiv.org/abs/2301.09152v2
• Date: Sat, 27 May 2023 09:11:48 GMT
• Title: Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data
• Authors: Shengchao Chen, Guodong Long, Tao Shen, Jing Jiang
• Abstract summary: To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data. This paper develops a foundation model across regions of understanding complex meteorological data and providing weather forecasting. A novel prompt learning mechanism has been adopted to satisfy low-resourced sensors' communication and computational constraints.
• Score: 37.549578998407675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data. Despite urgency, heterogeneous meteorological sensors across countries and regions, inevitably causing multivariate heterogeneity and data exposure, become the main barrier. This paper develops a foundation model across regions capable of understanding complex meteorological data and providing weather forecasting. To relieve the data exposure concern across regions, a novel federated learning approach has been proposed to collaboratively learn a brand-new spatio-temporal Transformer-based foundation model across participants with heterogeneous meteorological data. Moreover, a novel prompt learning mechanism has been adopted to satisfy low-resourced sensors' communication and computational constraints. The effectiveness of the proposed method has been demonstrated on classical weather forecasting tasks using three meteorological datasets with multivariate time series.

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