  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.
