StreamDA: Recurrent Spatiotemporal Modeling for Hourly Global Atmospheric Data Assimilation

Data assimilation, as the core of numerical weather prediction, is a computationally complex and extremely time-consuming process. In recent years, deep learning-based methods have substantially reduced the computational cost of data assimilation. However, existing deep learning methods still exhibit limitations in the temporal modeling of multi-temporal observations and the efficient fusion of multi-source heterogeneous observations. We propose StreamDA, a recurrent spatiotemporal modeling framework for hourly global data assimilation, powered by a Memory-based Temporal Fusion (MTF) module and a Coverage Modulation for Observation Fusion (CMOF) module. MTF retrieves temporal context from a sequence of historical latent analysis states and propagates it across time, achieving effective integration of multi-temporal observations under hourly cycling assimilation. CMOF combines local coverage with source-specific learnable weights to dynamically modulate observational contributions, improving spatial feature fusion of multi-source observations. In global assimilation experiments, StreamDA reduces mean relative RMSE across variables by 18.2% compared with the primary baseline model, with lower errors in 67 of 69 output variables. Furthermore, the model exhibits greater long-term stability and generates hourly, high-refresh analysis fields, providing robust support for real-time weather forecasting.