FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D
  Variational Assimilation
Yi Xiao Lei Bai Wei Xue Kang Chen Tao Han Wanli Ouyang
Weather forecasting is a crucial yet highly challenging task. With the
maturity of Artificial Intelligence (AI), the emergence of data-driven weather
forecasting models has opened up a new paradigm for the development of weather
forecasting systems. Despite the significant successes that have been achieved
(e.g., surpassing advanced traditional physical models for global medium-range
forecasting), existing data-driven weather forecasting models still rely on the
analysis fields generated by the traditional assimilation and forecasting
system, which hampers the significance of data-driven weather forecasting
models regarding both computational cost and forecasting accuracy. In this
work, we explore the possibility of coupling the data-driven weather
forecasting model with data assimilation by integrating the global AI weather
forecasting model, FengWu, with one of the most popular assimilation
algorithms, Four-Dimensional Variational (4DVar) assimilation, and develop an
AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can
incorporate observational data into the data-driven weather forecasting model
and consider the temporal evolution of atmospheric dynamics to obtain accurate
analysis fields for making predictions in a cycling manner without the help of
physical models. Owning to the auto-differentiation ability of deep learning
models, FengWu-4DVar eliminates the need of developing the cumbersome adjoint
model, which is usually required in the traditional implementation of the 4DVar
algorithm. Experiments on the simulated observational dataset demonstrate that
FengWu-4DVar is capable of generating reasonable analysis fields for making
accurate and efficient iterative predictions.