A Data-Driven Method for Hybrid Data Assimilation with Multilayer Perceptron

Abstract Accurate and timely weather prediction is of significance for autonomous vehicles, such as designing more appropriate sensors or other configurations and developing safer driving strategies. Generally, as the mainstream weather prediction method, numerical weather prediction (NWP) relies on high-quality spatio-temporal observations. However, the precise state of the real world is not measurable. Thus, how to obtain a proper initial condition estimation based on big geospatial-temporal data is a crucial procedure for NWP. Data assimilation (DA) has been a traditional solution to the problem, for the better performance of which various mathematical-physics models have been used. However, the computational effectiveness and efficiency are still largely compromised by the complicated and nonparallel integration process in existing DA methods. In this paper, we propose a novel data-driven method named HDA-MLP to address the DA problem. We first constructed a customized MLP by introducing the temporal peculiarities of the state variables to simulate and optimize pure 3DVar and EnKF. Then we blended the optimized analysis fields directly by implicitly updating the background error covariance matrix through another neural network model to alleviate the dependence on traditional DA methods. We conducted extensive experiments to investigate the effectiveness and efficiency of the proposal by utilizing two classical nonlinear dynamic models. Results reveal that our approach has better robustness and enhanced capability to capture the variation of state variables. Notably, the analysis quality and computational efficiency are significantly improved.