A Surrogate Data Assimilation Model for the Estimation of Dynamical
  System in a Limited Area
Wei Kang Liang Xu Hong Zhou
We propose a novel learning-based surrogate data assimilation (DA) model for
efficient state estimation in a limited area. Our model employs a feedforward
neural network for online computation, eliminating the need for integrating
high-dimensional limited-area models. This approach offers significant
computational advantages over traditional DA algorithms. Furthermore, our
method avoids the requirement of lateral boundary conditions for the
limited-area model in both online and offline computations. The design of our
surrogate DA model is built upon a robust theoretical framework that leverages
two fundamental concepts: observability and effective region. The concept of
observability enables us to quantitatively determine the optimal amount of
observation data necessary for accurate DA. Meanwhile, the concept of effective
region substantially reduces the computational burden associated with computing
observability and generating training data.