Integrating recurrent neural networks with data assimilation for scalable data-driven state estimation | CU Experts

Data assimilation (DA) is integrated with machine learning in order to; perform entirely data-driven online state estimation. To achieve this,; recurrent neural networks (RNNs) are implemented as surrogate models to; replace key components of the DA cycle in numerical weather prediction; (NWP), including the conventional numerical forecast model, the forecast; error covariance matrix, and the tangent linear and adjoint models. It; is shown how these RNNs can be initialized using DA methods to directly; update the hidden/reservoir state with observations of the target; system. The results indicate that these techniques can be applied to; estimate the state of a system for the repeated initialization of; short-term forecasts, even in the absence of a traditional numerical; forecast model. Further, it is demonstrated how these integrated RNN-DA; methods can scale to higher dimensions by applying domain localization; and parallelization, providing a path for practical applications in NWP.

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