Data assimilation model based on machine learning

Abstract

Data assimilation(DA) is a method mainly absorbs the observation data into the simulation model, integrates the errors of observation and simulation, and provides a more accurate state so as to reduce the forecast error. Data assimilation has been widely used in the fields of atmosphere and ocean. However, traditional data assimilation methods require a lot of computing resources and consume a long time. Machine learning is a data analysis method with strong learning ability and rapid prediction ability. Long Short-Term Memory network (LSTM) is a widely used machine learning model, which has good effect on time series prediction. In this paper, we use the historical data of data assimilation to train LSTM model and get the prediction model. The experimental results show that the LSTM model can learn the latent law from the historical data, the results of the model fit the real data well, and the calculation speed is greatly improved compared with the original data assimilation algorithm.