Open AccessHybrid data assimilation based on multilayer perceptron
Author(s) -
Jialin Lang,
Feng Qiu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1948/1/012161
Subject(s) - data assimilation , kalman filter , computer science , ensemble kalman filter , assimilation (phonology) , artificial intelligence , multilayer perceptron , machine learning , perceptron , artificial neural network , data mining , extended kalman filter , meteorology , geography , linguistics , philosophy
Data assimilation is widely used in weather forecasting, ocean forecasting, remote sensing observation and other fields. The result of data assimilation directly affects the accuracy of forecasting. Data assimilation algorithms have been studied extensively. The classical algorithms include 3D-Var, 4D-Var, Kalman filter and ensemble Kalman filter. New assimilation algorithms also emerge one after another. In recent years, people have tried to use a hybrid of various data assimilation methods. Deep learning is a hot research field in recent years. Deep learning is able to mine the hidden features of data for analysis and prediction. In this paper, we attempt to use the classical deep learning model-multilayer perceptron to extract the characteristics of traditional data assimilation algorithms to perform hybrid data assimilation. The experiments show that the results of this method are significantly improved compared with the traditional methods, and reveal the value of multilayer perceptron in the direction of hybrid data assimilation.