Abstract
Atmospheric systems are typically chaotic and their chaotic nature is an important limiting factor for weather forecasting and climate prediction. So far, there have been many studies on the simulation and prediction of chaotic systems using numerical simulation methods. However, there are many intractable problems in predicting chaotic systems using numerical simulation methods, such as initial value sensitivity, error accumulation, and unreasonable parameterization of physical processes, which often lead to forecast failure. With the continuous improvement of observational techniques, data assimilation has gradually become an effective method to improve the numerical simulation prediction. In addition, with the advent of big data and the enhancement of computing resources, machine learning has achieved great success. Studies have shown that deep neural networks are capable of mining and extracting the complex physical relationships behind large amounts of data to build very good forecasting models. Therefore, in this paper, we propose a prediction method for chaotic systems that combines deep neural networks and data assimilation. To test the effectiveness of the method, we use the model to perform forecasting experiments on the Lorenz96 model. The experimental results show that the prediction method that combines neural network and data assimilation is very effective in predicting the amount of state of Lorenz96. However, Lorenz96 is a relatively simple model, and our next step will be to continue the experiments on the complex system model to test the effectiveness of the proposed method in this paper and to further optimize and improve the proposed method.
Highlights
The prediction of chaotic systems is an important area of research [1,2,3]
Many numerical simulation methods have been used for the numerical simulation of chaotic systems
*Corresponding author’s e-mail: caoxiaoqun@nudt.edu.cn (CESM) [8,9,10,11]. These numerical prediction models have been highly successful and have greatly improved weather and climate prediction. They have improved the understanding of atmospheric chaotic systems
Summary
Predicting chaotic systems is a very difficult task because the essential laws of chaotic systems are still unknown and are extremely sensitive to initial values. In the case of weather systems, for example, many numerical weather prediction models, as well as climate models, have been used for weather forecasting and climate prediction, such as the Weather Research and Forecasting Model (WRF), the Global/Regional Assimilation and Prediction System model (GRAPES), and the Community Earth System Model (CESM) [8,9,10,11] These numerical prediction models have been highly successful and have greatly improved weather and climate prediction. They face many problems, such as error accumulation, irrational parameterization of physical processes, and huge computational effort
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