Publication Date:
2021-10-29
Description:
Accurate prediction of the East Asian summer monsoon (EASM) is beneficial to billions of people’s production and lives. Here convolutional neural networks (CNN) and transfer learning are used for predicting the EASM. The results of the constructed CNN regression model show that the prediction of the CNN regression model is highly consistent with the reanalysis dataset, with correlation coefficient of 0.78, which is higher than that of each of the current state-of-the-art dynamic models. The heat map method indicates that the robust precursor signals in the CNN regression model agree well with previous theoretical studies, and can provide the quantitative contribution of different signals for EASM prediction. The CNN regression model can predict the EASM one year ahead with a confidence level above 95%. The above method can not only improve the prediction of the EASM but also help to identify the involved physical predictors.
Print ISSN:
1748-9318
Electronic ISSN:
1748-9326
Topics:
Energy, Environment Protection, Nuclear Power Engineering
