Machine Learning-Based Prediction of Summer Extended-Range Precipitation and Possible Contribution of Soil Moisture over China

Abstract

Low accuracy of extended forecast remains an important scientific problem in the current stage， and qualified extended forecast is of great significance for disaster prevention and mitigation.In this study， the machine learning method was used to forecast the summer precipitation during the extension period （5~30 days） in China， and explore the possible contribution of soil moisture to extended forecast of precipitation.Based on the results， machine learning methods remarkably outweigh traditional linear models in terms of forecast accuracy， with Catboost， Lightgbm and Adaboost being the optimal machine learning methods.According to further analysis， the abnormal evaporation and sensible heat anomaly caused by the surface soil moisture anomaly in the Yangtze River Basin can lead to the atmospheric circulation and vertical movement anomaly， which eventually affects summer precipitation.The set of three optimal machine learning methods was applied to calculate the contribution rate of each forecasting factor in the model.It was found that the local soil moisture dominated the extended precipitation in the Yangtze River Basin from the 5th day to the 10th day， while the local soil moisture played a dominant role on previous precipitation from the 10th day to the 15th day， and the extended precipitation in the Yangtze River Basin during the period of Day 20~30 was basically controlled by large-scale circulation.Besides， the influence of non-local soil moisture on extended precipitation was evaluated， the results of which showed that the surface soil moisture in Indo-China Peninsula mainly contributed to the extended precipitation in the Yangtze River Basin from the 15th day to the 30th day.By adding the surface soil moisture of Indo-China Peninsula to the extended precipitation model in Northeast China， it was found that surface the soil moisture failed to improve the extended forecast accuracy of precipitation in this area， which verified the availability of the machine learning model.This study provides a certain reference for forecasting precipitation in the extended period and exploring the contribution rate of forecasting factors.

Keywords

• machine learning
• extended-range prediction
• summer precipitation
• soil moisture