An Enhanced Neural Network Forecasting System for July Precipitation over the Middle-Lower Reaches of the Yangtze River

Forecasting July precipitation using prophase winter sea surface temperature through a nonlinear machine learning model remains challenging. Given the scarcity of observed samples and more attention should be paid to anomalous precipitation events, the shallow neural network (NN) and several improving techniques are employed to establish the statistical forecasting system. To enhance the stability of predicted precipitation, the final output precipitation is an ensemble of multiple NN models with optimal initial seeds. The precipitation data from anomalous years are amplified to focus on anomalous events rather than normal events. Some artificial samples are created based on the relevant background theory to mitigate the problem of insufficient sample size for model training. Sensitivity experiments indicate that the above techniques could improve the stability and interpretability of the forecasting system. Rolling forecasts further indicate that the forecasting system is robust and half of the anomalous events can be successfully predicted. These improving techniques used in this study can be applied not only to the precipitation over the middle-lower reaches of the Yangtze River but also to other climate events.