Deep Learning Integration of Multi-Model Forecast Precipitation Considering Long Lead Times

Abstract

Reliable forecast precipitation can support disaster prevention and mitigation and sustainable socio-economic development. Improving forecast precipitation accuracy remains a challenge. Therefore, a novel method for multi-model forecast precipitation integration considering long lead times was proposed based on deep learning. First, the accuracy of numerical forecast precipitation was evaluated under different lead times. Secondly, an integrated model was built by coupling the attention mechanism and a long short-term memory neural network (LSTM). Finally, integrated forecast precipitation was obtained by taking high-precision numerical forecast precipitation as an input and examining its accuracy and applicability. Considering the example of the Yalong River, the results showed the following: (1) numerical forecast precipitation fails to forecast precipitation of a ≥10 mm/d intensity well, and is less applicable in streamflow forecast; (2) traditional machine learning methods for integrating multi-model forecast precipitation fail to forecast precipitation of a ≥25 mm/d intensity; (3) the LSTM-A integration model formed by attention weighting after the LSTM output can combine the advantages of numerical forecast precipitation under different intensities and improve the forecast precipitation accuracy for 7-day lead times; and (4) the LSTM-A integrated forecast precipitation has the best applicability in streamflow forecast, with an NSE above 0.82 and an MRE below 30% with 7-day lead times. These findings contribute to improving precipitation forecast accuracy at different intensities and enhancing defense against extreme weather events.