Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering

This is the authors' abstract. We don't add key points for this paper.

Forecasting precipitation remains challenging because of its large spatial and temporal variability, and the uncertainty in precipitation forecast leads to an important source of uncertainty in the prediction of other components of a hydrological system. In this study, in order to forecast subseasonal precipitation and better characterize the temporal variability of precipitation, a hybrid precipitation forecast model was developed based on (1) temporal clustering of subseasonal precipitation; and (2) coupling an improved seasonal autoregressive integrated moving average (ISARIMA) model to an artificial neural network (ANN) model to take the advantages of both models and capture precipitation persistence and statistics in each cluster. The performance of the proposed model was compared against different variations of conventional statistical models in the Rasht station with a humid climate and Gorgan station with a Mediterranean climate, both located south of the Caspian Sea in northern Iran. The model evaluation criteria indicated that the hybrid model can remarkably improve forecast accuracy. The root-mean square error score of the forecasted precipitation by the hybrid model against observations decreased 48% and 24% in the Rasht and Gorgan stations, respectively, when compared with the seasonal autoregressive integrated moving average (SARIMA) model and the index of agreement increased 32% and 17%, respectively, when compared with the ANN models. The proposed hybrid model can be a useful tool for forecasting subseasonal precipitation in humid and arid climates with persistent and nonpersistent precipitation patterns.