A New Forecast Model Based on the Analog Method for Persistent Extreme Precipitation

Abstract

his study aims to establish an analog prediction model for forecasting daily persistent extreme precipitation (PEP) during a PEP event (PEPE) using the temporal sequences of predictors with different weights applied in the atmospheric spatial field. The predictors are atmospheric variables in areas where the key influential systems of a PEPE are active in the THORPEX Interactive Grand Global Ensemble (TIGGE) dataset. By means of the cosine similarity measure and the cuckoo search technique, a forecast model was established and named the Key Influential Systems Based Analog Model (KISAM). Validations through threat scores (TSs) and root-mean-square errors for PEP during 17?25 June 2010 indicate that KISAM is able to identify the approaching PEP earlier and yield a more accurate forecast for the location and intensity of PEP than direct model output (DMO) at 3-day and longer lead times in the Yangtze?Huai River valley. For the independent PEPE case on 17?19 June 2010, KISAM is able to predict the PEPE about 8 days in advance. That is much earlier than with DMO. In addition, KISAM produces better intensity forecasts and predicts the extent of the PEPE better than DMO at the same lead time of 5 days. In terms of the forecast experiments during June 2010 and 2015, KISAM shows relatively stronger capacity than DMO in predicting the occurrence and intensity of extreme precipitation (EP) and PEP events at lead times of 1 week or even longer. Through validation of more EP, better performance of KISAM compared to DMO on average is further confirmed at 3-day and longer lead times.