Improving the Forecast Performance of the DSAEF_LTP Model by Incorporating TC Translation Speed Similarity

Abstract

The Dynamical–Statistical–Analog Ensemble Forecast model for Landfalling Typhoon Precipitation (DSAEF_LTP) was developed as a supplementary method to numerical weather prediction (NWP). A successful strategy for improving the forecasting skill of the DSAEF_LTP model is to include as many relevant variables as possible in the generalized initial value (GIV) of this model. In this study, a new variable, TC translation speed, is incorporated into the DSAEF_LTP model, producing a new version of this model named DSAEF_LTP-4. Then, the best scheme of the model for South China is obtained by applying this model to the forecast of the accumulated rainfall of 13 landfalling tropical cyclones (LTCs) that occurred over South China during 2012–14. In addition, the forecast performance of the best scheme is estimated by forecast experiments with eight LTCs in 2015–16 over South China, and then compared to that of the other versions of the DSAEF_LTP model and three NWP models (i.e., ECMWF, GFS, and T639). Results show further the improved performance of the DSAEF_LTP-4 model in simulating precipitation of ≥250 and ≥100 mm. However, the forecast performance of DSAEF_LTP-4 is less satisfactory than DSAEF_LTP-2. This is mainly because of a large proportion of TCs with anomalous tracks and more sensitivity to the characteristics of experiment samples of DSAEF_LTP-4. Of significance is that the DSAEF_LTP model performs better than three NWP models for LTCs with typical tracks.