Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.

Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.