Experiment with the dynamical–statistical–analog ensemble forecast model for landfalling typhoon gale over South China - Li, Lifang; Chen, Yuxu; Ren, Fumin; Liu, Chunxia; Ma, Yunqi; Wan, Qilin (Frontiers in Earth Science, 2022)
In this study, an experiment based on the Dynamical-Statistical-Analog Ensemble Forecast model for Landfalling Typhoon Gale (DSAEF_LTG model) was conducted to predict tropical cyclone (TC)-induced potential maximum gales in South China for the first time. A total of 21 TCs with maximum gales greater than or equal to 17.2 m/s (at least one station) during 2011–2018 were selected for this experiment. Among them, 16 TCs in 2011–2015 were selected as the training samples aimed at identifying the best forecast scheme, while 5 TCs in 2016–2018 were selected as the independent samples to verify the best forecast scheme. Finally, the forecast results were compared with four numerical weather prediction (NWP) models (i.e., CMA, ECMWF, JMA and NCEP) based on four forecasting skill scores (Threat Score, False Alarm Ratio, Missing Ratio and Bias Score) at thresholds above Beaufort Scale 7 and 10, and two more indicators (Mean Absolute Error and pearson correlation coefficient). The results revealed encouraging forecasting ability in South China for the DSAEF_LTG model. In general, the DSAEF_LTG model showed higher forecasting skill than the NWP models above the critical thresholds. While the DSAEF_LTG model was prone to false alarms, the NWP models were prone to missing alarms, especially for an intense scale (≥Beaufort Scale 10). In addition, the DSAEF_LTG model also performed best with the smallest forecasting error. Furthermore, the DSAEF_LTG model had distinct advantages in predicting target TCs with typical tracks and widespread gales, both in terms of the wind field pattern and the magnitude of central wind speeds. However, for sideswiping TCs with small-scale gales, the DSAEF_LTG model tended to over-predict and held no advantage over the NWP models, which could perhaps be improved by introducing more reasonable ensemble forecast schemes in further research.