DSAEF_LTP Model Experiment to Forecast the Accumulated Precipitation of Landfalling Northward-Moving Typhoons in China - Yao, Mei; Ma, Yunqi; Jia, Li; Ren, Fumin; Li, Guoping; Ding, Chenchen; Wang, Mingyang; McBride, John L. (Frontiers in Earth Science, 2021)
We designed two groups of experiments to test the forecast performance of the Dynamical-Statistical-Analog Ensemble Forecast (DSAEF_LTP) model for precipitation caused by landfalling northward-moving typhoons. The first group DSAEF_LTP-1 had the generalized initial value containing three factors (tropical cyclone track, landfall season and tropical cyclone intensity) while the second group DSAEF_LTP-2 added multiple choices of similarity regions. We selected 33 typhoons that brought about maximum daily precipitation ≥100 mm to the area north of the Yangtze River from 2004–2019. We used 22 tropical cyclones from 2004–2015 as training samples to identify the best scheme, which was then used to conduct independent sample forecasting experiments for 11 tropical cyclones from 2016–2019. The results were compared with those of four numerical models (ECMWF, GFS, GRAPES and SMS-WARMS). The simulation ability of the DSAEF_LTP model was significantly improved after adding the similarity regions. The TSsum (TS250 + TS100) for accumulated precipitation ≥250 and ≥100 mm increased from 0.1239 (0 + 0.1239) to 0.1883 (0.0526 + 0.1357). The forecast performance of the DSAEF_LTP for TS100 was 0.1355 for DSAEF_LTP-1 and 0.099 for DSAEF_LTP-2. Both exceeded the scores for two of the operational Numerical Models, GRAPES (0.0798) and SMS-WARMS (0.0943). The DSAEF_LTP model can capture the distribution patterns of the observed precipitation in most cases. The forecasting performance was good over the southern coast of China but was limited in the north. The development of vortex identification technology for residual vortices and the introduction of new environmental factors into the generalized initial value are required to improve the DSAEF_LTP model.