Introducing TC intensity into the DSAEF_LTP model and simulating precipitation of super‐typhoon Lekima (2019). (27th August 2020) - Jia, Li; Jia, Zuo; Ren, Fumin; Ding, Chenchen; Wang, Mingyang; Feng, Tian
Abstract: In this study, the Dynamical‐Statistical‐Analog Ensemble Forecast model for landfall typhoon precipitation (the DSAEF_LTP model), which has been developed by Ren et al., in 2020, is applied to precipitation simulations for super‐typhoon Lekima (2019), a tropical cyclone (TC) that produced heavy rainfall over eastern China. A new variable, TC intensity, is introduced into the generalized initial value (GIV) of the DSAEF_LTP model. Two different groups of simulation experiments, one group including the new TC intensity variable in the GIV and the other group excluding it, have been conducted. Results show that, with TC intensity, the ability of the DSAEF_LTP model to forecast heavy rainfall (i.e. accumulated precipitation exceeding thresholds of 250 and 100 mm) for Lekima is improved. Its threat score ( TS ) ranks the second compared with that of three numerical weather prediction models (i.e. ECMWF, GRAPES and GFS ). Further study shows that three factors prevent the DSAEF_LTP model from achieving more satisfactory results for Lekima : the historically rare occurrence of extreme precipitation in northern China during Lekima, the increase of forecast track error when Lekima moved northwards, and the calculation of similarity between tracks of Lekima and historical TCs within an improper region (i.e. similarity region). To solve the third problem, new similarity region schemes are adopted that produce higher TS, in which case TS of precipitation greater than 100 mm ( TS 100) ranks first and TS of precipitation greater than 250 mm ( TS 250) gets closer to the first place. Abstract : The Dynamical‐Statistical‐Analog Ensemble Forecast model for Landfalling Tropical cyclone Precipitation (the DSAEF_LTP model) is a model which creates forecasts by assembling the precipitation of historical individuals determined by the generalized initial value (GIV) which consists of TC track and landfall season. In this study, a new variable, tropical cyclone (TC) intensity, is introduced into the GIV of the DSAEF_LTP model and two different groups of simulation experiments for super‐typhoon Lekima (2019), one including TC intensity and the other excluding it, have been conducted. Results show that the ability of the DSAEF_LTP model to forecast heavy rainfall (i.e. accumulated precipitation exceeding thresholds of 250 and 100 mm) for Lekima is significantly improved when the TC intensity is included.