Handout (1.1 MB)
Ren Fumin1, Wenyu Qiu1,2, Ding Chenchen1, Xianling Jiang1, Liguang Wu2
1 State key laboratory on Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China;
2 Key Laboratory of Meteorological Disaster of Ministry of Education,
Nanjing University of Information Science and Technology, Nanjing 210044, China
The progress of numerical weather prediction (NWP) over the past three decades in tropical cyclone (TC) forecasting research and operation has been concentrated on accurate TC track prediction. Compared with TC track forecast, TC precipitation forecast recieved limited attentions during the past thirty years (Tuleya et al., 2007 and Lonfat et al., 2007).
In this study, in quantitative TC precipitation forecast, a new technique, named as “track-similarity-based Landfalling Tropical cyclone Precipitation Dynamical -Statistical Ensemble Forecast model (LTP_DSEF)”, has preliminarily been developed. The main idea of LTP_DSEF includes four steps ---- 1) To predict TC track, which means directly adopting the NWP TC track prediction, 2) Identification of track-similarity TCs, 3) Considering seasonal similarity, 4) Developing ensemble predictions of the accumulated precipitation using ensembles of TCs with the most similar tracks. Two key methods, tropical cyclone (TC) Track Similarity Area Index (TSAI) (Ren et al., 2017) for identifying TC track similarity and the Objective Synoptic Analysis Technique (OSAT) (Ren et al., 2001, 2007 and 2011) for partitioning TC precipitation, were adopted in the LTP_DSEF.
A test, which involved 21 TCs that brought more than 100mm daily precipitation in at least one station over South China during 2012-2016 with 2012-2014 for the modeling sample and 2015-2016 for the independent sample, and verified the precipitation forecast effect for the LTP_DSEF model and three numerical weather prediction (NWP) models (EC, GFS and T639). The results show that, for landfalling TC accumulated precipitation forecasts, the LTP_DSEF model is superior to the three NWP models, especially for intense precipitation at large thresholds (100 or 250 mm) in the modeling (2012–2014) and independent (2015–2016) samples.
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