A Statistical-Based Model for Typhoon Rain Hazard Assessment

• Published in: Atmosphere Vol. 13; no. 8; p. 1172
• Main Authors: Gu, Jiyang, Cui, Xizhong, Hong, Hanping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Atmospheric precipitations; Coastal flooding; Coastal zone; Datasets; Environmental aspects; Extreme weather; Flood damage; Hazard assessment; Hurricanes; Mapping; Mathematical models; Methods; Modelling; Precipitation; Radar; Radar data; Rain; rain hazard; Rainfall; Rainfall intensity; Risk assessment; Scaling; Scaling factors; simulation; Spatial variations; Statistical models; Topography; TRMM satellite; tropical cyclone; Tropical cyclone rainfall; Tropical cyclones; Tropical rainfall; Tropical Rainfall Measuring Mission (TRMM); Typhoon rainfall; Typhoons; Velocity; Weather stations; wind field; Canada; United States > US; China
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos13081172

Extreme typhoon rainfall can lead to damaging floods near the coastal region in mainland China. In the present study, we calibrate the parameters for a parametric hurricane rain model by using the precipitation radar (PR) data from the Tropical Rainfall Measuring Mission (TRMM) (i.e., PR-TRMM) and the TRMM microwave imager (TMI) data (i.e., TMI-TRMM). To show the applicability of the model for the tropical cyclone (TC) rain hazard assessment, we combine the developed rainfall intensity model with historical and synthetic TC tracks to estimate the T-year return period value of the accumulated rainfall in 24 h, QA24-T. We map QA24-100 for part of the coastal region in mainland China, showing that the spatial variation of QA24-100 is relatively smooth. It was found that the estimated QA24-100 using the model developed, based on the snapshots from PR-TRMM, is about 60% of that obtained using the model developed based on the snapshots from TMI-TRMM. This reflects the differences in the rainfall intensities reported in TMI-TRMM and PR-TRMM. As part of verification, we compare the estimated return period value to that obtained by using the record from surface meteorological stations at a few locations. The comparison indicates that, on average, QA24-100 based on gauge data is about 1.4 and 2.3 times that obtained using the model developed based on the snapshots from PR-TRMM and TRM-TRMM, respectively. This suggests that, for TC rain hazard estimation, one may consider the empirical scaling factor of 1.4 and 2.4 for the rainfall intensity models developed based on snapshots from PR-TRMM and TMI-TRMM, respectively.