A Potential Risk Index Dataset for Landfalling Tropical Cyclones over the Chinese Mainland (PRITC dataset V1.0)

Abstract

A dataset entitled “A potential risk index dataset for landfalling tropical cyclones over the Chinese mainland” (PRITC dataset V1.0) is described in this paper, as are some basic statistical analyses. Estimating the severity of the impacts of tropical cyclones (TCs) that make landfall on the Chinese mainland based on observations from 1401 meteorological stations was proposed in a previous study, including an index combining TC-induced precipitation and wind (IPWT) and further information, such as the corresponding category level (CAT_IPWT), an index of TC-induced wind (IWT), and an index of TC-induced precipitation (IPT). The current version of the dataset includes TCs that made landfall from 1949–2018; the dataset will be extended each year. Long-term trend analyses demonstrate that the severity of the TC impacts on the Chinese mainland have increased, as embodied by the annual mean IPWT values, and increases in TCinduced precipitation are the main contributor to this increase. TC Winnie (1997) and TC Bilis (2006) were the two TCs with the highest IPWT and IPT values, respectively. The PRITC V1.0 dataset was developed based on the China Meteorological Administration’s tropical cyclone database and can serve as a bridge between TC hazards and their social and economic impacts.

Record‐breaking rainfall accumulations in eastern China produced by Typhoon In‐fa (2021)

“…Observed WNP TC best-track dataset and TC-induced rainfall in mainland China are obtained from the TC database developed by the Shanghai Typhoon Institute (STI), CMA (Lu et al, 2021;Ying et al, 2014). The database records all TCs that have passed through the WNP and South China Sea since 1949 and is updated annually, providing useful information for understanding climate impacts of WNP TCs (Chen et al, 2021;Liu & Wang, 2020;Tang et al, 2021).…”

Typhoon Track, Intensity, and Structure: From Theory to Prediction

“…The forecast performances of Typhoon Rammasun (2014) andHato (2017) show that there exist defects in the subjective intensity forecast of the CMA, especially for lead times beyond 48 h. But forecasters can capture RI through local sea surface temperature and simulated warm core structure, which is beneficial for disaster reduction (Wang et al, 2019c). To bridge the gap between TC hazards and the associated socioeconomic impacts, "a potential risk index dataset for landfalling tropical cyclones over the Chinese mainland" (PRITC dataset V1.0) is produced (Chen et al, 2021a). The dataset includes TCs that made landfall from 1949-2018 and will be extended each year.…”