Rainfall forecast errors in different landfall stages of Super Typhoon Lekima (2019)

Abstract

The rainfall forecast performance of the Tropical Cyclone (TC) version Model of Global and Regional Assimilation PrEdiction System (GRAPES-TCM) of the China Meteorological Administration for landfalling Super Typhoon Lekima (2019) is studied by using the object-oriented verification method of conti-guous rain area (CRA). The major error sources and possible reasons for the rainfall forecast uncertainties in different landfall stages (including near landfall and moving further inland) are compared. Results show that different performance and errors of rainfall forecast exist in the different TC stages. In the near landfall stage the asymmetric rainfall distribution is hard to be simulated, which might be related to the too strong forecasted TC intensity and too weak vertical wind shear accompanied. As Lekima moves further inland, the rain pattern and volume errors gradually increase. The Equitable Threat Score of the 24 h forecasted rainfall over 100 mm declines quickly with the time-length over land. The diagnostic analysis shows that there exists an interaction between the TC and the mid-latitude westerlies, but too weak frontogenesis is simulated. The results of this research indicate that for the current numerical model, the forecast ability of persistent heavy rainfall is very limited, especially when the weakened landing TC moves further inland.

Keywords

landing tropical cyclone / rainfall forecast verification / contiguous rain area / Lekima

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Acknowledgments

The work was supported in part by Key Program for International S&T Cooperation Projects of China (No. 2017YFE0107700), the National Natural Science Foundation of China (Grant No. 41875080), Scientific Research Program of Shanghai Science and Technology Commission (No. 19dz1200101), and in part by Shanghai Talent Development Fund and Fujian Key Laboratory of Severe Weather Open Foundation (2020TFS01). We also thank the support from the Typhoon Scientific and Technological Innovation Group of Shanghai Meteorological Service.

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