Evaluation of forecast performance for Super Typhoon Lekima in 2019

Guomin CHEN, Xiping ZHANG, Qing CAO, Zhihua ZENG

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PDF(3039 KB)
Front. Earth Sci. ›› 2022, Vol. 16 ›› Issue (1) : 17-33. DOI: 10.1007/s11707-021-0900-2
RESEARCH ARTICLE
RESEARCH ARTICLE

Evaluation of forecast performance for Super Typhoon Lekima in 2019

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Abstract

The predictions for Super Typhoon Lekima (2019) have been evaluated from official forecasts, global models, regional models and ensemble prediction systems (EPSs) at lead times of 1–5 days. Track errors from most deterministic forecasts are smaller than their annual mean errors in 2019. Compared to the propagation speed, the propagation direction of Lekima (2019) was much easier to determine for the official agency and numerical weather prediction (NWP) models. The National Centers for Environmental Prediction Global Ensemble Forecast System (NCEP-GEFS), Japan Meteorological Agency Global Ensemble Prediction System (JMA-GEPS) and Meteorological Service of Canada Ensemble System (MSC-CENS) are underdispersed, and the Shanghai Typhoon Institute Typhoon Ensemble Data Assimilation and Prediction System (STI-TEDAPS) is overdispersed, while the ensemble prediction system from European Centre for Medium-Range Weather Forecasts (ECMWF) shows adequate dispersion at all lead times. Most deterministic forecasting methods underestimated the intensity of Lekima (2019), especially for the rapid intensification period after Lekima (2019) entered the East China Sea. All of the deterministic forecasts performed well at predicting the first landfall point at Wenling, Zhejiang Province with a lead time of 24 and 48 h.

Keywords

Typhoon Lekima (2019) / track / intensity / landfall point / forecast verification

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Guomin CHEN, Xiping ZHANG, Qing CAO, Zhihua ZENG. Evaluation of forecast performance for Super Typhoon Lekima in 2019. Front. Earth Sci., 2022, 16(1): 17‒33 https://doi.org/10.1007/s11707-021-0900-2

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Acknowledgments

The authors thank Dr. Rijin Wan of CMA-STI for providing us the forecast data for the deterministic method. We also thank Dr. Lina Bai of CMA-STI for providing us the best tracks of Lekima (2019). This paper is supported in part by the National Nature Science Foundation of China (Grant Nos. 41875069 and 41975067), the National Key R&D Program of China (Nos. 2018YFC1506406 and 2020YFE0201900) and the Shanghai S&T Research Program (No. 19dz1200101).

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