Abstract
Recently, a track-similarity-based Dynamical-Statistical Ensemble Forecast (LTP_DSEF) model has been developed in an attempt to predict heavy rainfall from Landfalling Tropical cyclones (LTCs). In this study, the LTP_DSEF model is applied to predicting heavy precipitation associated with 10 LTCs occurring over China in 2018. The best forecast scheme of the model with optimized parameters is obtained after testing 3452 different schemes for the 10 LTCs. Then, its performance is compared to that of three operational dynamical models. Results show that the LTP_DSEF model has advantages over the three dynamical models in predicting heavy precipitation accumulated after landfall, especially for rainfall amounts greater than 250 mm. The model also provides superior or slightly inferior heavy rainfall forecast performance for individual LTCs compared to the three dynamical models. In particular, the LTP_DSEF model can predict heavy rainfall with valuable threat scores associated with certain LTCs, which is not possible with the three dynamical models. Moreover, the model can reasonably capture the distribution of heavier accumulated rainfall, albeit with widespread coverage compared to observations. The preliminary results suggest that the LTP_DSEF model can provide useful forecast guidance for heavy accumulated rainfall of LTCs despite its limited variables included in the model.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 41675042), the Hainan Provincial Key R & D Program of China (Grant No. SQ2019KJHZ0028), the National Key R & D Program of China (Grant No. 2018YFC1507703), and the Project “Dynamical-Statistical Ensemble Technique for Predicting Landfalling Tropical Cyclones Precipitation”.
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Jia, Z., Ren, F., Zhang, D. et al. An application of the LTP_DSEF model to heavy precipitation forecasts of landfalling tropical cyclones over China in 2018. Sci. China Earth Sci. 63, 27–36 (2020). https://doi.org/10.1007/s11430-019-9390-6
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DOI: https://doi.org/10.1007/s11430-019-9390-6