Abstract
To improve the ensemble prediction system of the tropical regional atmosphere model for the South China Sea (TREPS) in predicting landfalling tropical cyclones (TCs), the impacts of three new implementing strategies for surface and model physics perturbations in TREPS were evaluated for 19 TCs making landfall in China during 2014–16. For sea surface temperature (SST) perturbations, spatially uncorrelated random perturbations were replaced with spatially correlated ones. The multiplier f, which is used to form perturbed tendency in the Stochastically Perturbed Parameterization Tendency (SPPT) scheme, was inflated in regions with evident convective activity (f-inflated SPPT). Lastly, the Stochastically Perturbed Parameterization (SPP) scheme with 14 perturbed parameters selected from the planetary boundary layer, surface layer, microphysics, and cumulus convection parameterizations was added. Overall, all these methods improved forecasts more significantly for non-intensifying than intensifying TCs. Compared with f-inflated SPPT, the spatially correlated SST perturbations generally showed comparable performance but were more (less) skillful for intensifying (non-intensifying) TCs. The advantages of the spatially correlated SST perturbations and f-inflated SPPT were mainly present in the deterministic guidance for both TC track and wind and in the probabilistic guidance for reliability of wind. For intensifying TCs, adding SPP led to mixed impacts with significant improvements in probability-matched mean of modest winds and in probabilistic forecasts of rainfall; while for non-intensifying TCs, adding SPP frequently led to positive impacts on the deterministic guidance for track, intensity, strong winds, and moderate rainfall and on the probabilistic guidance for wind and discrimination of rainfall.
摘 要
近来, 基于中国气象局南海台风数值预报系统(CMA-TRAMS)建立了一个水平分辨率约为9 km的中尺度集合预报系统(TREPS). 为了提高TREPS针对登陆台风的预报效果, 本文针对海表和模式物理实施了三种新的扰动策略, 并通过2014~2016年19个登陆我国的台风个例评估了新扰动实施策略对TREPS预报效果的影响. 针对海表温度(SST)扰动, 空间不相关的随机扰动被替换为具有空间相关特征的随机扰动. 参数化倾向随机扰动(SPPT)的乘数f用于构建扰动倾向, 将f在对流活动明显的地区进行缩放(称为f缩放SPPT). 为了将随机参数扰动(SPP)引入TREPS, 针对边界层、 地表、 微物理与积云对流参数化选择了14个扰动参数. 试验结果表明: 相比加强型台风, 这三种新扰动实施策略都对非加强型台风预报具有更明显的改进效果. 相比f缩放SPPT, 空间相关SST扰动总体上具有相似的预报表现, 但针对加强(非加强)型台风具有更好(差)的预报效果. 空间相关SST扰动与f缩放SPPT的预报优势主要在确定性产品(包括路径与风场)以及概率性产品(包括风场可靠性). 对于加强型台风, 引入SPP带来好坏参半的影响, 明显的预报优势主要在中等强度风场的概率匹配以及降水概率预报; 对于非加强型台风, 引入SPP经常对确定性产品(包括路径、 强度、 强风与中等强度降水)以及概率性产品(包括降水分辨力与风场)产生正影响.
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Acknowledgements
This research was sponsored by the National Key R&D Program of China through Grant No. 2017YFC1501603, the National Natural Science Foundation of China through Grant No. 41975136 and the Guangdong Basic and Applied Basic Research Foundation through Grant No. 2019A1515011118. The author is grateful to two anonymous reviewers and the Associate Editors-in-Chief for providing constructive suggestions, which greatly improved the quality of this paper. The author thanks Xu ZHANG, Zhizhen XU, Jianfeng Gu, and Zhongkuo ZHAO for helpful discussions. The support with high-performance computing from Tianhe-2 provided by the National Supercomputing Center in Guangzhou is acknowledged.
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Article Highlights
• New implementing strategies for SST and model physics perturbations improve forecasts more notably for non-intensifying than intensifying TCs.
• Spatially correlated SST perturbations and f-inflated SPPT improve deterministic forecasts of track/wind and probabilistic forecasts of wind for reliability.
• Adding SPP improves probabilistic forecasts of rainfall especially for discrimination.
This paper is a contribution to the special issue on Key Dynamic and Thermodynamic Processes and Prediction of Typhoon (KPPT).
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Zhang, X. Impacts of New Implementing Strategies for Surface and Model Physics Perturbations in TREPS on Forecasts of Landfalling Tropical Cyclones. Adv. Atmos. Sci. 39, 1833–1858 (2022). https://doi.org/10.1007/s00376-021-1222-8
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DOI: https://doi.org/10.1007/s00376-021-1222-8