Abstract
Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones, namely Higos, Nangka, Saudel, and Atsani, over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weather Bureau in 2020. The conditional nonlinear optimal perturbation (CNOP) method has been utilized in real-time to identify the sensitive regions for targeting observations adhering to the procedure of real-time field campaigns for the first time. The observing system experiments were conducted to evaluate the effect of dropsonde data and CNOP sensitivity on TC forecasts in terms of track and intensity, using the Weather Research and Forecasting model. It is shown that the impact of assimilating all dropsonde data on both track and intensity forecasts is case-dependent. However, assimilation using only the dropsonde data inside the sensitive regions displays unanimously positive effects on both the track and intensity forecast, either of which obtains comparable benefits to or greatly reduces deterioration of the skill when assimilating all dropsonde data. Therefore, these results encourage us to further carry out targeting observations for the forecast of tropical cyclones according to CNOP sensitivity.
摘要
针对2020年西北太平洋4个热带气旋: 海高斯、 浪卡、 沙德尔、 艾莎尼, 香港天文台和台湾中央气象局开展了目标观测外场试验、 并收获了宝贵的下投探空资料; 这是条件非线性最优扰动方法 (CNOP) 首次在业务外场试验中应用、 并提供目标观测敏感区. 利用 WRF 模式进行的观测系统试验结果表明: 同化所有下投探空资料对热带气旋路径和强度预报的影响呈现个例依赖性; 但只同化 CNOP 敏感区内下投探空资料却展示出一致的优势: 用较少资料获得相当的改善或削弱同化所有造成的恶化. 上述结果鼓励我们在将来基于 CNOP 敏感区开展更多的热带气旋目标观测外场试验.
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Data Availability Statement: The forecasts from the European Centre for Medium-Range Weather Forecasts were taken from the following source: https://apps.ecmwf.int/datasets/data/tigge/levtype=sfc/type=cf/. The dropsonde data were from the Hong Kong Observatory and the Central Weather Bureau. These data are available on request.
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Acknowledgements
The authors appreciate the anonymous reviewers very much for their insightful comments and suggestions. This work was jointly sponsored by the National Nature Scientific Foundation of China (Grant. Nos. 41930971 and 41775061) and the National Key Research and Development Program of China (Grant No. 2018YFC1506402).
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Article Highlights
• CNOP has been utilized for the first time to identify and produce the sensitive regions in real-time field campaigns for TCs in 2020.
• CNOP sensitivity helps obtain unanimously positive effects for both the track and intensity forecast compared to assimilating all dropsonde data.
This paper is a contribution to the special issue on the 14th International Conference on Mesoscale Convective Systems and High-Impact Weather.
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Qin, X., Duan, W., Chan, PW. et al. Effects of Dropsonde Data in Field Campaigns on Forecasts of Tropical Cyclones over the Western North Pacific in 2020 and the Role of CNOP Sensitivity. Adv. Atmos. Sci. 40, 791–803 (2023). https://doi.org/10.1007/s00376-022-2136-9
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DOI: https://doi.org/10.1007/s00376-022-2136-9