近海台风立体协同观测科学试验进展

doi:10.11867/j.issn.1001-8166.2022.053

地球科学进展 ›› 2022, Vol. 37 ›› Issue (8): 771 -785. doi: 10.11867/j.issn.1001-8166.2022.053

综述与评述下一篇

近海台风立体协同观测科学试验进展

赵兵科 ¹(

), 汤杰 ¹, 雷小途 ²(

), 张雪芬 ³, 段晚锁 ⁴, 李泓 ¹, 高志球 ⁵, 钱传海 ⁶, 鲍旭炜 ¹, 骆婧瑶 ¹, 张帅 ¹

^1.中国气象局上海台风研究所，上海 200030
^2.上海市气象局，上海 200030
^3.中国气象局气象探测中心，北京 100081
^4.中国科学院大气物理研究所，北京 100029
^5.南京信息工程大学，江苏南京 210044
^6.国家气象中心，北京 100081

收稿日期:2022-06-29 修回日期:2022-07-23 出版日期:2022-08-10
通讯作者: 雷小途 E-mail:zhaobk@typhoon.org.cn;leixt@typhoon.org.cn
基金资助:
国家重点研发计划项目“近海台风立体协同观测科学试验”(2018YFC1506400)

Progress on the Experiment of a Multi-platform Collaborative Field Campaign on Offshore Typhoon

Bingke ZHAO ¹( ), Jie TANG ¹, Xiaotu LEI ²( ), Xuefen ZHANG ³, Wansuo DUAN ⁴, Hong LI ¹, Zhiqiu GAO ⁵, Chuanhai QIAN ⁶, Xuwei BAO ¹, Jingyao LUO ¹, Shuai ZHANG ¹

^1.Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China
^2.Shanghai Meteorological Service, Shanghai 200030, China
^3.Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China
^4.The Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
^5.Nanjing University of Information Science & Technology, Nanjing 210044, China
^6.National Meteorological Centre, Beijing 100081, China

Received:2022-06-29 Revised:2022-07-23 Online:2022-08-10 Published:2022-09-13
Contact: Xiaotu LEI E-mail:zhaobk@typhoon.org.cn;leixt@typhoon.org.cn
About author:ZHAO Bingke (1963-), male, Mei County, Shaanxi Province, Professor. Research areas include typhoon observation and research. E-mail: zhaobk@typhoon.org.cn
Supported by:
the National Key Research and Development Program of China “The experiment of multi-platform collaborative filed campaign on offshore typhoon”(2018 YFC1506400)

全文 ( 46 ) 下载PDF ( 370 )

国家重点研发计划项目“近海台风立体协同观测科学试验”（2018 YFC1506400）针对防台减灾国家需要和缺乏台风直接探测资料这一制约我国台风学科发展和业务预报能力提升的关键瓶颈而立项，经过3年（2018年12月至2021年12月）的研究，全面完成了项目的目标任务。采用目标观测指导外场观测—基于观测资料的诊断和理论分析—数值模拟及观测资料验证—观测研究成果业务应用的技术路线，首次对近年来我国自主研发的新设备高空无人机及平流层飞艇等开展了台风探测适应性改造，完成近海台风“地—海—空—天”多基平台立体协同观测方案设计，组织实施了16个近海目标台风的多基平台协同的外场观测试验，基于多源直接观测资料的分析对台风模式物理过程参数化方案进行了改进，并应用于国家级的台风业务模式，效果显著，实现台风业务模式的路径和强度预报精度提高5%、降水预报精度提高3%~5%的预期目标。构建的台风多平台观测体系及协同观测方案，将为我国近海台风直接观测的业务建设奠定基础，并有望实现由目前的“跟跑”向“并跑”和部分“领跑”迈进。

关键词: 台风, 多平台协同的外场观测试验, 台风业务模式

The China National Key Research and Development Program of China “The experiment of a multi-platform collaborative field campaign on offshore typhoon (2018YFC1506400)” was established to meet the needs of the country for typhoon prevention and disaster reduction. Additionally, the project aims to solve the lack of direct typhoon observation data, which restricts the development of typhoon science and the improvement of operational forecasting ability in China. Since the establishment of the project in December 2018, field observation-diagnosis and theoretical analysis based on observational data-numerical simulation and data verification have been used to adapt and transform new typhoon detection equipment independently developed by China in recent years. This included high-altitude unmanned aerial vehicles and stratospheric airships and the complete design of the “land-ocean-air-sky” three-dimensional collaborative observation scheme for offshore typhoons. Furthermore, multi-platform collaborative field observation experiments were implemented for 16 offshore target typhoons and the parameterization scheme of the physical process of the typhoon model based on the analysis of multi-source direct observation data was modified and applied to the national-level typhoon operational numerical prediction model. This significantly improved the performance and forecast accuracy of the track and intensity of the typhoon operational numerical prediction model and precipitation forecast by 5% and 3%~5%, respectively. Here, the progress of the program is summarized and associated scientific issues are discussed. The typhoon multi-platform observation system and collaborative observation scheme constructed by the project will lay the foundation for the construction of operational direct typhoon observation in offshore areas of China and is expected to realize the progress from the current “follow-up” to “parallel” and partial “lead.”

Key words: Typhoon, Multi-platform collaborative field campaign, Typhoon operational numerical forecast model

中图分类号:

P444

1	WU Ying， CHEN Peiyan， LEI Xiaotu. A preliminary study on the benefit assessment of track and intensity forecast of landfall tropical cyclones［J］. Journal of Tropical Meteorology， 2017， 33（5）： 675-682.
	吴影，陈佩燕，雷小途. 登陆热带气旋路径和强度预报的效益评估初步研究［J］. 热带气象学报， 2017， 33（5）： 675-682.
2	LEI Xiaotu， ZHANG Xuefen， DUAN Wansuo， et al. Experiment on coordinated observation of offshore typhoon in China［J］. Advances in Earth Science， 2019， 34（7）： 671-678.
	雷小途，张雪芬，段晚锁，等. 近海台风立体协同观测科学试验［J］. 地球科学进展， 2019， 34（7）： 671-678.
3	CHEN T， ZHU M， ZHENG Z W. Adaptive path following control of a stratospheric airship with full-state constraint and actuator saturation［J］. Aerospace Science and Technology， 2019， 95： 105457.
4	GUO R， ZHANG X F， MA S Q， et al. Design and preliminary experiment of HALE UAV podded dropsonde system［C］// 2019 International Conference on Meteorology Observations （ICMO）. Chengdu， China： IEEE， 2019： 1-4.
5	CHEN H B， LI J， HE W Y， et al. IAP’s solar-powered unmanned surface vehicle actively passes through the center of typhoon Sinlaku （2020）［J］. Advances in Atmospheric Sciences， 2021， 38（4）： 538-545.
6	MU Mu， WANG Hongli， ZHOU Feifan. A preliminary application of conditional nonlinear optimal perturbation to adaptive observation［J］. Chinese Journal of Atmospheric Sciences， 2007， 31（6）： 1 102-1 112.
	穆穆，王洪利，周菲凡. 条件非线性最优扰动方法在适应性观测研究中的初步应用［J］. 大气科学， 2007， 31（6）： 1 102-1 112.
7	QIN X H， DUAN W S， XU H. Sensitivity to tendency perturbations of tropical cyclone short-range intensity forecasts generated by WRF［J］. Advances in Atmospheric Sciences， 2020， 37（3）： 291-306.
8	YAO Jiawei， DUAN Wansuo. Target observation of sea surface temperature for tropical cyclone intensity simulation［J］. Chinese Journal of Atmospheric Sciences， 2022， 46（1）： 83-97.
	姚佳伟，段晚锁. 台风强度模拟的海温目标观测研究［J］. 大气科学， 2022， 46（1）： 83-97.
9	QIN X H， DUAN W S. Forecast uncertainty of rapid intensification of typhoon Dujuan （201521） induced by uncertainty in the boundary layer［J］. Atmosphere， 2020， 11（11）： 1263.
10	WANG Ye， DUAN Wansuo. Influences of initial perturbation amplitudes and ensemble sizes on the ensemble forecasts made by CNOPs method［J］. Chinese Journal of Atmospheric Sciences， 2019， 43（4）： 915-929.
	汪叶，段晚锁. 初始扰动振幅和集合样本数对CNOPs集合预报的影响［J］. 大气科学， 2019， 43（4）： 915-929.
11	HUO Z H， DUAN W S， ZHOU F F. Ensemble forecasts of tropical cyclone track with orthogonal conditional nonlinear optimal perturbations［J］. Advances in Atmospheric Sciences， 2019， 36（2）： 231-247.
12	ZHANG X F， LI L X， YANG R K， et al. Comprehensive marine observing experiment based on high-altitude large unmanned aerial vehicle （south China Sea experiment 2020 of the “petrel project”）［J］. Advances in Atmospheric Sciences， 2021， 38（4）： 531-537.
13	TIAN D， ZHANG H， ZHANG W Y， et al. Wave glider observations of surface waves during three tropical cyclones in the South China Sea［J］. Water， 2020， 12（5）： 1331.
14	LIN L M， BAO X W， ZHANG S， et al. Correction to raindrop size distributions measured by PARSIVEL disdrometers in strong winds［J］. Atmospheric Research， 2021， 260： 105728.
15	HUANG H， ZHAO K， CHEN H N， et al. Improving time-efficiency of variational specific differential phase estimation［J］. IEEE Transactions on Geoscience and Remote Sensing， 2021， 59（7）： 5 642-5 664.
16	SHEN F F， MIN J Z， LI H， et al. Applications of radar data assimilation with hydrometeor control variables within the WRFDA on the prediction of landfalling hurricane IKE （2008）［J］. Atmosphere， 2021， 12（7）： 853.
17	GAO Z Q， ZHOU S H， ZHANG J B， et al. Parameterization of sea surface drag coefficient for all wind regimes using 11 aircraft eddy-covariance measurement databases［J］. Atmosphere， 2021， 12（11）： 1485.
18	GAO Z Q， PENG W W， GAO C Y， et al. Parabolic dependence of the drag coefficient on wind speed from aircraft eddy-covariance measurements over the tropical eastern Pacific［J］. Scientific Reports， 2020， 10（1）： 1805.
19	YE L， LI Y B， GAO Z Q. Surface layer drag coefficient at different radius ranges in tropical cyclones［J］. Atmosphere， 2022， 13（2）： 280.
20	POWELL M D， VICKERY P J， REINHOLD T A. Reduced drag coefficient for high wind speeds in tropical cyclones［J］. Nature， 2003， 422： 279-283.
21	VICKERY P J， WADHERA D， POWELL M D， et al. A hurricane boundary layer and wind field model for use in engineering applications［J］. Journal of Applied Meteorology and Climatology， 2009， 48： 381-405.
22	HOLTHUIJSEN L H， POWELL M D， PIETRZAK J D. Wind and waves in extreme hurricanes［J］. Journal of Geophysical Research： Oceans， 2012， 117： 1-15.
23	RICHTER D H， BOHAC R， STERN D P. An assessment of the flux profile method for determining air-sea momentum and enthalpy fluxes from dropsonde data in Tropical Cyclones［J］. Journal of the Atmospheric Sciences， 2016， 73（7）： 2 665-2 682.
24	RICHTER D H， WAINWRIGHT C， STERN D P， et al. Potential low bias in high-wind drag coefficient inferred from dropsonde data in hurricanes［J］. Journal of the Atmospheric Sciences， 2021， 78（7）： 2 339-2 352.
25	ZHANG Xiaohua， BI Xueyan， GAO Zhiqiu， et al. Parameterizations of drag coefficient and aerodynamic roughness length using the turbulence data collected during typhoons Hagupit and Chanthu［J］. Journal of Tropical Oceanography， 2021， 40（2）： 1-6.
26	ZENG Zhihua， FU Tianhua， XU Ming， et al. Evaluation of impacts of CWRF boundary layer parameterization on the simulation of tropical cyclones over offshore areas of East Asia［J］. Journal of Marine Meteorology， 2020， 40（3）： 17-26.
	曾智华，辅天华，徐明，等. CWRF边界层参数化对东亚近海热带气旋模拟的影响评估［J］. 海洋气象学报， 2020， 40（3）： 17-26.
27	BAO X W， WU L G， TANG B， et al. Variable raindrop size distributions in different rainbands associated with typhoon Fitow （2013）［J］. Journal of Geophysical Research： Atmospheres， 2019， 124（22）： 12 262-12 281.

[1]	刘德强,冯杰,丁瑞强,李建平. 台风目标观测研究进展回顾[J]. 地球科学进展, 2021, 36(6): 564-578.
[2]	谢彦君, 任福民, 李国平, 王铭杨, 杨慧. 影响中国双台风活动气候特征研究[J]. 地球科学进展, 2020, 35(1): 101-108.
[3]	雷小途,张雪芬,段晚锁,李泓,高志球,钱传海,赵兵科,汤杰. 近海台风立体协同观测科学试验[J]. 地球科学进展, 2019, 34(7): 671-678.
[4]	尹碧文, 任福民, 李国平. 1951—2014年西北太平洋双台风活动气候特征研究[J]. 地球科学进展, 2017, 32(6): 643-650.
[5]	陈华, 霍也. 台风变性过程中下游环流发展的个例对比研究[J]. 地球科学进展, 2016, 31(4): 409-421.
[6]	端义宏. 登陆台风精细结构的观测、预报与影响评估[J]. 地球科学进展, 2015, 30(9): 847-854.
[7]	端义宏. 登陆台风精细结构的观测、预报与影响评估[J]. 地球科学进展, 2015, 30(8): 847-854.
[8]	雷小途. 无人飞机在台风探测中的应用进展[J]. 地球科学进展, 2015, 30(2): 276-283.
[9]	张朝林，宋长青. “复杂构型涡旋移动动力学的研究”研究成果介绍[J]. 地球科学进展, 2013, 28(9): 1062-1063.
[10]	王晨稀. 边界层参数化影响“梅花”台风的敏感性试验[J]. 地球科学进展, 2013, 28(2): 197-208.
[11]	陈大可,雷小途,王伟,王桂华,韩桂军,周磊. 上层海洋对台风的响应和调制机理[J]. 地球科学进展, 2013, 28(10): 1077-1086.
[12]	赵兵科，鲁小琴，万日金，张维. 0608号超强台风“桑美”强度变化位涡诊断分析[J]. 地球科学进展, 2011, 26(5): 528-537.
[13]	詹奕哲,王振会,官莉, 张蕾. 卫星导风在台风路径预报中的应用进展[J]. 地球科学进展, 2011, 26(4): 386-393.
[14]	赵兵科,邵德民,鲁小琴,徐同,翁永元. 多通道地基微波辐射计在0713号“韦帕”台风登陆前后探测性能及特征分析[J]. 地球科学进展, 2009, 24(9): 1001-1008.
[15]	李杨,马舒庆,王国荣,孙兆滨. 无人机探测“海鸥”台风中心附近的资料初步分析[J]. 地球科学进展, 2009, 24(6): 675-679.

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