双多普勒雷达资料同化在飓风“艾克”预报中的应用研究

沈菲菲; 闵锦忠; 许冬梅; 戴泽军; 张冰; 陈依濛

doi:10.3969/j.issn.0253-4193.2016.11.006

双多普勒雷达资料同化在飓风“艾克”预报中的应用研究

doi: 10.3969/j.issn.0253-4193.2016.11.006

1.
南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏南京 210044
2.
南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际合作联合实验室/气象灾害预报预警与评估协同创新中心, 江苏南京 210044;美国国家大气研究中心, 博尔德 80302
3.
湖南省气象科学研究所, 湖南长沙 410118
4.
江苏省气象科研研究所, 江苏南京 210000
5.
南京信息工程大学应用气象学院, 江苏南京 210044

基金项目: 国家重点基础研究计划（973计划）项目（OPPAC-2013CB430102）；国家自然科学基金项目（41430427，41375025，41205082，41505089）；江苏省气象局北极阁基金项目（BJG201510）；气象灾害教育部重点实验室开放课题（KLME 1311）。

计量
- 文章访问数: 960
- HTML全文浏览量: 18
- PDF下载量: 756
- 被引次数: 0
出版历程
- 收稿日期: 2015-07-09
- 修回日期: 2015-09-23

Application of assimilating Dual Doppler radar data in forecast of Hurricane Ike

1.
Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China
2.
Key Laboratory of Meteorological Disaster, Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;National Center for Atmospheric Research, Boulder, Colorado 80307, USA
3.
Institute of Hunan Meteorology, Changsha 410118, China
4.
Jiangsu Research Institute of Meteorlogical Science, Nanjing 210044, China
5.
School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China

摘要

摘要: 本文采用美国国家大气研究中心（NCAR）开发的中尺度数值模式WRFV3.7及其三维变分同化系统WRF-3DVAR对2008年飓风“艾克”进行了数值模拟研究。利用多普勒天气雷达观测资料具有高时空分辨率的优点，将美国两部多普勒天气雷达资料进行速度退模糊等必要质量控制后同化进中尺度数值模式，考察雷达资料同化对飓风“艾克”预报的改进程度。试验结果表明：将雷达资料用于对流尺度分辨率下飓风初始化需要对变分同化系统中特征尺度化因子进行优化调整，使观测资料能够以较为合理的方式调整模式初始场并进而改进预报；雷达径向风同化可以有效调整模式初始场中的飓风动力和热力结构，而经过尺度化因子调整后的雷达径向风同化则在飓风观测中心位置产生较为合理的气旋性风场增量，提供更为确切的中小尺度信息，使模式初始场更加接近观测并进而改进对飓风路径和强度的预报。
- 飓风 /
- 雷达径向风 /
- WRF模式 /
- 循环同化
Abstract: The impacts of assimilation of radial velocity (V_r) data for the application of analyses and forecasts for Hurricane Ike (2008) are investigated using the framework of Weather Research and Forecasting (WRF) V3.7 and its three-dimensional variational data (3DVAR) assimilation system developed by the U.S. National Center for Atmospheric Research (NCAR). To evaluate the impact of using the high spatial and temporal resolution of the radar data on the forecast of Hurricane Ike, V_r observations from two coastal radars are pre-processed with quality control procedures before they are assimilated using 3DVAR. Results show that, it is necessary to tune the background error length scale factor to better spread out observations for hurricane radar data assimilations. It is found that V_r data are able to adjust the hurricane's thermal and dynamic structure significantly. With smaller length scale factor, a much clearer cyclonic circulation wind increment around the observed hurricane center can be observed, providing effective meso-and micro scale information for the analysis, which can further improve the hurricane track and intensity forecast.
- hurricane /
- radial velocity /
- WRF model /
- cycling assimilation

HTML全文

参考文献(23)

Kurihara Y, Bender M A, Ross R J. An initialization scheme of hurricane models by vortex specification[J]. Mon Wea Rev, 1993, 121: 2030-2045.

Zou X, Xiao Q. Studies on the initialization and simulation of a mature hurricane using a variational bogus data assimilation scheme[J]. J Atmos Sci, 2000, 57: 836-860.

Pu Z X, Braun S A. Evaluation of bogus vortex techniques with four-dimensional variational data assimilation[J]. Mon Wea Rev, 2001, 129: 2023-2039.

Xiao Q, Chen L, Zhang X. Evaluations of BDA scheme using the Advanced Research WRF (ARW) model[J]. J Appl Meteor Climatol, 2009, 48: 680-689.

Xiao Q, Kuo Y H, Sun J, et al. An approach of radar reflectivity data assimilation and its assessment with the inland QPF of typhoon Rusa (2002) at landfall[J]. J Appl Meteor Climatol, 2007, 46: 14-22.

Gu J F, Xiao Q N, Kuo Y H, et al. Assimilation and simulation of Typhoon Rusa (2002) using the WRF system[J]. Adv Atmos Sci, 2005, 22(3): 415-427.

Zhao Q, Jin Y. High-resolution radar data assimilation for Hurricane Isabel (2003) at landfall[J]. Bull Am Meteorol Soc, 2008, 89:1355-1372.

Zhao K, X Li, Xue M, et al. Short-term forecasting through intermittent assimilation of data from Taiwan and Mainland China coastal radars for typhoon Meranti (2010) at landfall[J]. J Geophy Res, 2012, 117: D06108.

陈锋, 冀春晓, 董美莹, 等. 雷达径向风速同化对台风麦莎模拟的影响[J]. 气象, 2012, 38(10): 1170-1181. Chen Feng, Ji Chunxiao, Dong Meiying, et al. The effects of radar radial velocity data assimilation on the simulation of Typhoon Matsa[J]. Meteorological Monthly, 2012,38(10):1170-1181.

李新峰, 赵坤, 王明筠, 等. 多普勒雷达资料循环同化在台风"鲇鱼"预报中的应用[J]. 气象科学, 2013, 33(3): 255-263. Li Xinfeng, Zhao Kun, Wang Mingjun, et al. Short-term forecasting of super typhoon Meji at landfall through cycling assimilation of China coastal radar data[J]. Journal of the Meteorological Sciences, 2013,33(3):255-263.

Lorenc A. The potential of the ensemble Kalman filter for NWP-a comparison with 4D-Var[J]. Quart J Roy Meteor Soc, 2003, 129, 3183-3204.

Sun J, Crook N A. Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part Ⅰ: Model development and simulated data experiments[J]. J Atmos Sci, 1997, 54(12): 1642-1661.

Sun J, Crook N A. Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part Ⅱ: Retrieval experiments of an observed Florida convective storm[J]. J Atmos Sci, 1998, 55(5): 835-852.

Marshall J S, Palmer W M K. The distribution of raindrops with size[J]. J Meteor, 1948, 5(4): 165-166.

Hong S Y, J Dudhia, Chen S H. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation[J]. Mon Wea Rev, 2004, 132: 103-120.

Grell G A, Devenyi D. A generalized approach to parameterizing convection combining ensemble and data assimilation techniques[J]. Geophys Res Lett, 2002, 29(14): 1693.

Noh Y, Cheon W G, Hong S Y, et al. Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data[J]. Bound-Lay Meteorol, 2003, 107: 401-427.

Parrish D F, Derber J C. The National Meteorological Center's spectral statistical interpolation analysis system[J]. Mon Wea Rev, 1992, 120(8): 1747-1763.

沈菲菲,闵锦忠,陈鹏,等. 多普勒雷达资料同化在台风"桑美"预报中的应用应用[J]. 海洋学报, 2015, 37(3): 25-36. Shen Feifei, Min Jinzhong, Chen Peng, et al. Experiments of assimilating Doppler radar data in forecast of typhoon Saomai[J]. Haiyang Xuebao, 2015,37(3):25-36.

沈菲菲,闵锦忠,许冬梅,等. WRF-Hybrid背景误差协方差调整在台风同化及预报中的应用研究[J]. 气象科学, 2015, 35(2): 150-159. Shen Feifei, Min Jinzhong, Xu Dongmei, et al. WRF-Hybrid background error covariance adjustment in typhoon assimilation and forecasting[J]. Journal of the Meteorological Sciences, 2015, 35(2): 150-159.

Xiao Q, Sun J. Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002[J]. Mon Wea Rev, 2007, 135: 3381-3404.

Snyder C, Zhang F. Assimilation of simulated Doppler radar observations with an ensemble Kalman filter[J]. Mon Wea Rev, 2003, 131: 1663-1677.

Tong M, Xue M. Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model: OSS experiments[J]. Mon Wea Rev, 2005, 133: 1789-1807.

施引文献

资源附件(0)

访问统计