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雷达资料在登陆台风“桑美”数值模拟中的应用
Application of Doppler Radar Data to the Landfalling Typhoon Saomai Simulation
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摘要: 将国内多普勒天气雷达的反射率因子及径向风资料引入ARPS-3DVar同化系统进行同化，针对2006年登陆浙江苍南并造成严重影响的超强台风“桑美”，探讨多普勒雷达资料同化对台风模拟初始场和预报场的改进作用。结果表明:利用ARPS-3DVar同化雷达资料可以明显改善6 h同化窗口内的降水、风场和回波结构，并能提高模式对中尺度雨团位置、强度的模拟能力；雷达资料初始场同化后模拟的台风涡旋和台风眼结构与位置更加接近实况，各物理量空间分布结构更具有明显中尺度特征，从而改善了台风路径和降水的预报。但模拟过程中台风强度模拟偏弱，有待进一步改进。Abstract: The mesoscale model ARPS and its data analyzing system ARPS 3DVar developed by CAPS of Oklahoma university has a good potential to utilize in China. Using ARPS and its 3DVar assimilation system, the Doppler weather radar (CINRAD SA) reflectivity and radial velocity are assimilated. In order to test the effects of Doppler radar data on the initial field and on the forecast field, numeric study is carried out on super typhoon Saomai (0608) which lands at east China and causes a large damage. Comparison between experiments with and without radar data assimilation shows that Doppler radar assimilation can help obtain more realistic precipitation, wind and reflectivity structures within 6 hour initial time windows. The radar assimilation by ARPS 3DVar has the ability to improve the forecast on the mesoscale rain cell position and intensity. The improvement on typhoon track forecast is due to the effective adjustment of the typhoon vortex and eye structure by radar data assimilation. The result of precipitation forecast is improved significantly, mainly because of the physical quantities in assimilation test displaying typical characteristics of mesoscale system. However, there are some inadequate aspects still needing improvements in the stimulation of typhoon intensity.
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图 1 同化窗口内2006年8月10日06:00 1 h降水量（单位:mm）（a）雷达反演，（b）同化试验，（c）控制试验
Fig. 1 1-hour precipitation in the initial time windows at 06:00 10 Aug 2006(unit:mm)(a) retrieved rainfall fromr adar observation, (b) assimilation test, (c) control test
图 2 同化窗口内2006年8月10日06:00海平面的流场和全风速（阴影，单位:m/s）（a）控制试验，（b）同化试验
Fig. 2 Streamline filed and wind speed (shadow, unit: m/s) in the initial time windows at 06:00 10 Aug 2006(a) control test, (b) assimilation test
图 3 2006年8月10日06:00温州多普勒天气雷达径向风速度（单位:m/s）
Fig. 3 Radial velocity observed by Wenzhou radar at 06:00 10 Aug 2006(unit:m/s)
图 4 同化窗口内雷达同化试验、控制试验模拟台风路径与实况台风路径
Fig. 4 The track of typhoon Saomai in the initial time windows simulated by radar assimilation test, by control tset and observation
图 5 同化窗口内2008年8月10日06:00组合反射率（单位:dBz）（a）同化试验，（b）温州雷达观测，（c）控制试验
Fig. 5 The composite reflectivity in the initial time windows at 06:00 10 Aug 2006(unit:dBz) (a) assimilationtest, (b) observed by Wenzhou radar, (c) controltes
图 6 同化窗口内2006年8月10日06:00沿台风中心垂直径向剖面上的物理量（a）同化试验涡度（阴影，单位:10-5s-1）和散度（等值线，单位:10-3s-1），（b）控制试验涡度（阴影，单位:10-5s-1）和散度（等值线，单位:10-3s-1），（c）同化试验qc（阴影，单位:g/kg）和uv-w风场（矢量，单位:m/s），（d）控制试验qc（阴影，单位:g/kg）和uv-w风场（矢量，单位:m/s），（e）同化试验θe（单位:K），（f）控制试验θe（单位:K）
Fig. 6 The cross section of physical variables along typhoon center at 06:00 10 Aug 2006, vorticity (shadow, unit:10-5s-1), divergence (contour, unit:10-3s-1) in assimilation test (a) and in control test (b), qc(shadow, unit:g/kg), uv-w wind (vector, unit:m/s) in assimilation test (c) and in control test (d), θe(unit:K) in assimilation test (e) and in control test (f)
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[1] 陈联寿, 孟智勇.我国热带气旋研究十年进展.大气科学, 2001, 25(3):420-432. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200103012.htm [2] Kasahara A, Balgovind R C, Katz B. Use of satellite radio-metric imagery data for improvement in the analysis of diver-gent wind in the tropics. MonWeaRev, 1988, 116:866-883. http://adsabs.harvard.edu/abs/1988MWRv..116..866K [3] Sun J, Crook N A. Dynamical and microphysical retrieval from Doppler radar observation using a cloud model and its adjoint. Part Ⅰ:Model development and simulated data experiments. J Atmos Sci, 1997, 54:1642-1661. doi: 10.1175/1520-0469(1997)054<1642:DAMRFD>2.0.CO;2 [4] Sun J, Crook N A, Dynamical and microphysical retrieval from Doppler radar observation using a cloud model and its adjoint.Part Ⅱ:Retrieval experiments of an observed Florida convective storm. J Atmos Sci, 1998, 55:835-852. doi: 10.1175/1520-0469(1998)055<0835:DAMRFD>2.0.CO;2 [5] Sun J, Crook N A. Real-time low-level wind and temperature analysis using single WSR-88D data. WeaForecasting, 2001, 16:117-132. doi: 10.1175/1520-0434(2001)016<0117:RTLLWA>2.0.CO;2 [6] 顾建峰, 薛纪善, 颜宏.多普勒雷达四维变分分析系统概述.热带气象学报, 2004, 20:1-13. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200401000.htm [7] 杨艳蓉, 李柏, 张沛源.多普勒雷达资料四维变分同化.应用气象学报, 2004, 15:95-110. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20040113&flag=1 [8] 张林, 倪允琪.雷达径向风资料的四维变分同化试验.大气科学, 2006, 30(3):433-440. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200603006.htm [9] 李柏.多普勒天气雷达资料分析及同化在暴雨中尺度天气系统数值模拟中的应用.北京:中国气象科学研究院, 2005. http://cdmd.cnki.com.cn/Article/CDMD-85101-2005092097.htm [10] Synder C, Zhang F. Assimilation of simulated Doppler radar observations with an ensemble Kalman filter. Mon Wea Rev, 2003, 131:1663-1677. doi: 10.1175//2555.1 [11] Zhang F, Synder C, Sun J. Impacts of initial estimate and observations on the convective-scale data assimilation with an ensemble Kalman filter. Mon Wea Rev, 2004, 132:1238-1253. doi: 10.1175/1520-0493(2004)132<1238:IOIEAO>2.0.CO;2 [12] Tong M, Xue M. Ensemble Kalman filter assimilation of Doppler radar data with a compressible nonhydrostatic model:OSSE experiments. Mon Wea Rev, 2005, 133:1789-1807. doi: 10.1175/MWR2898.1 [13] Gao J, Ming X, Wang Z, et al. The Initial Condition and Explicit Prediction of Convection Using ARPS Adjoint and Other Retrieval Methods with WSR-88D Data. AZ, Amer Mete Soc, 1998:176-178 http://www.autodiff.org/?module=Publications&submenu=list%20publications&id=gao1998tic [14] 邱崇践, 余金香, Qin Xu.多普勒雷达资料对中尺度系统短期预报的改进.气象学报, 2000, 58(2):244-249. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200002011.htm [15] Xue M, Wang D H, Gao J D, et al. The advanced regional prediction system (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor Atmos Phys, 2003, 82:139-170. doi: 10.1007/s00703-001-0595-6 [16] Hu Ming, Xue Ming. 3DVAR and cloud analysis with WSR-88D level-Ⅱ data for the prediction of the Fort Worth Tornadic thunderstorms.Part Ⅰ:Cloud analysis and its impact. Mon Wea Rev, 2005, 134:699-721. [17] Hu Ming, Xue Ming. 3DVAR and cloud analysis with WSR-88D level-Ⅱ data for the prediction of the Fort Worth Tornadic thunderstorms.Part Ⅱ:Impact of radial velocity analysis via 3DVAR. Mon Wea Rev, 2005, 134:675-698. [18] Xiao Qingnong, KuoY-H, Sun J, et al. Assimilation of Doppler Radar Observations and Its Impacts on Forecasting of the Landfalling Typhoon Rusa (2002)∥Proceeding of the Third European Conference on Radar Meteorology (ERAD) Together with the COST 717 Final Seminar, 2004:178-182. [19] 顾建峰.多普勒雷达资料三维变分直接同化方法研究.北京:中国气象科学研究院, 2006. http://cdmd.cnki.com.cn/Article/CDMD-10300-2006083028.htm [20] 万齐林, 薛纪善, 陈子通, 等.雷达TREC风的三维变分同化应用与试验.热带气象学报, 2005, 21(5):449-457. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200505000.htm [21] 万齐林, 薛纪善, 庄世宇.多普勒雷达风场信息变分同化的试验研究.气象学报, 2005, 63(2):129-145. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200502000.htm [22] 盛春岩, 浦一芬, 高守亭.多普勒天气雷达资料对中尺度模式短时预报的影响.大气科学, 2006, 30(1):93-107. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200601007.htm [23] 余贞寿, 廖胜石, 黄克慧, 等. 0414号台风"云娜"的数值模拟.气象学报, 2005, 63(6):903-914. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200506006.htm [24] Xue M, Droegemeier K K, Wong V, et al. The Advanced Regional Prediction System (ARPS) User's Guide.Center for Analysis and Prediction of Storms. Oklahoma: University of Oklahoma, 1995. [25] Gao Jidong, Xue Ming, Brewster K, et al. A three-dimensional variational data analysis method with recursive filter for Doppler radars. JAtmosOceaTech, 2004, 21(3):457-469. https://www.researchgate.net/publication/228763983_A_Three-Dimensional_Variational_Data_Analysis_Method_with_Recursive_Filter_for_Doppler_Radars [26] Zhang J, Carr F H, Brewster K. ADAS Cloud Analysis, Phoenix. 12th Conference on Numerical Weather Prediction. Phoenix, Amer Meteor Soc, 1998:185-188. [27] Zhang J. Moisture and Diabatic Initialization Based on Radar and Satellite Observations. Oklahoma:University of Oklaho-ma, 1999. [28] Smith P L, Jr Myers CG, Orville H D. Orville H D, Radar reflectivity factor calculations in numerical cloud models using bulk parameterization of precipitation process. J Appl Meteor, 1975, 14:1156-1165. doi: 10.1175/1520-0450(1975)014<1156:RRFCIN>2.0.CO;2 [29] 陈联寿, 罗哲贤.非对称结构与台风移动的若千联系.台风科学业务试验和天气动力学理论的研究 (第二分册).北京:气象出版社, 1996:371-374. [30] 朱佩君, 陈敏, 陶祖钰, 等.登陆台风Winnie (1997) 的数值模拟研究Ⅰ:结果检验和云系的模拟.气象学报, 2002, 60(5):553-559. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200205004.htm [31] 朱佩君, 陈敏, 陶祖钰, 等.登陆台风Winnie (1997) 的数值模拟研究Ⅱ:结构演变特征分析.气象学报, 2002, 60(5):560-567. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXB200205005.htm [32] 林毅, 刘铭, 刘爱鸣, 等.台风龙王中尺度暴雨成因分析.气象, 2007, 332:22-28. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200702003.htm [33] 于玉斌.我国近海热带气旋强度突变的机理研究.北京:中国气象科学研究院, 2007. http://cdmd.cnki.com.cn/Article/CDMD-10300-2007075487.htm [34] 张建海.台风Rananim (0414) 数值模拟试验及其相关问题的初步分析.南京:南京信息工程大学, 2006. -
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