卫星云图资料同化及在暴雨数值模拟中的试验研究
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摘要
强降水预报是中尺度数值预报的难点之一。资料同化是提高数值模式预报能力的有效方法。为了提高数值预报模式的强降水预报能力，本文在前人研究成果的基础上，主要从事应用性试验研究。将现有的静止气象卫星红外云图资料定量加入到模式中。首先，云图灰度的定量应用采用了传统的统计回归反演方法反演出中低层的温度和相对湿度要素，反演后的要素经变分同化后应用于模式中。其次，云图T_(BB)资料的定量应用采用了T_(BB)资料温度订正和T_(BB)资料相对湿度增强订正的方法，经订正后的温度和相对湿度直接应用于模式中。对2003年7月9日至10日发生在我国长江流域的一次特大暴雨天气过程用MM5模式进行了数值模拟。同化模拟试验结果表明，卫星云图资料同化后模拟预报的降水场结果有了相当程度的改善，预报的强降水与实况比较接近。本文中采用的T_(BB)资料温度订正方法和T_(BB)资料相对湿度增强订正方法可将与云参数相关的T_(BB)资料直接同化应用于模式，与统计反演后再应用的方法不同，相比之下物理意义更明确。而且模拟试验结果也表明，用T_(BB)资料温度订正法和在较强对流云区T_(BB)资料相对湿度增强订正方法可取得比传统的统计反演方法更好的效果。
It is one of the difficulties of mesoscale-numerical forecasting in rainfall. Data assimilation is an effective approach to improve the numerical forecasts skill. Based on earlier research results, this paper aims at improving the forecast skill for rainfall by experiments, which add satellite infrared cloud image into a forecast model MM5 quantificationally. Statistical regressive method are employed to retrieve temperature and humidity fields in low-level atmosphere, and then assimilated into MM5 by variational technique. Secondly a method is applied to add cloud TBB data to numerical model, which corrects the temperature and amplify the humidity simultaneously. Experiments for the case of the rainfall occurred in the Changjiang River during 9-10,July,2003, are carried out. Numerical experiments results on modeling the rainfall, show that: l)The rainfall field modeled is improved and much similar to real rain field when TBB data is assimiliated into initial fields. The correction methed mentioned above,employed in this paper can assimilate TBB data to numerical model directly, and it has phisical significance clearly other than regressive method also. 2) The experiments verify that correction method gives better results than conventional regressive method in intensively convective zone.
It is one of the difficulties of mesoscale-numerical forecasting in rainfall. Data assimilation is an effective approach to improve the numerical forecasts skill. Based on earlier research results, this paper aims at improving the forecast skill for rainfall by experiments, which add satellite infrared cloud image into a forecast model MM5 quantificationally. Statistical regressive method are employed to retrieve temperature and humidity fields in low-level atmosphere, and then assimilated into MM5 by variational technique. Secondly a method is applied to add cloud TBB data to numerical model, which corrects the temperature and amplify the humidity simultaneously. Experiments for the case of the rainfall occurred in the Changjiang River during 9-10,July,2003, are carried out. Numerical experiments results on modeling the rainfall, show that: l)The rainfall field modeled is improved and much similar to real rain field when TBB data is assimiliated into initial fields. The correction methed mentioned above,employed in this paper can assimilate TBB data to numerical model directly, and it has phisical significance clearly other than regressive method also. 2) The experiments verify that correction method gives better results than conventional regressive method in intensively convective zone.
引文
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6. Sasaki Y K and Goerss J. Satellite data assimilationusing data systems observations. Mon. Wea. Rev., 1982, 110: 1635-1644
7. Lyne W H, Swinbank R and Birch N T. A data as-similation experimentand the global circulation duringthe FGGE special observing periods. Quart. J. Roy.Meteor. Soc., 1982,108:575-594
8． 王诗文、杜远行．气象观测误差和四维同化数值模拟实验．气象学报，1984，42(4)：459-465
9. Dee D, Cohn S E, Dalcher A and Ghil M. An efficientalgorithm for estimating noise covariance indistributed systems. IEEE Trans. AutomaticControl, AC-1985, AC-30:1057-1065
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13. Le-Dinet F X and Talagrand O. Variational algorithmsfor analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 1986,38A:97-110
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31．周鉴本、李尚武．卫星观测辐射资料之一维变分反演法．大气科学(台湾)，1997,25(3)：337-356
32. Koch S, Aksakal A, Mcqueen J T. The influence of mesoscale humidity and evapotranspiration fields on a model forecast of a cold-squall line. Mon Wea Rev, 1997,125:384-409
33．景学义、江孜春．对流云图资料在局地暴雨数值模拟预报中的应用．南京气象学院学报，1998，21(4)：662-669
34．陈子通，沈桐立等．中尺度数值模式的资料同化系统(一)伴随模式系统的设计。南京气象学院学报，1998，21(2)：166-172
35．张大林．各种非绝热物理过程在中尺度模式中的应用．大气科学，1998,22(7)：548-561
36．龚建东、邱崇践．区域四维变分同化的数值实验．气象学报，1999,57(2)：131-141
37．朱国富．观测资料同化与有限区域模式初值降水预报．北京大学学报(自然科学版)，1999,35(1)：81-88
38．张菊芳．TOVS卫星反演资料变分分析的实验研究与应用．气象科学,1999,19(2)：206-212
39．洪景山．卫星探空资料(SATEM)在中央气象局有限区域预报系统之客观分析模组的应用．大学科学(台湾)，1999，27(1)：61-74
40．张守峰、王诗文．在台风业务中使用卫星云导风资料的实验．气象，1999,25(8)：22-25
41．翁永辉、徐祥德．TOVS资料的变分处理方法在青藏高原地区的数值实验．大气科学，1999,23(6)：703-712
42．范新岗，丑纪范．提为反问题的数值预报方法与实验Ⅰ：三类反问题及数值解法．大气科学，1999，23(5)：543-550
43．江吉喜、范梅珠．GMST_(BB)揭示的1998年长江大水的异常天气成因．南京气象学院学报，1999,22(3),331-337
44．国家气象中心、国家卫星气象中心．98中国大洪水与天气预报．北京：气象出版社，1999．274-274
45．程麟生，中尺度大气数值模式发展现状和应用前景[J]，高原气象，1999，18(3)：350-360
46．黎光清．东亚地区气象参数卫星遥感反演理论和方法研究：IISPRM和SRRM[J]．气象学报，1999,57(5)：594-603．
47．周霞琼、朱永提．非对称的绝热加热对热带气旋移动影响的数值研究．应用气象学报，1999,10(3)：283-291
48．朱民、郁凡、郑维忠等．卫星反演湿度场及其在暴雨预报中的初步应用分析．气象学报，2000，58(4)：470-478
49．闵锦忠、沈桐立、陈海山等．卫星云图资料反演的质量控制及变分同化
数值实验．应用气象学报,2000,11(4)：410-418
50．王跃山．客观分析和四维同化——站在新世纪的回望(1)客观分析概念辨析．气象科技，2000,28(3)：1-8
51．邱崇践．变分同化中使用背景场时尺度匹配的数值研究。大气科学，2001，25(1)：103-110
52．蔺雷、朱民、郁凡等．一种融合卫星反演水汽资料的方法在中尺度数值模式中的应用和数值试验个例．气象科学,2001,21(9)：279-290
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2. Morel P, Lefevre G, Rabreau G. On initialization and non- synoptic data assimilation. Tellus,1971,23 : 197～206
3． 丑纪范．天气数值预报中使用过程资料的问题．中国科学，1974，4(6)：635-644
4. Cacuci D G. Sensitivity theory for nonlinear systems. J. Math. Phys., 1981,22:2794～2802
5. Lyne W H, Swinbank R and Birch N T. A data as-similation experimentand the global circulation duringthe FGGE special observing periods. Quart. J. Roy.Meteor. Soc., 1982,108:575-594
6. Sasaki Y K and Goerss J. Satellite data assimilationusing data systems observations. Mon. Wea. Rev., 1982, 110: 1635-1644
7. Lyne W H, Swinbank R and Birch N T. A data as-similation experimentand the global circulation duringthe FGGE special observing periods. Quart. J. Roy.Meteor. Soc., 1982,108:575-594
8． 王诗文、杜远行．气象观测误差和四维同化数值模拟实验．气象学报，1984，42(4)：459-465
9. Dee D, Cohn S E, Dalcher A and Ghil M. An efficientalgorithm for estimating noise covariance indistributed systems. IEEE Trans. AutomaticControl, AC-1985, AC-30:1057-1065
10. Derber J C. The variational 4-D assomolation ofanalyses rsing filtered models as constraints:[Ph. D.thesis], Madison:Univ. of Wiscosin-Madison, 1985.142
11. Lewis JM,Derber JC. The use of adjoint equations to solve a variation adjustment problem with advective constraints.Tellus,1985,37A:309-322
12. Lorenc A C. Analysis methods for numerical weatherprediction. Quart. J. Roy. Meteor. Soc., 1986,112:1177-1194
13. Le-Dinet F X and Talagrand O. Variational algorithmsfor analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 1986,38A:97-110
14. Zhang D-L and M Fritsch. A case study of the sensitivity of numerical model simulation of mesoscale convective systems to varying initial conditions. Mon. Wea. Rev.,1986,114:2418-2431
15. Courtier P, Talagrand O. Varitional assimilation of m eteorological observations with adjoint vorticity equation. Q. J. R.Meteor. Soc, 1987,113:1329～1347
16. Talagrand O,P Courtier. Varitional assimilation of meteorological observations with adjoint vorticity equation. Ⅰ:Theory,Q. J. R. Meteor. Soc,1987,113:1311～1328
17．黄荣辉，吴津生．卫星资料在数值天气预报中的应用与四维同化分机方法的若干进展．国家卫星气象中心．卫星气象文集．北京：气象出版社，1987．23—36
18. Scofield, R. A., The NESDIS operational convective precipitation estimation technique, Mon. Wea. Rev.,1987,115(8),1773-1792
19. Mills G A, Davidson N E. Tropospheric moisture profiles form digital IR satellite imagery: System description and analysis/forecast impact. Aust Meteor Mag, 1987,35:109-118
20. Zhang, D.L., and J.M. Fritsch, Numerical Sensitivity Experiments of Varying Model Physics on the Structure, Evolution and Dynamics of Two Mesoscale Convective Systems[J], J. Atmos. Sci. 1988,45:261-293.
21．陈伯民、钱正安，关于积云参数化问题[J]，高原气象，1992，11：213-221
22．黎光清、张文建、董超华等．东亚地区气象参数同步物理反演方法的数值实验：SPRM和ISPRM大气科学，1993，17(4)：489-498
23. Garand L. A pattern recognition techique for retrieving humidity profiles from METEOSAT or GOES imagery. J Appl Meteor, 1993,32:1592-1607
24．邱崇践，郜吉东．模式误差对变分同化过程影响的数值试验。高原气象，1994，13(4)：449-456
25．蒲朝霞，丑纪范．对中尺度遥感资料进行四维同化的共轭方法及其数值试验。高原气象，1994，13(4)，419-429
26. Arkin,P.A. and R.Xie,The global precipitation climatology project,First algorithm intercomparison project,Bull. Amer. Meteor. Soc.,1994,75,401-419
27. Grell, G.A., Dudhia, J., R. Stauffer, A Description of the Fifth-Generation Penn State/NCAR Mesoscal Model(MM5)[C], NCAR Technical Note, 1994, NCAR/TU-398+STR, 138pp
28．郜吉东、丑纪范．数值模式的初值敏感性程度对四维同化的影响—基于Lorentz系统的研究．气象学报，1995，53(4)：471-479
29．闵锦忠．有限区域卫星云图资料的变分同化系统的设计及试验研究．南京气象学院硕士论文．1995
30．沈桐立、闵锦忠、吴诚鸥等．有限区域卫星云图资料变分分析的实验研究．高原气象，1996，15(1)：58-67
31．周鉴本、李尚武．卫星观测辐射资料之一维变分反演法．大气科学(台湾)，1997,25(3)：337-356
32. Koch S, Aksakal A, Mcqueen J T. The influence of mesoscale humidity and evapotranspiration fields on a model forecast of a cold-squall line. Mon Wea Rev, 1997,125:384-409
33．景学义、江孜春．对流云图资料在局地暴雨数值模拟预报中的应用．南京气象学院学报，1998，21(4)：662-669
34．陈子通，沈桐立等．中尺度数值模式的资料同化系统(一)伴随模式系统的设计。南京气象学院学报，1998，21(2)：166-172
35．张大林．各种非绝热物理过程在中尺度模式中的应用．大气科学，1998,22(7)：548-561
36．龚建东、邱崇践．区域四维变分同化的数值实验．气象学报，1999,57(2)：131-141
37．朱国富．观测资料同化与有限区域模式初值降水预报．北京大学学报(自然科学版)，1999,35(1)：81-88
38．张菊芳．TOVS卫星反演资料变分分析的实验研究与应用．气象科学,1999,19(2)：206-212
39．洪景山．卫星探空资料(SATEM)在中央气象局有限区域预报系统之客观分析模组的应用．大学科学(台湾)，1999，27(1)：61-74
40．张守峰、王诗文．在台风业务中使用卫星云导风资料的实验．气象，1999,25(8)：22-25
41．翁永辉、徐祥德．TOVS资料的变分处理方法在青藏高原地区的数值实验．大气科学，1999,23(6)：703-712
42．范新岗，丑纪范．提为反问题的数值预报方法与实验Ⅰ：三类反问题及数值解法．大气科学，1999，23(5)：543-550
43．江吉喜、范梅珠．GMST_(BB)揭示的1998年长江大水的异常天气成因．南京气象学院学报，1999,22(3),331-337
44．国家气象中心、国家卫星气象中心．98中国大洪水与天气预报．北京：气象出版社，1999．274-274
45．程麟生，中尺度大气数值模式发展现状和应用前景[J]，高原气象，1999，18(3)：350-360
46．黎光清．东亚地区气象参数卫星遥感反演理论和方法研究：IISPRM和SRRM[J]．气象学报，1999,57(5)：594-603．
47．周霞琼、朱永提．非对称的绝热加热对热带气旋移动影响的数值研究．应用气象学报，1999,10(3)：283-291
48．朱民、郁凡、郑维忠等．卫星反演湿度场及其在暴雨预报中的初步应用分析．气象学报，2000，58(4)：470-478
49．闵锦忠、沈桐立、陈海山等．卫星云图资料反演的质量控制及变分同化
数值实验．应用气象学报,2000,11(4)：410-418
50．王跃山．客观分析和四维同化——站在新世纪的回望(1)客观分析概念辨析．气象科技，2000,28(3)：1-8
51．邱崇践．变分同化中使用背景场时尺度匹配的数值研究。大气科学，2001，25(1)：103-110
52．蔺雷、朱民、郁凡等．一种融合卫星反演水汽资料的方法在中尺度数值模式中的应用和数值试验个例．气象科学,2001,21(9)：279-290
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