NOAA卫星ATOVS资料反演大气温、湿廓线及其在中尺度气象模式中的同化试验
|
摘要
本论文系统介绍了气象卫星的发展、遥感原理以及大气参数反演方法;详细介绍了NOAA/ATOVS传感器工作原理及其大气温度、湿度、风矢量廓线反演方法;对MM5中尺度气象数值预报模式、数据同化方法也作了简要介绍。
本论文旨在运行中国海洋大学NOAA/SeaWiFS卫星地面站SeaSpace TeraScan软件包中ATOVS大气温度、湿度、风矢量廓线反演软件,并在UNIX操作系统下编写了批处理程序,以增加卫星地面站遥感数据服务项目和数据用户。
为探讨NOAA/ATOVS大气反演参数在MM5中尺度气象数值预报模式中的数据同化,以2004年8月8～12日产生于西北太平洋的“云娜”台风路径数值预报为例,设计了同化试验方案,数据同化采用MM5自带的观测松弛方法(Obs-Nudging)。结果表明,NOAA/ATOVS大气温度、湿度、风矢量剖面数据在MM5模式中进行同化,其复杂性和计算量均可达到应用的要求。
The history of meteorological satellites, the principle of meteorological remote sensing and retrieval methods for atmospheric parameters are introduced comprehensively in this thesis. The principle of Advanced TIROS Operational Vertical Sounder (ATOVS) on board the new generation of National Oceanic and Atmospheric Adminisstration (NOAA) polar-orbiting satellites and the retrieval methods for temperature, humidity and wind profiles are given indetails. Meteorological mesoscale model and data assimilating methods are described briefly.The ATOVS processing software of SeaSpace / TeraScan Package for atmospheric temperature, humidity and wind retrieval profiles is tested and a batch program under UNIX is carried out to process the data automatically in order to add the products service of the satellite ground station of Ocean University of China to more users.In order to discuss the assimilation of the profile data retrieved from ATOVS into MM5 (5th meteorological mesoscale model), the simulations on the case of the route of Rananim typhoon occurred over the western Pacific ocean from 8 to 12, August 2004 are carried out. The method of data assimilation is Observation Nudging which is built in MM5. The result shows the complexity and calculational capacity meet to the application.
本论文旨在运行中国海洋大学NOAA/SeaWiFS卫星地面站SeaSpace TeraScan软件包中ATOVS大气温度、湿度、风矢量廓线反演软件,并在UNIX操作系统下编写了批处理程序,以增加卫星地面站遥感数据服务项目和数据用户。
为探讨NOAA/ATOVS大气反演参数在MM5中尺度气象数值预报模式中的数据同化,以2004年8月8～12日产生于西北太平洋的“云娜”台风路径数值预报为例,设计了同化试验方案,数据同化采用MM5自带的观测松弛方法(Obs-Nudging)。结果表明,NOAA/ATOVS大气温度、湿度、风矢量剖面数据在MM5模式中进行同化,其复杂性和计算量均可达到应用的要求。
The history of meteorological satellites, the principle of meteorological remote sensing and retrieval methods for atmospheric parameters are introduced comprehensively in this thesis. The principle of Advanced TIROS Operational Vertical Sounder (ATOVS) on board the new generation of National Oceanic and Atmospheric Adminisstration (NOAA) polar-orbiting satellites and the retrieval methods for temperature, humidity and wind profiles are given indetails. Meteorological mesoscale model and data assimilating methods are described briefly.The ATOVS processing software of SeaSpace / TeraScan Package for atmospheric temperature, humidity and wind retrieval profiles is tested and a batch program under UNIX is carried out to process the data automatically in order to add the products service of the satellite ground station of Ocean University of China to more users.In order to discuss the assimilation of the profile data retrieved from ATOVS into MM5 (5th meteorological mesoscale model), the simulations on the case of the route of Rananim typhoon occurred over the western Pacific ocean from 8 to 12, August 2004 are carried out. The method of data assimilation is Observation Nudging which is built in MM5. The result shows the complexity and calculational capacity meet to the application.
引文
1.周秀骥等,大气微波辐射及遥感原理,北京:科学出版社,1982年
2.曾庆存,大气红外遥感原理,北京:科学出版社,1974年
3.张培昌,王振会,大气微波遥感原理,北京:气象出版社,1995年
4.张瑞生,大气微波遥感,济南:山东科学技术出版社,1989年
5.陈渭民,卫星气象学,北京:气象出版社,2003年
6.P.K.Rao等编,许健民等译,气象卫星——系统、资料及其在环境中的应用,北京:气象出版社,1994年
7.冯士笮等,海洋科学导论,高等教育出版社,1999年
8.吴雪宝等,大气反演参数在大气预报中的应用试验.电波科学学报.2004,19(4):469-473
9.吴雪宝,张凤英,利用高光谱红外探测资料反演大气参数,气象科技,2003,31(4): 201-205
10.李万彪,吴龙涛,张呈祥,余嘉裕,气象卫星遥感探测海面大气温度垂直廓线,北京大学学报(自然科学版),2003,39(5):656-665
11.王振会.用改进的EOF方法和卫星AMSU反演大气温度廓线的一次数值实验,南京气象学院学报,1990,13(3):410-416
12.陈洪滨,林龙福,从118.75GHz附近六通道亮温反演大气温度廓线的数值模拟研究,大气科学,2003,27(5):894-900
13.李俊,曾庆存,有云时大气红外遥感及其反演问题Ⅰ.理论研究,大气科学,1997,21(3):341-347
14.李俊,曾庆存,有云时大气红外遥感及其反演问题Ⅱ.反演试验研究,大气科学.1997,21(4):396-400
15.李俊,曾庆存,晴空时大气红外遥感及其反演问题Ⅰ.理论研究.大气科学.1997,21(1):1-9
16.李俊,曾庆存,晴空时大气红外遥感及其反演问题Ⅱ.反演试验研究,大气科学,1997,21(2):214-222
17.李俊,黄思训,改进的偏差原则在大气红外遥感及反演中的应用,中国科学(D辑),2001,31(1):70-80
18.吴保锁等,NOAA极轨气象卫星大气参数的物理反演方法数值试验,红外研究,1990,9(3):233-241
19.黎光清等.东亚地区高分辨率物理反演方法数值试验:内容比较研究,大气科学,1991,15(1):84-93
20.黎光清等,东亚地区气象参数卫星遥感反演理论和方法Ⅰ:ISPRM和SPRM,气象科学,1999,57(5):594-603
21.黎光清等,东亚地区气象参数卫星遥感反演理论和方法Ⅱ:ISPRM和VSPRM2,气象科学,1999,57(6):668-680
22.方宗义,许健民,赵凤生,中国气象卫星和卫星气象研究的回顾和发展,气象学报,2004,62(5):550-560
23.金龙,人工神经网络技术发展及在大气科学领域的应用,气象科技,2004,32(6),385-392
24.江东,王建华,人工神经网络在遥感中的应用与发展,国土资源遥感,1999,2.1-7
25.曹晓钟,王强,神经网络在气象观测资料优化中的应用研究,高原气象,2002,21(1),96-101
26.杨学联,季晓阳,黄润恒等,卫星遥感资料在台风数值预报中的应用,海洋预报,2001, 18(4):1-8
27.董超华主编,气象卫星业务产品使用手册,气象出版社,1999
28.中国台风网(http://www.typhoon.gov.cn/)
29.0414号台风信息(http://www.fs121.com/record/ty0414.html)
30.高山红,2001:Kalman滤波与松弛逼近法在大气数据同化中的应用研究,博土论文,中国海洋大学
31.谢红琴,2003:MM5-卫星数据变分同化方法及气象预报应用研究,博士论文,中国海洋大学
32.陈东升,沈桐立,马革兰,何迪,气象资料同化的研究进展,南京气象学报,2004,
33.孟智勇,徐祥德,陈联寿,卫星亮温资料四维同化方案极其对“7·20”武汉特大暴雨的模拟实验,大气科学,2002,26(5):663-675。
34.张守峰,王诗文,在台风业务化系统中使用卫星云导风资料的实验,1999气象,25(8):22-25
35.潘宁,董超华,张问建,黎光清,变分同化及卫星资料同化,气象科技,2001,
36. NOAA KLM User's Guide (http://www2.ncdc.noaa.gov/docs/klm),NESDIS,1998
37. TeraScan System Operations Manual, SeaSpace Corporation
38. TeraScan Software Training Guide, SeaSpace Corporation
39. Smith W. L., An improved method for calculating tropospheric temperature and moisture from satellite radiometer measurements[J], Mort. Wea. Rev., 1968, 96: 387-396
40. Smith W. L., lterative solution of the radiation transfer equation for the temperature and absorbing gas profiles of an atmosphere[J], Appl. Opt., 1970, 9: 1993-1999
41. Fleming, H. E., and W. L. Smith, Inversion techniques for remote sensing of atmospheric temperature profiles, Fifth Symposium on Temperature, Instrument Society of America, Pittsburg, Pennsylvania, 1971, 2239-2250
42, Smith W. L. and Woolf H. M., The use of eigenvectors of statistical covariance matrices for interpreting satellite sounding radiometer observations[J], J. Atmos. Sci., 1976, 35(7): 1127-1140
43. Rodger C. D., Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation[J], Rev. Geophys. And Space Phys., 1976, 14: 609-624
44. Smith W. L. et al., Simultaneous retrieval of surface atmospheric parameters: A physical and analytically direct approach, Advancedij remote sensing retrieval methods, Deepack Publishing, Hampton, VA, 1985, 221-232
45. Smith W. L. and Woolf H. M. et al., The simultaneous retrieval export package, Technical Proceeding of the 2nd international TOVS Study Conference, 1985, 224-253
46. Smith W. L. and Woolf H. M. et al., Linear simultaneous solution for temperature and absorbing constituent profiles from radiance spectra, Applied Optics, 1991, 30(9): 1117-1123
47. Chedin, A and Scott, NA, The improved initialization inversion method[J], J. of Clim. and Appl. Meteor., 1985, 24(1): 128-143
48. Chahine, M T., A general relaxation method for inverse solution of the full radiance transfer equation[J], J. Atmos. Sci., 1972, 29: 741-74749. Fleming, H. E. er al., J. Clim and Appl. Metor., 1986, 25: 869-882
50. Susskind J, Joiner J and Chahine M T., Determination of temperature and moisture profiles in a cloudy atmosphere using AIRS/AMSU, NATO ASI Series 1993, Vol. 19., 149-161
51. Smith W L, Zhou D K and Harrison F W et al., Hyperspectral remote sensing of atmospheric profiles from satellites aircraft, Proc. of SPIE, 2001, V 4151: 94-102
52. Wark D Q, Fleming H E., Indirect measurements of atmospheric temperature profiles from satellites [J]: I. Introduction, Monthly Weather Review, 1996, 94: 351-362
53. Li J., and Huang H. L., Retrieval of atmospheric profiles from satellite sound measurements using the discrepancy principle [J], Appl. Opt., 1999,38,916-923
54. Li J., et al., International ATOVS processing package: The algorithm design and its preliminary performance, Proc. of SPIE, 1998, V 3501: 196-206
55. Li Jun et al., Global Soundings of the Atmosphere from ATOVS Measurements: The Algorithm and Validation [J], Journal of Applied Meteorology, 2000, 39(8): 1248-1268
56. Achtor Thomas et al., Software packages for direct broadcast data processing of ATOVS, MODIS and AIRS radiances, Proc. of SPIE, 2002, V 4891:546-548
57. Shi Lei, Retrieval of atmospheric temperature profiles from AMSU-A measurement using a neural network approach, Journal of Atmospheric and Oceanic Technology, 2001,18(3), 340-347
58. Jones C, P. Peterson, C. Cautier, A new method for deriving ocean surface specific humidity and air temperature: an artificial neural network approach. Journal of Applied Meteorology, 1999,38:1229-1245
59. Kondar M., N. Imasato, A. Shabata, A new method to determine near-sea surface air temperature by using satellite data, J. Geophys. Res., 1996, 101 (C6): 14349-14360
60. Anthes R. A., 1982. Tropical cyclones: their evolution, structure and effects. Meteor. Monogr. 41, Amer. Meteor. Soc, Boston, MA. pp.208.
61. Benjamin S. B., P. A. Miller (1990) An alternative sea-Level pressure reduction and a statistical comparison of geostrophic winds estimated with observed surface winds. Mon. Wea. Rev., 118,2099-2116.
62. Bratseth F. (1986) Statistical interpolation by means of successive corrections. Tellus, 38A, 439-447.
63. Chao W C, Chang L P. (1992) Development of a four-dimensional variational analysis system using the adjoint method at GLA. Part 1: dynamics. Mon. Wea. Rev., 26, 1 661-1 673.
64. Cressman G. (1959) An operational objective analysis system.. Mon. Wea. Rev., 87, 367-374.
65. Daley R. (1991) Atmospheric Data Analysis. Cambridge University Press, 457pp.
66. Dong Chaohua (1999) Handbook of satellite operational product usage. Meteorology Press (in Chinese), 273pp.
67. Evensen G (1992) Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model. J Geophys. Res., 97, 905-17 904.
68. Evensen G, Van Leeuwen P J. (1996) Assimilation of geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model. Mon Wea Rev, 124, 85-96.
69. Evensen G. (1994) Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 , 10 143-10 612.
70. Fast J. D., S. Zhong et al. (1996) Boundary layer evolution within a canyonland basin: Part II. Numerical simulations of nocturnal flows and heat budgets. J. Appl. Meteor., 35, 2162-2178.
71. Gandin L. (1963) Objective analysis of meteorological fields. Israel Program for Scientific Translation, 232pp.
72. Gao Shanhong, Zengmao Wu et al. (2000) Developments and applications of Kalman filters in meteorological data assimilation. Advance in earth sciences (in Chinese), 15, 571-575.
73. Gelaro R., C. A. Reynolds et al. (2000) A predictability study using geostationary satellite wind observations during NORPEX. Mon. Wea. Rev., 128, 3789-3807.
74. Goerss J. S., P. A. Phoebus (1992) The navy's operational atmospheric analysis. Wea. Forecasting, 7, 232-249.
75. Grell G. A., J. Dudhia et al. (1994) Description of the fifth-generation Penn state/NCAR mesoscale model (MM5). NCAR Tech. Note. NCAR/TN-398+STR.., 107pp.
76. Grell G. A., J. Dudhia, and D. R. Stauffer, 1994: Description of the fifth-generation Penn state/NCAR mesoscale model (MM5). NCAR Tech. Note. NCAR/TN-398+STR.phoon
77. Hoffman B N, Jean-Francois Louis et al. (1992) A method for implementing adjoint calculations in the discrete case. ECMWF Research Dept Tech Memo ,183pp.
78. Houtekamer P. L., H. L. Mitchell. (2001) A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation. Mon. Wea. Rev., 129(1), 123-137.
79. Jarvinen H., Jean-Noel Thepant et al. (1995) Quasi-continuous variational data assimilation. ECMWF Research Dept Tech Memo, 210pp.
80. Kalman R. E. (1960). J. Basic Eng. 82, 34-45.
81. Kalman R. E. and R. S. Bucy, R. S. (1961). J. Basic Eng. 83, 95-108.
82. Klemp J. B., D. R. Durran (1983) An upper boundary condition permitting internal gravity wave radiation in numerical mesoscale models. Mon. Wea. Rev. ,111, 430-444.
83. Lipton A. E., G. D. Modica (1999) Assimilation of visible-band satellite data for mesoscale forecasting in cloudy conditions. Mon. Wea. Rev., 127, 265-278.
84. Lorenc A. C., Ballard, S. P. et al. (2000) The Met. office global 3-Dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc, 126, 2991-3012.
85. Low-Nam, S., and C. Davis (2001) Development of a tropical cyclone bogussing scheme for the MM5 system. Preprint, The Eleventh PSU/NCAR Mesoscale Model Users' Workshop, June 25-27, Boulder, Colorado, 130-134.
86. Low-Nam, S., and C. Davis, 2001. Development of a tropical cyclone bogussing scheme for the MM5 system. Preprint, The Eleventh PSU/NCAR Mesoscale Model Users' Workshop, June 25-27, Boulder, Colorado, 130-134.
87. Meng Zhiyong, Xiangde Xu et al. (2002) T~(HH) -nudging four-dimensional data assimilation method and simulations on heavy rain process in Wuhan on 20 July, 1998. Chinese J. Atmos, Sci. (in Chinese), 26, 663-675.
88. Mitchell H. L., P. L. Houtekamer (2000) An adaptive ensemble Kalman Filter. Mon. Wea. Rev., 128,416-433.
89. Mitchell H. L, P. L. Houtekamer et al. (2002) Ensemble Size, Balance, and Model-Error Representation in an Ensemble Kalman Filter. Mon. Wea. Rev., 130, 2791-2808.
90. Nuss W. A., D. W. Titley (1994) Use of multiquadric interpolation for meteorological objective analysis. Mon. Wea. Rev., 122, 1611-1631.
91. Rabier F, Mahfouf J-F et al. (1997) Recent experimentation on 4D-Var and first results
from a simplified Kalman filter. ECMWF Research Dept Tech Memo , 240pp.
92. Rasmussen R., J. Reisner et al. (1994) Modelling winter clouds with the MM5 model. ECWMF Workshop, 317-337.
93. Reisner J., R. J. Rasmussen et al. (1998) Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124, 1071-1107.
94. Soden B. J., C. S. Velden et al. (2001) The impact of satellite winds on experimental GFDL hurricane model forecasts. Mon. Wea. Rev., 129, 835-852.
95. Staufer D. R., N. L. Seaman (1990) Use of four-dimensional data assimilation in a limited-area mesoscale model. Part 1: Experiments with synoptic-scale data. Mon. Wea. Rev., 118, 1250-1277.
96. Staufer D. R., N. L. Seaman et al. (1991) Use of four-dimensional data assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation within the planetary layer. Mon. Wea. Rev. ,119, 734-754 .
97. Tomassini G. Kelly , R. Saunders. (1999) Use and impact of satellite atmospheric motion winds on ECMWF analysis and forecasts. Mon. Wea. Rev., 127, 971-986.
98. Velden S., T. L. Older et al. (1998a) The impact of multispectral GOES-8 wind on Atlantic tropical cyclone track forecasts in 1995. Part I: datasets methodology, description, and case analysis. Mon. Wea. Rev., 126, 1202-1218.
99. Velden S., T. L. Older et al. (1998b) The impact of multispectral GOES-8 wind on Atlantic tropical cyclone track forecasts in 1995. Part II: NOGAPS forecasts. Mon. Wea. Rev., 126, 1219-1227.
100. Xiao Q., X. Zou et al. (2002) Impact of GMS-5 and GOES-9 satellite-derived winds on the prediction of aNORPEX extratropical cyclone. Mon. Wea. Rev., 130, 507-528.
101. Yucel 1., W. J. Shuttleworth, et al. (2003) Short-term performance of MM5 with cloud-cover assimilation from satellite observations. Mon. Wea. Rev. ,131, 1797-1810.
102. Zhang D. L., R.A. Anthes (1982) A high-resolution model of the planetary layer-sensitivity test and comparisons with SESAME-79 data. J. Appl. Meteor. ,21, 1594-1609.
103. Zhang Shoufeng, Shiwen Wang (1999) Applied experiments of the prediction of Typhoon tracks by using cloud motion wind vectors. Meteorological Monthly (in Chinese), 25(8), 22-25.
104. Zhang Xiaoyan, Wang Bin, Ji Zhongshen, Qingnong Xiao, and Zhang Xin, 2003: Initialization and simulation of a typhoon using 4-dimensional variational data assimilation —Research on typhoon herb,2003:
105. Zhang, X.Y., B. Wang, Z. Z. Ji, Q. Xiao, and X. Zhang, 2003. Initialization and simulation of a typhoon using 4-dimensional variational data assimilation — research on Typhoon Herb(1996), Advances in Atmospheric Sciences, 20:612-622.
106. Zou X., Q. Xiao (2000) Studies on the initialization and simulations of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57, 836-860.
107. Zou, X., and Q. Xiao, 2000. Studies on the initialization and simulations of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57: 836-860.
2.曾庆存,大气红外遥感原理,北京:科学出版社,1974年
3.张培昌,王振会,大气微波遥感原理,北京:气象出版社,1995年
4.张瑞生,大气微波遥感,济南:山东科学技术出版社,1989年
5.陈渭民,卫星气象学,北京:气象出版社,2003年
6.P.K.Rao等编,许健民等译,气象卫星——系统、资料及其在环境中的应用,北京:气象出版社,1994年
7.冯士笮等,海洋科学导论,高等教育出版社,1999年
8.吴雪宝等,大气反演参数在大气预报中的应用试验.电波科学学报.2004,19(4):469-473
9.吴雪宝,张凤英,利用高光谱红外探测资料反演大气参数,气象科技,2003,31(4): 201-205
10.李万彪,吴龙涛,张呈祥,余嘉裕,气象卫星遥感探测海面大气温度垂直廓线,北京大学学报(自然科学版),2003,39(5):656-665
11.王振会.用改进的EOF方法和卫星AMSU反演大气温度廓线的一次数值实验,南京气象学院学报,1990,13(3):410-416
12.陈洪滨,林龙福,从118.75GHz附近六通道亮温反演大气温度廓线的数值模拟研究,大气科学,2003,27(5):894-900
13.李俊,曾庆存,有云时大气红外遥感及其反演问题Ⅰ.理论研究,大气科学,1997,21(3):341-347
14.李俊,曾庆存,有云时大气红外遥感及其反演问题Ⅱ.反演试验研究,大气科学.1997,21(4):396-400
15.李俊,曾庆存,晴空时大气红外遥感及其反演问题Ⅰ.理论研究.大气科学.1997,21(1):1-9
16.李俊,曾庆存,晴空时大气红外遥感及其反演问题Ⅱ.反演试验研究,大气科学,1997,21(2):214-222
17.李俊,黄思训,改进的偏差原则在大气红外遥感及反演中的应用,中国科学(D辑),2001,31(1):70-80
18.吴保锁等,NOAA极轨气象卫星大气参数的物理反演方法数值试验,红外研究,1990,9(3):233-241
19.黎光清等.东亚地区高分辨率物理反演方法数值试验:内容比较研究,大气科学,1991,15(1):84-93
20.黎光清等,东亚地区气象参数卫星遥感反演理论和方法Ⅰ:ISPRM和SPRM,气象科学,1999,57(5):594-603
21.黎光清等,东亚地区气象参数卫星遥感反演理论和方法Ⅱ:ISPRM和VSPRM2,气象科学,1999,57(6):668-680
22.方宗义,许健民,赵凤生,中国气象卫星和卫星气象研究的回顾和发展,气象学报,2004,62(5):550-560
23.金龙,人工神经网络技术发展及在大气科学领域的应用,气象科技,2004,32(6),385-392
24.江东,王建华,人工神经网络在遥感中的应用与发展,国土资源遥感,1999,2.1-7
25.曹晓钟,王强,神经网络在气象观测资料优化中的应用研究,高原气象,2002,21(1),96-101
26.杨学联,季晓阳,黄润恒等,卫星遥感资料在台风数值预报中的应用,海洋预报,2001, 18(4):1-8
27.董超华主编,气象卫星业务产品使用手册,气象出版社,1999
28.中国台风网(http://www.typhoon.gov.cn/)
29.0414号台风信息(http://www.fs121.com/record/ty0414.html)
30.高山红,2001:Kalman滤波与松弛逼近法在大气数据同化中的应用研究,博土论文,中国海洋大学
31.谢红琴,2003:MM5-卫星数据变分同化方法及气象预报应用研究,博士论文,中国海洋大学
32.陈东升,沈桐立,马革兰,何迪,气象资料同化的研究进展,南京气象学报,2004,
33.孟智勇,徐祥德,陈联寿,卫星亮温资料四维同化方案极其对“7·20”武汉特大暴雨的模拟实验,大气科学,2002,26(5):663-675。
34.张守峰,王诗文,在台风业务化系统中使用卫星云导风资料的实验,1999气象,25(8):22-25
35.潘宁,董超华,张问建,黎光清,变分同化及卫星资料同化,气象科技,2001,
36. NOAA KLM User's Guide (http://www2.ncdc.noaa.gov/docs/klm),NESDIS,1998
37. TeraScan System Operations Manual, SeaSpace Corporation
38. TeraScan Software Training Guide, SeaSpace Corporation
39. Smith W. L., An improved method for calculating tropospheric temperature and moisture from satellite radiometer measurements[J], Mort. Wea. Rev., 1968, 96: 387-396
40. Smith W. L., lterative solution of the radiation transfer equation for the temperature and absorbing gas profiles of an atmosphere[J], Appl. Opt., 1970, 9: 1993-1999
41. Fleming, H. E., and W. L. Smith, Inversion techniques for remote sensing of atmospheric temperature profiles, Fifth Symposium on Temperature, Instrument Society of America, Pittsburg, Pennsylvania, 1971, 2239-2250
42, Smith W. L. and Woolf H. M., The use of eigenvectors of statistical covariance matrices for interpreting satellite sounding radiometer observations[J], J. Atmos. Sci., 1976, 35(7): 1127-1140
43. Rodger C. D., Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation[J], Rev. Geophys. And Space Phys., 1976, 14: 609-624
44. Smith W. L. et al., Simultaneous retrieval of surface atmospheric parameters: A physical and analytically direct approach, Advancedij remote sensing retrieval methods, Deepack Publishing, Hampton, VA, 1985, 221-232
45. Smith W. L. and Woolf H. M. et al., The simultaneous retrieval export package, Technical Proceeding of the 2nd international TOVS Study Conference, 1985, 224-253
46. Smith W. L. and Woolf H. M. et al., Linear simultaneous solution for temperature and absorbing constituent profiles from radiance spectra, Applied Optics, 1991, 30(9): 1117-1123
47. Chedin, A and Scott, NA, The improved initialization inversion method[J], J. of Clim. and Appl. Meteor., 1985, 24(1): 128-143
48. Chahine, M T., A general relaxation method for inverse solution of the full radiance transfer equation[J], J. Atmos. Sci., 1972, 29: 741-74749. Fleming, H. E. er al., J. Clim and Appl. Metor., 1986, 25: 869-882
50. Susskind J, Joiner J and Chahine M T., Determination of temperature and moisture profiles in a cloudy atmosphere using AIRS/AMSU, NATO ASI Series 1993, Vol. 19., 149-161
51. Smith W L, Zhou D K and Harrison F W et al., Hyperspectral remote sensing of atmospheric profiles from satellites aircraft, Proc. of SPIE, 2001, V 4151: 94-102
52. Wark D Q, Fleming H E., Indirect measurements of atmospheric temperature profiles from satellites [J]: I. Introduction, Monthly Weather Review, 1996, 94: 351-362
53. Li J., and Huang H. L., Retrieval of atmospheric profiles from satellite sound measurements using the discrepancy principle [J], Appl. Opt., 1999,38,916-923
54. Li J., et al., International ATOVS processing package: The algorithm design and its preliminary performance, Proc. of SPIE, 1998, V 3501: 196-206
55. Li Jun et al., Global Soundings of the Atmosphere from ATOVS Measurements: The Algorithm and Validation [J], Journal of Applied Meteorology, 2000, 39(8): 1248-1268
56. Achtor Thomas et al., Software packages for direct broadcast data processing of ATOVS, MODIS and AIRS radiances, Proc. of SPIE, 2002, V 4891:546-548
57. Shi Lei, Retrieval of atmospheric temperature profiles from AMSU-A measurement using a neural network approach, Journal of Atmospheric and Oceanic Technology, 2001,18(3), 340-347
58. Jones C, P. Peterson, C. Cautier, A new method for deriving ocean surface specific humidity and air temperature: an artificial neural network approach. Journal of Applied Meteorology, 1999,38:1229-1245
59. Kondar M., N. Imasato, A. Shabata, A new method to determine near-sea surface air temperature by using satellite data, J. Geophys. Res., 1996, 101 (C6): 14349-14360
60. Anthes R. A., 1982. Tropical cyclones: their evolution, structure and effects. Meteor. Monogr. 41, Amer. Meteor. Soc, Boston, MA. pp.208.
61. Benjamin S. B., P. A. Miller (1990) An alternative sea-Level pressure reduction and a statistical comparison of geostrophic winds estimated with observed surface winds. Mon. Wea. Rev., 118,2099-2116.
62. Bratseth F. (1986) Statistical interpolation by means of successive corrections. Tellus, 38A, 439-447.
63. Chao W C, Chang L P. (1992) Development of a four-dimensional variational analysis system using the adjoint method at GLA. Part 1: dynamics. Mon. Wea. Rev., 26, 1 661-1 673.
64. Cressman G. (1959) An operational objective analysis system.. Mon. Wea. Rev., 87, 367-374.
65. Daley R. (1991) Atmospheric Data Analysis. Cambridge University Press, 457pp.
66. Dong Chaohua (1999) Handbook of satellite operational product usage. Meteorology Press (in Chinese), 273pp.
67. Evensen G (1992) Using the extended Kalman filter with a multilayer quasi-geostrophic ocean model. J Geophys. Res., 97, 905-17 904.
68. Evensen G, Van Leeuwen P J. (1996) Assimilation of geosat altimeter data for the Agulhas current using the ensemble Kalman filter with a quasigeostrophic model. Mon Wea Rev, 124, 85-96.
69. Evensen G. (1994) Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res., 99 , 10 143-10 612.
70. Fast J. D., S. Zhong et al. (1996) Boundary layer evolution within a canyonland basin: Part II. Numerical simulations of nocturnal flows and heat budgets. J. Appl. Meteor., 35, 2162-2178.
71. Gandin L. (1963) Objective analysis of meteorological fields. Israel Program for Scientific Translation, 232pp.
72. Gao Shanhong, Zengmao Wu et al. (2000) Developments and applications of Kalman filters in meteorological data assimilation. Advance in earth sciences (in Chinese), 15, 571-575.
73. Gelaro R., C. A. Reynolds et al. (2000) A predictability study using geostationary satellite wind observations during NORPEX. Mon. Wea. Rev., 128, 3789-3807.
74. Goerss J. S., P. A. Phoebus (1992) The navy's operational atmospheric analysis. Wea. Forecasting, 7, 232-249.
75. Grell G. A., J. Dudhia et al. (1994) Description of the fifth-generation Penn state/NCAR mesoscale model (MM5). NCAR Tech. Note. NCAR/TN-398+STR.., 107pp.
76. Grell G. A., J. Dudhia, and D. R. Stauffer, 1994: Description of the fifth-generation Penn state/NCAR mesoscale model (MM5). NCAR Tech. Note. NCAR/TN-398+STR.phoon
77. Hoffman B N, Jean-Francois Louis et al. (1992) A method for implementing adjoint calculations in the discrete case. ECMWF Research Dept Tech Memo ,183pp.
78. Houtekamer P. L., H. L. Mitchell. (2001) A Sequential Ensemble Kalman Filter for Atmospheric Data Assimilation. Mon. Wea. Rev., 129(1), 123-137.
79. Jarvinen H., Jean-Noel Thepant et al. (1995) Quasi-continuous variational data assimilation. ECMWF Research Dept Tech Memo, 210pp.
80. Kalman R. E. (1960). J. Basic Eng. 82, 34-45.
81. Kalman R. E. and R. S. Bucy, R. S. (1961). J. Basic Eng. 83, 95-108.
82. Klemp J. B., D. R. Durran (1983) An upper boundary condition permitting internal gravity wave radiation in numerical mesoscale models. Mon. Wea. Rev. ,111, 430-444.
83. Lipton A. E., G. D. Modica (1999) Assimilation of visible-band satellite data for mesoscale forecasting in cloudy conditions. Mon. Wea. Rev., 127, 265-278.
84. Lorenc A. C., Ballard, S. P. et al. (2000) The Met. office global 3-Dimensional variational data assimilation scheme. Quart. J. Roy. Meteor. Soc, 126, 2991-3012.
85. Low-Nam, S., and C. Davis (2001) Development of a tropical cyclone bogussing scheme for the MM5 system. Preprint, The Eleventh PSU/NCAR Mesoscale Model Users' Workshop, June 25-27, Boulder, Colorado, 130-134.
86. Low-Nam, S., and C. Davis, 2001. Development of a tropical cyclone bogussing scheme for the MM5 system. Preprint, The Eleventh PSU/NCAR Mesoscale Model Users' Workshop, June 25-27, Boulder, Colorado, 130-134.
87. Meng Zhiyong, Xiangde Xu et al. (2002) T~(HH) -nudging four-dimensional data assimilation method and simulations on heavy rain process in Wuhan on 20 July, 1998. Chinese J. Atmos, Sci. (in Chinese), 26, 663-675.
88. Mitchell H. L., P. L. Houtekamer (2000) An adaptive ensemble Kalman Filter. Mon. Wea. Rev., 128,416-433.
89. Mitchell H. L, P. L. Houtekamer et al. (2002) Ensemble Size, Balance, and Model-Error Representation in an Ensemble Kalman Filter. Mon. Wea. Rev., 130, 2791-2808.
90. Nuss W. A., D. W. Titley (1994) Use of multiquadric interpolation for meteorological objective analysis. Mon. Wea. Rev., 122, 1611-1631.
91. Rabier F, Mahfouf J-F et al. (1997) Recent experimentation on 4D-Var and first results
from a simplified Kalman filter. ECMWF Research Dept Tech Memo , 240pp.
92. Rasmussen R., J. Reisner et al. (1994) Modelling winter clouds with the MM5 model. ECWMF Workshop, 317-337.
93. Reisner J., R. J. Rasmussen et al. (1998) Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124, 1071-1107.
94. Soden B. J., C. S. Velden et al. (2001) The impact of satellite winds on experimental GFDL hurricane model forecasts. Mon. Wea. Rev., 129, 835-852.
95. Staufer D. R., N. L. Seaman (1990) Use of four-dimensional data assimilation in a limited-area mesoscale model. Part 1: Experiments with synoptic-scale data. Mon. Wea. Rev., 118, 1250-1277.
96. Staufer D. R., N. L. Seaman et al. (1991) Use of four-dimensional data assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation within the planetary layer. Mon. Wea. Rev. ,119, 734-754 .
97. Tomassini G. Kelly , R. Saunders. (1999) Use and impact of satellite atmospheric motion winds on ECMWF analysis and forecasts. Mon. Wea. Rev., 127, 971-986.
98. Velden S., T. L. Older et al. (1998a) The impact of multispectral GOES-8 wind on Atlantic tropical cyclone track forecasts in 1995. Part I: datasets methodology, description, and case analysis. Mon. Wea. Rev., 126, 1202-1218.
99. Velden S., T. L. Older et al. (1998b) The impact of multispectral GOES-8 wind on Atlantic tropical cyclone track forecasts in 1995. Part II: NOGAPS forecasts. Mon. Wea. Rev., 126, 1219-1227.
100. Xiao Q., X. Zou et al. (2002) Impact of GMS-5 and GOES-9 satellite-derived winds on the prediction of aNORPEX extratropical cyclone. Mon. Wea. Rev., 130, 507-528.
101. Yucel 1., W. J. Shuttleworth, et al. (2003) Short-term performance of MM5 with cloud-cover assimilation from satellite observations. Mon. Wea. Rev. ,131, 1797-1810.
102. Zhang D. L., R.A. Anthes (1982) A high-resolution model of the planetary layer-sensitivity test and comparisons with SESAME-79 data. J. Appl. Meteor. ,21, 1594-1609.
103. Zhang Shoufeng, Shiwen Wang (1999) Applied experiments of the prediction of Typhoon tracks by using cloud motion wind vectors. Meteorological Monthly (in Chinese), 25(8), 22-25.
104. Zhang Xiaoyan, Wang Bin, Ji Zhongshen, Qingnong Xiao, and Zhang Xin, 2003: Initialization and simulation of a typhoon using 4-dimensional variational data assimilation —Research on typhoon herb,2003:
105. Zhang, X.Y., B. Wang, Z. Z. Ji, Q. Xiao, and X. Zhang, 2003. Initialization and simulation of a typhoon using 4-dimensional variational data assimilation — research on Typhoon Herb(1996), Advances in Atmospheric Sciences, 20:612-622.
106. Zou X., Q. Xiao (2000) Studies on the initialization and simulations of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57, 836-860.
107. Zou, X., and Q. Xiao, 2000. Studies on the initialization and simulations of a mature hurricane using a variational bogus data assimilation scheme. J. Atmos. Sci., 57: 836-860.