MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS: METHODS AND IDEAL TESTS

DING Wei-yu; WAN Qi-lin; ZHANG Chen-zhong; CHEN Zi-tong; HUANG Yan-yan

Abstract:

Clouds have important effects on the infrared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, but also minimize the initial error of cloud parameters by adjusting part of the infrared radiances data. On the basis of the Grapes-3D-var (Global and Regional Assimilation and Prediction Enhanced System), cloud liquid water, cloud ice water and cloud cover are added as the governing variables in the assimilation. Under the conditions of clear sky, partly cloudy cover and totally cloudy cover, the brightness temperature of 16 MODIS channels are assimilated respectively in ideal tests. Results show that when the simulated background brightness temperatures are lower than the observation, the analyzed field will increase the simulated brightness temperature by increasing its temperature and reducing its moisture, cloud liquid water, cloud ice water, and cloud cover. The simulated brightness temperature can be reduced if adjustment is made in the contrary direction. The adjustment of the temperature and specific humidity under the clear sky conditions conforms well to the design of MODIS channels, but it is weakened for levels under cloud layers. The ideal tests demonstrate that by simultaneously adding both cloud parameters and atmospheric parameters as governing variables during the assimilation of infrared radiances, both the cloud parameters and atmospheric parameters can be adjusted using the observed infrared radiances and conventional meteorological elements to make full use of the infrared observations.

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DING Wei-yu, WAN Qi-lin, ZHANG Chen-zhong, et al. MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS: METHODS AND IDEAL TESTS [J]. Journal of Tropical Meteorology, 2010, 16(4): 313-324.

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MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS: METHODS AND IDEAL TESTS

Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration

Abstract: Clouds have important effects on the infrared radiances transmission in that the inclusion of cloud effects in data assimilation can not only improve the quality of the assimilated atmospheric parameters greatly, but also minimize the initial error of cloud parameters by adjusting part of the infrared radiances data. On the basis of the Grapes-3D-var (Global and Regional Assimilation and Prediction Enhanced System), cloud liquid water, cloud ice water and cloud cover are added as the governing variables in the assimilation. Under the conditions of clear sky, partly cloudy cover and totally cloudy cover, the brightness temperature of 16 MODIS channels are assimilated respectively in ideal tests. Results show that when the simulated background brightness temperatures are lower than the observation, the analyzed field will increase the simulated brightness temperature by increasing its temperature and reducing its moisture, cloud liquid water, cloud ice water, and cloud cover. The simulated brightness temperature can be reduced if adjustment is made in the contrary direction. The adjustment of the temperature and specific humidity under the clear sky conditions conforms well to the design of MODIS channels, but it is weakened for levels under cloud layers. The ideal tests demonstrate that by simultaneously adding both cloud parameters and atmospheric parameters as governing variables during the assimilation of infrared radiances, both the cloud parameters and atmospheric parameters can be adjusted using the observed infrared radiances and conventional meteorological elements to make full use of the infrared observations.

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[1]	DING Wei-yu, WAN Qi-lin, DUAN Yi-hong. 3D-varassimilation of TRMM rain rate and its impact on the typhoonDujuan (0313) forecast [J]. Chin. J. Atmos. Sci., 2005, 29(4):600-608.
[2]	ZHUANG Zhao-rong, XUE Ji-shan. Assimilation ofcloud-derived winds and its impact on typhoon forecast [J]. J.Trop. Meteor., 2004, 20(3): 225-236.
[3]	ENTING I G. Inverse problems in atmospheric constituenttransport [M]. Cambridge: Cambridge University Press, 2002.
[4]	VUKICEVIC T, BRASWELL B H, SCHEIMEL D. Adiagnostic study of temperature controls on global terrestrialcarbon exchange [J]. Tellus (Serial B), 2001, 53: 150-170.
[5]	LI Jun, MENZELB W P, SCHREINERC A J. Variationalretrieval of cloud parameters from GOES sounder longwavecloudy radiance measurements [J]. J. Appl. Meteor., 2001,40(3): 312-330.
[6]	ANDERSSON E, PAILLEUX J, THéPAUT J N, et al. Useof cloud-cleared radiances in three/four-dimensionalvariational data assimilation [J]. Quart. J. Roy. Meteor. Soc.,1994. 120: 627-653.
[7]	OKAMOTO K, DERBER J C. Assimilation of SSM/Iradiances in the NCEP global data assimilation system [J].Mon. Wea. Rev., 2006, 134: 2 612-2 631.
[8]	WANG Zong-hao. Direct use of satellite soundingradiances in numerical weather prediction [J]. J. Appl. Meteor.Sci., 1995, 6(1): 101-108.
[9]	PAN Ning, DONG Chao-hua, ZHANG Wen-jian. Theexperiments on direct assimilating atovs radiance [J]. ActaMeteor. Sinica, 2003, 61(2): 226-236.
[10]	ZHANG Hua, XUE Jishan, ZHU Guo-fu, et al.Application of direct assimilation of ATOVS microwaveradiances to typhoon track prediction [J]. Adv. Atmos. Sci.,2004, 21(2): 283-290.
[11]	THOMAS J G, HERTENSTEIN R, VUKIEVI T. Anall-weather observational operator for radiance dataassimilation with mesoscale forecast models [J]. Mon. Wea.Rev., 2002. 130(7): 1 882-1 897.
[12]	DING Wei-yu, WAN Qi-lin. The simulation ofCHANCHU typhoon infrared channels [J]. Chin. J. Atmos.Sci., 2008, 32(3): 572-580.
[13]	VUKICEVIC T, GREENWALD T, ZUPANSKI M, et al.Mesoscale cloud state estimation from visible and infraredsatellite radiances [J]. Mon. Wea. Rev., 2004, 132(12): 3066-3 077.
[14]	SEEMANN S W, LI J, MENZEL W P, et al. Operationalretrieval of atmospheric temperature, moisture, and ozonefrom MODIS infrared radiances [J]. J. Appl. Meteor., 2003, 42:1 072-1 091.
[15]	BORMANN N, THéPAUT J-N. Impact of MODIS polarwinds in ECMWF's 4DVAR data assimilation system [J]. Mon.Wea. Rev., 2004. 132(4): 929-940.
[16]	JAKOB C. Cloud cover in the ECMWF reanalysis [J]. J.C l i m a t e , 1 9 9 9 , 1 2 ( 4 ) : 9 4 7 - 9 5 9 .
[17]	PARK S K, DROEGEMEIER K. Sensitivity analysis of a3D convective storm: implications for variational dataassimilation and forecast error [J]. Mon. Wea. Rev., 2000,128(1): 140-159.
[18]	LIEBE H J. MPM-an atmospheric millimeter-wavepropagation model [J]. Int. J. Infrared Millim. Waves, 1989,10(6): 631-650.
[19]	SMITH E A, SHI L. Surface forcing of the infraredcooling profile over the Tibetan plateau Part I: Influence ofrelative longwave radiative heating at high altitude [J]. J.Atmos. Sci., 49: 805-822.
[20]	RAISANEN P. Effective longwave cloud fraction andmaximum-random overlap of clouds: A problem and asolution[J]. Mon. Wea. Rev., 1998, 126, 3 336-3 340.
[21]	MORCRETTE J J. Radiation and cloud radiativeproperties in the ECMWF forecasting system [J]. J.Geophys.Res., 1991, 96(D): 9 121-9 132.

MODIS BRIGHTNESS TEMPERATURE DATA ASSIMILATION UNDER CLOUDY CONDITIONS: METHODS AND IDEAL TESTS

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