Cloud type comparisons of AIRS, CloudSat, and CALIPSO cloud height and amount

被引:86
作者
Kahn, B. H. [1 ]
Chahine, M. T. [1 ]
Stephens, G. L. [2 ]
Mace, G. G. [3 ]
Marchand, R. T. [4 ]
Wang, Z. [5 ]
Barnet, C. D. [6 ]
Eldering, A. [1 ]
Holz, R. E. [7 ]
Kuehn, R. E. [8 ]
Vane, D. G. [1 ]
机构
[1] CALTECH, Jet Prop Lab, Pasadena, CA USA
[2] Colorado State Univ, Dept Atmospher Sci, Ft Collins, CO 80523 USA
[3] Univ Utah, Dept Meteorol, Salt Lake City, UT 84112 USA
[4] Univ Washington, Joint Inst Study Atmosphere & Ocean, Seattle, WA 98195 USA
[5] Univ Wyoming, Dept Atmospher Sci, Laramie, WY 82071 USA
[6] NOAA, NESDIS, Silver Spring, MD USA
[7] Univ Wisconsin, CIMSS, Madison, WI USA
[8] NASA, Langley Res Ctr, Hampton, VA 23665 USA
关键词
D O I
10.5194/acp-8-1231-2008
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The precision of the two-layer cloud height fields derived from the Atmospheric Infrared Sounder (AIRS) is explored and quantified for a five-day set of observations. Coincident profiles of vertical cloud structure by CloudSat, a 94 GHz profiling radar, and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), are compared to AIRS for a wide range of cloud types. Bias and variability in cloud height differences are shown to have dependence on cloud type, height, and amount, as well as whether CloudSat or CALIPSO is used as the comparison standard. The CloudSat-AIRS biases and variability range from 4.3 to 0.5 +/- 1.2-3.6 km for all cloud types. Likewise, the CALIPSO-AIRS biases range from 0.6-3.0 +/- 1.2-3.6 km (-5.8 to -0.2 +/- 0.5-2.7 km) for clouds >= 7 km (<7 km). The upper layer of AIRS has the greatest sensitivity to Altocumulus, Altostratus, Cirrus, Cumulonimbus, and Nimbostratus, whereas the lower layer has the greatest sensitivity to Cumulus and Stratocumulus. Although the bias and variability generally decrease with increasing cloud amount, the ability of AIRS to constrain cloud occurrence, height, and amount is demonstrated across all cloud types for many geophysical conditions. In particular, skill is demonstrated for thin Cirrus, as well as some Cumulus and Stratocumulus, cloud types infrared sounders typically struggle to quantify. Furthermore, some improvements in the AIRS Version 5 operational retrieval algorithm are demonstrated. However, limitations in AIRS cloud retrievals are also revealed, including the existence of spurious Cirrus near the tropopause and low cloud layers within Cumulonimbus and Nimbostratus clouds. Likely causes of spurious clouds are identified and the potential for further improvement is discussed.
引用
收藏
页码:1231 / 1248
页数:18
相关论文
共 68 条
[51]   The cloudsat mission and the a-train - A new dimension of space-based observations of clouds and precipitation [J].
Stephens, GL ;
Vane, DG ;
Boain, RJ ;
Mace, GG ;
Sassen, K ;
Wang, ZE ;
Illingworth, AJ ;
O'Connor, EJ ;
Rossow, WB ;
Durden, SL ;
Miller, SD ;
Austin, RT ;
Benedetti, A ;
Mitrescu, C .
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2002, 83 (12) :1771-1790
[52]   Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds [J].
Susskind, J ;
Barnet, CD ;
Blaisdell, JM .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41 (02) :390-409
[53]   Accuracy of geophysical parameters derived from Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit as a function of fractional cloud cover [J].
Susskind, Joel ;
Barnet, Chris ;
Blaisdell, John ;
Iredell, Lena ;
Keita, Fricky ;
Kouvaris, Lou ;
Molnar, Gyula ;
Chahine, Moustafa .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2006, 111 (D9)
[54]   Comparison of NOAA's operational AVHRR-derived cloud amount to other satellite-derived cloud climatologies [J].
Thomas, SM ;
Heidinger, AK ;
Pavolonis, MJ .
JOURNAL OF CLIMATE, 2004, 17 (24) :4805-4822
[55]   Atmospheric Radiation Measurement site atmospheric state best estimates for Atmospheric Infrared Sounder temperature and water vapor retrieval validation [J].
Tobin, DC ;
Revercomb, HE ;
Knuteson, RO ;
Lesht, BM ;
Strow, LL ;
Hannon, SE ;
Feltz, WF ;
Moy, LA ;
Fetzer, EJ ;
Cress, TS .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2006, 111 (D9)
[56]  
Vaughan M. A., CALIOP ALGORITHM T 2
[57]  
Wang Z, 2001, J APPL METEOROL, V40, P1665, DOI 10.1175/1520-0450(2001)040<1665:CTAMPR>2.0.CO
[58]  
2
[59]   Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models [J].
Webb, M ;
Senior, C ;
Bony, S ;
Morcrette, JJ .
CLIMATE DYNAMICS, 2001, 17 (12) :905-922
[60]   Comparison of AIRS, MODIS, CloudSat and CALIPSO cloud top height retrievals [J].
Weisz, Elisabeth ;
Li, Jun ;
Menzel, W. Paul ;
Heidinger, Andrew K. ;
Kahn, Brian H. ;
Liu, Chian-Yi .
GEOPHYSICAL RESEARCH LETTERS, 2007, 34 (17)