Editorial Type:
Article
Article Type:
Research Article

A Physically Based Algorithm for Non-Blackbody Correction of Cloud-Top Temperature and Application to Convection Study

Chunpeng Wang Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan

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Zhengzhao Johnny Luo Department of Earth and Atmospheric Sciences, and NOAA Cooperative Remote Sensing Science and Technology Center, City College of New York, New York, New York

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Xiuhong Chen Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan

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Xiping Zeng Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Wei-Kuo Tao Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

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Xianglei Huang Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan

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Print Publication:
01 Jul 2014
DOI:
https://doi.org/10.1175/JAMC-D-13-0331.1
Page(s):
1844–1857
Restricted access

Abstract

Cloud-top temperature (CTT) is an important parameter for convective clouds and is usually different from the 11-μm brightness temperature due to non-blackbody effects. This paper presents an algorithm for estimating convective CTT by using simultaneous passive [Moderate Resolution Imaging Spectroradiometer (MODIS)] and active [CloudSat + Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements of clouds to correct for the non-blackbody effect. To do this, a weighting function of the MODIS 11-μm band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat and CALIPSO retrievals and temperature and humidity profiles based on ECMWF analyses into a radiation transfer model. Among 16 837 tropical deep convective clouds observed by CloudSat in 2008, the averaged effective emission level (EEL) of the 11-μm channel is located at optical depth ~0.72, with a standard deviation of 0.3. The distance between the EEL and cloud-top height determined by CloudSat is shown to be related to a parameter called cloud-top fuzziness (CTF), defined as the vertical separation between −30 and 10 dBZ of CloudSat radar reflectivity. On the basis of these findings a relationship is then developed between the CTF and the difference between MODIS 11-μm brightness temperature and physical CTT, the latter being the non-blackbody correction of CTT. Correction of the non-blackbody effect of CTT is applied to analyze convective cloud-top buoyancy. With this correction, about 70% of the convective cores observed by CloudSat in the height range of 6–10 km have positive buoyancy near cloud top, meaning clouds are still growing vertically, although their final fate cannot be determined by snapshot observations.

Corresponding author address: Chunpeng Wang, Dept. of Atmospheric, Oceanic and Space Sciences, University of Michigan, 2455 Hayward St., Ann Arbor, MI 48109-2143. E-mail: cpwang@umich.edu

Abstract

Cloud-top temperature (CTT) is an important parameter for convective clouds and is usually different from the 11-μm brightness temperature due to non-blackbody effects. This paper presents an algorithm for estimating convective CTT by using simultaneous passive [Moderate Resolution Imaging Spectroradiometer (MODIS)] and active [CloudSat + Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO)] measurements of clouds to correct for the non-blackbody effect. To do this, a weighting function of the MODIS 11-μm band is explicitly calculated by feeding cloud hydrometer profiles from CloudSat and CALIPSO retrievals and temperature and humidity profiles based on ECMWF analyses into a radiation transfer model. Among 16 837 tropical deep convective clouds observed by CloudSat in 2008, the averaged effective emission level (EEL) of the 11-μm channel is located at optical depth ~0.72, with a standard deviation of 0.3. The distance between the EEL and cloud-top height determined by CloudSat is shown to be related to a parameter called cloud-top fuzziness (CTF), defined as the vertical separation between −30 and 10 dBZ of CloudSat radar reflectivity. On the basis of these findings a relationship is then developed between the CTF and the difference between MODIS 11-μm brightness temperature and physical CTT, the latter being the non-blackbody correction of CTT. Correction of the non-blackbody effect of CTT is applied to analyze convective cloud-top buoyancy. With this correction, about 70% of the convective cores observed by CloudSat in the height range of 6–10 km have positive buoyancy near cloud top, meaning clouds are still growing vertically, although their final fate cannot be determined by snapshot observations.

Corresponding author address: Chunpeng Wang, Dept. of Atmospheric, Oceanic and Space Sciences, University of Michigan, 2455 Hayward St., Ann Arbor, MI 48109-2143. E-mail: cpwang@umich.edu
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  • Casey, S. P. F., E. J. Fetzer, and B. H. Kahn, 2012: Revised identification of tropical oceanic cumulus congestus as viewed by CloudSat. Atmos. Chem. Phys., 12, 1587–1595, doi:10.5194/acp-12-1587-2012.

    • Search Google Scholar
    • Export Citation

  • Deng, M., G. G. Mace, Z. Wang, and H. Okamoto, 2010: Tropical Composition, Cloud and Climate Coupling Experiment validation for cirrus cloud profiling retrieval using CloudSat radar and CALIPSO lidar. J. Geophys. Res., 115, D00J15, doi:10.1029/2009JD013104.

    • Search Google Scholar
    • Export Citation

  • Deng, M., G. G. Mace, Z. Wang, and R. P. Lawson, 2013: Evaluation of several A-Train ice cloud retrieval products with in situ measurements collected during the SPARTICUS campaign. J. Appl. Meteor. Climatol.,52, 1014–1030, doi:10.1175/JAMC-D-12-054.1.

  • Goody, R. M., and Y. L. Yung, 1995: Atmospheric Radiation: Theoretical Basis. 2nd ed. Oxford University Press, 519 pp.

  • Haynes, J. M., R. T. Marchand, Z. Luo, A. Bodas-Salcedo, and G. L. Stephens, 2007: A multi- purpose radar simulation package: QuickBeam. Bull. Amer. Meteor. Soc., 88, 1723–1727, doi:10.1175/BAMS-88-11-1723.

    • Search Google Scholar
    • Export Citation

  • Johnson, R. H., T. M. Rickenbach, S. A. Rutledge, P. E. Ciesielski, and W. H. Schubert, 1999: Trimodal characteristics of tropical convection. J. Climate, 12, 2397–2418, doi:10.1175/1520-0442(1999)012<2397:TCOTC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • King, M. D., Y. Kaufman, W. P. Menzel, and D. Tanré, 1992: Remote sensing of cloud, aerosol, and water vapor properties from the Moderate Resolution Imaging Spectroradiometer (MODIS). IEEE Trans. Geosci. Remote Sens., 30, 2–27, doi:10.1109/36.124212.

    • Search Google Scholar
    • Export Citation

  • Klemp, J. B., and R. B. Wilhelmson, 1978: The simulation of three-dimensional convective storm dynamics. J. Atmos. Sci., 35, 1070–1096, doi:10.1175/1520-0469(1978)035<1070:TSOTDC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Lang, S., W.-K. Tao, J. Simpson, and B. Ferrier, 2003: Modeling of convective–stratiform precipitation processes: Sensitivity to partitioning methods. J. Appl. Meteor., 42, 505–527, doi:10.1175/1520-0450(2003)042<0505:MOCSPP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Liu, X., W. L. Smith, D. K. Zhou, and A. Larar, 2006: Principal component-based radiative transfer model for hyperspectral sensors: Theoretical concept. Appl. Opt., 45, 201–209, doi:10.1364/AO.45.000201.

    • Search Google Scholar
    • Export Citation

  • Liu, X., D. K. Zhou, A. M. Larar, W. L. Smith, P. Schluessel, S. M. Newman, J. P. Taylor, and W. Wu, 2009: Retrieval of atmospheric profiles and cloud properties from IASI spectra using super-channels. Atmos. Chem. Phys.,9, 9121–9142, doi:10.5194/acp-9-9121-2009.

  • Luo, Y., R. Zhang, W. Qian, Z. Luo, and X. Hu, 2011: Intercomparison of deep convection over the Tibetan Plateau–Asian monsoon region and subtropical North America in boreal summer using CloudSat/CALIPSO data. J. Climate, 24, 2164–2177, doi:10.1175/2010JCLI4032.1.

    • Search Google Scholar
    • Export Citation

  • Luo, Z., G. Y. Liu, and G. L. Stephens, 2008: CloudSat adding new insight into tropical penetrating convection. Geophys. Res. Lett., 35, L19819, doi:10.1029/2008GL035330.

    • Search Google Scholar
    • Export Citation

  • Luo, Z., G. Y. Liu, and G. L. Stephens, 2010: Use of A-Train data to estimate convective buoyancy and entrainment rate. Geophys. Res. Lett., 37, L09804, doi:10.1029/2010GL042904.

    • Search Google Scholar
    • Export Citation

  • May, P. T., J. H. Mather, G. Vaughan, K. N. Bower, C. Jakob, G. M. McFarquhar, and G. G. Mace, 2008: The Tropical Warm Pool International Cloud Experiment. Bull. Amer. Meteor. Soc., 89, 629–645, doi:10.1175/BAMS-89-5-629.

    • Search Google Scholar
    • Export Citation

  • McGill, M. J., L. Li, W. D. Hart, G. M. Heymsfield, D. L. Hlavka, P. E. Racette, L. Tian, M. A. Vaughan, and D. M. Winker, 2004: Combined lidar-radar remote sensing: Initial results from CRYSTAL-FACE. J. Geophys. Res., 109, D07203, doi:10.1029/2003JD004030.

    • Search Google Scholar
    • Export Citation

  • Menzel, W. P., and Coauthors, 2008: MODIS global cloud-top pressure and amount estimation: Algorithm description and results. J. Appl. Meteor. Climatol., 47, 1175–1198, doi:10.1175/2007JAMC1705.1.

    • Search Google Scholar
    • Export Citation

  • Minnis, P., P. W. Heck, and E. F. Harrison, 1990: The 27–28 October 1986 FIRE IFO cirrus case study: Cloud parameter fields derived from satellite data. Mon. Wea. Rev., 118, 2426–2447, doi:10.1175/1520-0493(1990)118<2426:TOFICC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Minnis, P., C. R. Yost, S. Sun-Mack, and Y. Chen, 2008: Estimating the top altitude of optically thick ice clouds from thermal infrared satellite observations using CALIPSO data. Geophys. Res. Lett., 35, L12801, doi:10.1029/2008GL033947.

    • Search Google Scholar
    • Export Citation

  • Minnis, P., and Coauthors, 2012: Simulations of infrared radiances over a deep convective cloud system observed during TC4: Potential for enhancing nocturnal ice cloud retrievals. Remote Sens., 4, 3022–3054, doi:10.3390/rs4103022.

    • Search Google Scholar
    • Export Citation

  • Niu, J. G., Y. Ping, H. L. Huang, J. E. Davies, J. Li, B. A. Baumc, and Y. X. Hu, 2007: A fast infrared radiative transfer model for overlapping clouds. J. Quant. Spectrosc. Radiat. Transf., 103, 447–459, doi:10.1016/j.jqsrt.2006.05.009.

    • Search Google Scholar
    • Export Citation

  • Okamoto, H., S. Iwasaki, M. Yasui, H. Horie, H. Kuroiwa, and H. Kumagai, 2003: An algorithm for retrieval of cloud microphysics using 95-GHz cloud radar and lidar. J. Geophys. Res.,108, 4226, doi:10.1029/2001JD001225.

  • Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE Trans. Geosci. Remote Sens., 41, 459–473, doi:10.1109/TGRS.2002.808301.

    • Search Google Scholar
    • Export Citation

  • Rossow, W. B., A. W. Walker, D. E. Beuschel, and M. D. Roiter, 1996: International Satellite Cloud Climatology Project (ISCCP) Documentation of New Cloud Datasets. WMO/TD-737, World Climate Research Programme, Geneva, Switzerland, 115 pp. [Available online at http://isccp.giss.nasa.gov/docs/documents.html.]

  • Sassen, K., Z. Wang, and D. Liu, 2009: Cirrus clouds and deep convection in the tropics: Insights from CALIPSO and CloudSat. J. Geophys. Res., 114, D00H06, doi:10.1029/2009JD011916.

    • Search Google Scholar
    • Export Citation

  • Segelstein, D. J., 1981: The complex refractive index of water. M.S. thesis, Dept. of Physics, University of Missouri—Kansas City, 167 pp.

  • Sherwood, S. C., J.-H. Chae, P. Minnis, and M. McGill, 2004: Underestimation of deep convective cloud tops by thermal imagery. Geophys. Res. Lett., 31, L11102, doi:10.1029/2004GL019699.

    • Search Google Scholar
    • Export Citation

  • Soong, S.-T., and Y. Ogura, 1980: Response of trade wind cumuli to large-scale processes. J. Atmos. Sci., 37, 2035–2050, doi:10.1175/1520-0469(1980)037<2035:ROTCTL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Stamnes, K., S. C. Tsay, W. Wiscombe, and K. Jayaweera, 1988: A numerically stable algorithm for discrete-ordinate-method radiative transfer in multiple scattering and emitting layered media. Appl. Opt., 27, 2502–2509, doi:10.1364/AO.27.002502.

    • Search Google Scholar
    • Export Citation

  • Stephens, G. L., 1994: Remote Sensing of the Lower Atmosphere: An Introduction. Oxford University Press, 544 pp.

  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 1771–1790, doi:10.1175/BAMS-83-12-1771.

    • Search Google Scholar
    • Export Citation

  • Stephens, G. L., and Coauthors, 2008: CloudSat mission: Performance and early science after the first year of operation. J. Geophys. Res., 113, D00A18, doi:10.1029/2008JD009982.

    • Search Google Scholar
    • Export Citation

  • Takayabu, Y. N., S. Shige, W.-K. Tao, and N. Hirota, 2010: Shallow and deep latent heating modes over tropical oceans observed with TRMM PR spectral latent heating data. J. Climate, 23, 2030–2046, doi:10.1175/2009JCLI3110.1.

    • Search Google Scholar
    • Export Citation

  • Tao, W.-K., and J. Simpson, 1993: Goddard Cumulus Ensemble model: Part I. Model description. Terr. Atmos. Ocean. Sci., 4, 35–72.

  • Tao, W.-K., and Coauthors, 2003: Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model. Meteor. Atmos. Phys.,82, 97–137. [Available online at http://grims-model.org/front/bbs/paper/mps-4/MPS_2002-1_Tao_et_al.pdf.]

  • Wang, C., Z. J. Luo, and X. Huang, 2011: Parallax correction in collocating CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations: Method and application to convection study. J. Geophys. Res., 116, D17201, doi:10.1029/2011JD016097.

    • Search Google Scholar
    • Export Citation

  • Warren, S. G., 1984: Optical constants of ice from the ultraviolet to the microwave. Appl. Opt., 23, 1206–1225, doi:10.1364/AO.23.001206.

    • Search Google Scholar
    • Export Citation

  • Wei, H., P. Yang, J. Li, B. A. Baum, H.-L. Huang, S. Platnick, Y. Hu, and L. Strow, 2004: Retrieval of semitransparent ice cloud optical thickness from Atmospheric Infrared Sounder (AIRS) measurements. IEEE Trans. Geosci. Remote Sens., 42, 2254–2267, doi:10.1109/TGRS.2004.833780.

    • Search Google Scholar
    • Export Citation

  • Winker, D. M., M. A. Vaughan, A. H. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, 2009: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Oceanic Technol., 26, 2310–2323, doi:10.1175/2009JTECHA1281.1.

    • Search Google Scholar
    • Export Citation

  • Wood, N., 2008: Level 2B Radar-Visible Optical Depth Cloud Water Content (2B-CWC-RVOD) process description document: Version 5.1. CloudSat Project Algorithm Theoretical Basis Doc., 26 pp. [Available online at http://www.cloudsat.cira.colostate.edu/ICD/2B-CWC-RVOD/2B-CWC-RVOD_PDD_V5p1.pdf.]

  • Xiong, X., B. N. Wenny, A. Wu, and W. L. Barnes, 2009: MODIS onboard blackbody function and performance. IEEE Trans. Geosci. Remote Sens.,47, 4210–4222, doi:10.1109/TGRS.2009.2023317.

    • Search Google Scholar
    • Export Citation

  • Yang, P., B. Gao, B. A. Baum, Y. X. Hu, W. J. Wiscombe, S. Tsay, D. M. Winker, and S. L. Nasiri, 2001: Radiative properties of cirrus clouds in the infrared (8–13 μm) spectral region. J. Quant. Spectrosc. Radiat. Transf., 70, 473–504, doi:10.1016/S0022-4073(01)00024-3.

    • Search Google Scholar
    • Export Citation

  • Young, A. H., J. J. Bates, and J. A. Curry, 2012: Complementary use of passive and active remote sensing for detection of penetrating convection from CloudSat,CALIPSO, and Aqua MODIS. J. Geophys. Res., 117, D13205, doi:10.1029/2011JD016749.

    • Search Google Scholar
    • Export Citation

  • Yuan, T., J. V. Martins, Z. Li, and L. A. Remer, 2010: Estimating glaciation temperature of deep convective clouds with remote sensing data. Geophys. Res. Lett., 37, L08808, doi:10.1029/2010GL042753.

    • Search Google Scholar
    • Export Citation

  • Zeng, X., W.-K. Tao, M. Zhang, A. Y. Hou, S. Xie, S. Lang, X. Li, D. Starr, X. Li, and J. Simpson, 2009: An indirect effect of ice nuclei on atmospheric radiation. J. Atmos. Sci., 66, 41–61, doi:10.1175/2008JAS2778.1.

    • Search Google Scholar
    • Export Citation

  • Zeng, X., W.-K. Tao, S. W. Powell, R. A. Houze Jr., P. Ciesielski, N. Guy, H. Pierce, and T. Matsui, 2013: A comparison of the water budgets between clouds from AMMA and TWP- ICE. J. Atmos. Sci., 70, 487–503, doi:10.1175/JAS-D-12-050.1.

    • Search Google Scholar
    • Export Citation

  • Zuidema, P., 1998: The 600–800-mb minimum in tropical cloudiness observed during TOGA COARE. J. Atmos. Sci., 55, 2220–2228, doi:10.1175/1520-0469(1998)055<2220:TMMITC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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