• Ackerman, S., and Coauthors, 2002: Discriminating clear-sky from cloud with MODIS. Algorithm Theoretical Basis Doc. Products MOD35, ATBD-MOD-06, 112 pp.

    • Search Google Scholar
    • Export Citation
  • Barker, H. W., and P. Raisanen, 2005: Radiative sensitivities for cloud structural properties that are unresolved by conventional GCMs. Quart. J. Roy. Meteor. Soc., 131 , 31033122.

    • Search Google Scholar
    • Export Citation
  • Barker, H. W., B. A. Wielicki, and L. Parker, 1996: A parameterization for computing grid-averaged solar fluxes for inhomogeneous marine boundary layer clouds. Part II: Validation using satellite data. J. Atmos. Sci., 53 , 23042316.

    • Search Google Scholar
    • Export Citation
  • Barker, H. W., G. L. Stephens, and Q. Fu, 1999: The sensitivity of domain-averaged solar fluxes to assumptions about cloud geometry. Quart. J. Roy. Meteor. Soc., 125 , 21272152.

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., C. Fravalo, and J. P. Pinty, 1992: A model of marine boundary-layer cloudiness for mesoscale applications. J. Atmos. Sci., 49 , 17231744.

    • Search Google Scholar
    • Export Citation
  • Bechtold, P., J. W. M. Cuijpers, P. Mascart, and P. Trouilhet, 1995: Modeling of trade wind cumuli with a low-order turbulence model: Toward a unified description of Cu and Sc clouds in meteorological models. J. Atmos. Sci., 52 , 455463.

    • Search Google Scholar
    • Export Citation
  • Bennartz, R., 2007: Global assessment of marine boundary layer cloud droplet number concentration from satellite. J. Geophys. Res., 112 , D02201. doi:10.1029/2006JD007547.

    • Search Google Scholar
    • Export Citation
  • Cahalan, R. F., W. Ridgway, W. J. Wiscombe, T. L. Bell, and J. B. Snider, 1994: The albedo of fractal stratocumulus clouds. J. Atmos. Sci., 51 , 24342455.

    • Search Google Scholar
    • Export Citation
  • Considine, G., J. A. Curry, and B. Wielicki, 1997: Modeling cloud fraction and horizontal variability in marine boundary layer clouds. J. Geophys. Res., 102 , 1351713525.

    • Search Google Scholar
    • Export Citation
  • Duynkerke, P. G., H. Q. Zhang, and P. J. Jonker, 1995: Microphysical and turbulent structure of nocturnal stratocumulus as observed during ASTEX. J. Atmos. Sci., 52 , 27632777.

    • Search Google Scholar
    • Export Citation
  • Frey, R. A., S. A. Ackerman, Y. H. Liu, K. I. Strabala, H. Zhang, J. R. Key, and X. G. Wang, 2008: Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5. J. Atmos. Oceanic Technol., 25 , 10571072.

    • Search Google Scholar
    • Export Citation
  • Heidinger, A. K., 2003: Rapid daytime estimation of cloud properties over a large area from radiance distributions. J. Atmos. Oceanic Technol., 20 , 12371250.

    • Search Google Scholar
    • Export Citation
  • King, M. D., S-C. Tsay, S. E. Platnick, M. Wang, and K-N. Liou, 1997: Cloud retrieval algorithms for MODIS: Optical thickness, effective particle radius, and thermodynamic phase. MODIS Algorithm Theoretical Basis Doc. ATBD-MOD-05, NASA, 79 pp.

    • Search Google Scholar
    • Export Citation
  • King, M. D., S. E. Platnick, P. A. Hubanks, G. T. Arnold, E. G. Moody, G. Wind, and B. Wind, cited. 2006: Collection 005 change summary for the MODIS cloud optical property (06_OD) algorithm. [Available online at http://modis-atmos.gsfc.nasa.gov/C005_Changes/C005_CloudOpticalProperties_ver311.pdf].

    • Search Google Scholar
    • Export Citation
  • Koren, I., L. Oreopoulos, G. Feingold, L. A. Remer, and O. Altaratz, 2008: How small is a small cloud? Atmos. Chem. Phys., 8 , 38553864.

    • Search Google Scholar
    • Export Citation
  • Lu, M. L., and J. H. Seinfeld, 2006: Effect of aerosol number concentration on cloud droplet dispersion: A large-eddy simulation study and implications for aerosol indirect forcing. J. Geophys. Res., 111 , D02207. doi:10.1029/2005JD006419.

    • Search Google Scholar
    • Export Citation
  • Nakajima, T., and M. D. King, 1990: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory. J. Atmos. Sci., 47 , 18781893.

    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., and R. Davies, 1998a: Plane parallel albedo biases from satellite observations. Part I: Dependence on resolution and other factors. J. Climate, 11 , 919932.

    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., and R. Davies, 1998b: Plane parallel albedo biases from satellite observations. Part II: Parameterizations for bias removal. J. Climate, 11 , 933944.

    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., and H. W. Barker, 1999: Accounting for subgrid-scale cloud variability in a multi-layer 1D solar radiative transfer algorithm. Quart. J. Roy. Meteor. Soc., 125 , 301330.

    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., M. D. Chou, M. Khairoutdinov, H. W. Barker, and R. F. Cahalan, 2004: Performance of Goddard earth observing system GCM column radiation models under heterogeneous cloud conditions. Atmos. Res., 72 , 365382.

    • Search Google Scholar
    • Export Citation
  • Oreopoulos, L., R. F. Cahalan, and S. Platnick, 2007: The plane-parallel albedo bias of liquid clouds from MODIS observations. J. Climate, 20 , 51145125.

    • Search Google Scholar
    • Export Citation
  • Pawlowska, H., and J. L. Brenguier, 2000: Microphysical properties of stratocumulus clouds during ACE-2. Tellus, 52B , 868887.

  • Petty, G. W., 2004: A First Course in Atmospheric Radiation. Sundog, 445 pp.

  • Pincus, R., and S. A. Klein, 2000: Unresolved spatial variability and microphysical process rates in large-scale models. J. Geophys. Res., 105 , 2705927065.

    • Search Google Scholar
    • Export Citation
  • Pincus, R., S. A. McFarlane, and S. A. Klein, 1999: Albedo bias and the horizontal variability of clouds in subtropical marine boundary layers: Observations from ships and satellites. J. Geophys. Res., 104 , 61836191.

    • Search Google Scholar
    • Export Citation
  • Raisanen, P., G. A. Isaac, H. W. Barker, and I. Gultepe, 2003: Solar radiative transfer for stratiform clouds with horizontal variations in liquid-water path and droplet effective radius. Quart. J. Roy. Meteor. Soc., 129 , 21352149.

    • Search Google Scholar
    • Export Citation
  • Raisanen, P., H. W. Barker, M. F. Khairoutdinov, J. N. Li, and D. A. Randall, 2004: Stochastic generation of subgrid-scale cloudy columns for large-scale models. Quart. J. Roy. Meteor. Soc., 130 , 20472067.

    • Search Google Scholar
    • Export Citation
  • Randall, D. A., and Coauthors, 2007: Climate models and their evaluation. Climate Change 2007: The Physical Science Basis, S. Solomon et al., Eds., Cambridge University Press, 589–662.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and L. C. Garder, 1993: Cloud detection using satellite measurements of infrared and visible radiances for ISCCP. J. Climate, 6 , 23412369.

    • Search Google Scholar
    • Export Citation
  • Shonk, J. K. P., and R. J. Hogan, 2008: Tripleclouds: An efficient method for representing horizontal cloud inhomogeneity in 1D radiation schemes by using three regions at each height. J. Climate, 21 , 23522370.

    • Search Google Scholar
    • Export Citation
  • Stowe, L. L., P. A. Davis, and E. P. McClain, 1999: Scientific basis and initial evaluation of the CLAVR-1 global clear cloud classification algorithm for the advanced very high resolution radiometer. J. Atmos. Oceanic Technol., 16 , 656681.

    • Search Google Scholar
    • Export Citation
  • Taylor, J. R., 1982: An Introduction to Error Analysis. Oxford University Press, 270 pp.

  • Tompkins, A. M., 2002: A prognostic parameterization for the subgrid-scale variability of water vapor and clouds in large-scale models and its use to diagnose cloud cover. J. Atmos. Sci., 59 , 19171942.

    • Search Google Scholar
    • Export Citation
  • Wood, N. B., P. M. Gabriel, and G. L. Stephens, 2005: An assessment of the parameterization of subgrid-scale cloud effects on radiative transfer. Part II: Horizontal inhomogeneity. J. Atmos. Sci., 62 , 28952909.

    • Search Google Scholar
    • Export Citation
  • Wood, R., and J. P. Taylor, 2001: Liquid water path variability in unbroken marine stratocumulus. Quart. J. Roy. Meteor. Soc., 127 , 26352662.

    • Search Google Scholar
    • Export Citation
  • Wood, R., and D. L. Hartmann, 2006: Spatial variability of liquid water path in marine low cloud: The importance of mesoscale cellular convection. J. Climate, 19 , 17481764.

    • Search Google Scholar
    • Export Citation
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Estimation of Liquid Cloud Properties that Conserve Total-Scene Reflectance Using Satellite Measurements

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  • 1 Space Science and Engineering Center, Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin
  • | 2 Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin
  • | 3 NOAA/NESDIS/Center for Satellite Applications and Research, and University of Wisconsin—Madison, Madison, Wisconsin
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Abstract

A new method of deriving statistical moments related to the distribution of liquid water path over partially cloudy scenes is tested using a satellite cloud climatology. The method improves the ability to reconstruct total-scene visible reflectance when compared with an approach that relies on valid liquid water path retrievals, and thus it maintains physical consistency with the primary satellite observations when deriving cloud climatologies. A global application of the new method finds a mean bias of −0.008 ± 0.017 when reconstructing total-scene reflectance from liquid water path distributions, as compared with a bias of 0.05 ± 0.047 when using a conventional approach. Application of the method to a multidecadal cloud climatology suggests that this may provide a means of identifying data artifacts that could affect long-term cloud property trends. The conservation of reflectance plus the ease of applicability to various satellite datasets makes this method a valuable tool for model validation and comparison of satellite climatologies. Gaussian and gamma functions are used to approximate the distribution of horizontal subgrid-scale liquid water path for 1° × 1° scenes, and while both functions perform well for the majority of atmospheric conditions, it is found that the Gaussian distribution generates a negative bias for cases in which visible reflectance is very high and that neither function is able to represent liquid water path well in the few cases in which the observed distribution is bi- or multimodal.

Corresponding author address: Dr. Michael Foster, 1225 West Dayton Street, Madison, WI 53706. Email: mfoster@aos.wisc.edu

Abstract

A new method of deriving statistical moments related to the distribution of liquid water path over partially cloudy scenes is tested using a satellite cloud climatology. The method improves the ability to reconstruct total-scene visible reflectance when compared with an approach that relies on valid liquid water path retrievals, and thus it maintains physical consistency with the primary satellite observations when deriving cloud climatologies. A global application of the new method finds a mean bias of −0.008 ± 0.017 when reconstructing total-scene reflectance from liquid water path distributions, as compared with a bias of 0.05 ± 0.047 when using a conventional approach. Application of the method to a multidecadal cloud climatology suggests that this may provide a means of identifying data artifacts that could affect long-term cloud property trends. The conservation of reflectance plus the ease of applicability to various satellite datasets makes this method a valuable tool for model validation and comparison of satellite climatologies. Gaussian and gamma functions are used to approximate the distribution of horizontal subgrid-scale liquid water path for 1° × 1° scenes, and while both functions perform well for the majority of atmospheric conditions, it is found that the Gaussian distribution generates a negative bias for cases in which visible reflectance is very high and that neither function is able to represent liquid water path well in the few cases in which the observed distribution is bi- or multimodal.

Corresponding author address: Dr. Michael Foster, 1225 West Dayton Street, Madison, WI 53706. Email: mfoster@aos.wisc.edu

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