• Ackerman, T. P., and G. M. Stokes, 2003: The Atmospheric Radiation Measurement Program. Phys. Today, 56 , 3844.

  • Benjamin, S. G., and Coauthors, 2004: An hourly assimilation/forecast cycle: The RUC. Mon. Wea. Rev., 132 , 495518.

  • Cess, R. D., and Coauthors, 1996: Cloud feedback in atmospheric general circulation models: An update. J. Geophys. Res., 101 , (D8). 1279112794.

    • Search Google Scholar
    • Export Citation
  • Clothiaux, E. E., T. P. Ackerman, G. G. Mace, K. P. Moran, R. T. Marchand, M. A. Miller, and B. E. Martner, 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites. J. Appl. Meteor., 39 , 645665.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., and M-S. Yao, 1993: Efficient cumulus parameterization for long-term climate studies: The GISS scheme. The Representation of Cumulus Convection in Numerical Models, Meteor. Monogr., No. 46, Amer. Meteor. Soc., 181-184.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., M-S. Yao, W. Kovari, and K. K. W. Lo, 1996: A prognostic cloud water parameterization for global climate models. J. Climate, 9 , 270304.

    • Search Google Scholar
    • Export Citation
  • Del Genio, A. D., W. Kovari, M-S. Yao, and J. Jonas, 2005a: Cumulus microphysics and climate sensitivity. J. Climate, 18 , 23762387.

  • Del Genio, A. D., A. B. Wolf, and M-S. Yao, 2005b: Evaluation of regional cloud feedbacks using single-column models. J. Geophys. Res., 110 , D15S13. doi:10.1029/2004JD005011.

    • Search Google Scholar
    • Export Citation
  • Dong, X., B. Xi, and P. Minnis, 2006: A climatology of midlatitude continental clouds from the ARM SGP Central Facility. Part II: Cloud fraction and surface radiative forcing. J. Climate, 19 , 17651783.

    • Search Google Scholar
    • Export Citation
  • Dong, X., P. Minnis, B. Xi, S. Sun-Mack, and Y. Chen, 2008: Comparison of CERES-MODIS stratus cloud properties using ground-based measurements at the DOE ARM SGP site. J. Geophys. Res., 113 , D03204. doi:10.1029/2007JD008438.

    • Search Google Scholar
    • Export Citation
  • Gao, S., and X. Li, 2007: Cloud-Resolving Modeling of Convective Processes. Springer, 206 pp.

  • Hogan, R. J., C. Jakob, and A. J. Illingworth, 2001: Comparison of ECMWF winter-season cloud fraction with radar-derived values. J. Applied Meteor., 40 , 513525.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and C. Jakob, 1999: Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon. Wea. Rev., 127 , 25142531.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., and A. D. Del Genio, 2006: ARM’s support for GCM improvement: A white paper. UCRL-MI-224010, ARM-06-012.

  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87 , 343360.

  • Min, Q., P. Minnis, and M. M. Khaiyer, 2004: Comparison of cirrus optical depths from GOES-8 and surface measurements. J. Geophys. Res., 109 , D20119. doi:10.1029/2003JD004390.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., W. L. Smith Jr., D. P. Garber, J. K. Ayers, and D. R. Doelling, 1995: Cloud properties derived from GOES-7 for the spring 1994 ARM intensive observing period using version 1.0.0 of the ARM satellite data analysis program. NASA Ref. Publ. 1366, 59 pp.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2001: A near-real time method for deriving cloud and radiation properties from satellites for weather and climate studies. Preprints, 11th Conf. on Satellite Meteorology and Oceanography, Madison, WI, Amer. Meteor. Soc., 477–480.

    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2008: Cloud detection in non-polar regions for CERES using TRMM VIRS and Terra and Aqua MODIS data. IEEE Trans. Geosci. Remote Sens., 46 , 38573884.

    • Search Google Scholar
    • Export Citation
  • Moran, K. P., B. E. Martner, M. J. Post, R. A. Kropfli, D. C. Welsh, and K. B. Widener, 1998: An unattended cloud-profiling radar for use in climate research. Bull. Amer. Meteor. Soc., 79 , 443455.

    • Search Google Scholar
    • Export Citation
  • Palikonda, R., and Coauthors, 2006: NASA-Langley Web-based operational real-time cloud retrieval products from geostationary satellites. Remote Sensing of the Atmosphere and Clouds, S.-C. Tsay, T. Nakajima, and R. P. Singh, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 6408), doi:10.1117/12.700423.

    • Search Google Scholar
    • Export Citation
  • Randall, D. A., K. M. Xu, R. J. Somerville, and S. Iacobellis, 1996: Single-column models and cloud ensemble models as links between observations and climate models. J. Climate, 9 , 16831697.

    • Search Google Scholar
    • Export Citation
  • Randall, D. A., M. E. Schlesinger, V. Galin, V. Meleshko, J-J. Morcrette, and R. Wetherald, 2006: Cloud feedbacks. Frontiers in the Science of Climate Modeling, J. T. Kiehl and V. Ramanathan, Eds., Cambridge University Press, 217–250.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80 , 22612287.

  • Smith, W. L., P. Minnis, H. Finney, R. Palikonda, and M. M. Khaiyer, 2008: An evaluation of operational GOES-derived single-layer cloud top heights with ARSCL over the ARM Southern Great Plains site. Geophys. Res. Lett., 35 , L13820. doi:10.1029/2008GL034275.

    • Search Google Scholar
    • Export Citation
  • Sundqvist, H., E. Berge, and J. E. Kristjánsson, 1989: Condensation and cloud parameterization studies with a mesoscale numerical weather prediction model. Mon. Wea. Rev., 117 , 16411657.

    • Search Google Scholar
    • Export Citation
  • Warren, S. G., C. J. Hahn, J. London, R. M. Chervin, and R. L. Jenne, 1984: Atlas of simultaneous occurrence of different cloud types over land. NCAR Tech. Note NCAR/TN-241+STR, 209 pp.

    • Search Google Scholar
    • Export Citation
  • Wielicki, B. A., and Coauthors, 1996: Clouds and the Earth’s Radiant Energy System (CERES): An Earth Observing System experiment. Bull. Amer. Meteor. Soc., 77 , 853868.

    • Search Google Scholar
    • Export Citation
  • Xi, B., X. Dong, P. Minnis, and M. Khaiyer, 2010: A 10-year climatology of cloud fraction and vertical distribution derived from both surface and GOES observations over the DOE ARM SGP Site. J. Geophys. Res., 115 , D12124. doi:10.1029/2009JD012800.

    • Search Google Scholar
    • Export Citation
  • Xie, S., R. T. Cederwall, and M. Zhang, 2004: Developing long-term single-column model/cloud system-resolving model forcing data using numerical weather prediction products constrained by surface and top of the atmosphere observations. J. Geophys. Res., 109 , D01104. doi:10.1029/2003JD004045.

    • Search Google Scholar
    • Export Citation
  • Zhang, G. J., 2003: Lagrangian study of cloud properties and their relationships to meteorological parameters over the U.S. Southern Great Plains. J. Climate, 16 , 27002716.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. H., J. L. Lin, R. T. Cederwall, J. J. Yio, and S. C. Xie, 2001: Objective analysis of ARM IOP data: Method and sensitivity. Mon. Wea. Rev., 129 , 295311.

    • Search Google Scholar
    • Export Citation
  • Zhang, M. J., and Coauthors, 2005: Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J. Geophys. Res., 110 , D15S02. doi:10.1029/2004JD005021.

    • Search Google Scholar
    • Export Citation
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Evaluation of the NASA GISS Single-Column Model Simulated Clouds Using Combined Surface and Satellite Observations

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  • 1 Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota
  • | 2 NASA Langley Research Center, Hampton, Virginia
  • | 3 NASA Goddard Institute of Space Studies, New York, New York
  • | 4 Columbia University, New York, New York
  • | 5 Science Systems and Applications, Inc., Hampton, Virginia
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Abstract

Three years of surface and Geostationary Operational Environmental Satellite (GOES) data from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to evaluate the NASA GISS Single Column Model (SCM) simulated clouds from January 1999 to December 2001. The GOES-derived total cloud fractions for both 0.5° and 2.5° grid boxes are in excellent agreement with surface observations, suggesting that ARM point observations can represent large areal observations. Low (<2 km), middle (2–6 km), and high (>6 km) levels of cloud fractions, however, have negative biases as compared to the ARM results due to multilayer cloud scenes that can either mask lower cloud layers or cause misidentifications of cloud tops. Compared to the ARM observations, the SCM simulated most midlevel clouds, overestimated low clouds (4%), and underestimated total and high clouds by 7% and 15%, respectively. To examine the dependence of the modeled high and low clouds on the large-scale synoptic patterns, variables such as relative humidity (RH) and vertical pressure velocity (omega) from North American Regional Reanalysis (NARR) data are included. The successfully modeled and missed high clouds are primarily associated with a trough and ridge upstream of the ARM SGP, respectively. The PDFs of observed high and low occurrence as a function of RH reveal that high clouds have a Gaussian-like distribution with mode RH values of ∼40%–50%, whereas low clouds have a gammalike distribution with the highest cloud probability occurring at RH ∼75%–85%. The PDFs of modeled low clouds are similar to those observed; however, for high clouds the PDFs are shifted toward higher values of RH. This results in a negative bias for the modeled high clouds because many of the observed clouds occur at RH values below the SCM-specified stratiform parameterization threshold RH of 60%. Despite many similarities between PDFs derived from the NARR and ARM forcing datasets for RH and omega, differences do exist. This warrants further investigation of the forcing and reanalysis datasets.

Corresponding author address: Aaron Kennedy, Dept. of Atmospheric Sciences, Box 9006, University of North Dakota, 4149 Campus Rd., Grand Forks, ND 58202-9006. Email: aaron.kennedy@und.edu

Abstract

Three years of surface and Geostationary Operational Environmental Satellite (GOES) data from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site are used to evaluate the NASA GISS Single Column Model (SCM) simulated clouds from January 1999 to December 2001. The GOES-derived total cloud fractions for both 0.5° and 2.5° grid boxes are in excellent agreement with surface observations, suggesting that ARM point observations can represent large areal observations. Low (<2 km), middle (2–6 km), and high (>6 km) levels of cloud fractions, however, have negative biases as compared to the ARM results due to multilayer cloud scenes that can either mask lower cloud layers or cause misidentifications of cloud tops. Compared to the ARM observations, the SCM simulated most midlevel clouds, overestimated low clouds (4%), and underestimated total and high clouds by 7% and 15%, respectively. To examine the dependence of the modeled high and low clouds on the large-scale synoptic patterns, variables such as relative humidity (RH) and vertical pressure velocity (omega) from North American Regional Reanalysis (NARR) data are included. The successfully modeled and missed high clouds are primarily associated with a trough and ridge upstream of the ARM SGP, respectively. The PDFs of observed high and low occurrence as a function of RH reveal that high clouds have a Gaussian-like distribution with mode RH values of ∼40%–50%, whereas low clouds have a gammalike distribution with the highest cloud probability occurring at RH ∼75%–85%. The PDFs of modeled low clouds are similar to those observed; however, for high clouds the PDFs are shifted toward higher values of RH. This results in a negative bias for the modeled high clouds because many of the observed clouds occur at RH values below the SCM-specified stratiform parameterization threshold RH of 60%. Despite many similarities between PDFs derived from the NARR and ARM forcing datasets for RH and omega, differences do exist. This warrants further investigation of the forcing and reanalysis datasets.

Corresponding author address: Aaron Kennedy, Dept. of Atmospheric Sciences, Box 9006, University of North Dakota, 4149 Campus Rd., Grand Forks, ND 58202-9006. Email: aaron.kennedy@und.edu

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