Editorial Type:
Article
Article Type:
Research Article

A Comparison of MODIS-Derived Cloud Fraction with Surface Observations at Five SURFRAD Sites

Ning An College of Global Change and Earth System Science, Beijing Normal University, and Joint Center for Global Change Studies, Beijing, China

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Kaicun Wang College of Global Change and Earth System Science, Beijing Normal University, and Joint Center for Global Change Studies, Beijing, China

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Print Publication:
01 May 2015
DOI:
https://doi.org/10.1175/JAMC-D-14-0206.1
Page(s):
1009–1020
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Abstract

Clouds determine the amount of solar radiation incident to the surface. Accurately quantifying cloud fraction is of great importance but is difficult to accomplish. Satellite and surface cloud observations have different fields of view (FOVs); the lack of conformity of different FOVs may cause large discrepancies when comparing satellite- and surface-derived cloud fractions. From the viewpoint of surface-incident solar radiation, this paper compares Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 cloud-fraction data with three surface cloud-fraction datasets at five Surface Radiation Network (SURFRAD) sites. The correlation coefficients between MODIS and the surface cloud fractions are in the 0.80–0.91 range and vary at different SURFRAD sites. In a number of cases, MODIS observations show a large cloud-fraction bias when compared with surface data. The variances between MODIS and the surface cloud-fraction datasets are more apparent when small convective or broken clouds exist in the FOVs. The magnitude of the discrepancy between MODIS and surface-derived cloud fractions depends on the satellite’s view zenith angle (VZA). On average, relative to surface cloud-fraction data, MODIS observes a larger cloud fraction at VZA > 40° and a smaller cloud fraction at VZA < 20°. When comparing long-term MODIS averages with surface datasets, Aqua MODIS observes a higher annual mean cloud fraction, likely because convective clouds are better developed in the afternoon when Aqua is observing.

Corresponding author address: Dr. Kaicun Wang, State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. E-mail: kcwang@bnu.edu.cn

Abstract

Clouds determine the amount of solar radiation incident to the surface. Accurately quantifying cloud fraction is of great importance but is difficult to accomplish. Satellite and surface cloud observations have different fields of view (FOVs); the lack of conformity of different FOVs may cause large discrepancies when comparing satellite- and surface-derived cloud fractions. From the viewpoint of surface-incident solar radiation, this paper compares Moderate Resolution Imaging Spectroradiometer (MODIS) level-2 cloud-fraction data with three surface cloud-fraction datasets at five Surface Radiation Network (SURFRAD) sites. The correlation coefficients between MODIS and the surface cloud fractions are in the 0.80–0.91 range and vary at different SURFRAD sites. In a number of cases, MODIS observations show a large cloud-fraction bias when compared with surface data. The variances between MODIS and the surface cloud-fraction datasets are more apparent when small convective or broken clouds exist in the FOVs. The magnitude of the discrepancy between MODIS and surface-derived cloud fractions depends on the satellite’s view zenith angle (VZA). On average, relative to surface cloud-fraction data, MODIS observes a larger cloud fraction at VZA > 40° and a smaller cloud fraction at VZA < 20°. When comparing long-term MODIS averages with surface datasets, Aqua MODIS observes a higher annual mean cloud fraction, likely because convective clouds are better developed in the afternoon when Aqua is observing.

Corresponding author address: Dr. Kaicun Wang, State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China. E-mail: kcwang@bnu.edu.cn
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Journal of Applied Meteorology and Climatology

  • Augustine, J. A., J. J. DeLuisi, and C. N. Long, 2000: SURFRAD—A national surface radiation budget network for atmospheric research. Bull. Amer. Meteor. Soc., 81, 2341–2357, doi:10.1175/1520-0477(2000)081<2341:SANSRB>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Augustine, J. A., G. B. Hodges, C. R. Cornwall, J. J. Michalsky, and C. I. Medina, 2005: An update on SURFRAD—The GCOS Surface Radiation budget network for the continental United States. J. Atmos. Oceanic Technol., 22, 1460–1472, doi:10.1175/JTECH1806.1.

    • Search Google Scholar
    • Export Citation

  • Baker, M., 1997: Cloud microphysics and climate. Science, 276, 1072–1078, doi:10.1126/science.276.5315.1072.

  • Boers, R., M. J. de Haij, W. M. F. Wauben, H. K. Baltink, L. H. van Ulft, M. Savenije, and C. N. Long, 2010: Optimized fractional cloudiness determination from five ground-based remote sensing techniques. J. Geophys. Res., 115, D24116, doi:10.1029/2010JD014661.

    • Search Google Scholar
    • Export Citation

  • Dürr, B., and R. Philipona, 2004: Automatic cloud amount detection by surface longwave downward radiation measurements. J. Geophys. Res., 109, D05201, doi:10.1029/2003JD004182.

    • Search Google Scholar
    • Export Citation

  • Feister, U., H. Möller, T. Sattler, J. Shields, U. Görsdorf, and J. Güldner, 2010: Comparison of macroscopic cloud data from ground-based measurements using VIS/NIR and IR instruments at Lindenberg, Germany. Atmos. Res., 96, 395–407, doi:10.1016/j.atmosres.2010.01.012.

    • Search Google Scholar
    • Export Citation

  • Frey, R. A., S. A. Ackerman, Y. Liu, K. I. Strabala, H. Zhang, J. R. Key, and X. Wang, 2008: Cloud detection with MODIS. Part I: Improvements in the MODIS cloud mask for collection 5. J. Atmos. Oceanic Technol., 25, 1057–1072, doi:10.1175/2008JTECHA1052.1.

    • Search Google Scholar
    • Export Citation

  • Hahn, C. J., W. B. Rossow, and S. G. Warren, 2001: ISCCP cloud properties associated with standard cloud types identified in individual surface observations. J. Climate, 14, 11–28, doi:10.1175/1520-0442(2001)014<0011:ICPAWS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Henderson-Sellers, A., and K. McGuffie, 1990: Are cloud amounts estimated from satellite sensor and conventional surface-based observations related? Remote Sens., 11, 543–550, doi:10.1080/01431169008955038.

    • Search Google Scholar
    • Export Citation

  • Kassianov, E., C. N. Long, and M. Ovtchinnikov, 2005: Cloud sky cover versus cloud fraction: Whole-sky simulations and observations. J. Appl. Meteor., 44, 86–98, doi:10.1175/JAM-2184.1.

    • Search Google Scholar
    • Export Citation

  • Kotarba, A. Z., 2009: A comparison of MODIS-derived cloud amount with visual surface observations. Atmos. Res., 92, 522–530, doi:10.1016/j.atmosres.2009.02.001.

    • Search Google Scholar
    • Export Citation

  • Li, Z., M. Cribb, F. Chang, and A. Trishchenko, 2004: Validation of MODIS-retrieved cloud fractions using whole sky imager measurements at the three ARM sites. Proc. 14th Atmospheric Radiation Measurement (ARM) Science Team Meeting, Albuquerque, NM, Atmospheric Radiation Measurement (ARM) Program, 22–26. [Available online at https://www.arm.gov/publications/proceedings/conf14/extended_abs/li1-z.pdf.]

  • Long, C. N., 2010: Correcting for circumsolar and near-horizon errors in sky cover retrievals from sky images. Open Atmos. Sci. J., 4, 45–52, doi:10.2174/1874282301004010045.

    • Search Google Scholar
    • Export Citation

  • Long, C. N., T. P. Ackerman, K. L. Gaustad, and J. N. S. Cole, 2006a: Estimation of fractional sky cover from broadband shortwave radiometer measurements. J. Geophys. Res., 111, D11204, doi:10.1029/2005JD006475.

    • Search Google Scholar
    • Export Citation

  • Long, C. N., J. Sabburg, J. Calbó, and D. Pages, 2006b: Retrieving cloud characteristics from ground-based daytime color all-sky images. J. Atmos. Oceanic Technol., 23, 633–652, doi:10.1175/JTECH1875.1.

    • Search Google Scholar
    • Export Citation

  • Maddux, B., S. Ackerman, and S. Platnick, 2010: Viewing geometry dependencies in MODIS cloud products. J. Atmos. Oceanic Technol., 27, 1519–1528, doi:10.1175/2010JTECHA1432.1.

    • 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

  • Min, Q., T. Wang, C. N. Long, and M. Duan, 2008: Estimating fractional sky cover from spectral measurements. J. Geophys. Res., 113, D20208, doi:10.1029/2008JD010278.

    • Search Google Scholar
    • Export Citation

  • Pfister, G., R. McKenzie, J. Liley, A. Thomas, B. Forgan, and C. N. Long, 2003: Cloud coverage based on all-sky imaging and its impact on surface solar irradiance. J. Appl. Meteor., 42, 1421–1434, doi:10.1175/1520-0450(2003)042<1421:CCBOAI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riédi, 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, and L. C. Garder, 1993: Comparison of ISCCP and other cloud amounts. J. Climate, 6, 2394–2418, doi:10.1175/1520-0442(1993)006<2394:COIAOC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Slater, D., C. Long, and T. Tooman, 2001: Total sky imager/whole sky imager cloud fraction comparison. Proc. 11th ARM Science Team Meeting, Atlanta, GA, Atmospheric Radiation Measurement (ARM) Program. [Available online at https://www.arm.gov/publications/proceedings/conf11/extended_abs/slater_dw.pdf.]

  • Stubenrauch, C., and Coauthors, 2013: Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX Radiation Panel. Bull. Amer. Meteor. Soc., 94, 1031–1049, doi:10.1175/BAMS-D-12-00117.1.

    • Search Google Scholar
    • Export Citation

  • Warren, S. G., C. J. Hahn, J. London, R. M. Chervin, and R. L. Jenne, 1986: Global distribution of total cloud cover and cloud type amounts over land. NCAR Tech. Note NCAR/TN-273+STR, doi:10.5065/D6GH9FXB.

  • Wu, W., Y. Liu, M. P. Jensen, T. Toto, M. J. Foster, and C. N. Long, 2014: A comparison of multiscale variations of decade-long cloud fractions from six different platforms over the Southern Great Plains in the United States. J. Geophys. Res. Atmos., 119, 3438–3459, doi:10.1002/2013JD019813.

    • Search Google Scholar
    • Export Citation

  • Xie, Y., and Y. Liu, 2013: A new approach for simultaneously retrieving cloud albedo and cloud fraction from surface-based shortwave radiation measurements. Environ. Res. Lett., 8, 044023, doi:10.1088/1748-9326/8/4/044023.

    • Search Google Scholar
    • Export Citation

  • Xie, Y., Y. Liu, C. N. Long, and Q. Min, 2014: Retrievals of cloud fraction and cloud albedo from surface-based shortwave radiation measurements: A comparison of 16 year measurements. J. Geophys. Res. Atmos., 119, 8925–8940, doi:10.1002/2014JD021705.

    • Search Google Scholar
    • Export Citation
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