Daytime Cloud Overlap Detection from AVHRR and VIIRS

Michael J. Pavolonis Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Andrew K. Heidinger Office of Research and Applications, NOAA/NESDIS, Madison, Wisconsin

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Abstract

Two algorithms for detecting multilayered cloud systems with satellite data are presented. The first algorithm utilizes data in the 0.65-, 11-, and 12-μm regions of the spectrum that are available on the Advanced Very High Resolution Radiometer (AVHRR). The second algorithm incorporates two different techniques to detect cloud overlap: the same technique used in the first algorithm and an additional series of spectral tests that now include data from the 1.38- and 1.65-μm near-infrared regions that are available on the Moderate Resolution Imaging Spectroradiometer (MODIS) and will be available on the Visible/Infrared Imager/Radiometer Suite (VIIRS). VIIRS is the imager that will replace the AVHRR on the next generation of polar-orbiting satellites. Both algorithms were derived assuming that a scene with cloud overlap consists of a semitransparent ice cloud that overlaps a cloud composed of liquid water droplets. Each algorithm was tested on three different MODIS scenes. In all three cases, the second (VIIRS) algorithm was able to detect more cloud overlap than the first (AVHRR) algorithm. Radiative transfer calculations indicate that the VIIRS algorithm will be more effective than the AVHRR algorithm when the visible optical depth of the ice cloud is greater than 3. Both algorithms will work best when the visible optical depth of the water cloud is greater than 5. Model sensitivity studies were also performed to assess the sensitivity of each algorithm to various parameters. It was found that the AVHRR algorithm is most sensitive to cloud particle size and the VIIRS near-infrared test is most sensitive to cloud vertical location. When validating each algorithm using cloud radar data, the VIIRS algorithm was shown to be more effective at detecting cloud overlap than the AVHRR algorithm; however, the VIIRS algorithm was slightly more prone to false cloud overlap detection.

Corresponding author address: Michael Pavolonis, 1225 West Dayton St., Madison, WI 53706. mpav@ssec.wisc.edu

Abstract

Two algorithms for detecting multilayered cloud systems with satellite data are presented. The first algorithm utilizes data in the 0.65-, 11-, and 12-μm regions of the spectrum that are available on the Advanced Very High Resolution Radiometer (AVHRR). The second algorithm incorporates two different techniques to detect cloud overlap: the same technique used in the first algorithm and an additional series of spectral tests that now include data from the 1.38- and 1.65-μm near-infrared regions that are available on the Moderate Resolution Imaging Spectroradiometer (MODIS) and will be available on the Visible/Infrared Imager/Radiometer Suite (VIIRS). VIIRS is the imager that will replace the AVHRR on the next generation of polar-orbiting satellites. Both algorithms were derived assuming that a scene with cloud overlap consists of a semitransparent ice cloud that overlaps a cloud composed of liquid water droplets. Each algorithm was tested on three different MODIS scenes. In all three cases, the second (VIIRS) algorithm was able to detect more cloud overlap than the first (AVHRR) algorithm. Radiative transfer calculations indicate that the VIIRS algorithm will be more effective than the AVHRR algorithm when the visible optical depth of the ice cloud is greater than 3. Both algorithms will work best when the visible optical depth of the water cloud is greater than 5. Model sensitivity studies were also performed to assess the sensitivity of each algorithm to various parameters. It was found that the AVHRR algorithm is most sensitive to cloud particle size and the VIIRS near-infrared test is most sensitive to cloud vertical location. When validating each algorithm using cloud radar data, the VIIRS algorithm was shown to be more effective at detecting cloud overlap than the AVHRR algorithm; however, the VIIRS algorithm was slightly more prone to false cloud overlap detection.

Corresponding author address: Michael Pavolonis, 1225 West Dayton St., Madison, WI 53706. mpav@ssec.wisc.edu

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  • Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley. 1998. Discriminating clear sky from clouds with MODIS. J. Geophys. Res 103:3214132157.

    • Search Google Scholar
    • Export Citation
  • Baum, B. A. and J. D. Spinhirne. 2000. Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS, 3, Cloud overlap. J. Geophys. Res 105:1179311804.

    • Search Google Scholar
    • Export Citation
  • Baum, B. A., R. F. Arduini, B. A. Wielicki, P. Minnis, and S-C. Tsay. 1994. Multilevel cloud retrieval using multispectral HIRS and AVHRR data: Nighttime oceanic analysis. J. Geophys. Res 99:54995514.

    • Search Google Scholar
    • Export Citation
  • Baum, B. A. Coauthors 1995. Satellite remote sensing of multiple cloud layers. J. Atmos. Sci 52:42104230.

  • Baum, B. A., V. Tovinkere, J. Titlow, and R. M. Welch. 1997. Automated cloud classification of global AVHRR data using a fuzzy logic approach. J. Appl. Meteor 36:15191540.

    • Search Google Scholar
    • Export Citation
  • Bennartz, R. and J. Fischer. 2000. A modified k-distribution approach applied to narrow band water vapor and oxygen absorption estimates in the near infrared. J. Quant. Spectrosc. Radiat. Transfer 66:539553.

    • Search Google Scholar
    • Export Citation
  • Briegleb, B. P., P. Minnis, V. Ramanathan, and E. Harrison. 1986. Comparison of regional clear-sky albedos inferred from satellite observations and model computations. J. Climate Appl. Meteor 25:214226.

    • 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
  • Ellingson, R. G., J. Ellis, and S. Fels. 1991. The intercomparison of radiation codes used in climate models: Long wave results. J. Geophys. Res 96 D5:89298953.

    • Search Google Scholar
    • Export Citation
  • Gao, B. C., A. F. H. Goetz, and W. J. Wiscombe. 1993. Cirrus cloud detection from airborne imaging spectrometer data using 1.38 μm water vapor band. Geophys. Res. Lett 20:301304.

    • Search Google Scholar
    • Export Citation
  • Giraud, V., J. C. Fouquart, Y. Parol, and G. Seze. 1997. Large-scale analysis of cirrus clouds from AVHRR data: Assessment of both a microphysical index and the cloud-top temperature. J. Appl. Meteor 36:664675.

    • Search Google Scholar
    • Export Citation
  • Gupta, S. K., W. L. Darnell, and A. C. Wilber. 1992. A parameterization for longwave surface radiation from satellite data: Recent improvements. J. Appl. Meteor 31:13611367.

    • Search Google Scholar
    • Export Citation
  • Hahn, C. J., S. G. Warren, J. London, R. M. Chervin, and R. Jenne. 1982. Atlas of simultaneous occurrence of different cloud types over the ocean. NCAR Tech. Note TN-201+STR, 212 pp. [NTIS PB83-152074.].

    • Search Google Scholar
    • Export Citation
  • Hahn, C. J., S. G. Warren, J. London, R. M. Chervin, and R. Jenne. 1984. Atlas of simultaneous occurrence of different cloud types over land. NCAR Tech. Note TN-241 + STR, 214 pp.

    • Search Google Scholar
    • Export Citation
  • Heidinger, A. K., R. Frey, and M. J. Pavolonis. 2004. Relative merits of the 1.6 and 3.75 μm channels of the AVHRR/3 for cloud detection. Can. J. Remote Sens 30 (2):113.

    • Search Google Scholar
    • Export Citation
  • Inoue, T. 1985. On the temperature and effective emissivity determination of semi-transparent cirrus clouds by bi-spectral measurements in the 10 μm window region. J. Meteor. Soc. Japan 63:8899.

    • Search Google Scholar
    • Export Citation
  • Jacobowitz, H., L. L. Stowe, G. Ohring, A. K. Heidinger, K. Knapp, and N. R. Nalli. 2003. The Advanced Very High Resolution Radiometer Pathfinder Atmosphere (PATMOS) climate dataset: A resource for climate research. Bull. Amer. Meteor. Soc 84:785793.

    • Search Google Scholar
    • Export Citation
  • Key, J. R. and A. Schweiger. 1998. Tools for atmospheric radiative transfer: Streamer and FluxNet. Comput. Geosci 24:443451.

  • Key, J. R., P. Yang, B. A. Baum, and S. L. Nasiri. 2002. Parameterization of shortwave ice cloud optical properties for various particle habits. J. Geophys. Res.,107, 4181, doi:10.1029/2001JD000742.

    • Search Google Scholar
    • Export Citation
  • Kratz, D. P. 1995. The correlated k-distribution technique as applied to the AVHRR channels. J. Quant. Spectrosc. Radiat. Transfer 53:501517.

    • Search Google Scholar
    • Export Citation
  • Kriebel, K. T., G. Gesell, M. Kaestner, and H. Mannstein. 2003. The cloud analysis tool APOLLO: Improvements and validations. J. Remote Sens 24:23892408.

    • Search Google Scholar
    • Export Citation
  • Liang, X. Z. and W. C. Wang. 1997. Cloud overlap effects on general circulation model climate simulations. J. Geophys. Res 102:1103911047.

    • Search Google Scholar
    • Export Citation
  • Moine, P., A. Chedin, and N. A. Scott. 1987. Automatic classification of air mass type from satellite vertical sounding data: Application to NOAA-7 observations. Ocean–Air Interact 1:95108.

    • Search Google Scholar
    • Export Citation
  • Morcrette, J. J. and C. Jakob. 2000. The response of the ECMWF model to changes in the cloud overlap assumption. Mon. Wea. Rev 128:17011732.

    • Search Google Scholar
    • Export Citation
  • Ou, S. C., K. N. Liou, and B. A. Baum. 1996. Detection of multilayer cirrus cloud systems using AVHRR data: Verification based on FIRE II IFO composite measurements. J. Appl. Meteor 35:178191.

    • Search Google Scholar
    • Export Citation
  • Pilewskie, P. and S. Twomey. 1987. Cloud phase discrimination by reflectance measurements near 1.6 and 2.2 μm. J. Atmos. Sci 44:34193420.

    • 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 cloud data. World Climate Research Programme Tech. Doc., WMO, 115 pp.

    • Search Google Scholar
    • Export Citation
  • Schmidt, E. O., R. F. Arduini, B. A. Wielicki, R. S. Stone, and S. C. Tsay. 1995. Considerations for modeling thin cirrus effects via brightness temperature differences. J. Appl. Meteor 34:447459.

    • Search Google Scholar
    • Export Citation
  • Stephens, G. L. Coauthors 2002. The CloudSat mission and the A-Train. Bull. Amer. Meteor. Soc 83:17711790.

  • Takano, Y. and K. N. Liou. 1989. Solar radiative transfer in cirrus clouds. Part I: Singlescattering and optical properties of hexagonal ice crystals. J. Atmos. Sci 46:319.

    • Search Google Scholar
    • Export Citation
  • Tian, L. and J. A. Curry. 1989. Cloud overlap statistics. J. Geophys. Res 94:99259935.

  • Warren, S. G., C. J. Hahn, and J. London. 1985. Simultaneous occurrence of different cloud types. J. Climate Appl. Meteor 24:658667.

  • Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison. 1995. Mission to planet Earth: Role of clouds and radiation in climate. Bull. Amer. Meteor. Soc 76:21252153.

    • Search Google Scholar
    • Export Citation
  • Wiscombe, W. J. 1977. The delta-M method: Rapid yet accurate radiative flux calculations for strongly asymmetric phase functions. J. Atmos. Sci 34:14081422.

    • Search Google Scholar
    • Export Citation
  • Wylie, D. P. and W. P. Menzel. 1999. Eight years of high cloud statistics using HIRS. J. Climate 12:170184.

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