Editorial Type:
Article
Article Type:
Research Article

Cloud Detection with MODIS. Part II: Validation

S. A. Ackerman Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by S. A. Ackerman in
Current site
Google Scholar
PubMed Close
,
R. E. Holz Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by R. E. Holz in
Current site
Google Scholar
PubMed Close
,
R. Frey Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by R. Frey in
Current site
Google Scholar
PubMed Close
,
E. W. Eloranta Space Science and Engineering Center, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by E. W. Eloranta in
Current site
Google Scholar
PubMed Close
,
B. C. Maddux Department of Atmospheric and Oceanic Science, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by B. C. Maddux in
Current site
Google Scholar
PubMed Close
, and
M. McGill NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by M. McGill in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jul 2008
DOI:
https://doi.org/10.1175/2007JTECHA1053.1
Page(s):
1073–1086
Restricted access

Abstract

An assessment of the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm for Terra and Aqua satellites is presented. The MODIS cloud mask algorithm output is compared with lidar observations from ground [Arctic High-Spectral Resolution Lidar (AHSRL)], aircraft [Cloud Physics Lidar (CPL)], and satellite-borne [Geoscience Laser Altimeter System (GLAS)] platforms. The comparison with 3 yr of coincident observations of MODIS and combined radar and lidar cloud product from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site in Lamont, Oklahoma, indicates that the MODIS algorithm agrees with the lidar about 85% of the time. A comparison with the CPL and AHSRL indicates that the optical depth limitation of the MODIS cloud mask is approximately 0.4. While MODIS algorithm flags scenes with a cloud optical depth of 0.4 as cloudy, approximately 90% of the mislabeled scenes have optical depths less than 0.4. A comparison with the GLAS cloud dataset indicates that cloud detection in polar regions at night remains challenging with the passive infrared imager approach.

In anticipation of comparisons with other satellite instruments, the sensitivity of the cloud mask algorithm to instrument characteristics (e.g., instantaneous field of view and viewing geometry) and thresholds is demonstrated. As expected, cloud amount generally increases with scan angle and instantaneous field of view (IFOV). Nadir sampling represents zonal monthly mean cloud amounts but can have large differences for regional studies when compared to full-swath-width analysis.

Corresponding author address: Steven A. Ackerman, CIMSS, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. Email: stevea@ssec.wisc.edu

Abstract

An assessment of the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask algorithm for Terra and Aqua satellites is presented. The MODIS cloud mask algorithm output is compared with lidar observations from ground [Arctic High-Spectral Resolution Lidar (AHSRL)], aircraft [Cloud Physics Lidar (CPL)], and satellite-borne [Geoscience Laser Altimeter System (GLAS)] platforms. The comparison with 3 yr of coincident observations of MODIS and combined radar and lidar cloud product from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program Southern Great Plains (SGP) site in Lamont, Oklahoma, indicates that the MODIS algorithm agrees with the lidar about 85% of the time. A comparison with the CPL and AHSRL indicates that the optical depth limitation of the MODIS cloud mask is approximately 0.4. While MODIS algorithm flags scenes with a cloud optical depth of 0.4 as cloudy, approximately 90% of the mislabeled scenes have optical depths less than 0.4. A comparison with the GLAS cloud dataset indicates that cloud detection in polar regions at night remains challenging with the passive infrared imager approach.

In anticipation of comparisons with other satellite instruments, the sensitivity of the cloud mask algorithm to instrument characteristics (e.g., instantaneous field of view and viewing geometry) and thresholds is demonstrated. As expected, cloud amount generally increases with scan angle and instantaneous field of view (IFOV). Nadir sampling represents zonal monthly mean cloud amounts but can have large differences for regional studies when compared to full-swath-width analysis.

Corresponding author address: Steven A. Ackerman, CIMSS, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706. Email: stevea@ssec.wisc.edu

Save
Cover Journal of Atmospheric and Oceanic Technology
Journal of Atmospheric and Oceanic Technology

  • Ackerman, S. A., Strabala K. I. , Menzel W. P. , Frey R. A. , Moeller C. C. , and Gumley L. E. , 1998: Discriminating clear sky from clouds with MODIS. J. Geophys. Res., 103 , 32141–32157.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Barnes, W. L., Pagano T. S. , and Salomonson V. V. , 1998: Prelaunch characteristics of the Moderate Resolution Imaging Spectroradiometer (MODIS) on EOS-AM1. IEEE Trans. Geosci. Remote Sens., 36 , 1088–1100.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Clothiaux, E. E., Ackerman T. P. , Mace G. G. , Moran K. P. , Marchand R. T. , Miller M. A. , and Martner B. E. , 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 , 645–665.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Di Girolamo, L., and Davies R. , 1997: Cloud fraction errors caused by finite resolution measurements. J. Geophys. Res., 102 , 1739–1756.

  • Eloranta, E. W., 2005: High spectral resolution lidar. Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, K. Weitkamp, Ed., Springer Series in Optical Sciences, Springer-Verlag, 143–163.

    • Search Google Scholar
    • Export Citation

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Holz, R. E., Ackerman S. A. , Antonelli P. , Nagle F. , Knuteson B. O. , McGill M. , Hlavka D. L. , and Hart W. D. , 2006: An improvement to the high-spectral-resolution CO2-slicing cloud-top altitude retrieval. J. Atmos. Oceanic Technol., 23 , 653–670.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • King, M. D., and Coauthors, 1996: Airborne scanning spectrometer for remote sensing of cloud, aerosol, water vapor, and surface properties. J. Atmos. Oceanic Technol., 13 , 777–794.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • King, M. D., and Coauthors, 2003: Cloud and aerosol properties, precipitable water, and profiles of temperature and humidity from MODIS. IEEE Trans. Geosci. Remote Sens., 41 , 442–458.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Klein, S. A., and Hartmann D. L. , 1993: The seasonal cycle of low stratiform clouds. J. Climate, 6 , 1587–1606.

  • Lee, Y., Wahba G. , and Ackerman S. A. , 2004: Cloud classification of satellite radiance data by multicategory support vector machines. J. Atmos. Oceanic Technol., 21 , 159–169.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Li, Z., Li J. , Menzel W. P. , Schmit T. J. , and Ackerman S. A. , 2007: Comparison between current and future environmental satellite imagers on cloud classification using MODIS. Remote Sens. Environ., 108 , 311–326.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Liu, Y., Key J. , Frey R. , Ackerman S. , and Menzel W. , 2004: Nighttime polar cloud detection with MODIS. Remote Sens. Environ., 92 , 181–194.

  • Mahesh, A., Grey M. A. , Palm S. P. , Hart W. D. , and Spinhirne J. D. , 2004: Passive and active detection of clouds: Comparisons between MODIS and GLAS observations. J. Geophys. Res., 108 .L04108, doi:10.1029/2003GL018859.

    • Search Google Scholar
    • Export Citation

  • McGill, M., Hlavka D. , Hart W. , Scott V. S. , Spinhirne J. D. , and Schmid B. , 2002: Cloud physics lidar: Instrument description and initial measurement results. Appl. Opt., 41 , 3725–3734.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Minnis, P., 1989: Viewing zenith angle dependence of cloudiness determined from coincident GOES East and GOES West data. J. Geophys. Res., 94 , 2303–2320.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Minnis, P., and Harrison E. F. , 1984: Diurnal variability of regional cloud and clear-sky radiative parameters derived from GOES data. Part II: November 1978 cloud distributions. J. Climate Appl. Meteor., 23 , 1012–1051.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Minnis, P., Heck P. W. , Young D. F. , Fairall C. W. , and Snider J. B. , 1992: Stratocumulus cloud properties derived from simultaneous satellite and island-based instrumentation during FIRE. J. Appl. Meteor., 31 , 317–339.

    • Crossref
    • Search Google Scholar
    • Export Citation

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

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Rossow, W. B., 1989: Measuring cloud properties from space. A review. J. Climate, 2 , 201–213.

  • Rossow, W. B., Walker A. W. , and Gardner L. C. , 1993: Comparison of ISCCP and other cloud amounts. J. Climate, 6 , 2394–2418.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stokes, G. M., and Schwartz S. E. , 1994: The Atmospheric Radiation Measurement (ARM) Program: Programmatic background and design of the cloud and radiation test bed. Bull. Amer. Meteor. Soc., 75 , 1201–1221.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Thomas, S. M., Heidinger A. K. , and Pavolonis M. J. , 2004: Comparison of NOAA’s operational AVHRR-derived cloud amount to other satellite-derived cloud climatologies. J. Climate, 17 , 4805–4822.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wylie, D. P., Menzel W. P. , Woolf H. M. , and Strabala K. I. , 1994: Four years of global cirrus cloud statistics using HIRS. J. Climate, 7 , 1972–1986.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Zhao, G., and Di Girolamo L. , 2006: Cloud fraction errors for trade wind cumuli from EOS-Terra instruments. Geophys. Res. Lett., 33 .L20802, doi:10.1029/2006GL027088.

    • Search Google Scholar
    • Export Citation

  • Zwally, H. J., and Coauthors, 2002: ICESat’s laser measurements of polar ice, atmosphere, ocean, and land. J. Geodyn., 34 , 405–445.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2538 589 43
PDF Downloads 1826 408 23