Article Type:
Research Article

Differentiating between Clouds and Heavy Aerosols in Sun-Glint Regions

Keith D. Hutchison The University of Texas at Austin, Austin, Texas

Search for other papers by Keith D. Hutchison in
Current site
Google Scholar
PubMed Close
,
Bruce Hauss Northrop Grumman Aerospace Systems, NPOESS System Engineering, Redondo Beach, California

Search for other papers by Bruce Hauss in
Current site
Google Scholar
PubMed Close
,
Barbara D. Iisager Northrop Grumman Information Systems, NPOESS System Engineering, Redondo Beach, California

Search for other papers by Barbara D. Iisager in
Current site
Google Scholar
PubMed Close
,
Hiroshi Agravante Northrop Grumman Aerospace Systems, NPOESS System Engineering, Redondo Beach, California

Search for other papers by Hiroshi Agravante in
Current site
Google Scholar
PubMed Close
,
Robert Mahoney Northrop Grumman Aerospace Systems, NPOESS System Engineering, Redondo Beach, California

Search for other papers by Robert Mahoney in
Current site
Google Scholar
PubMed Close
,
Alain Sei Northrop Grumman Aerospace Systems, NPOESS System Engineering, Redondo Beach, California

Search for other papers by Alain Sei in
Current site
Google Scholar
PubMed Close
, and
John M. Jackson Northrop Grumman Aerospace Systems, NPOESS System Engineering, Redondo Beach, California

Search for other papers by John M. Jackson in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jun 2010
DOI:
https://doi.org/10.1175/2010JTECHA1368.1
Page(s):
1085–1094
Restricted access

Abstract

An approach is presented to distinguish between clouds and heavy aerosols in sun-glint regions with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. The approach extends the applicability of an algorithm that has already been applied successfully in areas outside the geometric and wind-induced sun-glint areas of the earth over both land and water surfaces. The successful application of this approach to include sun-glint regions requires an accurate cloud phase analysis, which can be degraded, especially in regions of sun glint, because of poorly calibrated radiances of the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Consequently, procedures have been developed to replace bad MODIS level 1B (L1B) data, which may result from saturation, dead/noisy detectors, or data dropouts, with radiometrically reliable values to create the Visible Infrared Imager Radiometer Suite (VIIRS) proxy sensor data records (SDRs). Cloud phase analyses produced by the NPOESS VIIRS cloud mask (VCM) algorithm using these modified VIIRS proxy SDRs show excellent agreement with features observed in color composites of MODIS imagery. In addition, the improved logic in the VCM algorithm provides a new capability to differentiate between clouds and heavy aerosols within the sun-glint cone. This ability to differentiate between clouds and heavy aerosols in strong sun-glint regions is demonstrated using MODIS data collected during the recent fires that burned extensive areas in southern Australia. Comparisons between heavy aerosols identified by the VCM algorithm with imagery and heritage data products show the effectiveness of the new procedures using the modified VIIRS proxy SDRs. It is concluded that it is feasible to accurately detect clouds, identify cloud phase, and distinguish between clouds and heavy aerosol using a single cloud mask algorithm, even in extensive sun-glint regions.

Corresponding author address: Keith Hutchison, Senior Research Fellow, The University of Texas at Austin, 3925 W. Braker Lane, Suite 200, Austin, TX 78759. Email: keithh@csr.utexas.edu

Abstract

An approach is presented to distinguish between clouds and heavy aerosols in sun-glint regions with automated cloud classification algorithms developed for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) program. The approach extends the applicability of an algorithm that has already been applied successfully in areas outside the geometric and wind-induced sun-glint areas of the earth over both land and water surfaces. The successful application of this approach to include sun-glint regions requires an accurate cloud phase analysis, which can be degraded, especially in regions of sun glint, because of poorly calibrated radiances of the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Consequently, procedures have been developed to replace bad MODIS level 1B (L1B) data, which may result from saturation, dead/noisy detectors, or data dropouts, with radiometrically reliable values to create the Visible Infrared Imager Radiometer Suite (VIIRS) proxy sensor data records (SDRs). Cloud phase analyses produced by the NPOESS VIIRS cloud mask (VCM) algorithm using these modified VIIRS proxy SDRs show excellent agreement with features observed in color composites of MODIS imagery. In addition, the improved logic in the VCM algorithm provides a new capability to differentiate between clouds and heavy aerosols within the sun-glint cone. This ability to differentiate between clouds and heavy aerosols in strong sun-glint regions is demonstrated using MODIS data collected during the recent fires that burned extensive areas in southern Australia. Comparisons between heavy aerosols identified by the VCM algorithm with imagery and heritage data products show the effectiveness of the new procedures using the modified VIIRS proxy SDRs. It is concluded that it is feasible to accurately detect clouds, identify cloud phase, and distinguish between clouds and heavy aerosol using a single cloud mask algorithm, even in extensive sun-glint regions.

Corresponding author address: Keith Hutchison, Senior Research Fellow, The University of Texas at Austin, 3925 W. Braker Lane, Suite 200, Austin, TX 78759. Email: keithh@csr.utexas.edu

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

  • Ackerman, S., and Coauthors, cited. 2002: Discriminating clear-sky from cloud with MODIS. Algorithm theoretical basis document (MOD35), version 4.0, 115 pp.

    • Search Google Scholar
    • Export Citation

  • Ackerman, S., and Coauthors, cited. 2006: Discriminating clear-sky from cloud with MODIS. Algorithm theoretical basis document (MOD35), version 5.0, 129 pp. [Available online at http://modis.gsfc.nasa.gov/data/atbd/atbd_mod06.pdf].

    • Search Google Scholar
    • Export Citation

  • Anderson, G. P., Clough S. A. , Kneizys F. X. , Chetwynd J. H. , and Shettle E. P. , 1986: AFGL atmospheric constituent profiles (0.120 km). Air Force Geophysics Laboratory, 46 pp.

    • Search Google Scholar
    • Export Citation

  • Dubovik, O., Holben B. , Eck T. F. , Smirnov A. , Kaufman Y. J. , King M. D. , Tanré D. , and Slutsker I. , 2002: Variability of absorption and optical properties of key aerosol types observed in worldwide locations. J. Atmos. Sci., 59 , 590–608.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hutchison, K. D., and Jackson J. M. , 2003: Cloud detection over desert regions using the 412 nanometer MODIS channel. Geophys. Res. Lett., 30 , 2187. doi:10.1029/2003GL018446.

    • Search Google Scholar
    • Export Citation

  • Hutchison, K. D., and Cracknell A. P. , 2006: VIIRS—A New Operational Cloud Imager. CRC Press, 218 pp.

  • Hutchison, K. D., Roskovensky J. K. , Jackson J. M. , Heidinger A. K. , Kopp T. J. , Pavolonis M. J. , and Frey R. , 2005: Automated cloud detection and typing of data collected by the Visible Infrared Imager Radiometer Suite (VIIRS). Int. J. Remote Sens., 20 , 4681–4706.

    • Search Google Scholar
    • Export Citation

  • Hutchison, K. D., Iisager B. , Kopp T. T. , and Jackson J. M. , 2008: Distinguishing aerosols from clouds in global, multispectral satellite data with automated cloud classification algorithms. J. Atmos. Oceanic Technol., 25 , 501–518.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hutchison, K. D., Mahoney R. L. , Vermote E. F. , Kopp T. J. , Jackson J. M. , Sei A. , and Iisager B. D. , 2009: A geometry-based approach for identifying cloud shadows in the VIIRS cloud mask algorithm for NPOESS. J. Atmos. Oceanic Technol., 26 , 1388–1397.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Jin, Z., and Stamnes K. , 1994: Radiative transfer in nonuniformly refracting layered media: Atmosphere–ocean system. Appl. Opt., 33 , 431–442.

  • Menzel, P., Frey R. , Baum B. , and Zhang H. , cited. 2006: Cloud-top properties and cloud phase. Algorithm theoretical basis document (MOD35), version 7.0. 56 pp. [Available online at http://modis.gsfc.nasa.gov/data/atbd/atbd_mod04.pdf].

    • Search Google Scholar
    • Export Citation

  • Pavolonis, M. J., and Heidinger A. K. , 2004: Daytime cloud overlap detection from AVHRR and VIIRS. J. Appl. Meteor., 43 , 762–778.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 128 63 4
PDF Downloads 144 20 2