• Ackerman, S. A., , Smith W. L. , , Revercomb H. E. , , and Spinhirne J. D. , 1990: The 27–28 October 1986 FIRE IFO cirrus case study: Spectral properties of cirrus clouds in the 8-12 μm window. Mon. Wea. Rev., 118, 23772388, doi:10.1175/1520-0493(1990)118<2377:TOFICC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Baker, N., 2014: Joint Polar Satellite System (JPSS) VIIRS cloud mask (VCM). Algorithm Theoretical Basis Doc. Revision D, 117 pp. [Available online at http://npp.gsfc.nasa.gov/sciencedocs/2015-06/474-00033_ATBD-VIIRS-Cloud-Mask_E.pdf.]

  • Frey, R. A., , Ackerman 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, 10571072, doi:10.1175/2008JTECHA1052.1.

    • Search Google Scholar
    • Export Citation
  • Gao, B.-C., , Goetz A. F. H. , , and Wiscombe W. J. , 1993: Cirrus cloud detection from airborne imaging spectrometer data using the 1.38 μm water vapor band. Geophys. Res. Lett., 20, 301304, doi:10.1029/93GL00106.

    • Search Google Scholar
    • Export Citation
  • Gao, B.-C., , Yang P. , , Han W. , , Li R. R. , , and Wiscombe W. J. , 2002: An algorithm using visible and 1.38-μm channels to retrieve cirrus cloud reflectances from aircraft and satellite data. IEEE Trans. Geosci. Remote Sens., 40, 16591668, doi:10.1109/TGRS.2002.802454.

    • Search Google Scholar
    • Export Citation
  • Holz, R. E., , Ackerman S. A. , , Nagle F. W. , , Frey R. , , Dutcher S. , , Kuehn R. E. , , Vaughan M. A. , , and Baum B. , 2008: Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP. J. Geophys. Res., 113, D00A19, doi:10.1029/2008JD009837.

    • Search Google Scholar
    • Export Citation
  • Hunt, W. H., , Winker D. M. , , Vaughan M. A. , , Powell K. A. , , Lucker P. L. , , and Weimer C. , 2009: CALIPSO lidar description and performance assessment. J. Atmos. Oceanic Technol., 26, 12141228, doi:10.1175/2009JTECHA1223.1.

    • Search Google Scholar
    • Export Citation
  • Hutchison, K. D., , Iisager B. D. , , and Hauss B. , 2012: The use of global synthetic data for pre-launch tuning of the VIIRS cloud mask algorithm. Int. J. Remote Sens., 33, 14001423, doi:10.1080/01431161.2011.571299.

    • Search Google Scholar
    • Export Citation
  • Jethva, H., , Torres O. , , Waquet F. , , Chand D. , , and Hu Y. , 2014: How do A-train sensors intercompare in the retrieval of above-cloud aerosol optical depth? A case study-based assessment. Geophys. Res. Lett., 41, 186192, doi:10.1002/2013GL058405.

    • Search Google Scholar
    • Export Citation
  • Liou, K. N., 2005: Cirrus clouds and climate. McGraw-Hill Yearbook of Science and Technology 2005. McGraw Hill Yearbook of Science and Technology Series, McGraw-Hill Professional, 51–53.

  • Menzel, W. P., and et al. , 2008: MODIS global cloud-top pressure and amount estimation: Algorithm description and results. J. Appl. Meteor. Climatol., 47, 11751198, doi:10.1175/2007JAMC1705.1.

    • Search Google Scholar
    • Export Citation
  • Nagle, F. W., , and Holz R. E. , 2009: Computationally efficient methods of collocating satellite, aircraft, and ground observations. J. Atmos. Oceanic Technol., 26, 15851595, doi:10.1175/2008JTECHA1189.1.

    • Search Google Scholar
    • Export Citation
  • Rossow, W. B., , and Schiffer R. A. , 1991: ISCCP cloud data products. Bull. Amer. Meteor. Soc., 72, 220, doi:10.1175/1520-0477(1991)072<0002:ICDP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Saunders, R. W., , and Kriebel K. T. , 1988: An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens., 9, 123150, doi:10.1080/01431168808954841.

    • Search Google Scholar
    • Export Citation
  • Soden, B., , and Bretherton F. P. , 1993: Upper tropospheric relative humidity from GOES 6.7 μm channel: Method and climatology for July 1987. J. Geophys. Res., 98, 16 66916 688, doi:10.1029/93JD01283.

    • Search Google Scholar
    • Export Citation
  • Sun, W., , Videen G. , , Kato S. , , Lin B. , , Lukashin C. , , and Hu Y. , 2011: A study of subvisual clouds and their radiation effect with a synergy of CERES, MODIS, CALIPSO, and AIRS data. J. Geophys. Res., 116, D22207, doi:10.1029/2011JD016422.

    • Search Google Scholar
    • Export Citation
  • Sun, W., , Videen G. , , and Mishchenko M. I. , 2014: Detecting super-thin clouds with polarized sunlight. Geophys. Res. Lett., 41, 688693, doi:10.1002/2013GL058840.

    • Search Google Scholar
    • Export Citation
  • Wan, Z., 2014: New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ., 140, 3645, doi:10.1016/j.rse.2013.08.027.

    • Search Google Scholar
    • Export Citation
  • Wu, X., , Bates J. J. , , and Singhkhalsa S. , 1993: A climatology of the water vapor band brightness temperatures for NOAA operational satellites. J. Climate, 6, 12821300, doi:10.1175/1520-0442(1993)006<1282:ACOTWV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xia, L., , Mao K. B. , , Ma Y. , , Zhao F. , , Jiang L. , , Shen X. , , and Qin Z. , 2014: An algorithm for retrieving land surface temperatures using VIIRS data in combination with multi-sensors. Sensors, 14, 21 38521 408, doi:10.3390/s141121385.

    • Search Google Scholar
    • Export Citation
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An Improved Algorithm for the Detection of Cirrus Clouds in the Tibetan Plateau Using VIIRS and MODIS Data

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  • 1 * National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
  • | 2 Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
  • | 3 Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Science, Beijing, China
  • | 4 Chinese Academy of Agricultural Data and Nutrition, Hong Kong, China
  • | 5 Space Star Technology Co., Ltd., Beijing, China
  • | 6 ** Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma, Norman, Oklahoma
  • | 7 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Research Institute, and Beijing Normal University, Beijing, China
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Abstract

Cirrus clouds play an important role in the global radiation budget balance. However, the existing MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) cirrus cloud test algorithms struggle to provide accurate cirrus cloud information for the Tibetan Plateau region. In this study, the 1.38-μm cirrus cloud test was improved by adding 11-μm brightness temperature and a multiday average land surface temperature test. An algorithm sensitivity analysis indicated that the proposed algorithm lowered the threshold of the existing 1.38-μm algorithm to 0.005 in the winter and did not produce any observable misclassifications. Compared to the existing 1.38-μm cirrus test algorithm, the accuracy validation indicated that the improved algorithm detected 31.7% more cirrus clouds than the existing VIIRS 1.38-μm cirrus test and yielded 14% fewer misclassifications than the MODIS 1.38-μm cirrus test.

Corresponding author address: K. B. Mao, National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Avenue, Haidian District, Beijing 100081, China. E-mail: maokebiao@caas.cn

Abstract

Cirrus clouds play an important role in the global radiation budget balance. However, the existing MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) cirrus cloud test algorithms struggle to provide accurate cirrus cloud information for the Tibetan Plateau region. In this study, the 1.38-μm cirrus cloud test was improved by adding 11-μm brightness temperature and a multiday average land surface temperature test. An algorithm sensitivity analysis indicated that the proposed algorithm lowered the threshold of the existing 1.38-μm algorithm to 0.005 in the winter and did not produce any observable misclassifications. Compared to the existing 1.38-μm cirrus test algorithm, the accuracy validation indicated that the improved algorithm detected 31.7% more cirrus clouds than the existing VIIRS 1.38-μm cirrus test and yielded 14% fewer misclassifications than the MODIS 1.38-μm cirrus test.

Corresponding author address: K. B. Mao, National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, 12 Zhongguancun South Avenue, Haidian District, Beijing 100081, China. E-mail: maokebiao@caas.cn
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