Editorial Type:
Article
Article Type:
Research Article

Cloud-Base Height Estimation from VIIRS. Part II: A Statistical Algorithm Based on A-Train Satellite Data

Yoo-Jeong Noh Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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John M. Forsythe Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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Steven D. Miller Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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Curtis J. Seaman Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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Yue Li Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin–Madison, Madison, Wisconsin

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Andrew K. Heidinger NOAA/NESDIS/Center for Satellite Applications and Research/Advanced Satellite Products Branch, Madison, Wisconsin

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Daniel T. Lindsey NOAA/NESDIS/Center for Satellite Applications and Research/Regional and Mesoscale Meteorology Branch, Fort Collins, Colorado

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Matthew A. Rogers Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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Philip T. Partain Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

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Print Publication:
01 Mar 2017
DOI:
https://doi.org/10.1175/JTECH-D-16-0110.1
Page(s):
585–598
Restricted access

Abstract

Knowledge of cloud-base height (CBH) is important to describe cloud radiative feedbacks in numerical models and is of practical relevance to the aviation community. Whereas satellite remote sensing with passive radiometers traditionally has provided a ready means for estimating cloud-top height (CTH) and cloud water path (CWP), assignment of CBH requires heavy assumptions on the distribution of CWP within the cloud profile. An attempt to retrieve CBH has been included as part of the VIIRS environmental data records, produced operationally as part of the Suomi–National Polar-Orbiting Partnership (SNPP) and the forthcoming Joint Polar Satellite System. Through formal validation studies tied to the program, it was found that the operational CBH algorithm failed to meet performance specifications in many cases. This paper presents a new methodology for retrieving CBH of the uppermost cloud layer, developed through statistical analyses relating cloud geometric thickness (CGT) to CTH and CWP. The semiempirical approach, which relates these parameters via piecewise fitting, enlists A-Train satellite data [CloudSat cloud profiling radar (CPR), CALIPSO/CALIOP, and Aqua MODIS]. CBH is provided as the residual difference between CTH and CGT. By eliminating cloud type–dependent assumptions on CWP distribution, artifacts common to the operational algorithm (which contribute to high errors) are reduced. Special accommodations are made for handling optically thin cirrus and deep convection. An application to SNPP VIIRS is demonstrated, and the results are compared against global CloudSat observations. From the VIIRS–CloudSat daytime matchups (September–October 2013 and January–May 2015), the new algorithm outperforms the operational SNPP VIIRS algorithm, particularly when the retrieved CTH is accurate. Best performance is expected for single-layer liquid-phase clouds.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Yoo-Jeong Noh, yoo-jeong.noh@colostate.edu

Abstract

Knowledge of cloud-base height (CBH) is important to describe cloud radiative feedbacks in numerical models and is of practical relevance to the aviation community. Whereas satellite remote sensing with passive radiometers traditionally has provided a ready means for estimating cloud-top height (CTH) and cloud water path (CWP), assignment of CBH requires heavy assumptions on the distribution of CWP within the cloud profile. An attempt to retrieve CBH has been included as part of the VIIRS environmental data records, produced operationally as part of the Suomi–National Polar-Orbiting Partnership (SNPP) and the forthcoming Joint Polar Satellite System. Through formal validation studies tied to the program, it was found that the operational CBH algorithm failed to meet performance specifications in many cases. This paper presents a new methodology for retrieving CBH of the uppermost cloud layer, developed through statistical analyses relating cloud geometric thickness (CGT) to CTH and CWP. The semiempirical approach, which relates these parameters via piecewise fitting, enlists A-Train satellite data [CloudSat cloud profiling radar (CPR), CALIPSO/CALIOP, and Aqua MODIS]. CBH is provided as the residual difference between CTH and CGT. By eliminating cloud type–dependent assumptions on CWP distribution, artifacts common to the operational algorithm (which contribute to high errors) are reduced. Special accommodations are made for handling optically thin cirrus and deep convection. An application to SNPP VIIRS is demonstrated, and the results are compared against global CloudSat observations. From the VIIRS–CloudSat daytime matchups (September–October 2013 and January–May 2015), the new algorithm outperforms the operational SNPP VIIRS algorithm, particularly when the retrieved CTH is accurate. Best performance is expected for single-layer liquid-phase clouds.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Yoo-Jeong Noh, yoo-jeong.noh@colostate.edu
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  • Baker, N., 2011: Joint Polar Satellite System (JPSS) VIIRS cloud base height algorithm theoretical basis document (ATBD). JPSS Ground Project Code 474-00045, NASA GSFC, 35 pp. [Available online at http://www.star.nesdis.noaa.gov/jpss/documents/ATBD/D0001-M01-S01-015_JPSS_ATBD_VIIRS-Cloud-Base-Height.pdf.]

  • Barker, H. W., M. P. Jerg, T. Wehr, S. Kato, D. P. Donovan, and R. J. Hogan, 2011: A 3D cloud-construction algorithm for the EarthCARE satellite mission. Quart. J. Roy. Meteor. Soc., 137, 1042–1058, doi:10.1002/qj.824.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Bennartz, R., 2007: Global assessment of marine boundary layer cloud droplet number concentration from satellite. J. Geophys. Res., 112, D02201, doi:10.1029/2006JD007547.

    • Search Google Scholar
    • Export Citation

  • Chakrapani, V., D. R. Doelling, A. D. Rapp, and P. Minnis, 2002: Cloud thickness estimation from GOES-8 satellite data over the ARM-SGP site. Proc. 12th ARM Science Meeting, St. Petersburg, FL, U.S. Dept. of Energy, 7 pp.

  • Ellrod, G. P., 2002: Estimation of low cloud base heights at night from satellite infrared and surface temperature data. Natl. Wea. Dig., 26 (1–2), 39–44.

    • Search Google Scholar
    • Export Citation

  • Forsythe, J. M., T. H. Vonder Haar, and D. L. Reinke, 2000: Cloud-base height estimates using a combination of meteorological satellite imagery and surface reports. J. Appl. Meteor., 39, 2336–2347, doi:10.1175/1520-0450(2000)039<2336:CBHEUA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Forsythe, J. M., J. B. Dodson, P. T. Partain, S. Q. Kidder, and T. H. Vonder Haar, 2012: How total precipitable water vapor anomalies relate to cloud vertical structure. J. Hydrometeor., 13, 709–721, doi:10.1175/JHM-D-11-049.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Goldberg, M. D., H. Kilcoyne, H. Cikanek, and A. Metha, 2013: Joint Polar Satellite System: The United States next generation civilian polar-orbiting environmental satellite system. J. Geophys. Res. Atmos., 118, 13 463–13 475, doi:10.1002/2013JD020389.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hansen, B., 2007: A fuzzy logic–based analog forecasting system for ceiling and visibility. Wea. Forecasting, 22, 1319–1330, doi:10.1175/2007WAF2006017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Heidinger, A. K., 2013: ABI cloud height. Version 3.0, NOAA/NESDIS/STAR Algorithm Theoretical Basis Doc., 79 pp. [Available online at http://www.star.nesdis.noaa.gov/goesr/docs/ATBD/Cloud_Height.pdf.]

  • Heidinger, A. K., and M. J. Pavolonis, 2009: Gazing at cirrus clouds for 25 years through a split window. Part I: Methodology. J. Appl. Meteor. Climatol., 48, 1100–1116, doi:10.1175/2008JAMC1882.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Heidinger, A. K., Y. Li, B. A. Baum, R. E. Holz, S. Platnick, and P. Yang, 2015: Retrieval of cirrus cloud optical depth under day and night conditions from MODIS Collection 6 cloud property data. Remote Sens., 7, 7257–7271, doi:10.3390/rs70607257.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hillger, D., and Coauthors, 2013: First-light imagery from Suomi NPP VIIRS. Bull. Amer. Meteor. Soc., 94, 1019–1029, doi:10.1175/BAMS-D-12-00097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hutchison, K. D., 2002: The retrieval of cloud base heights from MODIS and three-dimensional cloud fields from NASA’s EOS Aqua mission. Int. J. Remote Sens., 23, 5249–5265, doi:10.1080/01431160110117391.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Hutchison, K. D., E. Wong, and S. C. Ou, 2006: Cloud base height retrieval during nighttime conditions with MODIS data. Int. J. Remote Sens., 27, 2847–2862, doi:10.1080/01431160500296800.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • King, M. D., S.-C. Tsay, S. E. Platnick, M. Wang, and K. N. Liou, 1997: Cloud retrieval algorithms for MODIS: Optical thickness, effective particle radius, and thermodynamic phase. Version 5, MODIS Algorithm Theoretical Basis Doc. ATBD-MOD-05, 79 pp.

  • L’Ecuyer, T. S., and J. H. Jiang, 2010: Touring the atmosphere aboard the A-Train. Phys. Today, 63, 36, doi:10.1063/1.3463626.

  • Marchand, R., G. G. Mace, T. Ackerman, and G. Stephens, 2008: Hydrometeor detection using Cloudsat—An Earth-orbiting 94-GHz cloud radar. J. Atmos. Oceanic Technol., 25, 519–533, doi: 10.1175/2007JTECHA1006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Marzban, C., S. Leyton, and B. Colman, 2007: Ceiling and visibility forecasts via neural networks. Wea. Forecasting, 22, 466–479, doi:10.1175/WAF994.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • McCleese, D. J., and L. W. Wilson, 1976: Cloud top height from temperature sounding instruments. Quart. J. Roy. Meteor. Soc., 102, 781–790, doi:10.1002/qj.49710243408.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Mecikalski, J. R., and Coauthors, 2007: Aviation applications for satellite-based observations of cloud properties, convection initiation, in-flight icing, turbulence, and volcanic ash. Bull. Amer. Meteor. Soc., 88, 1589–1607, doi:10.1175/BAMS-88-10-1589.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Menzel, W. P., W. L. Smith, and T. R. Stewart, 1983: Improved cloud motion wind vector and altitude assignment using VAS. J. Appl. Meteor. Climatol., 22, 377–384, doi:10.1175/1520-0450(1983)022<0377:ICMWVA>2.0.CO;2.

    • Crossref
    • 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.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Miller, S. D., and Coauthors, 2014: Estimating three-dimensional cloud structure via statistically blended satellite observations. J. Appl. Meteor. Climatol., 53, 437–455, doi:10.1175/JAMC-D-13-070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Minnis, P., and Coauthors, 2011: CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data—Part I: Algorithms. IEEE Trans. Geosci. Remote Sens., 49, 4374–4400, doi:10.1109/TGRS.2011.2144601.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Nayak, M., M. Witkowski, D. Vane, T. Livermore, and M. Rokey, 2012: CloudSat anomaly recovery and operational lessons learned. Proc. 12th Int. Conf. on Space Operations (Space Ops 2012), Stockholm, Sweden, CNES, 1295798. [Available online at www.spaceops2012.org/proceedings/documents/id1295798-Paper-001.pdf.]

    • Crossref
    • Export Citation

  • Partain, P., 2007: Cloudsat ECMWF-AUX auxiliary data process description and interface control document. CloudSat Project, CIRA, Colorado State University, 11 pp. [Available online at http://www.cloudsat.cira.colostate.edu/sites/default/files/products/files/ECMWF-AUX_PDICD.P_R04.20070718.pdf.]

  • Pavolonis, M. J., and A. K. Heidinger, 2004: Daytime cloud overlap detection from AVHRR and VIIRS. J. Appl. Meteor., 43, 762–778, doi:10.1175/2099.1.

  • Randall, D. A., 1989: Cloud parameterization for climate models: Status and prospects. Atmos. Res., 23, 345–362, doi:10.1016/0169-8095(89)90025-2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Seaman, C. J., Y.-J. Noh, S. D. Miller, A. K. Heidinger, and D. T. Lindsey, 2017: Cloud-base height estimation from VIIRS. Part I: Operational algorithm validation against CloudSat. J. Atmos. Oceanic Technol., 34, 567–583, doi:10.1175/JTECH-D-16-0109.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Slingo, A., and J. M. Slingo, 1988: The response of a general circulation model to cloud longwave forcing. I: Introduction and initial experiments. Quart. J. Roy. Meteor. Soc., 114, 1027–1062, doi:10.1002/qj.49711448209.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Stephens, G. L., and Coauthors, 2002: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bull. Amer. Meteor. Soc., 83, 1771–1790, doi:10.1175/BAMS-83-12-1771.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Sun, X. J., H. R. Li, H. W. Barker, R. W. Zhang, Y. B. Zhou, and L. Liu, 2016: Satellite-based estimation of cloud-base heights using constrained spectral radiance matching. Quart. J. Roy. Meteor. Soc., 142, 224–232, doi:10.1002/qj.2647.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Vislocky, R. L., and J. M. Fritsch, 1997: An automated, observations-based system for short-term prediction of ceiling and visibility. Wea. Forecasting, 12, 31–43, doi:10.1175/1520-0434(1997)012<0031:AAOBSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Walther, A., and A. K. Heidinger, 2012: Implementation of the daytime cloud optical and microphysical properties algorithm (DCOMP) in PATMOS-x. J. Appl. Meteor. Climatol., 51, 1371–1390, doi:10.1175/JAMC-D-11-0108.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Walther, A., A. K. Heidinger, and S. Miller, 2013: The expected performance of cloud optical and microphysical properties derived from Suomi NPP VIIRS day/night band lunar reflectance. J. Geophys. Res. Atmos., 118, 13 230–13 240, doi:10.1002/2013JD020478.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Weisz, E., J. Li, J. Li, D. K. Zhou, H.-L. Huang, M. D. Goldberg, and P. Yang, 2007: Cloudy sounding and cloud-top height retrieval from AIRS alone single field-of-view radiance measurements. Geophys. Res. Lett., 34, L12802, doi:10.1029/2007GL030219.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Wilheit, T. T., and K. D. Hutchison, 2000: Retrieval of cloud base heights from passive microwave and cloud top temperature data. IEEE Trans. Geosci. Remote Sens., 38, 1253–1259, doi:10.1109/36.843017.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Winker, D. M., M. A. Vaughan, A. H. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young, 2009: Overview of the CALIPSO mission and CALIOP data processing algorithms. J. Atmos. Oceanic Technol., 26, 2310–2323, doi:10.1175/2009JTECHA1281.1.

    • Crossref
    • Search Google Scholar
    • Export Citation

  • Yao, Z., J. Li, E. Weisz, A. Heidinger, and C. Liu, 2013: Evaluation of single field-of-view cloud top height retrievals from hyperspectral infrared sounder radiances with CloudSat and CALIPSO measurements. J. Geophys. Res. Atmos., 118, 9182–9190, doi:10.1002/jgrd.50681.

    • Crossref
    • Search Google Scholar
    • Export Citation
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