Cloud-Base Height Estimation from VIIRS. Part I: Operational Algorithm Validation against CloudSat

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

Search for other papers by Curtis J. Seaman in
Current site
Google Scholar
PubMed
Close
,
Yoo-Jeong Noh Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

Search for other papers by Yoo-Jeong Noh in
Current site
Google Scholar
PubMed
Close
,
Steven D. Miller Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, Colorado

Search for other papers by Steven D. Miller in
Current site
Google Scholar
PubMed
Close
,
Andrew K. Heidinger NOAA/NESDIS/Center for Satellite Applications and Research/Advanced Satellite Products Branch, Madison, Wisconsin

Search for other papers by Andrew K. Heidinger in
Current site
Google Scholar
PubMed
Close
, and
Daniel T. Lindsey NOAA/NESDIS/Center for Satellite Applications and Research/Regional and Mesoscale Meteorology Branch, Fort Collins, Colorado

Search for other papers by Daniel T. Lindsey in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The operational VIIRS cloud-base height (CBH) product from the Suomi–National Polar-Orbiting Partnership (SNPP) satellite is compared against observations of CBH from the cloud profiling radar (CPR) on board CloudSat. Because of the orbits of SNPP and CloudSat, these instruments provide nearly simultaneous observations of the same locations on Earth for a ~4.5-h period every 2–3 days. The methodology by which VIIRS and CloudSat observations are spatially and temporally matched is outlined. Based on four 1-month evaluation periods representing each season from June 2014 to April 2015, statistics related to the VIIRS CBH retrieval performance have been collected. Results indicate that when compared against CloudSat, the VIIRS CBH retrieval does not meet the error specifications set by the Joint Polar Satellite System (JPSS) program, with a root-mean-square error (RMSE) of 3.7 km for all clouds globally. More than half of all matching VIIRS pixels and CloudSat profiles have CBH errors exceeding the 2-km error requirement. Underscoring the significance of these statistics, it is shown that a simple estimate based on a constant cloud geometric thickness of 2 km outperforms the current operational CBH algorithm. It was found that the performance of the CBH product is impacted by the accuracy of upstream retrievals [primarily cloud-top height (CTH)] and the a priori information used by the CBH retrieval algorithm. However, even when CTH errors were small, CBH errors still exceed the JPSS program error specifications with an RMSE of 2.3 km.

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: Curtis J. Seaman, curtis.seaman@colostate.edu

Abstract

The operational VIIRS cloud-base height (CBH) product from the Suomi–National Polar-Orbiting Partnership (SNPP) satellite is compared against observations of CBH from the cloud profiling radar (CPR) on board CloudSat. Because of the orbits of SNPP and CloudSat, these instruments provide nearly simultaneous observations of the same locations on Earth for a ~4.5-h period every 2–3 days. The methodology by which VIIRS and CloudSat observations are spatially and temporally matched is outlined. Based on four 1-month evaluation periods representing each season from June 2014 to April 2015, statistics related to the VIIRS CBH retrieval performance have been collected. Results indicate that when compared against CloudSat, the VIIRS CBH retrieval does not meet the error specifications set by the Joint Polar Satellite System (JPSS) program, with a root-mean-square error (RMSE) of 3.7 km for all clouds globally. More than half of all matching VIIRS pixels and CloudSat profiles have CBH errors exceeding the 2-km error requirement. Underscoring the significance of these statistics, it is shown that a simple estimate based on a constant cloud geometric thickness of 2 km outperforms the current operational CBH algorithm. It was found that the performance of the CBH product is impacted by the accuracy of upstream retrievals [primarily cloud-top height (CTH)] and the a priori information used by the CBH retrieval algorithm. However, even when CTH errors were small, CBH errors still exceed the JPSS program error specifications with an RMSE of 2.3 km.

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: Curtis J. Seaman, curtis.seaman@colostate.edu
Save
  • Austin, R. T., A. J. Heymsfield, and G. L. Stephens, 2009: Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature. J. Geophys. Res., 114, D00A23, doi:10.1029/2008JD010049.

    • Search Google Scholar
    • Export Citation
  • Baker, M. B., 1997: Cloud microphysics and climate. Science, 276, 10721078, doi:10.1126/science.276.5315.1072.

  • 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.]

  • Baker, N., 2012: Joint Polar Satellite System (JPSS) VIIRS cloud top algorithm theoretical basis document (ATBD). Revision A, JPSS Ground Project Code 474-00041, NASA-GSFC, 73 pp. [Available online at http://www.star.nesdis.noaa.gov/jpss/documents/ATBD/D0001-M01-S01-012_JPSS_ATBD_VIIRS-Cloud-Top_A.pdf.]

  • Baker, N., 2013: Joint Polar Satellite System (JPSS) operational algorithm description (OAD) document for VIIRS perform parallax correction (PPC) intermediate product (IP) software. Revision B, JPSS Ground Project Code 474-00088, NASA GSFC, 28 pp. [Available online at http://npp.gsfc.nasa.gov/sciencedocs/2015-06/474-00088_OAD-VIIRS-PPC-IP_B.pdf.]

  • Baker, N., 2014: Joint Polar Satellite System (JPSS) VIIRS cloud mask (VCM) algorithm theoretical basis document (ATBD). Revision E, JPSS Ground Project Code 474-00033, NASA GSFC, 101 pp. [Available online at http://www.star.nesdis.noaa.gov/jpss/documents/ATBD/D0001-M01-S01-011_JPSS_ATBD_VIIRS-Cloud-Mask_E.pdf.]

  • Bankert, R. L., M. Hadjimichael, A. P. Kuciauskas, W. T. Thompson, and K. Richardson, 2004: Remote cloud ceiling assessment using data mining methods. J. Appl. Meteor., 43, 19291946, doi:10.1175/JAM2177.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bendix, J., B. Thies, J. Cermak, and T. Nauss, 2005: Ground fog detection from space based on MODIS daytime data—A feasibility study. Wea. Forecasting, 20, 9891005, doi:10.1175/WAF886.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Calvert, C. G., M. J. Pavolonis, S. Hubbard, C. M. Gravelle, and S. S. Lindstrom, 2016: The GOES-R/JPSS approach for identifying hazardous low clouds: Overview and operational impacts. 12th Annual Symp. on New Generation Operational Environmental Satellite Systems, New Orleans, LA, Amer. Meteor. Soc., 8.4. [Available online at https://ams.confex.com/ams/96Annual/webprogram/Paper290493.html.]

  • Cao, C., J. Xiong, S. Blonski, Q. Liu, S. Uprety, X. Shao, Y. Bai, and F. Weng, 2013a: Suomi NPP VIIRS sensor data record verification, validation and long-term performance monitoring. J. Geophys. Res. Atmos., 118, 11 66411 678, doi:10.1002/2013JD020418.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cao, C., and Coauthors, 2013b: Visible Infrared Imaging Radiometer Suite (VIIRS) sensor data record (SDR) user’s guide. Version 1.2, NOAA Tech. Rep. NESDIS 142A, 46 pp. [Available online at http://www.star.nesdis.noaa.gov/jpss/documents/UserGuides/VIIRS_SDR_Users_Guide.pdf.].

  • Eberhard, W. L., 1986: Cloud signals from lidar and rotating beam ceilometer compared with pilot ceiling. J. Atmos. Oceanic Technol., 3, 499512, doi:10.1175/1520-0426(1986)003<0499:CSFLAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ellrod, G. P., 2002: Estimation of low cloud base height at night from satellite infrared and surface temperature data. Natl. Wea. Dig., 26 (12), 3944.

    • Search Google Scholar
    • Export Citation
  • Fitch, K. E., K. D. Hutchison, K. S. Bartlett, R. S. Wacker, and K. C. Gross, 2016: Assessing VIIRS cloud base height products with data collected at the Department of Energy Atmospheric Radiation Measurement sites. Int. J. Remote Sens., 37, 26042620, doi:10.1080/01431161.2016.1182665.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Forsythe, J. F., T. H. Vonder Haar, and D. Reinke, 2000: Cloud-base height estimates using a combination of meteorological satellite imagery and surface reports. J. Appl. Meteor., 39, 23362347, doi:10.1175/1520-0450(2000)039<2336:CBHEUA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fuchs, B. R., and Coauthors, 2015: Environmental controls on storm intensity and charge structure in multiple regions of the continental United States. J. Geophys. Res. Atmos., 120, 65756596, doi:10.1002/2015JD023271.

    • 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 46313 475, doi:10.1002/2013JD020389.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gultepe, I., and Coauthors, 2009: The Fog Remote Sensing and Modeling Field Project. Bull. Amer. Meteor. Soc., 90, 341359, doi:10.1175/2008BAMS2354.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haynes, J. M., T. S. L’Ecuyer, G. L. Stephens, S. D. Miller, C. Mitrescu, N. B. Wood, and S. Tanelli, 2009: Rainfall retrieval over the ocean with spaceborne W-band radar. J. Geophys. Res., 114, D00A22, doi:10.1029/2008JD009973.

    • Search Google Scholar
    • Export Citation
  • Heidinger, A. K., M. J. Pavolonis, R. E. Holz, B. A. Baum, and S. Berthier, 2010a: Using CALIPSO to explore the sensitivity to cirrus height in the infrared observations from NPOESS/VIIRS and GOES‐R/ABI. J. Geophys. Res., 115, D00H20, doi:10.1029/2009JD012152.

    • Search Google Scholar
    • Export Citation
  • Heidinger, A. K., M. J. Pavolonis, R. E. Holz, B. A. Baum, and S. Berthier, 2010b: Correction to “Using CALIPSO to explore the sensitivity to cirrus height in the infrared observations from NPOESS/VIIRS and GOES‐R/ABI.” J. Geophys. Res., 115, D12299, doi:10.1029/2010JD014461.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Herzegh, P., G. Wiener, R. Bateman, J. Cowie, and J. Black, 2015: Data fusion enables better recognition of ceiling and visibility hazards in aviation. Bull. Amer. Meteor. Soc., 96, 526532, doi:10.1175/BAMS-D-13-00111.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Heymsfield, A. J., D. Winker, and G.-J. van Zadelhoff, 2005: Extinction-ice water content-effective radius algorithms for CALIPSO. Geophys. Res. Lett., 32, L10807, doi:10.1029/2005GL022742.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hillger, D., and Coauthors, 2013: First-light imagery from Suomi NPP VIIRS. Bull. Amer. Meteor. Soc., 94, 10191029, doi:10.1175/BAMS-D-12-00097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hutchison, K., 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, 52495265, doi:10.1080/01431160110117391.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hutchison, K., E. Wong, and C. Ou, 2006: Cloud base heights retrieved during night-time conditions with MODIS data. Int. J. Remote Sens., 27, 28472862, doi:10.1080/01431160500296800.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Inoue, M., A. D. Fraser, N. Adams, S. Carpenter, and H. E. Phillips, 2015: An assessment of numerical weather prediction–derived low-cloud-base height forecasts. Wea. Forecasting, 30, 486497, doi:10.1175/WAF-D-14-00052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, D. B., 1980: The influence of cloud-base temperature and pressure on droplet concentration. J. Atmos. Sci., 37, 20792085, doi:10.1175/1520-0469(1980)037<2079:TIOCBT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kokhanovsky, A. A., and V. V. Rozanov, 2005: Cloud bottom altitude determination from a satellite. IEEE Trans. Geosci. Remote Sens. Lett., 2, 280283, doi:10.1109/LGRS.2005.846837.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lawson, R. P., and B. A. Baker, 2006: Improvement in determination of ice water content from two-dimensional particle imagery. Part II: Applications to collected data. J. Appl. Meteor. Climatol., 45, 12911303, doi:10.1175/JAM2399.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leyton, S. M., and J. M. Fritsch, 2004: The impact of high-frequency surface weather observations on short-term probabilistic forecasts of ceiling and visibility. J. Appl. Meteor., 43, 145156, doi:10.1175/1520-0450(2004)043<0145:TIOHSW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liou, K.-N., 1992: Radiation and Cloud Processes in the Atmosphere: Theory, Observation and Modeling. Oxford Monogr. Geol. Geophys., Vol. 20, Oxford University Press, 504 pp.

  • Mace, G. G., and Q. Zhang, 2014: The CloudSat radar–lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results. J. Geophys. Res. Atmos., 119, 94419462, doi:10.1002/2013JD021374.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 519533, doi:10.1175/2007JTECHA1006.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miles, N. L., J. Verlinde, and E. E. Clothiaux, 2000: Cloud droplet size distributions in low-level stratiform clouds. J. Atmos. Sci., 57, 295311, doi:10.1175/1520-0469(2000)057<0295:CDSDIL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miller, S. D., and Coauthors, 2014: Estimating three-dimensional cloud structure from statistically blended active and passive sensor observations. J. Appl. Meteor. Climatol., 53, 437455, doi:10.1175/JAMC-D-13-070.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 1997: Cloud optical property retrieval (subsystem 4.3). CERES Algorithm Theoretical Basis Doc. 4.3, Release 2.2, 60 pp. [Available online at http://ceres.larc.nasa.gov/atbd.php.]

  • 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 http://www.spaceops2012.org/proceedings/documents/id1295798-Paper-001.pdf.]

    • Crossref
    • Export Citation
  • Noh, Y.-J., C. J. Seaman, T. H. Vonder Haar, D. R. Hudak, and P. Rodriguez, 2011: Comparisons and analyses of aircraft and satellite observations for wintertime mixed-phase clouds. J. Geophys. Res., 116, D18207, doi:10.1029/2010JD015420.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noh, Y.-J., and Coauthors, 2017: Cloud-base height estimation from VIIRS. Part II: A statistical algorithm based on A-Train satellite data. J. Atmos. Oceanic., 34, 585598, doi:10.1175/JTECH-D-16-0110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ou, S. C., K. N. Liou, and T. R. Caudill, 1998: Remote sounding of multilayer cirrus cloud systems using AVHRR data collected during FIRE-II-IFO. J. Appl. Meteor., 37, 241254, doi:10.1175/1520-0450-37.3.241.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ou, S. C., Y. Takano, K. N. Liou, G. J. Higgins, A. George, and R. Slonaker, 2003: Remote sensing of cirrus cloud optical thickness and effective particle size for the National Polar-orbiting Operational Environmental Satellite System Visible/Infrared Imager Radiometer Suite: Sensitivity to instrument noise and uncertainties in environmental parameters. Appl. Opt., 42, 72027214, doi:10.1364/AO.42.007202.

    • Search Google Scholar
    • Export Citation
  • Pandey, P. C., E. G. Njoku, and J. W. Waters, 1983: Inference of cloud temperature and thickness by microwave radiometry from space. J. Climate Appl. Meteor., 22, 18941898, doi:10.1175/1520-0450(1983)022<1894:IOCTAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reinke, D. L., and T. H. Vonder Haar, 2011: Probability of cloud-free-line-of-sight derived from CloudSat and CALIPSO cloud observations. 2011 EUMETSAT Meteorological Satellite Conf., Oslo, Norway, EUMETSAT. [Available online at http://www.eumetsat.int/website/home/News/ConferencesandEvents/DAT_2039705.html.]

  • Schmit, T. J., M. M. Gunshor, W. P. Menzel, J. J. Gurka, J. Li, and A. S. Bachmeier, 2005: Introducing the next-generation Advanced Baseline Imager on GOES-R. Bull. Amer. Meteor. Soc., 86, 10791096, doi:10.1175/BAMS-86-8-1079.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seaman, C., D. Hillger, T. Kopp, R. Williams, S. Miller, and D. Lindsey, 2014: Visible Infrared Imaging Radiometer Suite (VIIRS) imagery environmental data record (EDR) user’s guide. Version 1.1, NOAA Tech. Rep. NESDIS 147, 30 pp. [Available online at http://www.star.nesdis.noaa.gov/jpss/documents/UserGuides/VIIRS_Imagery_EDR_Users_Guide.pdf.]

  • 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, 10271062, doi:10.1002/qj.49711448209.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., 1994: Remote Sensing of the Lower Atmosphere. Oxford University Press, 540 pp.

  • 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, 17711790, doi:10.1175/BAMS-83-12-1771.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stephens, G. L., and Coauthors, 2008: CloudSat mission: Performance and early science after the first year of operation. J. Geophys. Res., 113, D00A18, doi:10.1029/2008JD009982.

    • 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, 224232, doi:10.1002/qj.2647.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tanelli, S., S. L. Durden, E. Im, K. S. Pak, D. G. Reinke, P. Partain, J. M. Haynes, and R. T. Marchand, 2008: CloudSat’s Cloud Profiling Radar after two years in orbit: Performance, calibration, and processing. IEEE Trans. Geosci. Remote Sens., 46, 35603573, doi:10.1109/TGRS.2008.2002030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walther, A., A. K. Heidinger, and S. D. 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 23013 240, doi:10.1002/2013JD020478.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilheit, T. T., and K. D. Hutchison, 1997: Retrieval of cloud base heights from passive microwave data constrained by infrared based cloud information. Int. J. Remote Sens., 18, 32633278, doi:10.1080/014311697217071.

    • 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, 23102323, doi:10.1175/2009JTECHA1281.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolfe, R. E., G. Q. Lin, M. Nishihama, K. P. Tewari, J. C. Tilton, and A. R. Isaacman, 2013: Suomi NPP VIIRS prelaunch and on-orbit geometric calibration and characterization. J. Geophys. Res. Atmos., 118, 11508–11521, doi:10.1002/jgrd.50873.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., D. Rosenfeld, X. Yu, G. Liu, J. Dai, and X. Xu, 2014: Satellite retrieval of convective cloud base temperature based on the NPP/VIIRS Imager. Geophys. Res. Lett., 41, 13081313, doi:10.1002/2013GL058970.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1004 334 17
PDF Downloads 771 163 5