Editorial Type:
Article
Article Type:
Research Article

Evaluation of Cloud-Phase Retrieval Methods for SEVIRI on Meteosat-8 Using Ground-Based Lidar and Cloud Radar Data

Erwin L. A. Wolters Royal Netherlands Meteorological Institute, De Bilt, Netherlands

Search for other papers by Erwin L. A. Wolters in
Current site
Google Scholar
PubMed Close
,
Robert A. Roebeling Royal Netherlands Meteorological Institute, De Bilt, Netherlands

Search for other papers by Robert A. Roebeling in
Current site
Google Scholar
PubMed Close
, and
Arnout J. Feijt Royal Netherlands Meteorological Institute, De Bilt, Netherlands

Search for other papers by Arnout J. Feijt in
Current site
Google Scholar
PubMed Close
Online Publication:
01 Jun 2008
Print Publication:
01 Jun 2008
DOI:
https://doi.org/10.1175/2007JAMC1591.1
Page(s):
1723–1738
Restricted access

Abstract

Three cloud-phase determination algorithms from passive satellite imagers are explored to assess their suitability for climate monitoring purposes in midlatitude coastal climate zones. The algorithms are the Moderate Resolution Imaging Spectroradiometer (MODIS)-like thermal infrared cloud-phase method, the Satellite Application Facility on Climate Monitoring (CM-SAF) method, and an International Satellite Cloud Climatology Project (ISCCP)-like method. Using one year (May 2004–April 2005) of data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the first Meteosat Second Generation satellite (Meteosat-8), retrievals of the methods are compared with collocated and synchronized ground-based cloud-phase retrievals obtained from cloud radar and lidar observations at Cabauw, Netherlands. Three aspects of the satellite retrievals are evaluated: 1) instantaneous cloud-phase retrievals, 2) monthly-averaged water and ice cloud occurrence frequency, and 3) diurnal cycle of cloud phase for May–August 2004. For the instantaneous cases, all methods have a very small bias for thick water and ice cloud retrievals (∼5%). The ISCCP-like method has a larger bias for pure water clouds (∼10%), which is likely due to the 260-K threshold leading to misdetection of water clouds existing at lower temperatures. For the monthly-averaged water and ice cloud occurrence, the CM-SAF method is best capable of reproducing the annual cycle, mainly for the water cloud occurrence frequency, for which an almost constant positive bias of ∼8% was found. The ISCCP- and MODIS-like methods have more problems in detecting the annual cycle, especially during the winter months. The difference in annual cycle detection among the three methods is most probably related to the use of visible/near-infrared reflectances that enable a more direct observation of cloud phase. The diurnal cycle in cloud phase is reproduced well by all methods. The MODIS-like method reproduces the diurnal cycle best, with correlations of 0.89 and 0.86 for water and ice cloud occurrence frequency, respectively.

Corresponding author address: E. Wolters, Royal Netherlands Meteorological Institute, De Bilt NL-3730 AE, Netherlands. Email: wolterse@knmi.nl

Abstract

Three cloud-phase determination algorithms from passive satellite imagers are explored to assess their suitability for climate monitoring purposes in midlatitude coastal climate zones. The algorithms are the Moderate Resolution Imaging Spectroradiometer (MODIS)-like thermal infrared cloud-phase method, the Satellite Application Facility on Climate Monitoring (CM-SAF) method, and an International Satellite Cloud Climatology Project (ISCCP)-like method. Using one year (May 2004–April 2005) of data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on the first Meteosat Second Generation satellite (Meteosat-8), retrievals of the methods are compared with collocated and synchronized ground-based cloud-phase retrievals obtained from cloud radar and lidar observations at Cabauw, Netherlands. Three aspects of the satellite retrievals are evaluated: 1) instantaneous cloud-phase retrievals, 2) monthly-averaged water and ice cloud occurrence frequency, and 3) diurnal cycle of cloud phase for May–August 2004. For the instantaneous cases, all methods have a very small bias for thick water and ice cloud retrievals (∼5%). The ISCCP-like method has a larger bias for pure water clouds (∼10%), which is likely due to the 260-K threshold leading to misdetection of water clouds existing at lower temperatures. For the monthly-averaged water and ice cloud occurrence, the CM-SAF method is best capable of reproducing the annual cycle, mainly for the water cloud occurrence frequency, for which an almost constant positive bias of ∼8% was found. The ISCCP- and MODIS-like methods have more problems in detecting the annual cycle, especially during the winter months. The difference in annual cycle detection among the three methods is most probably related to the use of visible/near-infrared reflectances that enable a more direct observation of cloud phase. The diurnal cycle in cloud phase is reproduced well by all methods. The MODIS-like method reproduces the diurnal cycle best, with correlations of 0.89 and 0.86 for water and ice cloud occurrence frequency, respectively.

Corresponding author address: E. Wolters, Royal Netherlands Meteorological Institute, De Bilt NL-3730 AE, Netherlands. Email: wolterse@knmi.nl

Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Arking, A., and J. D. Childs, 1985: Retrieval of cloud cover parameters from multispectral satellite images. J. Climate Appl. Meteor., 24 , 322–333.

    • Search Google Scholar
    • Export Citation

  • Baum, B. A., P. F. Soulen, K. I. Strabala, M. D. King, S. A. Ackerman, W. P. Menzel, and P. Yang, 2000: Remote sensing of cloud properties using MODIS Airborne Simulator imagery during SUCCESS: 2. Cloud thermodynamic phase. J. Geophys. Res., 105 , 11781–11792.

    • Search Google Scholar
    • Export Citation

  • Chepfer, H., P. Goloub, J. Spinhirne, P. H. Flamant, M. Lavorato, L. Sauvage, G. Brogniez, and J. Pelon, 2000: Cirrus cloud properties derived from POLDER-1/ADEOS polarized radiances: First validation using a ground-based lidar network. J. Appl. Meteor., 39 , 154–168.

    • Search Google Scholar
    • Export Citation

  • De Haan, J. F., P. B. Bosma, and J. W. Hovenier, 1987: The adding method for multiple scattering calculations of polarized light. Astron. Astrophys., 183 , 371–391.

    • Search Google Scholar
    • Export Citation

  • Doelling, D. R., L. Nguyen, and P. Minnis, 2004: Calibration comparisons between SEVIRI, MODIS, and GOES data. Proc. 2004 EUMETSAT Meteor. Satellite Conf., Prague, Czech Republic, European Organisation for the Exploitation of Meteorological Satellites, 77–83.

  • Downing, H. D., and D. Williams, 1975: Optical constants of water in the infrared. J. Geophys. Res., 80 , 1656–1661.

  • Efron, B., and R. J. Tibshirani, 1993: An Introduction to the Bootstrap. CRC Press, 436 pp.

  • EUMETSAT, 2007: A planned change to the MSG Level 1.5 image production radiance definition. EUMETSAT Sci. Rep. EUM/OPS-MSG/TEN/06/0519, 9 pp. [Available online at http://www.eumetsat.int/groups/ops/documents/document/pdf_msg_planned_change_level15.pdf.].

  • Hansen, J. E., and J. B. Pollack, 1970: Near-infrared light scattering by terrestrial clouds. J. Atmos. Sci., 27 , 265–281.

  • Hansen, J. E., and L. D. Travis, 1974: Light scattering in planetary atmospheres. Space Sci. Rev., 16 , 527–610.

  • Hess, M., and M. Wiegner, 1994: COP: A data library of optical properties of hexagonal ice crystals. Appl. Opt., 33 , 7740–7746.

  • Hogan, R. J., A. J. Illingworth, E. J. O’Connor, and J. P. V. Poiares Baptista, 2003: Characteristics of mixed-phase clouds, part II: A climatology from ground-based lidar. Quart. J. Roy. Meteor. Soc., 129 , 2117–2134.

    • Search Google Scholar
    • Export Citation

  • Illingworth, A. J., and Coauthors, 2007: Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations. Bull. Amer. Meteor. Soc., 88 , 883–898.

    • Search Google Scholar
    • Export Citation

  • Jolivet, D., and A. J. Feijt, 2005: Quantification of the accuracy of liquid water path fields derived from NOAA 16 Advanced Very High Resolution Radiometer over three ground stations using microwave radiometers. J. Geophys. Res., 110 .D11204, doi:10.1029/2004JD005205.

    • Search Google Scholar
    • Export Citation

  • Knap, W. H., P. Stammes, and R. B. A. Koelemeijer, 2002: Cloud thermodynamic phase determination from near-infrared spectra of reflected sunlight. J. Atmos. Sci., 59 , 83–96.

    • Search Google Scholar
    • Export Citation

  • Loeb, N. G., and J. A. Coakley Jr., 1998: Inference of marine stratus cloud optical depths from satellite measurements: Does 1D theory apply? J. Climate, 11 , 215–233.

    • Search Google Scholar
    • Export Citation

  • Minnis, P., D. P. Garber, D. F. Young, R. F. Arduini, and Y. Takano, 1998: Parameterizations of reflectance and effective emittance for satellite remote sensing of cloud properties. J. Atmos. Sci., 55 , 3313–3339.

    • Search Google Scholar
    • Export Citation

  • Mittermaier, M. P., and A. J. Illingworth, 2003: Comparison of model-derived and radar-observed freezing-level heights: Implications for vertical reflectivity profile-correction schemes. Quart. J. Roy. Meteor. Soc., 129 , 83–95.

    • Search Google Scholar
    • Export Citation

  • Nakajima, T., and M. D. King, 1990: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part 1: Theory. J. Atmos. Sci., 47 , 1878–1893.

    • Search Google Scholar
    • Export Citation

  • Naud, C. M., A. D. Del Genio, and M. Bauer, 2006: Observational constraints on the cloud thermodynamic phase in midlatitude storms. J. Climate, 19 , 5273–5288.

    • Search Google Scholar
    • Export Citation

  • Pilewskie, P., and S. Twomey, 1987: Discrimination of ice from water in clouds by optical remote sensing. Atmos. Res., 21 , 113–122.

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

    • Search Google Scholar
    • Export Citation

  • Pruppacher, H. R., and J. D. Klett, 1997: Microphysics of Clouds and Precipitation. 2nd ed. Kluwer Academic, 954 pp.

  • Rauber, R. M., and A. Tokay, 1991: An explanation for the existence of supercooled water at the top of cold clouds. J. Atmos. Sci., 48 , 1005–1023.

    • Search Google Scholar
    • Export Citation

  • Riédi, J. C., P. Goloub, and R. T. Marchand, 2001: Comparison of POLDER cloud phase retrievals to active remote sensor measurements at the ARM SGP site. Geophys. Res. Lett., 28 , 2185–2188.

    • Search Google Scholar
    • Export Citation

  • Roebeling, R. A., A. J. Feijt, and P. Stammes, 2006a: Cloud property retrievals for climate monitoring: Implications of differences between Spinning Enhanced Visible and Infrared Imager (SEVIRI) on Meteosat-8 and Advanced Very High Resolution Radiometer (AVHRR) on NOAA-17. J. Geophys. Res., 111 .D20210, doi:10.1029/2005JD006990.

    • Search Google Scholar
    • Export Citation

  • Roebeling, R. A., N. A. J. Schutgens, and A. J. Feijt, 2006b: Analysis of uncertainties in SEVIRI cloud property retrievals for climate monitoring. Preprints. 12th Conf. on Atmospheric Radiation, Madison, WI, Amer. Meteor. Soc., P4.51. [Available online at http://ams.confex.com/ams/pdfpapers/112865.pdf.].

    • Search Google Scholar
    • Export Citation

  • Rossow, W. B., and R. A. Schiffer, 1999: Advances in understanding clouds from ISCCP. Bull. Amer. Meteor. Soc., 80 , 2261–2287.

  • Stammes, P., 2001: Spectral radiance modelling in the UV-visible range. IRS 2000: Current Problems in Atmospheric Radiation (Proceedings of the International Radiation Symposium), W. L. Smith and Y. M. Timofeyev, Eds., A. Deepak Publishing, 385–388.

    • Search Google Scholar
    • Export Citation

  • Strabala, K. I., S. A. Ackerman, and W. P. Menzel, 1994: Cloud properties inferred from 8–12-μm data. J. Appl. Meteor., 33 , 212–229.

    • Search Google Scholar
    • Export Citation

  • Turner, D. D., 2005: Arctic mixed-phase cloud properties from AERI lidar observations: Algorithm and results from SHEBA. J. Appl. Meteor., 44 , 427–444.

    • Search Google Scholar
    • Export Citation

  • Turner, D. D., S. A. Ackerman, B. A. Baum, P. E. Rivercombe, and P. Yang, 2003: Cloud phase determination using ground-based AERI observations at SHEBA. J. Appl. Meteor., 42 , 701–715.

    • Search Google Scholar
    • Export Citation

  • Warren, S. G., 1984: Optical constants of ice from the ultraviolet to the microwave. Appl. Opt., 23 , 1206–1225.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1845 1476 70
PDF Downloads 235 56 12