Editorial Type:
Article
Article Type:
Research Article

Using MSG-SEVIRI Cloud Physical Properties and Weather Radar Observations for the Detection of Cb/TCu Clouds

Cintia Carbajal Henken Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands

Search for other papers by Cintia Carbajal Henken in
Current site
Google Scholar
PubMed Close
,
Maurice J. Schmeits Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands

Search for other papers by Maurice J. Schmeits in
Current site
Google Scholar
PubMed Close
,
Hartwig Deneke Leibniz Institute for Tropospheric Research, Leipzig, Germany

Search for other papers by Hartwig Deneke in
Current site
Google Scholar
PubMed Close
, and
Rob A. Roebeling Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands

Search for other papers by Rob A. Roebeling in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Jul 2011
DOI:
https://doi.org/10.1175/2011JAMC2601.1
Page(s):
1587–1600
Restricted access

Abstract

A new automated daytime cumulonimbus/towering cumulus (Cb/TCu) cloud detection method for the months of May–September is presented that combines information on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG) satellites and weather radar reflectivity factors. First, a pixel-based convective cloud mask (CCM) is constructed on the basis of cloud physical properties [cloud-top temperature, cloud optical thickness (COT), effective radius, and cloud phase] derived from SEVIRI. Second, a logistic regression model is applied to determine the probability of Cb/TCu clouds for the collection of pixels that pass the CCM. In this model, MSG-SEVIRI cloud physical properties and weather radar reflectivity factors are used as potential predictor sources. The predictand is derived from aviation routine weather reports (METAR) made by human observers at Amsterdam Airport Schiphol for 2004–07. Results show that the CCM filters out >70% of the “no” events (no Cb/TCu cloud) and that >93% of the “yes” events (Cb/TCu cloud) are retained. Most skillful predictors are derived from radar reflectivity factors and the COT of high resolution. The derived probabilities from the combined MSG and radar method clearly show skill over sample climatology. Probability thresholds are used to convert derived probabilities into derived group memberships (i.e., yes/no Cb/TCu clouds). When comparing verification scores between the combined MSG and radar method and either the radar-only method or the MSG-only method, the combined MSG and radar method shows slightly better performance. When comparing the combined MSG and radar method with the current Royal Netherlands Meteorological Institute (KNMI) radar-based Cb/TCu cloud detection method, the two methods show comparable probability of detection, but the former shows a false-alarm ratio that is about 8% lower. Moreover, a big advantage of the newly developed method is that it provides probabilities, in contrast to the current KNMI method.

Current affiliation: Institute for Space Sciences, Freie Universität Berlin, Berlin, Germany.

Corresponding author address: Dr. Rob A. Roebeling, EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, Germany. E-mail: rob.roebeling@eumetsat.int

Abstract

A new automated daytime cumulonimbus/towering cumulus (Cb/TCu) cloud detection method for the months of May–September is presented that combines information on cloud physical properties retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG) satellites and weather radar reflectivity factors. First, a pixel-based convective cloud mask (CCM) is constructed on the basis of cloud physical properties [cloud-top temperature, cloud optical thickness (COT), effective radius, and cloud phase] derived from SEVIRI. Second, a logistic regression model is applied to determine the probability of Cb/TCu clouds for the collection of pixels that pass the CCM. In this model, MSG-SEVIRI cloud physical properties and weather radar reflectivity factors are used as potential predictor sources. The predictand is derived from aviation routine weather reports (METAR) made by human observers at Amsterdam Airport Schiphol for 2004–07. Results show that the CCM filters out >70% of the “no” events (no Cb/TCu cloud) and that >93% of the “yes” events (Cb/TCu cloud) are retained. Most skillful predictors are derived from radar reflectivity factors and the COT of high resolution. The derived probabilities from the combined MSG and radar method clearly show skill over sample climatology. Probability thresholds are used to convert derived probabilities into derived group memberships (i.e., yes/no Cb/TCu clouds). When comparing verification scores between the combined MSG and radar method and either the radar-only method or the MSG-only method, the combined MSG and radar method shows slightly better performance. When comparing the combined MSG and radar method with the current Royal Netherlands Meteorological Institute (KNMI) radar-based Cb/TCu cloud detection method, the two methods show comparable probability of detection, but the former shows a false-alarm ratio that is about 8% lower. Moreover, a big advantage of the newly developed method is that it provides probabilities, in contrast to the current KNMI method.

Current affiliation: Institute for Space Sciences, Freie Universität Berlin, Berlin, Germany.

Corresponding author address: Dr. Rob A. Roebeling, EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, Germany. E-mail: rob.roebeling@eumetsat.int
Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • Amorati, R., P. Alberoni, V. Levizzani, and S. Nanni, 2000: IR-based satellite and radar rainfall estimates of convective storms over northern Italy. Meteor. Appl., 7, 1–18.

    • Search Google Scholar
    • Export Citation

  • Arkin, P., 1979: The relationship between fractional coverage of high cloud and rainfall accumulations during GATE over the B-scale array. Mon. Wea. Rev., 107, 1382–1387.

    • Search Google Scholar
    • Export Citation

  • Bankert, R., 1994: Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network. J. Appl. Meteor., 33, 909–918.

    • Search Google Scholar
    • Export Citation

  • Baum, B., V. Tovinkere, J. Titlow, and R. Welch, 1997: Automated cloud classification of global AVHRR data using a fuzzy logic approach. J. Appl. Meteor., 36, 1519–1540.

    • Search Google Scholar
    • Export Citation

  • Berendes, T., J. Mecikalski, W. MacKenzie Jr., K. Bedka, and U. Nair, 2008: Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering. J. Geophys. Res., 113, D20207, doi:10.1029/2008JD010287.

    • Search Google Scholar
    • Export Citation

  • Billet, J., M. DeLisi, B. Smith, and C. Gates, 1997: Use of regression techniques to predict hail size and the probability of large hail. Wea. Forecasting, 12, 154–164.

    • Search Google Scholar
    • Export Citation

  • Bolliger, M., P. Binder, and A. Rossa, 2003: Tracking cloud patterns by Meteosat rapid scan imagery in complex terrain. Meteor. Z., 12, 73–80.

    • Search Google Scholar
    • Export Citation

  • Carbajal Henken, C. K., M. J. Schmeits, E. L. A. Wolters, and R. A. Roebeling, 2009: Detection of Cb and TCu clouds using MSG-SEVIRI cloud physical properties and weather radar observations. KNMI WR 2009-04, 97 pp. [Available online at http://www.knmi.nl/publications/showAbstract.php?id=7203.]

    • Search Google Scholar
    • Export Citation

  • Dasgupta, S., and U. De, 2007: Binary logistic regression models for short term prediction of premonsoon convective developments over Kolkata (India). Int. J. Climatol., 27, 831.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Deneke, H., and R. Roebeling, 2010: Downscaling of Meteosat SEVIRI 0.6 and 0.8 micron channel radiances utilizing the high-resolution visible channel. Atmos. Chem. Phys. Discuss., 10, 10 707–10 740.

    • Search Google Scholar
    • Export Citation

  • Deneke, H., W. Knap, and C. Simmer, 2009: Multiresolution analysis of the temporal variance and correlation of transmittance and reflectance of an atmospheric column. J. Geophys. Res., 114, D17206, doi:10.1029/2008JD011680.

    • Search Google Scholar
    • Export Citation

  • Donovan, M., E. Williams, C. Kessinger, G. Blackburn, P. Herzegh, R. Bankert, S. Miller, and F. Mosher, 2006: The identification and verification of hazardous convective cells over oceans using visible and infrared satellite observations. Preprints, 12th Conf. on Aviation Range and Aerospace Meteorology, Atlanta, GA, Amer. Meteor. Soc., P1.20. [Available online at http://ams.confex.com/ams/pdfpapers/103764.pdf.]

    • Search Google Scholar
    • Export Citation

  • Hansen, J., and J. Hovenier, 1974: Interpretation of the polarization of Venus. J. Atmos. Sci., 31, 1137–1160.

  • Herzegh, P., E. Williams, T. Lindholm, F. Mosher, C. Kessinger, R. Sharman, J. Hawkins, and D. Johnson, 2002: Development of automated aviation weather products for oceanic/remote regions: Scientific and practical challenges, research strategies, and first steps. Preprints, 10th Conf. on Aviation, Range, and Aerospace Meteorology, Portland, OR, Amer. Meteor. Soc., 57–60.

    • Search Google Scholar
    • Export Citation

  • Inoue, T., 1985: On the temperature and effective emissivity determination of semi-transparent cirrus clouds by bi-spectral measurements in the 10 μm window region. J. Meteor. Soc. Japan, 63, 88–99.

    • Search Google Scholar
    • Export Citation

  • Jolivet, D., and A. Feijt, 2003: Cloud thermodynamic phase and particle size estimation using the 0.67 and 1.6 μm channels from meteorological satellites. Atmos. Chem. Phys. Discuss., 3, 4461–4488.

    • Search Google Scholar
    • Export Citation

  • Katz, R., and A. Murphy, 2005: Economic Value of Weather and Climate Forecasts. Cambridge University Press, 240 pp.

  • King, P., W. Hogg, and P. Arkin, 1995: The role of visible data in improving satellite rain-rate estimates. J. Appl. Meteor., 34, 1608–1621.

    • Search Google Scholar
    • Export Citation

  • Kurino, T., 1997: A satellite infrared technique for estimating deep/shallow precipitation. Adv. Space Res., 19, 511–514.

  • Lensky, I., and D. Rosenfeld, 1997: Estimation of precipitation area and rain intensity based on the microphysical properties retrieved from NOAA AVHRR data. J. Appl. Meteor., 36, 234–242.

    • Search Google Scholar
    • Export Citation

  • Leroy, M., 2006: Status of the automatic observation on aerodrome and ongoing improvements in France. Proc. TECO-2006—WMO Tech. Conf. on Meteorological and Environmental Instruments and Methods of Observation, Geneva, Switzerland, World Meteorological Organization, 8 pp. [Available online at http://www.wmo.int/pages/prog/www/IMOP/publications/IOM-94-TECO2006/1(3)_Leroy_France.pdf.]

    • Search Google Scholar
    • Export Citation

  • Levizzani, V., and M. Setvák, 1996: Multispectral, high-resolution satellite observations of plumes on top of convective storms. J. Atmos. Sci., 53, 361–369.

    • Search Google Scholar
    • Export Citation

  • Mecikalski, J., and K. Bedka, 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Wea. Rev., 134, 49–78.

    • Search Google Scholar
    • Export Citation

  • Mecikalski, J., and Coauthors, 2002: NASA Advanced Satellite Aviation-Weather Products (ASAP) study report. NASA Tech. Rep., 65 pp. [Available from Schwerdtferger Library, 1225 West Dayton St., University of Wisconsin—Madison, Madison, WI 53706.]

    • Search Google Scholar
    • Export Citation

  • Mecikalski, J., 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.

    • Search Google Scholar
    • Export Citation

  • Mecikalski, J., K. Bedka, S. Paech, and L. Litten, 2008: A statistical evaluation of GOES cloud-top properties for nowcasting convective initiation. Mon. Wea. Rev., 136, 4899–4914.

    • Search Google Scholar
    • Export Citation

  • Mecikalski, J., W. M. Mackenzie, M. Koenig, and S. Muller, 2010: Cloud-top properties of growing cumulus prior to convective initiation as measured by Meteosat Second Generation. Part II. Use of visible reflectance. J. Appl. Meteor. Climatol., 49, 2544–2558.

    • Search Google Scholar
    • Export Citation

  • Merenti-Välimäki, H., and P. Laininen, 2002: Analysing effects of meteorological variables on weather codes by logistic regression. Meteor. Appl., 9, 191–197.

    • Search Google Scholar
    • Export Citation

  • Mosher, F., 2002: Detection of deep convection around the globe. Preprints, 10th Conf. on Aviation, Range, and Aerospace Meteorology, Portland, OR, Amer. Meteor. Soc., 289–292.

    • Search Google Scholar
    • Export Citation

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

    • Search Google Scholar
    • Export Citation

  • Negri, A., R. Adler, and P. Wetzel, 1984: Rain estimation from satellites: An examination of the Griffith–Woodley technique. J. Climate Appl. Meteor., 23, 102–116.

    • Search Google Scholar
    • Export Citation

  • Roberts, R., and S. Rutledge, 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562–584.

    • Search Google Scholar
    • Export Citation

  • Roebeling, R., A. Feijt, and P. Stammes, 2006: 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

  • Rosenfeld, D., and G. Gutman, 1994: Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data. Atmos. Res., 34, 259–283.

    • Search Google Scholar
    • Export Citation

  • Rosenfeld, D., W. Woodley, A. Lerner, G. Kelman, and D. Lindsey, 2008: Satellite detection of severe convective storms by their retrieved vertical profiles of cloud particle effective radius and thermodynamic phase. J. Geophys. Res., 113, D04208, doi:10.1029/2007JD008600.

    • Search Google Scholar
    • Export Citation

  • Schmeits, M., K. Kok, D. Vogelezang, and R. van Westrhenen, 2008: Probabilistic forecasts of (severe) thunderstorms for the purpose of issuing a weather alarm in the Netherlands. Wea. Forecasting, 23, 1253–1267.

    • Search Google Scholar
    • Export Citation

  • Schmetz, J., S. Tjemkes, M. Gube, and L. Van de Berg, 1997: Monitoring deep convection and convective overshooting with Meteosat. Adv. Space Res., 19, 433–441.

    • Search Google Scholar
    • Export Citation

  • Schmetz, J., P. Pili, S. Tjemkes, D. Just, J. Kerkmann, S. Rota, and A. Ratier, 2002: An introduction to Meteosat Second Generation (MSG). Bull. Amer. Meteor. Soc., 83, 977–992.

    • Search Google Scholar
    • Export Citation

  • Setvák, M., and C. A. Doswell, 1991: The AVHRR channel 3 cloud top reflectivity of convective storms. Mon. Wea. Rev., 119, 841–847.

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

    • Search Google Scholar
    • Export Citation

  • Tag, P., R. Bankert, and L. Brody, 2000: An AVHRR multiple cloud-type classification package. J. Appl. Meteor., 39, 125–134.

  • Thies, B., T. Nauß, and J. Bendix, 2008: Precipitation process and rainfall intensity differentiation using Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager data. J. Geophys. Res., 113, D23206, doi:10.1029/2008JD010464.

    • Search Google Scholar
    • Export Citation

  • Tjemkes, S., L. Van de Berg, and J. Schmetz, 1997: Warm water vapour pixels over high clouds as observed by Meteosat. Contrib. Atmos. Phys., 70, 15–21.

    • Search Google Scholar
    • Export Citation

  • Wauben, W., H. K. Baltink, M. de Haij, N. Maat, and H. The, 2006: Status, evaluation and new developments of the automated cloud observations in the Netherlands. TECO-2006—WMO Tech. Conf. on Meteorological and Environmental Instruments and Methods of Observation, Geneva, Switzerland, World Meteorological Organization, 10 pp. [Available online at http://www.wmo.int/pages/prog/www/IMOP/publications/IOM-94-TECO2006/1(7)_Wauben_Netherlands.pdf.]

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.

  • Zinner, T., H. Mannstein, and A. Tafferner, 2008: Cb-TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi-channel Meteosat-8 SEVIRI data. Meteor. Atmos. Phys., 101, 191–210.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 558 174 17
PDF Downloads 434 135 4