An Enhanced Geostationary Satellite–Based Convective Initiation Algorithm for 0–2-h Nowcasting with Object Tracking

John R. Walker Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by John R. Walker in
Current site
Google Scholar
PubMed
Close
,
Wayne M. MacKenzie Jr. Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama, and Earth Resources Technology, Inc., Laurel, Maryland

Search for other papers by Wayne M. MacKenzie Jr. in
Current site
Google Scholar
PubMed
Close
,
John R. Mecikalski Department of Atmospheric Science, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by John R. Mecikalski in
Current site
Google Scholar
PubMed
Close
, and
Christopher P. Jewett Earth System Science Center, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by Christopher P. Jewett in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

This paper describes an enhanced 0–2-h convective initiation (CI) nowcasting algorithm known as Satellite Convection Analysis and Tracking, version 2 (SATCASTv2). Tracking of developing cumulus cloud “objects” in advance of CI was developed as a means of reducing errors caused by tracking single satellite pixels of cumulus clouds, as identified in Geostationary Operational Environmental Satellite (GOES) output. The method rests on the idea that cloud objects at one time, when extrapolated forward in space and time using mesoscale atmospheric motion vectors, will overlap with the same actual cloud objects at a later time. Significant overlapping confirms that a coherent cumulus cloud is present and trackable in GOES data and that it is persistent enough that various infrared threshold–based tests may be performed to assess cloud growth. Validation of the new object-tracking approach to nowcasting CI was performed over four regions in the United States: 1) Melbourne, Florida; 2) Memphis, Tennessee; 3) the central United States/Great Plains; and 4) the northeastern United States as a means of evaluating algorithm performance in various convective environments. In this study, 9943 CI nowcasts and 804 CI events were analyzed. Optimal results occurred in the central U.S./Great Plains domain, where the probability of detection (POD) and false-alarm ratio (FAR) reached 85% and 55%, respectively, for tracked cloud objects. The FARs were partially attributed to difficulties inherent to the CI nowcasting problem. PODs were seen to decrease for CI events in Florida. Discussion is provided on how SATCASTv2 performed, as well as on how certain problems may be mitigated, especially in light of enhanced geostationary-satellite systems.

Corresponding author address: John R. Walker, University of Alabama in Huntsville, Earth System Science Center, 320 Sparkman Dr., Cramer Hall/#3075, Huntsville, AL 35805. E-mail: jwalker@nsstc.uah.edu

Abstract

This paper describes an enhanced 0–2-h convective initiation (CI) nowcasting algorithm known as Satellite Convection Analysis and Tracking, version 2 (SATCASTv2). Tracking of developing cumulus cloud “objects” in advance of CI was developed as a means of reducing errors caused by tracking single satellite pixels of cumulus clouds, as identified in Geostationary Operational Environmental Satellite (GOES) output. The method rests on the idea that cloud objects at one time, when extrapolated forward in space and time using mesoscale atmospheric motion vectors, will overlap with the same actual cloud objects at a later time. Significant overlapping confirms that a coherent cumulus cloud is present and trackable in GOES data and that it is persistent enough that various infrared threshold–based tests may be performed to assess cloud growth. Validation of the new object-tracking approach to nowcasting CI was performed over four regions in the United States: 1) Melbourne, Florida; 2) Memphis, Tennessee; 3) the central United States/Great Plains; and 4) the northeastern United States as a means of evaluating algorithm performance in various convective environments. In this study, 9943 CI nowcasts and 804 CI events were analyzed. Optimal results occurred in the central U.S./Great Plains domain, where the probability of detection (POD) and false-alarm ratio (FAR) reached 85% and 55%, respectively, for tracked cloud objects. The FARs were partially attributed to difficulties inherent to the CI nowcasting problem. PODs were seen to decrease for CI events in Florida. Discussion is provided on how SATCASTv2 performed, as well as on how certain problems may be mitigated, especially in light of enhanced geostationary-satellite systems.

Corresponding author address: John R. Walker, University of Alabama in Huntsville, Earth System Science Center, 320 Sparkman Dr., Cramer Hall/#3075, Huntsville, AL 35805. E-mail: jwalker@nsstc.uah.edu
Save
  • Ackerman, S. A., 1996: Global satellite observations of negative brightness temperature differences between 11 and 6.7 μm. J. Atmos. Sci., 53, 28032812.

    • Search Google Scholar
    • Export Citation
  • Ackerman, S. A., R. A. Frey, and W. L. Smith, 1992: Radiation budget studies using collocated observations from AVHRR, HIRS/2, and ERBE instruments. J. Geophys. Res., 97, 11 51311 525.

    • Search Google Scholar
    • Export Citation
  • Bachmann, S., and D. Zrnić, 2007: Spectral density of polarimetric variables separating biological scatterers in the VAD display. J. Atmos. Oceanic Technol., 24, 11861198.

    • Search Google Scholar
    • Export Citation
  • Banacos, P. C., and D. M. Schultz, 2005: The use of moisture flux convergence in forecasting convective initiation: Historical and operational perspectives. Wea. Forecasting, 20, 351366.

    • 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, 11 78111 792.

    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., and J. R. Mecikalski, 2005: Application of satellite winds to mesoscale phenomena and nowcasting. J. Appl. Meteor., 44, 17611772.

    • Search Google Scholar
    • Export Citation
  • Bedka, K. M., C. S. Velden, R. A. Petersen, W. F. Feltz, and J. R. Mecikalski, 2009: Comparisons of satellite-derived atmospheric motion vectors, rawinsondes, and NOAA Wind Profiler observations. J. Appl. Meteor. Climatol., 48, 15421561.

    • Search Google Scholar
    • Export Citation
  • Berendes, T. A., J. R. Mecikalski, W. M. Mackenzie, K. M. Bedka, and U. S. 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
  • Browning, K. A., and D. Atlas, 1965: Initiation of precipitation in vigorous convective clouds. J. Atmos. Sci., 22, 678683.

  • Chang, F., and Z. Li, 2003: Detecting and evaluating the effect over overlaying thin cirrus cloud on MODIS retrieved water-cloud droplet effective radius. Extended Abstracts, 13th ARM Science Team Meeting Proceedings, Broomfield, CO, U.S. Dept. of Energy, 6 pp. [Available online at http://www.arm.gov/publications/proceedings/conf13/extended_abs/chang1-fl.pdf.]

  • Easterling, D. R., and P. J. Robinson, 1985: The diurnal variation of thunderstorm activity in the United States. J. Climate Appl. Meteor., 24, 10481058.

    • Search Google Scholar
    • Export Citation
  • Engel, C., and E. Ebert, 2007: Performance of hourly operational consensus forecasts (OCFS) in the Australian region. Wea. Forecasting, 22, 13451359.

    • Search Google Scholar
    • Export Citation
  • Harris, R. J., J. R. Mecikalski, W. M. MacKenzie Jr., P. A. Durkee, and K. E. Nielsen, 2010: The definition of GOES infrared lightning initiation interest fields. J. Appl. Meteor. Climatol., 49, 25272543.

    • Search Google Scholar
    • Export Citation
  • Huang, X., Y. Ma, and G. A. Mills, 2008: Verification of mesoscale NWP forecasts of abrupt wind changes. Centre for Australian Weather and Climate Research Tech. Rep. CTR008, 67 pp.

  • Li, X., C.-H. Sui, and K.-N. Lau, 2002: Precipitation efficiency in the tropical deep convective regime: A 2-D cloud resolving modeling study. J. Meteor. Soc. Japan, 80, 205212.

    • Search Google Scholar
    • Export Citation
  • Marshall, J. S., and S. Radhakant, 1978: Radar precipitation maps as lightning indicators. J. Appl. Meteor., 17, 206212.

  • Mecikalski, J. R., and K. M. Bedka, 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Wea. Rev., 134, 4978.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., S. J. Paech, K. M. Bedka, and L. A. Litten, 2008: A statistical evaluation of GOES cloud-top properties for nowcasting convective initiation. Mon. Wea. Rev., 136, 48994914.

    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., 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 I: Infrared fields. J. Appl. Meteor. Climatol., 49, 521534.

    • Search Google Scholar
    • Export Citation
  • Mueller, C., T. Saxen, R. Roberts, J. Wilson, T. Betancourt, S. Dettling, N. Oien, and J. Yee, 2003: NCAR Auto-Nowcast System. Wea. Forecasting, 18, 545561.

    • Search Google Scholar
    • Export Citation
  • Murray, J. J., 2002: Aviation weather applications of Earth Science Enterprise data. Earth Obs. Mag., 11 (8), 2630.

  • Prata, A. J., 1989: Observations of volcanic ash clouds in the 10-12 μm window using AVHRR/2 data. Int. J. Remote Sens., 10, 751761.

    • Search Google Scholar
    • Export Citation
  • Roberts, R. D., and S. Rutledge, 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562584.

    • Search Google Scholar
    • Export Citation
  • Roberts, R. D., and Coauthors, 2008: REFRACTT 2006. Bull. Amer. Meteor. Soc., 89, 15351548.

  • Saunders, R., 1986: An automated scheme for the removal of cloud contamination from AVHRR radiances over western Europe. Int. J. Remote Sens., 7, 867886.

    • Search Google Scholar
    • Export Citation
  • Schmetz, J., S. A. Tjemkes, M. Gube, and L. van de Berg, 1997: Monitoring deep convection and convective overshooting with METEOSAT. Adv. Space Res., 19, 433441.

    • Search Google Scholar
    • Export Citation
  • 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.

    • Search Google Scholar
    • Export Citation
  • Sieglaff, J. M., L. M. Cronce, W. F. Feltz, K. M. Bedka, M. J. Pavolonis, and A. K. Heidinger, 2011: Nowcasting convective storm initiation using satellite-based box-averaged cloud-top cooling and cloud-type trends. J. Appl. Meteor. Climatol., 50, 110126.

    • 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, 212229.

    • Search Google Scholar
    • Export Citation
  • Trentmann, J., and Coauthors, 2009: Multi-model simulations of a convective situation in low-mountain terrain in central Europe. Meteor. Atmos. Phys., 103, 95103.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., C. M. Hayden, S. J. Nieman, W. P. Menzel, S. Wanzong, and J. S. Goerss, 1997: Upper-tropospheric winds derived from geostationary satellite water vapor observations. Bull. Amer. Meteor. Soc., 78, 173195.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., T. Olander, and S. Wanzong, 1998: The impact of multispectral GOES-8 wind information on Atlantic tropical cyclone track forecasts in 1995. Part I: Dataset methodology, description, and case analysis. Mon. Wea. Rev., 126, 12021218.

    • Search Google Scholar
    • Export Citation
  • Wakimoto, R. M., and J. K. Lew, 1993: Observations of a Florida waterspout during CaPE. Wea. Forecasting, 8, 412423.

  • Walker, J. R., and J. R. Mecikalski, 2011: Algorithm theoretical basis document (ATBD) for convective initiation. NOAA NESDIS Center for Satellite Applications and Research, 40 pp. [Available online at http://www.nsstc.uah.edu/SATCAST/docs/GOES-R%20AWG%20ATBD%20Aviation%20ConvectiveInitiation%20v2.0.pdf.]

  • Weckwerth, T. M., and C. B. Parson, 2006: A review of convective initiation and motivation for IHOP_2002. Mon. Wea. Rev., 134, 522.

  • Wilson, J. W., and W. E. Schreiber, 1986: Initiation of convective storms by radar–observed boundary-layer convergence lines. Mon. Wea. Rev., 114, 25162536.

    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., and C. K. Mueller, 1993: Nowcasts of thunderstorm initiation and evolution. Wea. Forecasting, 8, 113131.

  • Wilson, J. W., J. A. Moore, G. B. Foote, B. Martner, A. R. Rodi, T. Uttal, and J. M. Wilczak, 1988: Convective Initiation and Downburst Experiment (CINDE). Bull. Amer. Meteor. Soc., 69, 13281348.

    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., G. B. Foote, N. A. Crook, J. C. Fankhauser, C. G. Wade, J. D. Tuttle, C. K. Mueller, and S. K. Kruger, 1992: The role of boundary–layer convergence zones and horizontal rolls in the initiation of thunderstorms: A case study. Mon. Wea. Rev., 120, 17851815.

    • Search Google Scholar
    • Export Citation
  • 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, 191210.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1079 261 28
PDF Downloads 933 233 32