Editorial Type:
Article
Article Type:
Research Article

Probabilistic 0–1-h Convective Initiation Nowcasts that Combine Geostationary Satellite Observations and Numerical Weather Prediction Model Data

John R. Mecikalski Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by John R. Mecikalski in
Current site
Google Scholar
PubMed Close
,
John K. Williams National Center for Atmospheric Research,* Boulder, Colorado

Search for other papers by John K. Williams in
Current site
Google Scholar
PubMed Close
,
Christopher P. Jewett Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by Christopher P. Jewett in
Current site
Google Scholar
PubMed Close
,
David Ahijevych National Center for Atmospheric Research, Boulder, Colorado

Search for other papers by David Ahijevych in
Current site
Google Scholar
PubMed Close
,
Anita LeRoy Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by Anita LeRoy in
Current site
Google Scholar
PubMed Close
, and
John R. Walker Atmospheric Science Department, University of Alabama in Huntsville, Huntsville, Alabama

Search for other papers by John R. Walker in
Current site
Google Scholar
PubMed Close
Print Publication:
01 May 2015
DOI:
https://doi.org/10.1175/JAMC-D-14-0129.1
Page(s):
1039–1059
Restricted access

Abstract

The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: John R. Mecikalski, Atmospheric Science Department, University of Alabama in Huntsville, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805-1912. E-mail: johnm@nsstc.uah.edu

Abstract

The Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

The National Center for Atmospheric Research is sponsored by the National Science Foundation.

Corresponding author address: John R. Mecikalski, Atmospheric Science Department, University of Alabama in Huntsville, National Space Science and Technology Center, 320 Sparkman Drive, Huntsville, AL 35805-1912. E-mail: johnm@nsstc.uah.edu
Save
Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

  • AMS Council, 2008: Enhancing weather information with probability forecasts. Bull. Amer. Meteor. Soc., 89, 1049–1053. [Available online at http://www.ametsoc.org/policy/2008enhancingweatherinformation_amsstatement.pdf.]

    • Search Google Scholar
    • Export Citation

  • Autones, F., 2012: Product user manual for “Rapid Development Thunderstorms” (RDT-PGE11 v3.0d). EUMETSAT Network of Satellite Application Facilities, Météo France, 27 pp. [Available online at http://www.nwcsaf.org/scidocs/Documentation/SAF-NWC-CDOP2-MFT-SCI-PUM-11_v3.0d.pdf.]

  • Bedka, K. M., and J. R. Mecikalski, 2005: Application of satellite-derived atmospheric motion vectors for estimating mesoscale flows. J. Appl. Meteor., 44, 1761–1772, doi:10.1175/JAM2264.1.

    • Search Google Scholar
    • Export Citation

  • Bedka, K. M., J. Brunner, R. Dworak, W. Feltz, J. Otkin, and T. Greenwald, 2010: Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients. J. Appl. Meteor. Climatol., 49, 181–202, doi:10.1175/2009JAMC2286.1.

    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., and Coauthors, 2009: Rapid Refresh/Rapid Update Cycle (RR/RUC) technical review. NOAA/ESRL/GSD Internal Review, 168 pp. [Available online at http://ruc.noaa.gov/pdf/RR-RUC-TR_11_3_2009.pdf.]

  • Berendes, T. A., J. R. Mecikalski, W. M. Mackenzie, K. M. Bedka, and U. S. Nair, 2008: Convective cloud detection in satellite imagery using standard deviation limited adaptive clustering. J. Geophys. Res., 113, D20207, doi:10.1029/2008JD010287.

    • Search Google Scholar
    • Export Citation

  • Breiman, L., 2001: Random forests. Mach. Learn., 45, 5–32, doi:10.1023/A:1010933404324.

  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probabilities. Mon. Wea. Rev., 78, 1–3, doi:10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., and N. Dotzek, 2007: The spatial distribution of severe convective storms and an analysis of their secular changes. Climate Extremes and Society, H. F. Diaz and R. Murnane, Eds., Cambridge University Press, 35–53.

  • Brooks, H. E., C. A. Doswell III, and R. A. Maddox, 1992: On the use of mesoscale and cloud-scale models in operational forecasting. Wea. Forecasting, 7, 120–132, doi:10.1175/1520-0434(1992)007<0120:OTUOMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., C. A. Doswell III, and J. Cooper, 1994: On the environments of tornadic and nontornadic mesocyclones. Wea. Forecasting, 9, 606–618, doi:10.1175/1520-0434(1994)009<0606:OTEOTA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Brooks, H. E., C. A. Doswell III, and M. P. Kay, 2003: Climatological estimates of local daily tornado probability for the United States. Wea. Forecasting, 18, 626–640, doi:10.1175/1520-0434(2003)018<0626:CEOLDT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Browning, K. A., and D. Atlas, 1965: Initiation of precipitation in vigorous convective clouds. J. Atmos. Sci., 22, 678–683, doi:10.1175/1520-0469(1965)022<0678:IOPIVC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Coniglio, M. C., H. E. Brooks, S. J. Weiss, and S. F. Corfidi, 2007: Forecasting the maintenance of quasi-linear mesoscale convective systems. Wea. Forecasting, 22, 556–570, doi:10.1175/WAF1006.1.

    • Search Google Scholar
    • Export Citation

  • Curran, E. B., R. L. Holle, and R. E. López, 2000: Lightning casualties and damages in the United States from 1959 to 1994. J. Climate, 13, 3448–3464, doi:10.1175/1520-0442(2000)013<3448:LCADIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dance, S., E. Ebert, and D. Scurrah, 2010: Thunderstorm strike probability nowcasting. J. Atmos. Oceanic Technol., 27, 79–93, doi:10.1175/2009JTECHA1279.1.

    • Search Google Scholar
    • Export Citation

  • Dattatreya, G. R., 2009: Decision trees. Artificial Intelligence Methods in the Environmental Sciences, S. E. Haupt, C. Marzban, and A. Pasini, Eds., Springer, 424 pp.

  • Díaz-Uriarte, R., and S. A. de Andrés, 2006: Gene selection and classification of microarray data using random forest. BMC Bioinf., 7, 3, doi:10.1186/1471-2105-7-3.

    • Search Google Scholar
    • Export Citation

  • Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785–797, doi:10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Dixon, P., A. Mercer, J. Choi, and J. Allen, 2011: Tornado risk analysis: Is Dixie Alley an extension of Tornado Alley? Bull. Amer. Meteor. Soc., 92, 433–441, doi:10.1175/2010BAMS3102.1.

    • Search Google Scholar
    • Export Citation

  • Duda, D. P., and P. Minnis, 2009: Basic diagnosis and prediction of persistent contrail occurrence using high-resolution numerical weather analyses/forecasts and logistic regression. Part II: Evaluation of sample models. J. Appl. Meteor. Climatol., 48, 1790–1802, doi:10.1175/2009JAMC2057.1.

    • Search Google Scholar
    • Export Citation

  • Evans, J. E., and E. R. Ducot, 2006: Corridor Integrated Weather System. Lincoln Lab. J., 16, 59–80. [Available online at https://www.ll.mit.edu/publications/journal/pdf/vol16_no1/16_1_4EvansDucot.pdf.]

    • Search Google Scholar
    • Export Citation

  • Feng, Y., Y. Wang, T. Peng, and J. Yan, 2007: An algorithm on convective weather potential in the early rainy season over the Pearl River Delta in China. Adv. Atmos. Sci., 24, 101–110, doi:10.1007/s00376-007-0101-2.

    • Search Google Scholar
    • Export Citation

  • Fritsch, J. M., and Coauthors, 1998: Quantitative precipitation forecasting: Report of the Eighth Prospectus Development Team, U.S. Weather Research Program. Bull. Amer. Meteor. Soc., 79, 285–299, doi:10.1175/1520-0477(1998)079<0285:QPFROT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Goodman, S. J., and Coauthors, 2013: The GOES-R Geostationary Lightning Mapper (GLM). Atmos. Res., 125–126, 34–49, doi:10.1016/j.atmosres.2013.01.006.

    • Search Google Scholar
    • Export Citation

  • Harris, R. J., J. R. Mecikalski, W. M. MacKenzie, P. A. Durkee, and K. E. Nielsen, 2010: The definition of GOES infrared lightning initiation interest fields. J. Appl. Meteor. Climatol., 49, 2527–2543, doi:10.1175/2010JAMC2575.1.

    • Search Google Scholar
    • Export Citation

  • Hosmer, D. W., and S. Lemeshow, 1989: Applied Logistic Regression. John Wiley & Sons, 307 pp.

  • Hu, S., S. Gu, X. Zhuang, and H. Luo, 2007: Automatic identification of storm cells using Doppler radars. Acta Meteor. Sin., 21, 353–365.

    • Search Google Scholar
    • Export Citation

  • Iskenderian, H., and Coauthors, 2010: Satellite data applications for nowcasting of convective initiation. 14th Conf. on Aviation, Range, and Aerospace Meteorology, Atlanta, GA, Amer. Meteor. Soc., 5.2. [Available online at https://ams.confex.com/ams/90annual/recordingredirect.cgi/id/11827.]

  • Iskenderian, H., L. Bickmeier, J. Mecikalski, and C. P. Jewett, 2012: Satellite data applications for nowcasting of cloud-to-ground lightning initiation. 18th Conf. on Satellite Meteorology, Oceanography and Climatology/First Joint AMS-Asia Satellite Meteorology Conf., New Orleans, LA, Amer. Meteor. Soc., 13C.4. [Available online at https://ams.confex.com/ams/92Annual/recordingredirect.cgi/id/20310.]

  • Joe, P., M. Falla, P. V. Rijn, L. Stamadianos, T. Falla, D. Magosse, L. Ing, and J. Dobson, 2003: Radar data processing for severe weather in the national radar project of Canada. Preprints, 21st Conf. on Severe Local Storms, San Antonio, TX, Amer. Meteor. Soc., P4.13. [Available online at http://ams.confex.com/ams/pdfpapers/47421.pdf.]

  • Lakshmanan, V., T. Smith, K. Hondl, G. J. Stumpf, and A. Witt, 2006: A real-time, three-dimensional, rapid updating, heterogeneous technique radar merger technique for reflectivity, velocity, and derived products. Wea. Forecasting, 21, 802–823, doi:10.1175/WAF942.1.

    • Search Google Scholar
    • Export Citation

  • Lakshmanan, V., T. Smith, G. J. Stumpf, and K. Hondl, 2007: The Warning Decision Support System–Integrated Information (WDSS-II). Wea. Forecasting, 22, 596–608, doi:10.1175/WAF1009.1.

    • Search Google Scholar
    • Export Citation

  • Lensky, I. M., and D. Rosenfeld, 2006: The time-space exchangeability of satellite retrieved relations between cloud top temperature and particle effective radius. Atmos. Chem. Phys., 6, 2887–2894, doi:10.5194/acp-6-2887-2006.

    • Search Google Scholar
    • Export Citation

  • Lima, M. A., and J. W. Wilson, 2008: Convective storm initiation in a moist tropical environment. Mon. Wea. Rev., 136, 1847–1864, doi:10.1175/2007MWR2279.1.

    • Search Google Scholar
    • Export Citation

  • Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291–303.

  • 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, 49–78, doi:10.1175/MWR3062.1.

    • Search Google Scholar
    • Export Citation

  • Mecikalski, J. R., K. M. Bedka, S. J. Paech, and L. A. Litten, 2008: A statistical evaluation of GOES cloud-top properties for predicting convective initiation. Mon. Wea. Rev., 136, 4899–4914, doi:10.1175/2008MWR2352.1.

    • Search Google Scholar
    • Export Citation

  • Mecikalski, J. R., W. M. Mackenzie, M. Koenig, and S. Muller, 2010a: 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, 521–534, doi:10.1175/2009JAMC2344.1.

    • Search Google Scholar
    • Export Citation

  • Mecikalski, J. R., W. M. Mackenzie, M. Koenig, and S. Muller, 2010b: 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, doi:10.1175/2010JAMC2480.1.

    • Search Google Scholar
    • Export Citation

  • Mecikalski, J. R., P. Minnis, and R. Palikonda, 2013: Use of satellite derived cloud properties to quantify growing cumulus beneath cirrus clouds. Atmos. Res., 120–121, 192–201, doi:10.1016/j.atmosres.2012.08.017.

    • Search Google Scholar
    • Export Citation

  • Merino, A., L. López, J. L. Sánchez, E. García-Ortega, E. Cattani, and V. Leivizzani, 2014: Daytime identification of summer hailstorm cells from MSG data. Nat. Hazards Earth Syst. Sci., 14, 1017–1033, doi:10.5194/nhess-14-1017-2014.

    • Search Google Scholar
    • Export Citation

  • Merk, D., and T. Zinner, 2013: Detection of convective initiation using Meteosat SEVIRI: Implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM. Atmos. Meas. Tech., 6, 1903–1918, doi:10.5194/amt-6-1903-2013.

    • Search Google Scholar
    • Export Citation

  • Minnis, P., and Coauthors, 2011a: 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.

    • Search Google Scholar
    • Export Citation

  • Minnis, P., and Coauthors, 2011b: CERES Edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data—Part II: Examples of average results and comparisons with other data. IEEE Trans. Geosci. Remote Sens., 49, 4401–4430, doi:10.1109/TGRS.2011.2144602.

    • Search Google Scholar
    • Export Citation

  • Mueller, C. K., J. W. Wilson, and N. A. Crook, 1993: The utility of sounding and mesonet data to nowcast thunderstorm initiation. Wea. Forecasting, 8, 132–146, doi:10.1175/1520-0434(1993)008<0132:TUOSAM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Mueller, C. K., T. Saxen, R. Roberts, J. Wilson, T. Betancourt, S. Dettling, N. Oien, and J. Yee, 2003: NCAR Auto-Nowcast System. Wea. Forecasting, 18, 545–561, doi:10.1175/1520-0434(2003)018<0545:NAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Nisi, L., P. Ambrosetti, and L. Clementi, 2014: Nowcasting severe convection in the Alpine region: The COALITION approach. Quart. J. Roy. Meteor. Soc., 140, 1684–1699, doi:10.1002/qj.2249.

    • Search Google Scholar
    • Export Citation

  • Pal, M., 2005: Random forest classifier for remote sensing classification. Int. J. Remote Sens., 26, 217–222, doi:10.1080/01431160412331269698.

    • Search Google Scholar
    • Export Citation

  • Purdom, J. F. W., 1976: Some uses of high resolution GOES imagery in the mesoscale forecasting of convection and its behavior. Mon. Wea. Rev., 104, 1474–1483, doi:10.1175/1520-0493(1976)104<1474:SUOHRG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Purdom, J. F. W., 1982: Subjective interpretations of geostationary satellite data for nowcasting. Nowcasting, K. Browning, Ed., Academic Press, 149–166.

  • Roberts, R. D., and S. Rutledge, 2003: Nowcasting storm initiation and growth using GOES-8 and WSR-88D data. Wea. Forecasting, 18, 562–584, doi:10.1175/1520-0434(2003)018<0562:NSIAGU>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Roberts, R. D., A. R. S. Anderson, E. Nelson, B. G. Brown, J. W. Wilson, M. Pocernich, and T. Saxen, 2012: Impacts of forecaster involvement on convective storm initiation and evolution nowcasting. Wea. Forecasting, 27, 1061–1089, doi:10.1175/WAF-D-11-00087.1.

    • Search Google Scholar
    • Export Citation

  • Rosenfeld, D., W. L. Woodley, A. Lerner, G. Kelman, and D. T. 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

  • Ruzanski, E., V. Chandrasekar, and Y. Wang, 2011: The CASA nowcasting system. J. Atmos. Oceanic Technol., 28, 640–655, doi:10.1175/2011JTECHA1496.1.

    • Search Google Scholar
    • Export Citation

  • Setvák, M., R. M. Rabin, C. A. Doswell, and V. Levizzani, 2003: Satellite observations of convective storm tops in the 1.6, 3.7, and 3.9 μm spectral bands. Atmos. Res.,67–68, 607–627, doi:10.1016/S0169-8095(03)00076-0.

  • Setvák, M., K. Bedka, D. T. Lindsey, A. Sokol, Z. Charvát, J. Šťástka, and P. K. Wang, 2013: A-Train observations of deep convective storm tops. Atmos. Res., 123, 229–248, doi:10.1016/j.atmosres.2012.06.020.

    • 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, 110–126, doi:10.1175/2010JAMC2496.1.

    • Search Google Scholar
    • Export Citation

  • Siewert, C., and K. Kuhlman, 2011: Hazardous Weather Testbed—Final evaluation. NOAA Rep., 21 pp. [Available online at http://www.goes-r.gov/users/docs/pg-activities/PGFR-HWT-2011-Final.pdf.]

  • Steiner, M., R. Bateman, D. Megenhardt, Y. Liu, M. Xu, M. Pocernich, and J. Krozel, 2010: Translation of ensemble weather forecasts into probabilistic air traffic capacity impact. Air Traffic Quart., 18, 229–254.

    • 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, doi:10.1175/1520-0450(1994)033<0212:CPIFD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Terborg, A., and C. Gravelle, 2012: GOES-R desk final evaluation. Aviation Weather Testbed 2012 Summer Experiment, 20 pp. [Available online at http://www.goes-r.gov/users/docs/pg-activities/PGFR-AWC-2012-Final.pdf.]

  • Terborg, A., K. Calhoun, C. Gravelle, and W. Line, 2013: Hazardous Weather Testbed—GOES-R Proving Ground final evaluation. NOAA Rep., 27 pp. [Available online at http://www.goes-r.gov/users/docs/pg-activities/PGFR-HWT-2013-Final.pdf.]

  • Topić, G., and T. Šmuc, 2014: Parallel random forest algorithm usage. Accessed 26 June 2014. [Available online at http://code.google.com/p/parf/wiki/Usage.]

  • Wakimoto, R. M., and H. V. Murphey, 2009: Analysis of a dryline during IHOP: Implications for convection initiation. Mon. Wea. Rev., 137, 912–936, doi:10.1175/2008MWR2584.1.

    • Search Google Scholar
    • Export Citation

  • Walker, J. R., W. M. MacKenzie, J. R. Mecikalski, and C. P. Jewett, 2012: An enhanced geostationary satellite–based convective initiation algorithm for 0–2-h nowcasting with object tracking. J. Appl. Meteor. Climatol., 51, 1931–1949, doi:10.1175/JAMC-D-11-0246.1.

    • Search Google Scholar
    • Export Citation

  • Wang, P. K., S.-H. Su, M. Setvák, L.-H. Lin, and R. M. Rabin, 2010: Ship wave signature at the cloud top of deep convective storms. Atmos. Res., 97, 294–302, doi:10.1016/j.atmosres.2010.03.015.

    • Search Google Scholar
    • Export Citation

  • Weckwerth, T. M., and D. B. Parsons, 2006: A review of convection initiation and motivation for IHOP_2002. Mon. Wea. Rev., 134, 5–22, doi:10.1175/MWR3067.1.

    • Search Google Scholar
    • Export Citation

  • Weckwerth, T. M., and Coauthors, 2004: An overview of the International H2O Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253–277, doi:10.1175/BAMS-85-2-253.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Elsevier, 676 pp.

  • Williams, J. K., 2014: Using random forests to diagnose aviation turbulence. Mach. Learn., 95, 51–70, doi:10.1007/s10994-013-5346-7.

  • Williams, J. K., D. Ahijevych, S. Dettling, and M. Steiner, 2008: Combining observations and model data for short-term storm forecasting. Remote Sensing Applications for Aviation Weather Hazard Detection and Decision Support., W. Feltz and J. Murray, Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 7088), 708805, doi:10.1117/12.795737.

  • Wilson, J. W., and W. E. Schreiber, 1986: Initiation of convective storms by radar–observed boundary layer convergent lines. Mon. Wea. Rev., 114, 2516–2536, doi:10.1175/1520-0493(1986)114<2516:IOCSAR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wilson, J. W., and C. K. Mueller, 1993: Nowcasts of thunderstorm initiation and evolution. Wea. Forecasting, 8, 113–131, doi:10.1175/1520-0434(1993)008<0113:NOTIAE>2.0.CO;2.

    • 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, 1785–1815, doi:10.1175/1520-0493(1992)120<1785:TROBLC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wilson, J. W., N. A. Crook, C. K. Mueller, J. Sun, and M. Dixon, 1998: Nowcasting thunderstorms: A status report. Bull. Amer. Meteor. Soc., 79, 2079–2099, doi:10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Wilson, J. W., Y. Feng, M. Chen, and R. D. Roberts, 2010: Nowcasting challenges during the Beijing Olympics: Successes, failures, and implications for future nowcasting systems. Wea. Forecasting, 25, 1691–1714, doi:10.1175/2010WAF2222417.1.

    • Search Google Scholar
    • Export Citation

  • Wolfson, M. M., and D. A. Clark, 2006: Advanced aviation weather forecasts. Lincoln Lab. J.,16, 31–58. [Available online at http://www.ll.mit.edu/publications/journal/pdf/vol16_no1/16_1_3Wolfson.pdf.]

  • Zhang, J., and Coauthors, 2011: National Mosaic and Multi-Sensor QPE (NMQ) System: Description, results, and future plans. Bull. Amer. Meteor. Soc., 92, 1321–1338, doi:10.1175/2011BAMS-D-11-00047.1.

    • Search Google Scholar
    • Export Citation

  • Ziegler, C. L., E. N. Rasmussen, M. S. Buban, Y. P. Richardson, L. J. Miller, and R. M. Rabin, 2007: The “Triple Point” on 24 May 2002 during IHOP. Part II: Ground–radar and in situ boundary layer analysis of cumulus development and convection initiation. Mon. Wea. Rev., 135, 2443–2472, doi:10.1175/MWR3411.1.

    • 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, 191–210, doi:10.1007/s00703-008-0290-y.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 5218 2142 70
PDF Downloads 1702 244 28