Lightning Prediction for Australia Using Multivariate Analyses of Large-Scale Atmospheric Variables

Bryson C. Bates CSIRO Oceans and Atmosphere, Wembley, and School of Agriculture and Environment, The University of Western Australia, Crawley, Western Australia, Australia

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Andrew J. Dowdy Bureau of Meteorology, Melbourne, Victoria, Australia

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Richard E. Chandler Department of Statistical Science, University College London, London, United Kingdom

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Abstract

Lightning is a natural hazard that can lead to the ignition of wildfires, disruption and damage to power and telecommunication infrastructures, human and livestock injuries and fatalities, and disruption to airport activities. This paper examines the ability of six statistical and machine-learning classification techniques to distinguish between nonlightning and lightning days at the coarse spatial and temporal scales of current general circulation models and reanalyses. The classification techniques considered were 1) a combination of principal component analysis and logistic regression, 2) classification and regression trees, 3) random forests, 4) linear discriminant analysis, 5) quadratic discriminant analysis, and 6) logistic regression. Lightning-flash counts at six locations across Australia for 2004–13 were used, together with atmospheric variables from the ERA-Interim dataset. Tenfold cross validation was used to evaluate classification performance. It was found that logistic regression was superior to the other classifiers considered and that its prediction skill is much better than using climatological values. The sets of atmospheric variables included in the final logistic-regression models were primarily composed of spatial mean measures of instability and lifting potential, along with atmospheric water content. The memberships of these sets varied among climatic zones.

© 2018 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: Andrew J. Dowdy, andrew.dowdy@bom.gov.au

Abstract

Lightning is a natural hazard that can lead to the ignition of wildfires, disruption and damage to power and telecommunication infrastructures, human and livestock injuries and fatalities, and disruption to airport activities. This paper examines the ability of six statistical and machine-learning classification techniques to distinguish between nonlightning and lightning days at the coarse spatial and temporal scales of current general circulation models and reanalyses. The classification techniques considered were 1) a combination of principal component analysis and logistic regression, 2) classification and regression trees, 3) random forests, 4) linear discriminant analysis, 5) quadratic discriminant analysis, and 6) logistic regression. Lightning-flash counts at six locations across Australia for 2004–13 were used, together with atmospheric variables from the ERA-Interim dataset. Tenfold cross validation was used to evaluate classification performance. It was found that logistic regression was superior to the other classifiers considered and that its prediction skill is much better than using climatological values. The sets of atmospheric variables included in the final logistic-regression models were primarily composed of spatial mean measures of instability and lifting potential, along with atmospheric water content. The memberships of these sets varied among climatic zones.

© 2018 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: Andrew J. Dowdy, andrew.dowdy@bom.gov.au
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  • Allen, J. T., D. J. Karoly, and G. A. Mills, 2011: A severe thunderstorm climatology for Australia and associated thunderstorm environments. Aust. Meteor. Ocean J., 61, 143158, https://doi.org/10.22499/2.6103.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bates, B. C., A. J. Dowdy, and R. E. Chandler, 2017: Classification of Australian thunderstorms using multivariate analyses of large-scale atmospheric variables. J. Appl. Meteor. Climatol., 56, 19211937, https://doi.org/10.1175/JAMC-D-16-0271.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Belsley, D. A., 1991: Conditioning Diagnostics, Collinearity and Weak Data in Regression. John Wiley and Sons, 396 pp.

  • Blouin, K. D., M. D. Flannigan, X. Wang, and B. Kochtubajda, 2016: Ensemble lightning prediction models for the province of Alberta, Canada. Int. J. Wildland Fire, 25, 421432, https://doi.org/10.1071/WF15111.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Breiman, L., 2001: Random forests. Mach. Learn., 45, 532, https://doi.org/10.1023/A:1010933404324.

  • Brier, G. W, 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78, 13, https://doi.org/10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burrows, W. R., C. Price, and L. J. Wilson, 2005: Warm season lightning probability prediction for Canada and the northern United States. Wea. Forecasting, 20, 971988, https://doi.org/10.1175/WAF895.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chambers, C. R. S., G. B. Brassington, I. Simmonds, and K. Walsh, 2014: Precipitation changes due to the introduction of eddy-resolved sea surface temperatures into simulations of the “Pasha Bulker” Australian east coast low of June 2007. Meteor. Atmos. Phys., 125, 115, https://doi.org/10.1007/s00703-014-0318-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Christian, H. J., and Coauthors, 2003: Global frequency and distribution of lightning as observed from space by the Optical Transient Detector. J. Geophys. Res., 108, 4005, https://doi.org/10.1029/2002JD002347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davies, L., C. Jakob, P. May, V. V. Kumar, and S. Xie, 2013: Relationships between the large-scale atmosphere and the small-scale convective state for Darwin, Australia. J. Geophys. Res. Atmos., 118, 11 53411 545, https://doi.org/10.1002/jgrd.50645.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • De Rooy, W. C., and Coauthors, 2013: Entrainment and detrainment in cumulus convection: An overview. Quart. J. Roy. Meteor. Soc., 139, 119, https://doi.org/10.1002/qj.1959.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dowdy, A. J., 2015: Large-scale modelling of environments favourable for dry lightning occurrence. MODSIM2015, 21st International Congress on Modelling and Simulation, T. Weber, M. J. McPhee, and R. S. Anderssen, Eds., Modelling and Simulation Society of Australia and New Zealand, 1524–1530.

  • Dowdy, A. J., and G. A. Mills, 2009: Atmospheric states associated with the ignition of lightning-attributed fires. Collaboration for Australian Weather and Climate Research Tech. Rep. 019, 34 pp, http://www.cawcr.gov.au/technical-reports/CTR_019.pdf.

  • Dowdy, A. J., and Y. Kuleshov, 2014: Climatology of lightning activity in Australia: Spatial and seasonal variability. Aust. Meteor. Ocean J., 6, 914.

    • Search Google Scholar
    • Export Citation
  • Dowdy, A. J., and J. L. Catto, 2017: Extreme weather caused by concurrent cyclone, front and thunderstorm occurrences. Sci. Rep., 7, 40359, https://doi.org/10.1038/srep40359.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanstrum, B. N., G. A. Mills, A. Watson, J. P. Monteverdi, and C. A. Doswell III, 2002: The cool-season tornadoes of California and southern Australia. Wea. Forecasting, 17, 705722, https://doi.org/10.1175/1520-0434(2002)017<0705:TCSTOC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hilbe, J. M., 2009: Logistic Regression Models. Chapman and Hall/CRC, 656 pp.

    • Crossref
    • Export Citation
  • Lynn, B. H., Y. Yair, C. Price, G. Kelman, and A. J. Clark, 2012: Predicting cloud-to-ground and intracloud lightning in weather forecast models. Wea. Forecasting, 27, 14701488, https://doi.org/10.1175/WAF-D-11-00144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mazany, R. A., S. Businger, S. I. Gutman, and W. Roeder, 2002: A lightning prediction index that utilizes GPS integrated precipitable water vapor. Wea. Forecasting, 17, 10341047, https://doi.org/10.1175/1520-0434(2002)017<1034:ALPITU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Muñoz, Á. G., J. Díaz-Lobatón, X. Chourio, and M. J. Stock, 2016: Seasonal prediction of lightning activity in north western Venezuela: Large-scale versus local drivers. Atmos. Res., 172–173, 147162, https://doi.org/10.1016/j.atmosres.2015.12.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niall, S., and K. Walsh, 2005: The impact of climate change on hailstorms in southeastern Australia. Int. J. Climatol., 25, 19331952, https://doi.org/10.1002/joc.1233.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pezza, A. B., L. A. Garde, A. P. Veiga, and I. Simmonds, 2014: Large scale features and energetics of the hybrid subtropical low ‘Duck’ over the Tasman Sea. Climate Dyn., 42, 453466, https://doi.org/10.1007/s00382-013-1688-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rakov, V. A., and M. A. Uman, 2003: Lightning: Physics and Effects. Cambridge University Press, 687 pp.

    • Crossref
    • Export Citation
  • Romps, D. M., J. T. Seeley, D. Vollaro, and J. Molinari, 2014: Projected increase in lightning strikes in the United States due to global warming. Science, 346, 851854, https://doi.org/10.1126/science.1259100.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sánchez, J. L., R. Fraile, M. T. de la Fuente, and J. L. Marcos, 1998: Discriminant analysis applied to the forecasting of thunderstorms. Meteor. Atmos. Phys., 68, 187195, https://doi.org/10.1007/BF01030210.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sousa, J. F., M. Fragoso, S. Mendes, J. Corte-Real, and J. A. Santos, 2013: Statistical–dynamical modeling of the cloud-to-ground lightning activity in Portugal. Atmos. Res., 132–133, 4664, https://doi.org/10.1016/j.atmosres.2013.04.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stolz, D. C., S. A. Rutledge, J. R. Pierce, and S. C. van den Heever, 2017: A global lightning parameterization based on statistical relationships among environmental factors, aerosols, and convective clouds in the TRMM climatology. J. Geophys. Res., 122, 74617492, https://doi.org/10.1002/2016JD026220.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thornton, J. A., K. S. Virts, R. H. Holzworth, and T. P. Mitchell, 2017: Lightning enhancement over major oceanic shipping lanes. Geophys. Res. Lett., 44, 91029111, https://doi.org/10.1002/2017GL074982.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Venables, W. N., and B. D. Ripley, 2002: Modern Applied Statistics with S. 4th ed. Springer, 495 pp.

    • Crossref
    • Export Citation
  • Yair, Y., B. Lynn, C. Price, V. Kotroni, K. Lagouvardos, E. Morin, A. Mugnai, and M. del Carmen Llasat, 2010: Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) Model dynamic and microphysical fields. J. Geophys. Res., 115, D04205, https://doi.org/10.1029/2008JD010868.

    • Search Google Scholar
    • Export Citation
  • Youden, W. J., , 1950: Index for rating diagnostic tests. Cancer, 3, 3235, https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zepka, G. S., O. Pinto Jr., and A. C. V. Saraiva, 2014: Lightning forecasting in southern Brazil using the WRF Model. Atmos. Res., 135–136, 344362, https://doi.org/10.1016/j.atmosres.2013.01.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
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