Skip to main content
Log in

Development of a perfect prognosis probabilistic model for prediction of lightning over south-east India

  • Published:
Journal of Earth System Science Aims and scope Submit manuscript

A prediction model based on the perfect prognosis method was developed to predict the probability of lightning and probable time of its occurrence over the south-east Indian region. In the perfect prognosis method, statistical relationships are established using past observed data. For real time applications, the predictors are derived from a numerical weather prediction model. In the present study, we have developed the statistical model based on Binary Logistic Regression technique. For developing the statistical model, 115 cases of lightning that occurred over the south-east Indian region during the period 2006–2009 were considered. The probability of lightning (yes or no) occurring during the 12-hour period 0900–2100 UTC over the region was considered as the predictand. The thermodynamic and dynamic variables derived from the NCEP Final Analysis were used as the predictors. A three-stage strategy based on Spearman Rank Correlation, Cumulative Probability Distribution and Principal Component Analysis was used to objectively select the model predictors from a pool of 61 potential predictors considered for the analysis. The final list of six predictors used in the model consists of the parameters representing atmospheric instability, total moisture content in the atmosphere, low level moisture convergence and lower tropospheric temperature advection. For the independent verifications, the probabilistic model was tested for 92 days during the months of May, June and August 2010. The six predictors were derived from the 24-h predictions using a high resolution Weather Research and Forecasting model initialized with 00 UTC conditions. During the independent period, the probabilistic model showed a probability of detection of 77% with a false alarm rate of 35%. The Brier Skill Score during the independent period was 0.233, suggesting that the prediction scheme is skillful in predicting the lightning probability over the south-east region with a reasonable accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Banacos P C and Schultz D M 2005 The use of moisture flux convergence in forecasting convective initiation: Historical and operational perspectives; Wea. Forecasting 20 351–366.

    Article  Google Scholar 

  • Bhowmik Roy S K, Soma Sen Roy and Kundu P K 2007 Analysis of large-scale conditions associated with convection over the Indian monsoon region; Int. J. Climatol., doi: 10.1002/joc.1567.

  • Bothwell P D 2002 Prediction of cloud-to-ground lightning in the western United States; Ph.D. thesis, University of Oklahoma, 178p.

  • Bothwell P D 2005 Development of an operational statistical scheme to predict the location and intensity of lightning; Conference on Meteorological Applications of Lightning Data, San Diego, CA, Amer. Meteor. Soc., 6p.

  • Bothwell P D 2008 Predicting the location and intensity of lightning using an experimental automated statistical methods; Third Conference on Meteorological Applications of Lightning Data, New Orleans, LA, Amer. Meteor. Soc., 6p.

  • Bothwell P D and Buckey D R 2009 Using the perfect prognosis technique for predicting cloud-to-ground lightning in mainland Alaska; Fourth Conference on Meteorological Applications of Lightning Data, Phoenix, AZ, Amer. Meteor. Soc., 10p.

  • Bright D R, Wandishin M, Jewell R and Weiss S 2005 A physically based parameter for lightning prediction and its calibration in ensemble forecasts; Preprints, Conference on Meteorological Applications of Lightning Data, Amer. Meteor. Soc., San Diego, CA (CD-ROM 4.3).

  • Brier G W 1950 Verification of forecasts expressed in terms of probability; Mon. Wea. Rev. 75 1–3.

    Article  Google Scholar 

  • Burrows W R, Price C and Wilson L J 2005 Warm season lightning probability prediction for Canada and the northern United States; Wea. Forecasting 20 971–988.

    Article  Google Scholar 

  • Chatterjee P, Pradhan D and De U K 2008 Simulation of local severe storm by mesoscale model MM5; Ind. J. Radio Space Phys. 37 419–433.

    Google Scholar 

  • De U S, Dube R K and Prakasa Rao G S 2005 Extreme weather events over India in the last 100 years; J. Ind. Geophys. Union 9(3) 173–187.

    Google Scholar 

  • Dasgupta S and De U K 2007 Binary logistic regression models for short term prediction of premonsoon convective developments over Kolkata (India); Int. J. Climatol. 27 831–836.

    Article  Google Scholar 

  • Ghosh S, Sen P K and De U K 1999 Identification of significant parameters for the prediction of pre-monsoon thunderstorms at Calcutta; Int. J. Climatol. 19 673–681.

    Article  Google Scholar 

  • Hughes K K 2001 Development of MOS thunderstorm and severe thunderstorm forecast equations with multiple data sources; Preprints, 18th Conf. on Weather Analysis and Forecasting, Fort Lauderdale, FL, Amer. Meteor. Soc., pp. 191–195.

  • Klein W H 1971 Computer prediction of precipitation probability in the United States; J. Appl. Meteor. 10 903–915.

    Article  Google Scholar 

  • Lambert W C, Wheeler M and Roeder W 2005 Objective lightning forecasting at Kennedy Space Center and Cape Canaveral Air Force Station using cloud-to-ground lightning surveillance system data; Preprints, Conf. on Meteorological Applications of Lightning Data, San Diego, CA, Amer. Meteor. Soc., 4.1.

  • Litta A J and Mohanty U C 2008 Simulation of a severe thunderstom event during the field experiment of STORM programme 2006 using WRF-NMM model; Curr. Sci. 95(2) 204–215.

    Google Scholar 

  • Manohar G K, Kandalgaonkar S S and Tinmaker M I R 1999 Thunderstorm activity over India and the Indian southwest monsoon; J. Geophys. Res. 104 4169–4188.

    Article  Google Scholar 

  • Mazany R A, Businger S, Gutman S I and Roeder W 2002 A lightning prediction index that utilizes GPS integrated precipitable water vapor; Wea. Forecasting 17 1034–1047.

    Article  Google Scholar 

  • McCann D W 1994 Windex – a new index for forecasting microburst potential; Wea. Forecasting 9 532–541.

    Article  Google Scholar 

  • Mukhopadhyay P, Singh H A K and Mahakur M 2009 The interaction of large scale and mesoscale environment leading to formation of intense thunderstorms over Kolkata, Part I: Doppler radar and satellite observations; J. Earth Syst. Sci. 118 441–466.

    Article  Google Scholar 

  • Nath A, Manohar G K, Dani K K and Devara P C S 2009 A study of lightning activity over land and oceanic regions of India; J. Earth Syst. Sci. 118 467–481.

    Article  Google Scholar 

  • Neumann C J and Nicholson J R 1972 Multivariate regression techniques applied to thunderstorm forecasting at the Kennedy Space Center; Preprints, Int. Conf. on Aerospace and Aeronautical Meteorology, Washington, DC, Amer. Meteor. Soc., pp. 6–13.

  • Price C and Rind D 1992 A simple lightning parameterization for calculating global lightning distributions; J. Geophys. Res. 97 9919–9933.

    Article  Google Scholar 

  • Rajeevan M, Kesarkar A, Thampi S B, Rao T N, Radhakrishna B and Rajasekhar M 2010 Sensitivity of WRF cloud microphysics to simulations of a severe thunderstorm event over south-east India; Ann. Geophys. 28 603–619.

    Article  Google Scholar 

  • Ranalkar M R and Chaudari H S 2009 Seasonal variation of lightning activity over Indian subcontinent; Meteor. Atmos. Phys. 104 125–134, doi: 10.1007/500703- 009-0026-7.

    Article  Google Scholar 

  • Reap R M and MacGorman D R 1989 Cloud-to-ground lightning: Climatological characteristics and relationships to modelfields, radar observations, and severe local storms; Mon. Weather Rev. 117 518–535.

    Article  Google Scholar 

  • Reap R M 1994a Analysis and prediction of lightning strike distributions associated with synoptic map types over Florida; Mon. Weather Rev. 122 1698–1715.

    Article  Google Scholar 

  • Reap R M 1994b 4-h NGM based probability and categorical forecasts of thunderstorms and severe local storms for the contiguous U.S; NWS Technical Procedures Bulletin 419 14p.

  • Shafer P E and Fuelberg H E 2006 A statistical procedure to forecast warm season lightning over portions of the Florida peninsula; Wea. Forecasting 21 851–868.

    Article  Google Scholar 

  • Shafer P E and Fuelberg H E 2008 A perfect prognosis scheme for forecasting warm-season lightning over Florida; Mon. Weather Rev. 136 1817–1846.

    Article  Google Scholar 

  • Srivastava K, Roy Bhowmik S K, Hatwar H R, Das A K and Kumar A 2008 Simulation of mesoscale structure of thunderstorm using ARPS model; Mausam 59 1–14.

    Google Scholar 

  • Tinmaker M I R, Kaushar A and Beig G 2009 Relationship between lightning activity over peninsular India and sea surface temperature; J. Appl. Meteor. 49 828–835.

    Google Scholar 

  • Wilks D S 2006 Statistical methods in the Atmospheric Sciences, International Geophysics Series, Academic Press, 91, 627p.

  • Wilson J W, Crook N A, Mueller C K, Sun J and Dixon M 1998 Nowcasting thunderstorms: A status report; Bull. Am. Meteor. Soc. 79 2079–2099.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M Rajeevan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rajeevan, M., Madhulatha, A., Rajasekhar, M. et al. Development of a perfect prognosis probabilistic model for prediction of lightning over south-east India. J Earth Syst Sci 121, 355–371 (2012). https://doi.org/10.1007/s12040-012-0173-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12040-012-0173-y

Keywords

Navigation