Skip to main content
SpringerLink
Log in

A composite stability index for dichotomous forecast of thunderstorms

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Thunderstorms are the perennial feature of Kolkata (22° 32′ N, 88° 20′ E), India during the premonsoon season (April–May). Precise forecast of these thunderstorms is essential to mitigate the associated catastrophe due to lightning flashes, strong wind gusts, torrential rain, and occasional hail and tornadoes. The present research provides a composite stability index for forecasting thunderstorms. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant indices with threshold ranges for the prevalence of such thunderstorms. The analyses reveal that the lifted index (LI) within the range of −5 to −12 °C, convective inhibition energy (CIN) within the range of 0–150 J/kg and convective available potential energy (CAPE) within the ranges of 2,000 to 7,000 J/kg are the most pertinent indices for the prevalence thunderstorms over Kolkata during the premonsoon season. A composite stability index, thunderstorm prediction index (TPI) is formulated with LI, CIN, and CAPE. The statistical skill score analyses show that the accuracy in forecasting such thunderstorms with TPI is 99.67 % with lead time less than 12 h during training the index whereas the accuracies are 89.64 % with LI, 60 % with CIN and 49.8 % with CAPE. The performance diagram supports that TPI has better forecast skill than its individual components. The forecast with TPI is validated with the observation of the India Meteorological Department during the period from 2007 to 2009. The real-time forecast of thunderstorms with TPI is provided for the year 2010.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

Similar content being viewed by others

References

  • Abraham A, Philip NS, Joseph B (2001) Will we have a wet summer? Long-term rain forecasting using Soft Computing models. In: Kerchoffs EJH & Snorek M (eds) Modeling and simulation 2001. Society for Computer Simulation International: Prague, Czech Republic. pp 1044–1048

  • Anderson T, Anderson M, Jacobson C, Nilsson S (1989) Thermodynamic indices for forecasting thunderstorms in southern Sweden. Meteor Mag 118:141–146

    Google Scholar 

  • Anthes RA (1976) Numerical prediction of severe storms—certainty, probability, or dream. Bull Amer Met Soc 57:423–430

    Google Scholar 

  • Chaudhuri S (2007) Chaotic graph theory approach for identification of convective available potential energy (CAPE) patterns required for the genesis of severe thunderstorm. Adv Complex Syst 10:413–422

    Article  Google Scholar 

  • Chaudhuri S (2008) Preferred type of cloud in the genesis of severe thunderstorms—a soft computing approach. Atmos Res 88:149–156

    Article  Google Scholar 

  • Chaudhuri S, Middey A (2009) Applicability of bipartite graph model for thunderstorms forecast over Kolkata. Adv Meteorol 2009:1–12

    Article  Google Scholar 

  • Chaudhuri S (2010a) Convective energies in forecasting severe thunderstorms with one hidden layer neural net and variable learning rate back propagation algorithm. Asia Pacific J Atmos Sci 46(2):173–183

    Article  Google Scholar 

  • Chaudhuri S (2010b) Predictability of severe thunderstorms with fractal dimension approach. Asian J Water Air Environ Pollut 7(4):81–87

    Google Scholar 

  • Chaudhuri S (2011a) Implementation of intuitionistic fuzzy logic to assess the predictability of Severe thunderstorms. Asia-Pacific J Atmos Sci 47(5):477–483

    Article  Google Scholar 

  • Chaudhuri S (2011b) A probe for consistency in CAPE and CINE during the prevalence of severe thunderstorms: statistical-fuzzy coupled approach. Atmos Clim Sci 4(1):197–205

    Google Scholar 

  • Chaudhuri S, Middey A (2011a) Nowcasting thunderstorms with graph spectral distance and entropy estimation. Meteorol Appl Vol 18(2):238–249

    Article  Google Scholar 

  • Chaudhuri S, Middey A (2011b) Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms. Meteorol Atmos Phys 114:139–149

    Article  Google Scholar 

  • Chaudhuri S, Anirban M, Sayantika G, Soumita B (2012) Appraisal of the prevalence of severe tropical storms over Indian Ocean by screening the features of tropical depressions. Nat Hazards 61(2):745–756

    Article  Google Scholar 

  • Doswell CA (1987) The distinction between large-scale and mesoscale contribution to severe convection: a case study example. Wea Forecast 2:3–16

    Article  Google Scholar 

  • Elshorbagy A, Simonovic SP, Panu US (2000) Performance evaluation of artificial neural networks for runoff prediction. J Hydrol Eng ASCE 5(4):424–427

    Article  Google Scholar 

  • Fernando DAK, Jayawardena AW (1998) Runoff forecasting using RBF networks with OLS algorithm. J Hydrol Eng 3(3):203–209

    Article  Google Scholar 

  • Hsieh WW, Tang T (1998) Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull Am Meteorol Soc 79:1855–1869

    Article  Google Scholar 

  • Huntrieser H, Schiesser HH, Schmid W, Waldvogel A (1997) Comparison of traditional and newly developed thunderstorm indices for Switzerland. Wea Forecast 12:108–125

    Article  Google Scholar 

  • IMD (1941) Nor’westers of Bengal, India Meteorological Department. Tech Note No. 10

  • Jacovides CP, Yonetani T (1990) An evaluation of stability indices for thunderstorm prediction in Greater Cyprus. Weather Forecast 5(4):559–569

    Article  Google Scholar 

  • Jin L, Yao C, Huang YX (2008) A nonlinear artificial intelligence ensemble prediction model for typhoon intensity. Mon Weather Rev 136:4541–4554

    Article  Google Scholar 

  • Johns RH, Doswell CA III (1992) Severe local storms forecasting. Wea Forecast 7:588–612

    Article  Google Scholar 

  • Jolliffe IT, Stephenson DB (2003) Forecast verification: a practitioner's guide in atmospheric science. Wiley, Chichester. ISBN 0-471-49759-2

    Google Scholar 

  • Lal R (1990) Forecasting of severe convective activity over Lucknow in premonsoon season. Mausam 41:455–458

    Google Scholar 

  • Lee RR, Passner JE (1993) The development and verification of TIPS: an expert system to forecast thunderstorm occurrence. Wea Forecast 8:271–280

    Article  Google Scholar 

  • Lilly DK (1990) Numerical prediction of thunderstorms—has its time come? Quart J Roy Meteor Soc 116:779–798

    Google Scholar 

  • Marzban C, Stumpf G (1998) A neural network for damaging wind prediction. Weather Forecast 13:151–163

    Article  Google Scholar 

  • Marzban C, Witt A (2001) A Bayesian neural network for severe hail size prediction. Weather Forecast 16:600–610

    Article  Google Scholar 

  • McCann DW (1992) A neural network short-term forecast of significant thunderstorms. Weather Forecast 7:525–534

    Article  Google Scholar 

  • Neumann CJ (1971) The thunderstorm forecasting system at the Kennedy Space Center. J Appl Meteorol 10:921–936

    Article  Google Scholar 

  • Orlanski I (1975) A rational subdivision of scales for atmospheric processes. Bull Am Meteorol Soc 56(5):527–530

    Google Scholar 

  • Pawlak Z, Skowron (2007) Rough sets and Boolean reasoning. Inf Sci 177(1):41–73

    Article  Google Scholar 

  • Pepple RA, Lamb PJ (1989) Tropospheric static stability and North American growing season rainfall. Mon Wea Rev 117:1156–1180

    Article  Google Scholar 

  • Rajurkar MP, Kothyari UC, Chaube UC (2004) Modeling of the daily rainfall–runoff relationship with artificial neural network. J Hydrol 285(1–4):96–113

    Article  Google Scholar 

  • Rasmussen EN, Blanchard DO (1998) A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea Forecast 13:1148–1164

    Article  Google Scholar 

  • Ravi N, Mohanty UC, Madan OP, Paliwal RK (1999) Forecasting of thunderstorms in the pre monsoon season at Delhi. Met Appl 6:29–38

    Article  Google Scholar 

  • Reap RM, Foster DS (1979) Automated 12–36 hour probability forecasts of thunderstorms and severe local storms. J Appl Meteor 18:1304–1315

    Article  Google Scholar 

  • Riemann-Campe K, Blender R, Fraedrich K (2010) Global memory analysis in observed and simulated CAPE and CIN. Int J Climatol 31:1099–1107

    Article  Google Scholar 

  • Roebber P (2009) Visualizing multiple measures of forecast quality. Weather Forecast 24:601–608

    Article  Google Scholar 

  • Schultz P (1989) Relationship of several stability indices to convective weather events in Northest Colorado. Wea Forecast 4:73–80

    Article  Google Scholar 

  • Stone HM (1985) A comparison among various thermodynamic parameters for the prediction of convective activity, Part I, NOAA Technical Memorandum, NWS-ER 68, Washington D C

  • Tokar AS, Markus M (2000) Precipitation–runoff modeling using artificial neural networks and conceptual models. J Hydrol Eng 5(2):156–161

    Article  Google Scholar 

  • Trafalis TB, Richman MB, Santosa B (2006) Learning networks for tornado detection. Int J Gen Syst 35:93–107

    Article  Google Scholar 

  • Tyagi B, Krishna NV, Satyanarayana ANV (2011) Study of thermodynamic indices in forecasting pre-monsoon thunderstorms over Kolkata during STORM pilot phase 2006–2008. Nat Hazard 56:681–698

    Article  Google Scholar 

  • Wang Y, Yu TY, Yeary M, Shapiro A, Nemati S, Foster M, Andra DL Jr, Jain M (2008) Tornado detection using a neuro-fuzzy system to integrate shear and spectral signatures. J Atmos Ocean Technol 25(7):1136–1148

    Article  Google Scholar 

  • Zhao Q, Cook J, Xu Q, Harasti PR (2008) Improving short-term storm predictions by assimilating both radar radial-wind and reflectivity observations. Weather Forecast 23(3):373–391

    Article  Google Scholar 

Download references

Acknowledgments

The first author gratefully acknowledges Indian Meteorological Department (IMD) and Department of Atmospheric Sciences, University of Wyoming (http://www.weather.uwyo.edu) for providing the data archives. The financial support rendered by CSIR is acknowledged. The authors express special gratitude to the anonymous reviewers for their substantial remarks that increased the completeness and clarity of the manuscript.

Author information

Authors and Affiliations

  1. Department of Atmospheric Science, University of Calcutta, Kolkata, 700-019, India

    Sutapa Chaudhuri & Anirban Middey

Authors
  1. Sutapa Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anirban Middey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sutapa Chaudhuri.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chaudhuri, S., Middey, A. A composite stability index for dichotomous forecast of thunderstorms. Theor Appl Climatol 110, 457–469 (2012). https://doi.org/10.1007/s00704-012-0640-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-012-0640-z

Keywords

Search

Navigation