A Probe for Consistency in CAPE and CINE During the Prevalence of Severe Thunderstorms:Statistical – Fuzzy Coupled Approach

Author(s)
Sutapa Chaudhuri

ABSTRACT

Thunderstorms of pre-monsoon season (April – May) over Kolkata (22° 32’N, 88° 20’E), India are invariably accompanied with lightning flashes, high wind gusts, torrential rainfall, occasional hail and tornadoes which significantly affect the life and property on the ground and aviation aloft. The societal and economic impact due to such storms made accurate prediction of the weather phenomenon a serious concern for the meteorologists of India. The initiation of such storms requires sufficient moisture in lower troposphere, high surface temperature, conditional instability and a source of lift to initiate the convection. Convective available potential energy (CAPE) is a measure of the energy realized when conditional instability is released. It plays an important role in meso-scale convective systems. Convective inhibition energy (CINE) on the other hand acts as a possible barrier to the release of convection even in the presence of high value of CAPE. The main idea of the present study is to see whether a consistent quantitative range of CAPE and CINE can be identified for the prevalence of such thunderstorms that may aid in operational forecast. A statistical – fuzzy coupled method is implemented for the purpose. The result reveals that a definite range of CINE within 0 – 150 Jkg^{-1} is reasonably pertinent whereas no such range of CAPE depicts any consistency for the occurrence of severe thunderstorms over Kolkata. The measure of CINE mainly depends upon the altitude of the level of free convection (LFC), surface temperature (T) and surface mixing ratio (q). The box-and-whisker plot of LFC, T and q are drawn to select the most dependable parameter for the consistency of CINE in the prevalence of such thunderstorms. The skills of the parameters are evaluated through skill score analyses. The percentage error during validation with the observation of 2010 is estimated to be 0% for the range of CINE and 3.9% for CAPE.

Thunderstorms of pre-monsoon season (April – May) over Kolkata (22° 32’N, 88° 20’E), India are invariably accompanied with lightning flashes, high wind gusts, torrential rainfall, occasional hail and tornadoes which significantly affect the life and property on the ground and aviation aloft. The societal and economic impact due to such storms made accurate prediction of the weather phenomenon a serious concern for the meteorologists of India. The initiation of such storms requires sufficient moisture in lower troposphere, high surface temperature, conditional instability and a source of lift to initiate the convection. Convective available potential energy (CAPE) is a measure of the energy realized when conditional instability is released. It plays an important role in meso-scale convective systems. Convective inhibition energy (CINE) on the other hand acts as a possible barrier to the release of convection even in the presence of high value of CAPE. The main idea of the present study is to see whether a consistent quantitative range of CAPE and CINE can be identified for the prevalence of such thunderstorms that may aid in operational forecast. A statistical – fuzzy coupled method is implemented for the purpose. The result reveals that a definite range of CINE within 0 – 150 Jkg

Cite this paper

nullS. Chaudhuri, "A Probe for Consistency in CAPE and CINE During the Prevalence of Severe Thunderstorms:Statistical – Fuzzy Coupled Approach,"*Atmospheric and Climate Sciences*, Vol. 1 No. 4, 2011, pp. 197-205. doi: 10.4236/acs.2011.14022.

nullS. Chaudhuri, "A Probe for Consistency in CAPE and CINE During the Prevalence of Severe Thunderstorms:Statistical – Fuzzy Coupled Approach,"

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[30] S. Chaudhuri, “Artificial Neural Network Model to Fore- cast Maximum Wind Speed Associated with Severe Thunderstorms,” Vatabaran, AFAC Journal of Meteorology, Vol. 30, No. 1, 2006, pp. 14-19.

[31] S. Chaudhuri, “A Hybrid Model to Estimate the Depth of Potential Convective Instability during Severe Thunder- storms,” Soft Computing, Vol. 10, No. 8, 2006, pp. 643- 648. doi:10.1007/s00500-005-0532-6

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[34] S. Chaudhuri, “Consequences of Surface Parameters due to the Occurrence of Severe Thunderstorms―A View through Rough Set Theory,” Science & Culture, Vol. 73, No. 11-12, 2007, pp. 391-395.

[35] S. Chaudhuri, “Preferred Type of Cloud in the Genesis of Severe Thunderstorms―A Soft Computing Approach,” Atmospheric Research, Vol. 88, No. 2, 2008, pp. 149-156. doi:10.1016/j.atmosres.2007.10.008

[36] S. Chaudhuri and A. Middey, “The Applicability of Bipartite Graph Model for Thunderstorm Forecast over Kolkata,” Advances in Meteorology, Vol. 2009, 2009, pp. 1-12. doi:10.1155/2009/270530

[37] S. Chaudhuri, “Convective Energies in Forecasting Severe Thunderstorms with One Hidden Layer Neural Net and Variable Learning Rate Back Propagation Algorithm,” Asia-Pacific Journal of Atmospheric Sciences, Vol. 46, No. 2, 2010, pp. 173-183.

[38] S. Chaudhuri, “Predictability of Severe Thunderstorms with Fractal Dimension Approach,” Asian Journal of Wa- ter, Air & Environmental Pollution, Vol. 7, No. 4, 2010, pp. 81-87.

[39] S. Chaudhuri and A. Middey, “Nowcasting Thunderstorms with Graph Spectral Distance and Entropy Estimation,” Meteorological Applications, Vol. 18, No. 2, 2011, pp. 238-249. doi:10.1002/met.240

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[2] IMD, “Nor’westers of West Bengal,” India Meteorological Department Tech, Note 10, 1941.

[3] H. R. Byers and R. R Jr. Braham, “The Thunderstorm,” U. S. Government Printing Office, Washington D. C., 1949, p. 287.

[4] S. Mull and Y. P. Rao, “Effect of Vertical Acceleration on Pressure during Thunderstorms,” Quarterly Journal of the Royal Meteorological Society, Vol. 71, 1948, pp. 419-421.

[5] B. N. Desai and Y. P. Rao, “On the Cold Pools and Their Role in the Development of Nor’westers over West Bengal and East Pakistan,” Indian Journal of Meteorology and Geophysics, Vol. 5, 1954, pp. 243-248.

[6] C. Ramaswamy, “On the Subtropical Jet Stream and Its Role in the Development of Large-Scale Convection,” Tellus, Vol. 8, No. 1, 1956, pp. 26-60. doi:10.1111/j.2153-3490.1956.tb01194.x

[7] P. Koteswaram and V. Srinivasan, “Thunderstorms over Gangetic West Bengal in the Pre-Monsoon Season and the Synoptic Factors Favorable for Their Formation,” Indian Journal of Meteorology and Geophysics, Vol. 10, 1958, pp. 275-282.

[8] A. K. Mukherjee and A. K. Chowdhury, “Excessive Overshooting of Cb,” Indian Journal of Meteorology and Geophysics, Vol. 30, 1979, pp. 485-492.

[9] S. Ghosh, P. K. Sen and U. K. De, “Identification of Sig- nificant Parameters for the Prediction of Pre-Monsoon Thunderstorms at Calcutta,” International Journal of Climatology, Vol. 19, No. 6, 1999, pp. 673-681. doi:10.1002/(SICI)1097-0088(199905)19:6<673::AID-JOC384>3.0.CO;2-O

[10] S. Chaudhuri, “Identification of the Level of Downdraft Formation during Severe Thunderstorms: A Frequency Domain Analysis,” Meteorology and Atmospheric Physics, Vol. 102, No. 1-2, 2008a, pp. 123-129. doi:10.1007/s00703-008-0014-3

[11] S. Chatterjee, S. Ghosh and U. K. De, “Reduction of Number of Parameters and Forecasting Convective Development over Kolkata (22.53° N, 88.33° E), India during Pre-Monsoon Season: An Application of Multivariate Technique,” Indian Journal of Radio & Space Physics, Vol. 38, 2009, pp. 275-282.

[12] S. Chaudhuri and M. Biswas, “Pattern of Meteorological Parameters during Severe Thunderstorms―A Frequency Domain Analysis,” Mausam, Vol. 60, No. 1, 2008, pp. 1-10.

[13] P. Chatterjee, D. Pradhan and U. K. De, “Simulation of Local Severe Storm by Mesoscale Model MM5,” Indian Journal of Radio & Space Physics, Vol. 37, 2008, pp. 419-433.

[14] A. J. Litta and U. C. Mohanty, “Simulation of a Severe Thunderstorm Event during the Field Experiment of STORM Programme 2006, Using WRF-NMM Model,” Current Science, Vol. 95, No. 2, 2008, pp. 204-215.

[15] P. Mukhopadhyay, H. A. K. Singh and M. Mahakur, “The Interaction of Large Scale and Mesoscale Environment Leading to Formation of Intense Thunderstorms over Kolkata. Part I: Doppler Radar and Satellite Observations,” Journal of Earth System Science, Vol. 118, No. 5, 2009, pp. 441-466. doi:10.1007/s12040-009-0046-1

[16] L. A. Zadeh, “Probability Measures of Fuzzy Events,” Journal of Mathematical Analysis and Applications, Vol. 23, No. 2, 1965, pp. 421-427. doi:10.1016/0022-247X(68)90078-4

[17] D. W. McCann, “A Neural Network Short-Term Forecast of Significant Thunderstorms,” Weather and Forecasting, Vol. 7, No. 3, 1992, pp. 525-534. doi:10.1175/1520-0434(1992)007<0525:ANNSTF>2.0.CO;2

[18] C. Marzban and G. Stumpf, “A Neural Networks for Damaging Wind Prediction,” Weather and Forecasting, Vol. 13, No. 1, 1998, pp. 151-163. doi:10.1175/1520-0434(1998)013<0151:ANNFDW>2.0.CO;2

[19] W. W. Hsieh and T. Tang, “Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography,” Bulletin of the American Meteoro- logical Society, Vol. 79, No. 9, 1998, pp. 1855-1869. doi:10.1175/1520-0477(1998)079<1855:ANNMTP>2.0.CO;2

[20] D. A. K. Fernando and A. W. Jayawardena, “Runoff Forecasting Using RBF Networks with OLS Algorithm,” Journal of Hydrologic Engineering, Vol. 3, No. 3, 1998, pp. 203-209. doi:10.1061/(ASCE)1084-0699(1998)3:3(203)

[21] A. S. Elshorbagy, P. Simonovic and U. S. Panu, “Performance Evaluation of Artificial Neural Networks for Runoff Prediction,” Journal of Hydrologic Engineering ASCE, Vol. 5, No. 4, 2000, pp. 424-427. doi:10.1061/(ASCE)1084-0699(2000)5:4(424)

[22] A. S. Tokar and M. Markus, “Precipitation-Runoff Modeling Using Artificial Neural Networks and Conceptual Models,” Journal of Hydrologic Engineering, Vol. 5, No. 2, 2000, pp. 156-161. doi:10.1061/(ASCE)1084-0699(2000)5:2(156)

[23] C. Marzban and A. Witt, “A Bayesian Neural Network for Severe Hail Size Prediction,” Weather and Forecasting, Vol. 16, No. 5, 2001, pp. 600-610. doi:10.1175/1520-0434(2001)016<0600:ABNNFS>2.0.CO;2

[24] A. Abraham, N. S. Philip and B. Joseph, “Will We Have a Wet Summer? Long Term Rain Forecasting Using Soft Computing Models,” In: E. J. H. Kerchoffs and M. Snorek, eds., Modeling and Simulation 2001, Publication of the Society for Computer Simulation International, Prague, 2001, pp. 1044-1048.

[25] M. P. Rajurkar , U. C. Kothyari and U. C. Chaube, “Mod- eling of the Daily Rainfall-Runoff Relationship with Artificial Neural Network,” Journal of Hydrology, Vol. 285, No. 1-4, 2001, pp. 96-113. doi:10.1016/j.jhydrol.2003.08.011

[26] L. Jin, C. Yao and Y. K. Huang, “A Nonlinear Artificial Intelligence Ensemble Prediction Model for Typhoon Intensity,” Monthly Weather Review, Vol. 136, No. 12, 2008, pp. 4541-4554. doi:10.1175/2008MWR2269.1

[27] Y. Wang, T. Y. Yu, M. Yeary, A. Shapiro, S. Nemati, M. Foster, D. L. Jr Andra and M. Jain, “Tornado Detection Using a Neuro-Fuzzy System to Integrate Shear and Spec- tral Signatures,” Journal of Atmospheric and Oceanic Technology, Vol. 25, No. 7, 2008, pp. 1136-1148. doi:10.1175/2007JTECHA1022.1

[28] S. Chaudhuri, “Genetic Algorithm to Recognize Apt Energy for the Genesis of Severe Thunderstorms,” Vatabaran, AFAC Journal of Meteorology, Vol. 29, No. 2, 2008, pp. 1-8.

[29] S. Chaudhuri, “Ampliative Reasoning to View the Prevalence of Severe Thunderstorms,” Mausam-Quarterly Journal of Meteorology, Hydrology & Geophysics, Vol. 57, No. 3, 2006, pp. 523-526.

[30] S. Chaudhuri, “Artificial Neural Network Model to Fore- cast Maximum Wind Speed Associated with Severe Thunderstorms,” Vatabaran, AFAC Journal of Meteorology, Vol. 30, No. 1, 2006, pp. 14-19.

[31] S. Chaudhuri, “A Hybrid Model to Estimate the Depth of Potential Convective Instability during Severe Thunder- storms,” Soft Computing, Vol. 10, No. 8, 2006, pp. 643- 648. doi:10.1007/s00500-005-0532-6

[32] S. Chaudhuri and S. Aich Bhowmik, “CAPE―A Link between Thermodynamics and Microphysics for the Oc- currence of Severe Thunderstorms,” Mausam-Quarterly Journal of Meteorology, Hydrology & Geophysics, Vol. 57, No. 2, 2006, pp. 249-254.

[33] S. Chaudhuri, “Chaotic Graph Theory Approach for Identification of Convective Available Potential Energy (CAPE) Patterns Required for the Genesis of Severe Thunderstorms,” Advances in Complex Systems, Vol. 10, No. 3, 2007, pp. 413-422. doi:10.1142/S0219525907001215

[34] S. Chaudhuri, “Consequences of Surface Parameters due to the Occurrence of Severe Thunderstorms―A View through Rough Set Theory,” Science & Culture, Vol. 73, No. 11-12, 2007, pp. 391-395.

[35] S. Chaudhuri, “Preferred Type of Cloud in the Genesis of Severe Thunderstorms―A Soft Computing Approach,” Atmospheric Research, Vol. 88, No. 2, 2008, pp. 149-156. doi:10.1016/j.atmosres.2007.10.008

[36] S. Chaudhuri and A. Middey, “The Applicability of Bipartite Graph Model for Thunderstorm Forecast over Kolkata,” Advances in Meteorology, Vol. 2009, 2009, pp. 1-12. doi:10.1155/2009/270530

[37] S. Chaudhuri, “Convective Energies in Forecasting Severe Thunderstorms with One Hidden Layer Neural Net and Variable Learning Rate Back Propagation Algorithm,” Asia-Pacific Journal of Atmospheric Sciences, Vol. 46, No. 2, 2010, pp. 173-183.

[38] S. Chaudhuri, “Predictability of Severe Thunderstorms with Fractal Dimension Approach,” Asian Journal of Wa- ter, Air & Environmental Pollution, Vol. 7, No. 4, 2010, pp. 81-87.

[39] S. Chaudhuri and A. Middey, “Nowcasting Thunderstorms with Graph Spectral Distance and Entropy Estimation,” Meteorological Applications, Vol. 18, No. 2, 2011, pp. 238-249. doi:10.1002/met.240

[40] D. S. Wilks, “Statistical Methods in the Atmospheric Sciences,” 2nd Edition, Elsevier, Oxford, 2006.

[41] I. T. Jolliffe and D. B. Stephenson, “Forecast Verification: A Practitioner’s Guide in Atmospheric Science,” John Wiley and Sons, Chichester, 2003.

[42] D. S. Wilks, “Statistical Methods in Atmospheric Sci- ences,” Academic Press, Cambridge, 1995, pp. 114-122.