Abstract
The specific forecast of occurrences and the associated consequences of thunderstorms is still a difficult task for both NWP models and professional weather forecasters due to the small spatial and temporal scales involved. In operational forecast, many indices are being used to assess the stability of the atmosphere and predict the possibility of thunderstorm development. It is also well established that the Doppler weather radar (DWR) has the capability of capturing the fast developing convective systems such as thunderstorms. The instability indices as well as the DWR data are utilized in the present study to estimate the speed of squall associated with thunderstorms during the pre monsoon season over Kolkata (22° 32′N, 88° 20′E), India. The ranges of the selected indices and the DWR data are estimated using the normal probability distribution function. The statistical skill score analysis is implemented to select the instability indices relevant for estimating the squall speed of thunderstorms over Kolkata. The threshold ranges of the selected indices and the DWR data are used as the inputs while the target output being the squall speed associated with thunderstorms. The method of rough set theory is adopted in this study to identify the best combination of the instability indices and DWR data for estimating the squall speed. The method of rough set theory is capable of dealing with inconsistency in the data set, if any, while simulates the condition — decision support system. The certainty factor of the rough set theory is computed in this study for the condition which is the coupled influence of the instability indices and DWR data on the decision that is, the squall speed associated with thunderstorms. The results are validated with the observations of 2010.
Similar content being viewed by others
References
Abraham, N. S. Philip, and B. Joseph, 2001: “Will we have a wet summer? Long term rain forecasting using soft computing models,” in Modeling and Simulation 2001, E. J. H Kerchoffs and M. Snorek, Eds., The Society for Computer Simulation International, Prague, Czech Republic, 1044–1048.
Andersson, T., M. Andersson, C. Jacobsson, and S. Nilsson, 1989: Thermodynamic indices for forecasting thunderstorms in southern Sweden. Meteoro. Magazine, 118, 141–146.
Bhatnagar A. K., P. R. Rao, S. Kalyanasundaram, S. B. Thampi, R. Suresh, and J. P. Gupta, 2003: Doppler radar — A detecting tool and measuring instrument in meteorology. Current Science, 85, 256–264.
Chatterjee, S., S. Ghosh, and U. K. De, 2009: Reduction of number of parameters and forecasting convective developments at Kolkata (22.53°N, 88.33°E), India during pre-monsoon season: An application of multivariate techniques. Indian J. Radio Space Phys., 38, 275–282.
Chaudhuri, S., 2006: Predictability of chaos inherent in the occurrence of severe thunderstorms. Adv. in Complex Syst., 9, 77–85.
_____, 2008a: Identification of the level of downdraft formation during severe thunderstorms: a frequency domain analysis. Meteor. Atmos. Phys., 102, 123–129.
_____, 2008b: Preferred type of cloud in the genesis of severe thunderstorms-a soft computing approach. Atmos. Res., 88, 149–156.
_____, 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., 462, 173–183.
_____, 2010b: Predictability of severe thunderstorms with fractal dimension approach. Asian J. Water, Air & Environ. Pollution, 7, 81–87.
_____, and A. Middey, 2011: Adaptive neuro-fuzzy inference system to forecast peak gust speed during thunderstorms. Meteor. Atmos. Phys., 114, 139–149.
_____, ______, S. Goswami, and S. Banerjee, 2012: Appraisal of the prevalence of severe tropical storms over Indian Ocean by screening the features of tropical depressions. Nat. Hazards, DOI 10.1007/s11069-011-0068-2.
_____, and Anirban Middey, 2012: A composite stability index for dichotomous forecast of thunderstorms. Theor. Appl. Climatol., DOI 10.1007/s00704-012-0640-z.
Cornford, S. G., and C. S. Spavins, 1973: Some measurements of cumulonimbus tops in the pre-monsoon season in north-east India. Meteoro. Magazine, 102, 314–332.
De, A. C., and S. N. Sen, 1961: A radar study of preog-monsoon thunderstorms (Norwesters) over Gangetic West Bengal. Indian J. Meteoro. Geophys., 12, 51.
Desai, B. N., and Y. P. Rao, 1954: On the cold pools and their role in the development of Nor’westers overWest Bengal and East Pakistan. Indian J. Meteoro. Geophys, 5, 243–248.
Dubrovsky, M., 1994: Probabilistic prediction of thunderstorm occurrence. Meteoro. Zpravy., 47, 103–112.
Floyd, J., 1838: Account of the hurricane or whirlwind of the 8 April 1838. J. Asiatic Soc. of Bengal, 7, 422–429.
Gardner, M. W., and S. R. Dorling, 1998: Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences.” Atmos. Environ., 32, 2627–2636.
Ghosh, S., P. K. Sen, and U. K. De, 1999: Identification of significant parameters for the prediction of pre-monsoon thunderstorms at Calcutta, India. Int. J. Climatol., 19, 673–681.
Hornik, K., 1991: Approximation capabilities of multilayer feed forward networks. Neural Networks, 4, 251–257.
Hsieh, W. W., and T. Tang, 1998: Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography. Bull. Amer. Meteor. Soc., 79, 1855–1869.
Huntrieser, H., H. H. Schiesser, W. Schmid, and A. Waldvogel, 1997: Comparison of traditional and newly developed thunderstorm indices for Switzerland. Wea. Forecasting, 12, 108–125.
Jacovides, C. P., and T. Yonetani, 1990: An evaluation of Stability indices for Thunderstorm prediction in Greater Cyprus. Wea. Forecasting, 5, 559–569.
Kalsi, S. R., and R. C. Bhatia, 1992: Satellite observations of thunderstorm complexes in weakly forced environments. Vayu Mandal, 22, 65–76.
Kandalgaokar, S. S., M. I. R. Tinmaker, M. K. Kulkarni, and A. Nath, 2002: Thunderstorm activity and sea surface temperature over the Island stations and along the east and west coast of India. Mausam., 53, 245.
Koteswaram, P., and A. C. De, 1959: Vertical development of precipitation echoes from cumulus clouds near Calcutta during the post monsoon season. Indian J. Meteoro. Geophys., 10, 173.
Lee, R. R., and J. E. Passner, 1993: The development and verification of TIPS: An expert system to forecast thunderstorm occurrence. Wea. Forecasting, 8, 271–280.
Lidga, M. G. H., 1951: Radar storm observation. Compendium of Meteorology., Boston, American Meteorological society, 1273–1275.
Litta, A. J., S. M. Ididcula, U. C. Mohanty, and S. K. Prasad, 2012: Comparison of Thunderstorm Simulations from WRF-NMM and WRFARW Models over East Indian Region. The Scientific World Journal, ID 951870, doi:10.1100/2012/951870.
Manohar, G. K., S. S. Kahdalgaonkar, and M. I. R. Tinmaker, 1999: Thunderstorm activity over India and the Indian southwest monsoon. J. Geophys. Res., 104, 4169–4188.
Marzban, C., and A. Witt, 2001: A Bayesian neural network for severe-hail size prediction. Wea. Forecasting, 16, 600–610.
McCann, D. W., 1992: A Neural Network Short-Term Forecast of Significant Thunderstorms. Wea. Forecasting, 7, 525–534.
Mukhopadhya P., J. Sanjay, and S. S. Singh, 2003: Objective forecast of thundery / non thundery days using conventional indices over three north east Indian stations. Mausam, 54, 867–880.
_____, H. A. K. Singh, and S. S. Singh, 2005: Two severe Nor’westers in April 2003 over Kolkata, India using Doppler radar observations and satellite imageries. Weather, 60, 343–353.
_____, ______, M. Mahakur, 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.
Mull, S., H. Mitra, and S. M. Kulshrestha, 1963: Tropical thunderstorms and radar echoes. Indian J. Meteoro. Geophys, 14, 23.
Neumann, C. J., 1971: The thunderstorm forecasting system at the Kennedy Space Center. J. Appl. Meteorol., 10, 921–936.
Orlanski, I., 1975: A rational subdivision of scales for atmospheric processes. Bull. Amer. Meteor. Soc., 56, 527–530.
Pawlak, Z., 2002: Rough set theory and its application. J. telecommunications and information technology, 7–10.
_____, and Skowron, 2007: Rough set and Boolean reasoning. Information Science, 177, 41–73.
Pradhan, D., U. K. De, and U. V. Singh, 2012: Development of nowcasting technique and evaluation of convective indices for thunderstorm prediction in Gangetic West Bengal (India) using Doppler Weather Radar and Upper air data. Mausam, 2, 299–318.
Rasmussen, E. N., and D. O. Blanchard, 1998: A Baseline Climatology of Sounding-Derived Supercell andTornado Forecast Parameters, Wea. Forecasting, 13, 1148–1164.
Reap, R. M., and D. S. Foster, 1979: Automated 12–36 hour probability forecasts of thunderstorms and severe local storms. J. Appl. Meteorol., 18, 1304–1315.
Schultz, P., 1989: Relationship of several stability indices to convective weather events in Northest Colorado. Wea. Forecasting, 4, 73–80.
Sinha, V., and D. Pradhan, 2006: Supercell storm at Kolkata, India and neighbourh ood-Analysis of thermodynamic conditions, evolution, structure and movement. Indian J. Radio and Space phys., 35, 270–279.
Stone, H. M., 1985: A comparison among various thermodynamic parameters for the prediction of convective activity, Part I. NOAA Technical Memorandum, NWS-ER 68, Washington D. C.
Zhao, Q., J. Cook, and P. R. Harasti, 2008: Improving Short term predictions by assimilating radar radial-wind and reflectivity observations. Wea. forecasting, 23, 373–391.
Waldteufel, P., and H. Corbin, 1979: On the analysis of Single Doppler Radar data. J. Appl. Meteorol., 18, 532–542.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chaudhuri, S., Goswami, S. & Middey, A. The coupled influence of instability indices and DWR data in estimating the squall speed of thunderstorms. Asia-Pacific J Atmos Sci 49, 451–465 (2013). https://doi.org/10.1007/s13143-013-0041-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13143-013-0041-y