—Thunderstorm forecasting is a challenging job. Machine learning techniques are being applied nowadays in meteorological fields for prediction purpose. This study presents the application of different machine learning tools based on multiple correlation, Multi-layer Perceptron (MLP), K-nearest neighbor (K-nn) method, and modified K-nn method to predict seasonal severe thunderstorms associated with squall occurring in Kolkata, North-East India. The models are trained and tested with the radiosonde data recorded in the early morning at 00:00UTC. The predictors are moisture difference and dry adiabatic lapse rate at different geopotential heights of the atmosphere. Our aim in this paper is to find how much correctly one can nowcast 10 to 14 hours before the ‘occurrence’/ ‘no occurrence’ of evening squall-storms by using a few upper air diagnostic predictors. Modified K-nn method is found to yield very promising prognostic information with high prediction accuracy. The results indicate that forecasting can be done correctly up to 82.02% both for ‘squall-storm/no storm’ events, and up to 91.11% for ‘squall-storm’ events using modified K-nn based approach. In this article, modified K-nn method is proved as the best method in comparison with the other methods for the squall-storm prediction.
—Terms—Back propagation, K-nearest neighbor, multilayer perceptron, multiple correlation, squall-storm.
H. Chakrabarty is with Surendranath College, and he is also with Institute of Radiophysics and Electronics, Calcutta University, Kolkata, India (e-mail: firstname.lastname@example.org). C. A. Murthy is with Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India (e-mail: email@example.com). A. DasGupta is with S. K. Mitra Center for Research in Space Environment, Calcutta University, Kolkata, India (e-mail: firstname.lastname@example.org).
Cite:Himadri Chakrabarty, C. A. Murthy, and Ashish Das Gupta, "Application of Pattern Recognition Techniques to Predict Severe Thunderstorms," International Journal of Computer Theory and Engineering vol. 5, no. 6, pp. 850-855, 2013.