Table of Contents
International Journal of Atmospheric Sciences
Volume 2013 (2013), Article ID 525383, 14 pages
Research Article

Artificial Neural Network Model in Prediction of Meteorological Parameters during Premonsoon Thunderstorms

1Department of Computer Science, Cochin University of Science and Technology, Cochin, Kerala 682 022, India
2School of Earth Ocean and Climate Sciences, Indian Institute of Technology Bhubaneswar, A2-707, Toshali Bhawan, Satyanagar, Bhubaneswar, Odisha 751007, India

Received 21 May 2013; Revised 6 October 2013; Accepted 10 October 2013

Academic Editor: Hui Wang

Copyright © 2013 A. J. Litta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Forecasting thunderstorm is one of the most difficult tasks in weather prediction, due to their rather small spatial and temporal extension and the inherent nonlinearity of their dynamics and physics. Accurate forecasting of severe thunderstorms is critical for a large range of users in the community. In this paper, experiments are conducted with artificial neural network model to predict severe thunderstorms that occurred over Kolkata during May 3, 11, and 15, 2009, using thunderstorm affected meteorological parameters. The capabilities of six learning algorithms, namely, Step, Momentum, Conjugate Gradient, Quick Propagation, Levenberg-Marquardt, and Delta-Bar-Delta, in predicting thunderstorms and the usefulness for the advanced prediction were studied and their performances were evaluated by a number of statistical measures. The results indicate that Levenberg-Marquardt algorithm well predicted thunderstorm affected surface parameters and 1, 3, and 24 h advanced prediction models are able to predict hourly temperature and relative humidity adequately with sudden fall and rise during thunderstorm hour. This demonstrates its distinct capability and advantages in identifying meteorological time series comprising nonlinear characteristics. The developed model can be useful in decision making for meteorologists and others who work with real-time thunderstorm forecast.