Optimized Thunderstorm and Lightning Detection System

Rasika Kalbende, Nitin Shelke

Abstract: Thunderstorm and lightning is a sudden electrical expulsion manifested by a blaze of lightening with a muffled sound. It is one of the most stunning mesoscale weather phenomena in the atmosphere which occurs seasonally. Every thunderstorm produce lightening, this kills more people every year than tornadoes, and prediction of thunderstorms is said to be the most complicated task in weather forecasting, due to its limited spatial and temporal extension either dynamically or physically. Many of the researchers proposed various methodologies like STP model, MOM model, CG model, LM model, QKP model, DBD model and so on for the detection, but neither of them could provide an accurate prediction. The proposed system is to meet the satellite images obtained from dataset in order to predict whether the cloud images produces thunderstorms or not. The proposed system improves the prediction rate to a greater extent, on the basis of some statistical analysis. For more Accuracy, we are going to do comparative study of different model using Hybrid Approach.

Keywords: Clustering, Haar wavelet transform, K-Medoids, Remote Sensing, Image Processing, PAM

How to Cite?: Rasika Kalbende, Nitin Shelke, "Optimized Thunderstorm and Lightning Detection System", Volume 6 Issue 5, May 2017, International Journal of Science and Research (IJSR), Pages: 840-844, https://www.ijsr.net/getabstract.php?paperid=ART20173348, DOI: https://dx.doi.org/10.21275/ART20173348