Detection of Wavelengths for Hail Identification Using Satellite Imagery of Clouds

Weather forecasting is a formidable challenge in the field of science as it depends on multiple parameters which are dynamic and chaotic. The rain, snow and hails are different climatic conditions that depend on the atmospheric parameters and are major forms of precipitation. Hailstorms are measured using traditional radar. Radar based hail measurements face major problems as the signals are weak and face attenuation issues with strong echoes. Hence, satellite or digital images are one of the efficient sources in the prediction of hail. In the process of image acquisition from the satellite imagery it would often find barriers like noise, burrs and so on, obscure or even cover the original image of an area or can reduce the image quality which include lot of noise. Therefore wavelet transform is used to enhance the image or to eliminate striping noise. They have advantages over traditional wavelet methods in analyzing physical situations where the signals are discontinuities. One of the wavelet transform used in the prediction of hail for a satellite or digital image is the haar wavelet transform. Differentiation between rain and hail depends on the square root balance sparsity norm threshold value obtained on compressing and de-noising the satellite image. The proposed model yields an average accuracy of 89.15 % in the identification of hail.