Hue Channel Mesoscale Convective System Identification Techniques using Kalpana Geostationary Satellite Observations
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Abstract
Climate change is leading to sudden extreme weather events like floods and heavy rain to occur. One of the most significant rain-bearing systems, Mesoscale Convective Systems (MCSs), is in charge of catastrophic rainfall and flood events that may cause loss of life and property. MCSs are one of the vital components of the climate system on Earth. Many studies contributed to two significant steps, MCS identification and tracking. There are a variety of algorithms focused on tracking whereas for MCS identification only limited methods are present. They contribute to global as well as regional climate patterns by transporting heat and moisture. It is necessary to identify MCS properly to track MCS occurrences over time and comprehend their typical lifecycle to get early warnings of their existence. Three MCS identification techniques based on the Hue channel of the Hue Saturation Value (HSV) color model are implemented in this research, and their effectiveness is assessed. These techniques execute segmentation based on Hue channel Thresholding (HT), K means clustering combined with Hue channel Thresholding (KMCHT) and the modified Source Apportionment Technique combined with Hue channel Thresholding (SATHT). Image pixel values are used to depict infrared brightness temperature data obtained from the Indian geostationary satellite Kalpana-1. The generated ground truth images and performance measurements are used to assess the effectiveness of the methods. The proposed SATHT method for multiple cloud segmentation results in superior performance metrics than the HT and KMCHT approaches.
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