Rainfall Estimation From Satellite Images Using Cloud Classifications

The majority of human activities, from agricultural activities to heavy industries, depends on rainfall as this is the key source of water supply. In order to precisely measure rainfall precipitation, a trustworthy method is needed. The satellite monitors the atmosphere from top and gives visual data on cloud coverage. University of California engineers designed the mobile application iRain. Irvine provides the public with access to the same precise rainfall data. Skymet App is an existing Indian app that forecast weather news and analysis based on satellite images. In order to estimate rainfall, we provide a neural network-based real-time precipitation retrieval approach. It divides the cloud patch features into categories based on cloud height, area extension, and temperature inferred from satellite images. Our solution to this problem is an application developed based on the requirements suggested by APSDMA. The app will calculate Precipitation estimation using satellite data, where cloud samples will be taken and logged in a database. Also it divides the cloud images into discrete cloud patches, extract the cloud characteristics (coldness, temperature etc.) and categorize the cloud patches into groups from which rainfall will be estimated.