A study of atmospheric gradient for the analysis of weather events and prediction of rainfall in the tropical region

Abstract

Based on the statistical analysis of gradient orientation preceding a rain event, we have designed an algorithm using gradient convergence and flux for forecasting vii the rainfall over a region. In this study, we have incorporated precipitable water vapor alongside the atmospheric gradient in order to augment the accuracy of the forecasting algorithm with a 6-hour prediction window as PWV experiences an increase in its value before precipitation over an area. The single-layer dual-parameter algorithm with gradient convergence and PWV provides a high true detection rate of 90.4% alongside a substantially low false alarm rate of 21.9%. We can achieve almost similar accuracy with gradient flux as well in combination with PWV for predicting rain over a region in next quarter of a day. Furthermore, a two-layer algorithm has also been developed with both PWV and atmospheric gradient which precisely predicts the region to be afflicted by rainfall in the next 6 hours. Deep learning model has also been implemented to forecast the rain events for the following 6 hours with various atmospheric parameters which attains a substantial prediction accuracy of around 80% for tropical regions.