HIR Nowcasting using ConvLSTM

This repository presents a deep learning-based framework for high-intensity rainfall (HIR) nowcasting, leveraging geostationary satellite data from GEO-KOMPSAT-2A (GK2A) and Global Precipitation Measurement (GPM) IMERG. Three Convolutional Long Short-Term Memory 2D (ConvLSTM2D) models are developed using brightness temperature (BT), cloud analysis, and rain rate products. These models achieve critical success indices (CSI) up to 50% and F1 scores above 70%, demonstrating the potential for HIR prediction globally, especially in regions without ground-based radar coverage.