Reducing Latency in Satellite-Based Precipitation Estimates Using GOES-16 and Machine Learning

Abstract

AbstractAccurate representation of spatiotemporal precipitation patterns is essential for developing hydrological applications, particularly in operational hydrology and early warning systems. In regions with scarce in situ precipitation data, freely ...Practical ApplicationsUnderstanding precipitation patterns is crucial for managing water resources, predicting floods, and mitigating drought impacts. However, in many regions, reliable ground-based precipitation data are limited, making it difficult to ...