Precipitation Estimation

Precipitation estimation aims to accurately determine the amount and spatial distribution of rainfall, crucial for various applications from flood prediction to irrigation management. Current research heavily utilizes machine learning, employing convolutional neural networks (CNNs), including 3D variations and those incorporating attention mechanisms, along with other algorithms like LightGBM and ensemble methods, to merge data from diverse sources such as rain gauges, radar, and satellites. These advancements improve the accuracy and resolution of precipitation estimates, particularly for extreme events, leading to better hydrological modeling, improved water resource management, and enhanced disaster preparedness. The focus is on addressing challenges posed by complex terrain and limited data availability through innovative data fusion and model development.