Satellite Precipitation

Satellite precipitation data, crucial for hydrological modeling and climate studies, often suffers from inaccuracies and limited spatial resolution. Current research focuses on improving these datasets by merging satellite data with ground-based measurements using advanced machine learning techniques, such as ensemble learning methods incorporating gradient boosting machines, random forests, and neural networks, to enhance accuracy and provide uncertainty estimates. This work is significant because reliable, high-resolution precipitation data is essential for improving weather forecasting, water resource management, and understanding the impacts of climate change.