A Geostationary Satellite Precipitation Estimation Machine Learning Model With Dynamic Sample Balancing for Fengyun-4B (opens in new tab)

High-precision, real-time precipitation estimation is critical for improving the accuracy of meteorological forecasting and disaster warnings. However, mainstream satellite remote sensing inversion techniques are often limited by their reliance on cloud-top information. In addition, estimation accuracy is further constrained by the severe category imbalance in precipitation samples and the widespread use of low-spatial-resolution reanalysis data, making it difficult for existing products to m...