From Drops to Data: Advancing Global Precipitation Estimates with the LETKF Algorithm
2025.01.15
Estimating global precipitation is vital for managing water-related disasters, yet it is often challenging due to sparse rain gauge data in certain areas. To improve these predictions, Assistant Professor Yuka Muto and Professor Shunji Kotsuki, a research duo from Chiba University, developed a new tool using the Local Ensemble Transform Kalman Filter technique for rain gauge observations and reanalysis precipitation. Their method offers promising results for improving disaster management and sustainable water supply strategies.