Assessing a machine learning-based downscaling framework for obtaining 1km daily precipitation from GPM data.

ABSTRACT: Hydro-meteorological monitoring through satellites in arid and semi-arid regions is constrained by the coarse spatial resolution of precipitation data, which impedes detailed analyses. The objective of this study is to evaluate various machine learning techniques for developing a downscaling framework that generates high spatio-temporal resolution precipitation products. Focusing on the Hai River Basin, we evaluated three machine learning approaches-Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Back Propagation (BP) neural networks. These methods integrate environmental variables including land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), Precipitable Water Vapor (PWV), and albedo, to downscale the 0.1° spatial reso

SUBMITTER: Sun T 

PROVIDER: S-EPMC11403431 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature