Limitation of super-resolution machine learning approach to precipitation downscaling.

ABSTRACT: The present study explores the potential of super-resolution machine learning (ML) models for precipitation downscaling from 100 to 12.5 km at hourly timescale using the Conformal Cubic Atmospheric Model (CCAM) data over the Australian domain. Two approaches were examined: the perfect approach, which trains the ML model using coarsened high-resolution data as input (i.e., CCAM 12.5 km data coarsened to 100 km), and the imperfect approach, which uses original coarse-resolution data as input (i.e., CCAM model simulation at 100 km resolution) and in both the cases high-resolution data (i.e., CCAM 12.5 km simulation) is used as target. In the perfect case, the ML model (ML_Perfect) accurately reproduces high-resolution climatology and extremes. However, the ML_Perfect model

SUBMITTER: Reddy PJ 

PROVIDER: S-EPMC12358561 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature