Machine unlearning: bias correction in neural network downscaled storms
Year: 2025
Type: article
Abstract: Accurate precipitation at fine spatial resolutions is essential for hydrologic modeling and risk assessment, yet most precipitation data are available at coarse scales. Dynamical downscaling can improve spatial resolution but is computationally expensive while statistical downscaling struggles to reproduce high-resolution characteristics. Machine learning has been shown to offer an operational alternative for transforming coarse data into fine-scale fields, especially honoring spatiotemporal precipitation dependencies. Here, we show that combining machine learning with post-processing bias correction approaches—a form of “machine unlearning”— yields improved performance. We evaluate four machine-learning models—Linear Network (LNet), Fully Connected Network (FCNet), Convolutional Neural Network (UNet), and Wasserstein Generative Adversarial Network (WGAN)—for downscaling precipitation using synthetic benchmark storms with known marginal and spatiotemporal properties. Using synthetic fields provides full control over storm characteristics and enables rigorous evaluation. Raw outputs from all models struggle to reproduce wet/dry boundaries, statistics, and extremes, and only WGAN captures the complex spatiotemporal structure of fine-scale storms. We then apply linear and nonlinear bias corrections to enforce zeros, match the mean of positive values, and align full marginal distributions, including tails. This demonstrates that post-processing is crucial for reliable neural network outputs in operational settings. The results highlight WGAN’s potential for operational downscaling while emphasizing the need for systematic post-processing and careful validation before real-world application.
Source: Journal of Hydrology
Institutions: Hamburg University of Technology, University of Virginia
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
Estimated APC paid: $4,030
OpenAlex ID: W4416777141