Physically constrained generative adversarial networks for improving precipitation fields from Earth system models

2022/10/03 by Philipp Hess, Markus Drüke, Stefan Petri +3 · 1 voice · 1 citation
Earth and Planetary Sciences · Environmental Science · #Climate variability and models #Meteorological Phenomena and Simulations #Tropical and Extratropical Cyclones Research

paper · doi:10.1038/s42256-022-00540-1

crossref issued 2022/10/03 · crossref published 2022/10/03 · crossref published-online 2022/10/03 · openalex publication_date 2022/10/03 · crossref created 2022/10/03 · crossref deposited 2022/10/22 · openalex created_date 2025/10/10 · crossref indexed 2026/07/15 · openalex updated_date 2026/07/23

Citations

• CyCADA: Cycle-Consistent Adversarial Domain Adaptation
• MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting
• The ERA5 global reanalysis
• Bias correction of temperature and precipitation over China for RCM simulations using the QM and QDM methods
• Skilful precipitation nowcasting using deep generative models of radar

Cited by

• Improving Data Fidelity via Diffusion Model-based Correction and Super-Resolution

Discussions

• Hess et al. (2022). “Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models.” doi.org/10.1038/s422... [bsky, 0 points, 1 comments]