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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Physically constrained generative adversarial networks for improving precipitation fields from Earth system models
Philipp Hess, Markus Drüke, Stefan Petri, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 10, pp. 828-839
Open Access | Times Cited: 48

Showing 1-25 of 48 citing articles:

Risks of synchronized low yields are underestimated in climate and crop model projections
Kai Kornhuber, Corey Lesk, Carl‐Friedrich Schleussner, et al.
Nature Communications (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 81
Rewards, risks and responsible deployment of artificial intelligence in water systems
Catherine E. Richards, Asaf Tzachor, Shahar Avin, et al.
Nature Water (2023) Vol. 1, Iss. 5, pp. 422-432
Open Access | Times Cited: 72
Iterative integration of deep learning in hybrid Earth surface system modelling
Min Chen, Zhen Qian, Niklas Boers, et al.
Nature Reviews Earth & Environment (2023) Vol. 4, Iss. 8, pp. 568-581
Closed Access | Times Cited: 56
Improving CMIP6 Atmospheric River Precipitation Estimation by Cycle‐Consistent Generative Adversarial Networks
Yuan Tian, Yang Zhao, Jianping Li, et al.
Journal of Geophysical Research Atmospheres (2024) Vol. 129, Iss. 14
Closed Access | Times Cited: 15
High-resolution meteorology with climate change impacts from global climate model data using generative machine learning
Grant Buster, Brandon Benton, Andrew Glaws, et al.
Nature Energy (2024) Vol. 9, Iss. 7, pp. 894-906
Closed Access | Times Cited: 11
Generative deep learning for data generation in natural hazard analysis: motivations, advances, challenges, and opportunities
Zhengjing Ma, Gang Mei, Nengxiong Xu
Artificial Intelligence Review (2024) Vol. 57, Iss. 6
Open Access | Times Cited: 9
Opinion: Why all emergent constraints are wrong but some are useful – a machine learning perspective
Peer Nowack, Duncan Watson‐Parris
Atmospheric chemistry and physics (2025) Vol. 25, Iss. 4, pp. 2365-2384
Open Access | Times Cited: 1
Collaboration between artificial intelligence and Earth science communities for mutual benefit
Min Chen, Zhen Qian, Niklas Boers, et al.
Nature Geoscience (2024) Vol. 17, Iss. 10, pp. 949-952
Closed Access | Times Cited: 8
Generative machine learning methods for multivariate ensemble postprocessing
Jieyu Chen, Tim Janke, Florian Steinke, et al.
The Annals of Applied Statistics (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 7
Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks: Climatology, Interannual Variability, and Extremes
Ya Wang, Gang Huang, Baoxiang Pan, et al.
Advances in Atmospheric Sciences (2024) Vol. 41, Iss. 7, pp. 1299-1312
Closed Access | Times Cited: 7
Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network
Yutong Chen, Ya Wang, Gang Huang, et al.
Geophysical Research Letters (2024) Vol. 51, Iss. 2
Open Access | Times Cited: 6
Improving the accuracy of flood forecasting for Northeast China by the correction of global forecast rainfall based on deep learning
Songliang Chen, Youcan Feng, Qinglin Mao, et al.
Journal of Hydrology (2024) Vol. 640, pp. 131733-131733
Closed Access | Times Cited: 6
Deep Learning for Bias‐Correcting CMIP6‐Class Earth System Models
Philipp Hess, Stefan Lange, Christof Schötz, et al.
Earth s Future (2023) Vol. 11, Iss. 10
Open Access | Times Cited: 14
Toward a Learnable Climate Model in the Artificial Intelligence Era
Gang Huang, Ya Wang, Yoo‐Geun Ham, et al.
Advances in Atmospheric Sciences (2024) Vol. 41, Iss. 7, pp. 1281-1288
Closed Access | Times Cited: 4
Machine learning for the physics of climate
Annalisa Bracco, Julien Brajard, Henk A. Dijkstra, et al.
Nature Reviews Physics (2024) Vol. 7, Iss. 1, pp. 6-20
Closed Access | Times Cited: 4
Future Projections of Temperature Extremes and Urban Heat Island in Paris Using Deep Learning
Frederico Johannsen, Pedro M. M. Soares, Gaby S. Langendijk
(2025)
Closed Access
Intraseasonal synchronization of extreme rainfall between North India and the Sahel
Felix Strnad, Kieran M. R. Hunt, Niklas Boers, et al.
Quarterly Journal of the Royal Meteorological Society (2025)
Open Access
Fast, scale-adaptive and uncertainty-aware downscaling of Earth system model fields with generative machine learning
Philipp Hess, Michael Aich, Baoxiang Pan, et al.
Nature Machine Intelligence (2025)
Open Access
Reconstructing historical climate fields with deep learning
Nils Bochow, Anna Poltronieri, Martin Rypdal, et al.
Science Advances (2025) Vol. 11, Iss. 14
Open Access
Predictive modeling of climate change impacts using Artificial Intelligence: a review for equitable governance and sustainable outcome
Kingsley Ukoba, Oluwatayo Racheal Onisuru, Tien‐Chien Jen, et al.
Environmental Science and Pollution Research (2025)
Open Access
Multi-Variable Hard Physical Constraints for Climate Model Downscaling
José González-Abad, Álex Hernández-García, Paula Harder, et al.
Proceedings of the AAAI Symposium Series (2024) Vol. 2, Iss. 1, pp. 62-67
Open Access | Times Cited: 3
Emerging artificial intelligence applications: metaverse, IoT, cybersecurity, healthcare - an overview
Neha Sharma, Neeru Jindal
Multimedia Tools and Applications (2023) Vol. 83, Iss. 19, pp. 57317-57345
Closed Access | Times Cited: 7
Global Precipitation Correction Across a Range of Climates Using CycleGAN
Jeremy McGibbon, Spencer K. Clark, Brian Henn, et al.
Geophysical Research Letters (2024) Vol. 51, Iss. 4
Open Access | Times Cited: 2
Multivariate bias correction and downscaling of climate models with trend-preserving deep learning
Fang Wang, Di Tian
Climate Dynamics (2024) Vol. 62, Iss. 10, pp. 9651-9672
Open Access | Times Cited: 2
Foundations of Generative AI
Tina Babu, Rekha R Nair, P M Ebin
Advances in computational intelligence and robotics book series (2024), pp. 136-166
Closed Access | Times Cited: 1
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