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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:

Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science
Antonios Mamalakis, Imme Ebert‐Uphoff, Elizabeth A. Barnes
Lecture notes in computer science (2022), pp. 315-339
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Explainable AI Methods - A Brief Overview
Andreas Holzinger, Anna Saranti, Christoph Molnar, et al.
Lecture notes in computer science (2022), pp. 13-38
Open Access | Times Cited: 232
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction
Sunnie S. Y. Kim, Elizabeth Anne Watkins, Olga Russakovsky, et al.
(2023), pp. 1-17
Open Access | Times Cited: 80
Pushing the frontiers in climate modelling and analysis with machine learning
Veronika Eyring, William D. Collins, Pierre Gentine, et al.
Nature Climate Change (2024) Vol. 14, Iss. 9, pp. 916-928
Closed Access | Times Cited: 32
How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences
Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, et al.
Earth s Future (2024) Vol. 12, Iss. 7
Open Access | Times Cited: 27
Explainability can foster trust in artificial intelligence in geoscience
Jesper Dramsch, Monique M. Kuglitsch, Miguel‐Ángel Fernández‐Torres, et al.
Nature Geoscience (2025)
Closed Access | Times Cited: 2
AI for climate impacts: applications in flood risk
Anne Jones, Julian Kuehnert, Paolo Fraccaro, et al.
npj Climate and Atmospheric Science (2023) Vol. 6, Iss. 1
Open Access | Times Cited: 33
A machine learning model that outperforms conventional global subseasonal forecast models
Lei Chen, Xiaohui Zhong, Hao Li, et al.
Nature Communications (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 12
Towards understanding the robust strengthening of ENSO and more frequent extreme El Niño events in CMIP6 global warming simulations
Ulla K. Heede, Alexey V. Fedorov
Climate Dynamics (2023)
Open Access | Times Cited: 17
Viewing Soil Moisture Flash Drought Onset Mechanism and Their Changes Through XAI Lens: A Case Study in Eastern China
Jiajin Feng, Jun Li, Chong‐Yu Xu, et al.
Water Resources Research (2024) Vol. 60, Iss. 6
Open Access | Times Cited: 4
Characterizing climate pathways using feature importance on echo state networks
Katherine Goode, Daniel Ries, Kellie McClernon
Statistical Analysis and Data Mining The ASA Data Science Journal (2024) Vol. 17, Iss. 4
Open 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
Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting
Muhammad Asif, Monique M. Kuglitsch, Ivanka Pelivan, et al.
Water Resources Management (2025)
Open Access
Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling
Ryan O’Loughlin, Dan Li, Richard Neale, et al.
Geoscientific model development (2025) Vol. 18, Iss. 3, pp. 787-802
Open Access
Enhancing Tree Species Mapping in Arkansas’ Forests Through Machine Learning and Satellite Data Fusion: A Google Earth Engine–Based Approach
Abdullah Al Saim, Mohamed H. Aly
Journal of Geovisualization and Spatial Analysis (2025) Vol. 9, Iss. 1
Open Access
Healthcare transformed: a comprehensive survey of artificial intelligence trends in healthcare industries
Abida Parveen, Kannan Govindan
Elsevier eBooks (2024), pp. 395-424
Closed Access | Times Cited: 3
Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Ankur Mahesh, Travis O’Brien, Burlen Loring, et al.
Geoscientific model development (2024) Vol. 17, Iss. 8, pp. 3533-3557
Open Access | Times Cited: 2
Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts
Laura Mansfield, Aman Gupta, Adam C. Burnett, et al.
Journal of Advances in Modeling Earth Systems (2023) Vol. 15, Iss. 10
Open Access | Times Cited: 6
Using Explainable Artificial Intelligence to Quantify “Climate Distinguishability” After Stratospheric Aerosol Injection
Antonios Mamalakis, Elizabeth A. Barnes, James W. Hurrell
Geophysical Research Letters (2023) Vol. 50, Iss. 20
Open Access | Times Cited: 5
Moving beyond post-hoc XAI: Lessons learned from dynamical climate modeling
Ryan O’Loughlin, Dan Li, Travis O’Brien
(2024)
Open Access | Times Cited: 1
Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning
Michail Mamalakis, Abhirup Banerjee, Surajit Ray, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 30, pp. 18841-18862
Open Access | Times Cited: 1
Cross-validation, Symbolic Regression, Pareto include
Ryan O’Loughlin, Dan Li, Travis O’Brien
(2024)
Open Access
Explainable Artificial Intelligence for a Better Understanding of Naturalist Data

(2024), pp. 73-102
Closed Access
Comment on egusphere-2023-2969
Julie Jebeile
(2024)
Open Access
Comment on egusphere-2023-2969
Imme Ebert-Uphoff
(2024)
Open Access
Comment on egusphere-2023-2969
Yumin Liu
(2024)
Open Access
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