15 citations as recorded by crossref.

1. Improving interpretation of sea-level projections through a machine-learning-based local explanation approach J. Rohmer et al. 10.5194/tc-16-4637-2022
2. Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements 10.1038/s41467-022-32693-3
3. Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration J. Becker et al. 10.1525/elementa.2022.00025
4. Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc A. Bishnoi et al. 10.5194/gmd-17-261-2024
5. A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning A. Edrich et al. 10.1007/s11069-024-06563-8
6. A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends L. Gouldsbrough et al. 10.5194/acp-24-3163-2024
7. Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al. 10.5194/gmd-17-2387-2024
8. Correcting ozone biases in a global chemistry–climate model: implications for future ozone Z. Liu et al. 10.5194/acp-22-12543-2022
9. Unveiling the Transparency of Prediction Models for Spatial PM2.5 over Singapore: Comparison of Different Machine Learning Approaches with eXplainable Artificial Intelligence M. Sunder et al. 10.3390/ai4040040
10. Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation M. Shams Eddin & J. Gall 10.5194/gmd-17-2987-2024
11. A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers J. Roussillon et al. 10.3389/fmars.2023.1077623
12. Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980–2019 R. Liu et al. 10.1021/acs.est.3c05424
13. Graph Machine Learning for Improved Imputation of Missing Tropospheric Ozone Data C. Betancourt et al. 10.1021/acs.est.3c05104
14. Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022
15. Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset S. Stadtler et al. 10.3390/make4010008