Articles | Volume 17, issue 9
https://doi.org/10.5194/gmd-17-4017-2024
© Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License.
Special issue:
Development and technical paper
| 
16 May 2024
Development and technical paper |
| 16 May 2024
| 16 May 2024
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
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Cited articles
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: Tensorflow: A system for large-scale machine learning, in: 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, US, 2–4 November 2016, 265–283, https://arxiv.org/abs/1603.04467 (last access: 6 September 2023), 2016. a
Alexeev, D.: PyTorch bindings for Fortran (v0.4), GitHub [code], https://github.com/alexeedm/pytorch-fortran (last access: 6 September 2023), 2023. a
Arnold, C., Sharma, S., and Weigel, T.: DKRZ-AIM/dkrz-hereon-icon-superdropnet: Integrating SuperdropNet (v0.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.10069121, 2023a. a
Arnold, C., Sharma, S., and Weigel, T.: ICON Code v 2.6.5 including coupling schemes for integrating SuperdropNet, Zenodo [code], https://doi.org/10.5281/zenodo.8348256, 2023b. a
Arnold, C., Sharma, S., and Weigel, T.: Data set for: Efficient and Stable Coupling of the SuperdropNet Deep Learning-based Cloud Microphysics (v0.1.0) to the ICON Climate and Weather Model (v2.6.5), Zenodo [data set], https://doi.org/10.5281/zenodo.8348266, 2023c. a
