Articles | Volume 19, issue 13
https://doi.org/10.5194/gmd-19-6027-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
08 Jul 2026
Methods for assessment of models |
| 08 Jul 2026
| 08 Jul 2026
A self-supervised precipitation forecast verification based on contrastive learning
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Cited articles
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Cassola, F., Ferrari, F., and Mazzino, A.: Numerical simulations of Mediterranean heavy precipitation events with the WRF model: a verification exercise using different approaches, Atmos. Res., 164, 210–225, https://doi.org/10.1016/j.atmosres.2015.05.010, 2015.
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G.: A simple framework for contrastive learning of visual representations, in: Proceedings of the 8th International Conference on Learning Representations (ICLR), 26 April–1 May 2020, Addis Ababa, Ethiopia, 1597–1607, arXiv, https://doi.org/10.48550/arXiv.2002.05709 2020.
Chen, Y., Wang, Y., Huang, G., and Tian, Q.: Coupling physical factors for precipitation forecast in China with graph neural network, Geophys. Res. Lett., 51, e2023GL106676, https://doi.org/10.1029/2023GL106676, 2024.
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