USMILE publications


  • Behrens, G., Beucler, T., Gentine, P., Iglesias-Suarez, F., Pritchard, M. & Eyring, V. Non-Linear Dimensionality Reduction with a Variational Autoencoder Decoder to Understand Convective Processes in Climate Models. Accepted,, 2022.
  • Grundner, A., Beucler, T., Gentine, P., Iglesias-Suarez, F., Giorgetta, M., A., & Eyring V. Deep Learning Based Cloud Cover Parameterization for ICON,, 2021.
  • Kaps, A., Lauer, A., Camps-valls, G., Gentine, P., Gómez-Chova, L., & Eyring, V. Machine-learned cloud classes from satellite data for process-oriented climate model evaluation. IEEE Transactions on Geoscience and Remote Sensing (submitted),, 2022.

Peer-Reviewed Publications 2022

  • Díaz, E. Adsura, J.E., Martínez, Á.M, Piles, M., & Camps-Valls, G. Inferring causal relations from observational long-term carbon and water fluxes records. Scientific Reports. 12, 2045-2322,, 2022.
  • Persello, C., Wegner, J.D., Hänsch, R., Tuia, D., Ghamisi, P., Koeva, M., & Camps-Valls, G. Deep Learning and Earth Observation to Support the Sustainable Development Goals. IEEE Geoscience and Remote Sensing Magazine, 2-30, 10.1109/MGRS.2021.3136100, 2022.
  • Svendsen, D. H., Piles, M., Muñoz-Marí, J., Luengo, D., Martino, L., &, Camps-Valls G. Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions., IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15, 10.1109/TGRS.2021.3059550, 2022.

Peer-Reviewed Publications 2021

  • Camps-Valls, G. Perspective on Deep Learning for Earth Sciences. Generalization with Deep Learning. Ch. 7, 159-173, 10.1142/9789811218842_0007, 2021.
  • Camps-Valls, G., Tuia, D., Zhu, X., X., Reichstein, M. (eds). Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science and Geosciences. Wiley & Sons,, 2021
  • Eyring, V., Mishra, V., Griffith, G.P., Chen, L., Keenan, T., Turetsky, M.R., Brown, S., Jotzo, F., Moore, F.C. & Van der Linden, S. Reflections and projections on a decade of climate science. Nature Clim. Change. 11, 279–285,, 2021.
  • Gentine, P., Eyring, V., & Beucler, T. Deep Learning for the Parametrization of Subgrid Processes in Climate Models. In Deep learning for the Earth Sciences (eds G. Camps-Valls, D. Tuia, X.X. Zhu and M. Reichstein),, 2021.
  • Gottfriedsen, J., Berrendorf, M., Gentine, P., Hassler, B., Reichstein, M., Weigel, K., & Eyring, V. On the Generalization of Agricultural Drought Classification from Climate Data, Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) Workshop 2021 “Tackling Climate Change with Machine Learning”, 6.-14. December 2021,
  • Kraft, B., Jung, M., Körner, M., Koirala, S., & Reichstein, M., 2021, Towards hybrid modeling of the global hydrological cycle. Hydrology and Earth System Sciences Discussions, 1-40.
  • Svendsen, D. H., Hernández-Lobato, D. , Martino, L., Laparra, V., Moreno-Martínez, A., & Camps-Valls, G. Inference over radiative transfer models using variational and expectation maximization methods. Machine Learning, 1-17, 10.1007/s10994-021-05999-4, 2021.
  • Trifunov, V., T., Shadaydeh, M., Runge, J., Reichstein, M., & Denzler, J. A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model. IEEE Access. 107873-107883, 10.1109/ACCESS.2021.3101129, 2021.
  • Trifunov, V., T., Shadaydeh, M., Barz, B., & Denzler, J. Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021.
  • Tuia, D., Roscher, R., Wegner, J.D., Jacobs, N., Zhu, X.X., & Camps-Valls, G. Towards a Collective Agenda on AI for Earth Science Data Analysis. IEEE Geoscience and Remote Sensing Magazine, 9, 2, 88-104, 10.1109/MGRS.2020.3043504, 2021.

Peer-Reviewed Publications 2020

  • Kraft, B., Jung, M., Körner, M., & Reichstein, M., 2020, Hybrid modeling: Fusion of a deep approach and physics-based model for global hydrological modeling.The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 1537-1544, 2020
  • Schlund, M., V. Eyring, G.-Camps-Valls, P. Friedlingstein, P. Gentine, & M. Reichstein, Constraining uncertainty in projected gross primary production with machine learning. JGR: Biogeosc. 125, e2019JG005619, doi:10.1029/2019JG005619, 2020.

Supporting the USMILE proposal

  • Eyring, V., Cox, P. M., Flato, G. M., Gleckler, P. J., Abramowitz, G., Caldwell, P., Collins, W. D., Gier, B., Hall, A. D., Hoffman, F. M., Hurtt, G. C., Jahn, A., Jones, C. D., Klein, S. A., Krasting, J., Kwiatkowski, L., Lorenz, R., Maloney, E., Meehl, G. A., Pendergrass, A., Pincus, R., Ruane, A. C., Russell, J. L., Sanderson, B., Santer, B., Sherwood, S. C., Simpson, I., Stouffer, R. & Williamson, M. S. Taking climate model evaluation to the next level. Nature Clim. Change 9, 102–110, 2019.
  • Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G. & Yacalis, G. Could Machine Learning Break the Convection Parameterization Deadlock? Geophysical Research Letters 45, 5742-5751, doi:10.1029/2018gl078202, 2018.
  • Reichstein, M., Camps-Valls, G., Stevens, B., Denzler, J., Carvalhais, N., Jung, M. & Prabhat. Deep learning and process understanding for data-driven Earth System Science. Nature 566, 195–204, 2019.