Understanding and Modelling the Earth System with Machine Learning

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Pubblicazioni

Towards Causal Discovery for Earth System Sciences

Autori: Emiliano Diaz
Pubblicato in: 2023
Editore: Universitat de València

Consistent long-term observational datasets of soil moisture and vegetation reveal trends and variability in soil moisture, improve carbon cycle models, and constrain climate models.

Autori: Olya Skulovich
Pubblicato in: 2024
Editore: Columbia University

Causal discovery of Atlantic-Pacific interactions in observations and CMIP6 models

Autori: Karmouche, Soufiane
Pubblicato in: 2024
Editore: University of Bremen
DOI: 10.26092/elib/2833

Understanding and Modelling Convection with Machine Learning

Autori: Behrens, Gunnar
Pubblicato in: 2024
Editore: University of Bremen
DOI: 10.26092/elib/3050

A comparison of drought indices in CMIP6 climate projections

Autori: Lukas Ruhe
Pubblicato in: Master Thesis, 2022
Editore: University of Bremen, Germany

Detecting Activity of Tropical Cyclones with the Unsupervised Maximally Divergent Interval Algorithm

Autori: Simon Zitzmann
Pubblicato in: Master Thesis, 2020
Editore: LMU Munich

Data-driven cloud cover parameterizations for the ICON earth system model using deep learning and symbolic regression

Autori: Grundner, Arthur
Pubblicato in: Dissertation, 2024
Editore: University of Bremen
DOI: 10.26092/elib/2821

Machine-learning based observational cloud products for process-oriented climate model evaluation

Autori: Kaps, Arndt
Pubblicato in: 2024
Editore: University of Bremen
DOI: 10.26092/elib/2997

Understanding Land-Atmosphere Interactions Across Multiple Scales

Autori: Yu Huang
Pubblicato in: 2024
Editore: Columbia university

Estimating Information in Earth System Data with Machine Learning

Autori: Emmanuel J. Johnson
Pubblicato in: 2021
Editore: University of Valencia

Constraining uncertainties in multi-model projections of the future climate with observations

Autori: Manuel Schlund
Pubblicato in: Dissertation, 2021
Editore: University of Bremen
DOI: 10.26092/elib/941

Carbon system state determines warming potential of emissions

Autori: Alexander J. Winkler, Ranga Myneni, Christian Reimers, Markus Reichstein, Victor Brovkin
Pubblicato in: PLOS ONE, Numero 19, 2024, Pagina/e e0306128, ISSN 1932-6203
Editore: Public Library of Science
DOI: 10.1371/journal.pone.0306128

Detecting extreme temperature events using Gaussian mixture models

Autori: Paçal, A., Hassler, B., Weigel, K., Kurnaz, M. L., Wehner, M. F., Eyring, V.
Pubblicato in: Journal of Geophysical Research: Atmospheres, Numero Volume 128; Numero 18; 22.09.2023, 2023, Pagina/e e2023JD038906, ISSN 2169-897X
Editore: ADVANCING EARTH AND SPACE SCIENCES
DOI: 10.1029/2023jd038906

Regulation of the global carbon and water cycles through vegetation structural and physiological dynamics

Autori: Wantong Li, Gregory Duveiller, Sebastian Wieneke, Matthias Forkel, Pierre Gentine, Markus Reichstein, Shuli Niu, Mirco Migliavacca, Rene Orth
Pubblicato in: Environmental Research Letters, Numero 19, 2024, Pagina/e 073008, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ad5858

Soil dryness matters to ecosystem photosynthesis when and where it does

Autori: Jiangong Liu, Qiren Wang, Weiwei Zhan, Xu Lian, Pierre Gentine
Pubblicato in: Nature water submitted, 2024, ISSN 1758-678X
Editore: Nature Publishing Group
DOI: 10.21203/rs.3.rs-5147541/v1

Deep Learning and Earth Observation to Support the Sustainable Development Goals

Autori: Persello, C., Wegner, J.D., Hänsch, R., Tuia, D., Ghamisi, P., Koeva, M., Camps-Valls, G.
Pubblicato in: IEEE Geoscience and Remote Sensing Magazine, 2022, Pagina/e 2-30, ISSN 2168-6831
Editore: IEEE Geosciene and Remote Sensing Society
DOI: 10.1109/mgrs.2021.3136100

Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology

Autori: Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, Veronika Eyring
Pubblicato in: Earth System Science Data, Numero 16, 2024, Pagina/e 3001-3016, ISSN 1866-3516
Editore: Copernicus GmbH
DOI: 10.5194/essd-16-3001-2024

Reliance on fossil fuels increases during extreme temperature events in the continental United States

Autori: Wenli Zhao, Biqing Zhu, Steven J. Davis, Philippe Ciais, Chaopeng Hong, Zhu Liu, Pierre Gentine
Pubblicato in: Communications Earth & Environment, Numero 4, 2023, ISSN 2662-4435
Editore: NA
DOI: 10.1038/s43247-023-01147-z

Structural learning of simple staged trees

Autori: Manuele Leonelli, Gherardo Varando
Pubblicato in: Data Mining and Knowledge Discovery, Numero 38, 2024, Pagina/e 1520-1544, ISSN 1384-5810
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10618-024-01007-0

Evaluation of native Earth system model output with ESMValTool v2.6.0

Autori: Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, Veronika Eyring
Pubblicato in: Geoscientific Model Development, Numero 16, 2023, Pagina/e 315-333, ISSN 1991-9603
Editore: Copernicus GmbH
DOI: 10.5194/gmd-16-315-2023

Pushing the frontiers in climate modelling and analysis with machine learning

Autori: Veronika Eyring, William D. Collins, Pierre Gentine, Elizabeth A. Barnes, Marcelo Barreiro, Tom Beucler, Marc Bocquet, Christopher S. Bretherton, Hannah M. Christensen, Katherine Dagon, David John Gagne, David Hall, Dorit Hammerling, Stephan Hoyer, Fernando Iglesias-Suarez, Ignacio Lopez-Gomez, Marie C. McGraw, Gerald A. Meehl, Maria J. Molina, Claire Monteleoni, Juliane Mueller, Michael S. Pritch
Pubblicato in: Nature Climate Change, Numero 14, 2024, Pagina/e 916-928, ISSN 1758-678X
Editore: Nature Publishing Group
DOI: 10.1038/s41558-024-02095-y

Interpretable Multiscale Machine Learning‐Based Parameterizations of Convection for ICON

Autori: Helge Heuer, Mierk Schwabe, Pierre Gentine, Marco A. Giorgetta, Veronika Eyring
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero 16, 2024, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2024ms004398

Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0

Autori: Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, Manuel Schlund
Pubblicato in: Geoscientific Model Development, Numero 18, 2025, Pagina/e 1169-1188, ISSN 1991-9603
Editore: Copernicus Publications
DOI: 10.5194/gmd-18-1169-2025

Inferring causal relations from observational long-term carbon and water fluxes records

Autori: Díaz, E. Adsura, J.E., Martínez, Á.M, Piles, M., Camps-Valls, G
Pubblicato in: Scientific Reports, Numero 12, 2022, Pagina/e 1610, ISSN 2045-2322
Editore: Nature Publishing Group
DOI: 10.1038/s41598-022-05377-7

Constraining uncertainty in projected gross primary production with machine learning

Autori: M. Schlund, V. Eyring, G.-Camps-Valls, P. Friedlingstein, P. Gentine, & M. Reichstein
Pubblicato in: American Geophysical Union / Wiley Periodicals, 2020, ISSN 2576-604X
Editore: American Geophysical Union / Wiley Periodicals
DOI: 10.1029/2019jg005619

Evidence for widespread thermal acclimation of canopy photosynthesis

Autori: Jiangong Liu, Youngryel Ryu, Xiangzhong Luo, Benjamin Dechant, Benjamin Stocker, Trevor Keenan, Pierre Gentine, Xing Li, Bolun Li, Sandy Harrison, Iain Prentice
Pubblicato in: Nature Ecology - accepted, 2024, ISSN 1758-678X
Editore: Nature Publishing Group
DOI: 10.21203/rs.3.rs-4013319/v1

A hybrid generative adversarial network for weakly-supervised cloud detection in multispectral images

Autori: Jun Li, Zhaocong Wu, Qinghong Sheng, Bo Wang, Zhongwen Hu, Shaobo Zheng, Gustau Camps-Valls, Matthieu Molinier
Pubblicato in: Remote Sensing of Environment, Numero 280, 2022, Pagina/e 113197, ISSN 0034-4257
Editore: Elsevier BV
DOI: 10.1016/j.rse.2022.113197

MetaFlux: Meta-learning global carbon fluxes from sparse spatiotemporal observations

Autori: Juan Nathaniel, Jiangong Liu, Pierre Gentine
Pubblicato in: Scientific Data, Numero 10, 2023, ISSN 2052-4463
Editore: NA
DOI: 10.1038/s41597-023-02349-y

Spatiotemporal upscaling of sparse air-sea pCO2 data via physics-informed transfer learning

Autori: Siyeon Kim, Juan Nathaniel, Zhewen Hou, Tian Zheng, Pierre Gentine
Pubblicato in: Scientific Data, Numero 11, 2024, ISSN 2052-4463
Editore: NA
DOI: 10.1038/s41597-024-03959-w

Hybrid modeling: Fusion of a deep approach and physics-based model for global hydrological modeling

Autori: Kraft, B., Jung, M., Körner, M., & Reichstein, M.
Pubblicato in: The International Archives of Photogrammetry, Remote Sensing and Spatial Information Science, Numero 21949034, 2020, ISSN 2194-9034
Editore: Copernicus Publications
DOI: 10.5194/isprs-archives-xliii-b2-2020-1537-2020

Satellite Analyses Unravel the Multi-Decadal Impact of Dam Management on Tropical Floodplain Vegetation

Autori: Luca Salerno, Álvaro Moreno-Martínez, Emma Izquierdo-Verdiguier, Nicholas Clinton, Annunziato Siviglia, and Carlo Camporeale
Pubblicato in: Frontiers in Environmental Science, Numero 2296665X, 2022, ISSN 2296-665X
Editore: Frontiers
DOI: 10.3389/fenvs.2022.871530

Pairwise causal discovery with support measure machines

Autori: Gherardo Varando, Salvador Catsis, Emiliano Diaz, Gustau Camps-Valls
Pubblicato in: Applied Soft Computing, Numero 150, 2024, Pagina/e 111030, ISSN 1568-4946
Editore: Elsevier BV
DOI: 10.1016/j.asoc.2023.111030

Deep Learning With Noisy Labels for Spatiotemporal Drought Detection

Autori: Jordi Cortés-Andrés, Miguel-Ángel Fernández-Torres, Gustau Camps-Valls
Pubblicato in: IEEE Transactions on Geoscience and Remote Sensing, Numero 62, 2024, Pagina/e 1-13, ISSN 0196-2892
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tgrs.2024.3504340

Learning latent functions for causal discovery

Autori: E Diaz. JE Johnson, G Varando, G Camps-Valls
Pubblicato in: Machine Learning: Science and Technology, 2023, ISSN 2632-2153
Editore: IOP Science
DOI: 10.1088/2632-2153/ace151

Climate-invariant machine learning

Autori: Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng, Jerry Lin, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O’Gorman, J. David Neelin, Nicholas J. Lutsko, Michael Pritchard
Pubblicato in: Science Advances, Numero 10, 2024, ISSN 2375-2548
Editore: NA
DOI: 10.1126/sciadv.adj7250

Learning Atmospheric Boundary Layer Turbulence

Autori: Sara Shamekh, Pierre Gentine
Pubblicato in: JAMES in review, 2023, ISSN 1758-678X
Editore: Nature Publishing Group
DOI: 10.22541/essoar.168748456.60017486/v1

A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections

Autori: Tibau, X., Reimers, C., Gerhardus, A., Denzler, J., Eyring, V., Runge, J.
Pubblicato in: Environmental Data Science, Numero 26344602, 2022, ISSN 2634-4602
Editore: Cambridge University Press
DOI: 10.1017/eds.2022.11

Generative networks for spatio-temporal gap filling of Sentinel-2 reflectances

Autori: Maria Gonzalez-Calabuig, Miguel-Ángel Fernández-Torres, Gustau Camps-Valls
Pubblicato in: ISPRS Journal of Photogrammetry and Remote Sensing, Numero 220, 2025, Pagina/e 637-648, ISSN 0924-2716
Editore: Elsevier BV
DOI: 10.1016/j.isprsjprs.2025.01.016

Causal hybrid modeling with double machine learning—applications in carbon flux modeling

Autori: Kai-Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls
Pubblicato in: Machine Learning: Science and Technology, Numero 5, 2024, Pagina/e 035021, ISSN 2632-2153
Editore: IOP Science
DOI: 10.1088/2632-2153/ad5a60

X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X

Autori: Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Jiří Dušek, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck
Pubblicato in: Biogeosciences, Numero 21, 2025, Pagina/e 5079-5115, ISSN 1726-4189
Editore: Biogeosciences
DOI: 10.5194/bg-21-5079-2024

Causally‐Informed Deep Learning to Improve Climate Models and Projections

Autori: Fernando Iglesias‐Suarez, Pierre Gentine, Breixo Solino‐Fernandez, Tom Beucler, Michael Pritchard, Jakob Runge, Veronika Eyring
Pubblicato in: Journal of Geophysical Research: Atmospheres, Numero 129, 2024, ISSN 2169-897X
Editore: AGU
DOI: 10.1029/2023jd039202

Quantifying uncertainty in high resolution biophysical variable retrieval maps

Autori: Laura Martínez-Ferrer, Álvaro Moreno-Martínez, Manuel Campos-Taberner, Francisco Javier García-Haro, Jordi Munoz-Marí, Steven W. Running, John Kimball, Nicholas Clinton, Gustau Camps-Valls
Pubblicato in: Remote Sensing of Environment (RSE), 2022, ISSN 0034-4257
Editore: Elsevier BV

Assessing and improving the transferability of current global spatial prediction models.

Autori: Ludwig, Marvin and Moreno-Martinez, Alvaro and Hölzel, Norbert and Pebesma, Edzer and Meyer, Hanna
Pubblicato in: Global Ecology and Biogeography, 2023, ISSN 1466-822X
Editore: Blackwell Publishing Inc.
DOI: 10.1111/geb.13635

Role of locality, fidelity and symmetry regularization in learning explainable representations

Autori: Michele Ronco, Gustau Camps-Valls
Pubblicato in: Neurocomputing, Numero 562, 2023, Pagina/e 126884, ISSN 0925-2312
Editore: Elsevier BV
DOI: 10.1016/j.neucom.2023.126884

Reflections and projections on a decade of climate science

Autori: V. Eyring, V. Mishra, G. Griffith, L. Chen, T. F. Keenan, M. R. Turetsky, S. Brown, F. Jotzo, F. C. Moore, and S. van der Linden
Pubblicato in: Nature Publishing Group / Springer Nature, 2021, ISSN 1758-6798
Editore: Nature Publishing Group / Springer Nature
DOI: 10.1038/s41558-021-01020-x

Causal inference for time series

Autori: J., Runge, A., Gerhardus, G., Varando, V., Eyring, G., Camps-Valls
Pubblicato in: nature reviews earth & environment, Numero Volume 4; 07.2023, 2023, Pagina/e 487-505, ISSN 2662-138X
Editore: Springer Nature
DOI: 10.1038/s43017-023-00431-y

Within-season crop monitoring at continental scale utilizing new gap-filled Landsat temporal series

Autori: C. Rajadel-Lambistos, E. Izquierdo-Verdiguier, A. Moreno-Martínez, M. P. Maneta, S. Begueria, J. S. Kimball, N. Clinton, C. Atzberger, G. Camps-Valls, S.W. Running
Pubblicato in: International Journal of Digital Earth, Numero 17, 2025, ISSN 1753-8947
Editore: Taylor & Francis
DOI: 10.1080/17538947.2024.2359577

Collaboration between artificial intelligence and Earth science communities for mutual benefit

Autori: Min Chen, Zhen Qian, Niklas Boers, Felix Creutzig, Gustau Camps-Valls, Klaus Hubacek, Christophe Claramunt, John P. Wilson, Stefano Nativi, Anthony J. Jakeman, R. Dietmar Müller, Michael Batty, Chenghu Zhou, Fahu Chen, Qiao Wang, Fan Zhang, C. Michael Barton, Josef Strobl, Michael Meadows, Carlo Ratti, Philipp Hess, Qingsong Xu, Zhixin Zhang, Qiushi Gu, A-Xing Zhu, Hui Lin, Linwang Yuan, Guonian
Pubblicato in: Nature Geoscience, Numero 17, 2024, Pagina/e 949-952, ISSN 1752-0894
Editore: Nature Publishing Group
DOI: 10.1038/s41561-024-01550-x

Converging Findings of Climate Models and Satellite Observations on the Positive Impact of European Forests on Cloud Cover

Autori: Luca Caporaso, Gregory Duveiller, Graziano Giuliani, Filippo Giorgi, Martin Stengel, Emanuele Massaro, Matteo Piccardo, Alessandro Cescatti
Pubblicato in: Journal of Geophysical Research: Atmospheres, Numero 129, 2024, ISSN 2169-897X
Editore: Wiley Online Library
DOI: 10.1029/2023jd039235

Changing effects of external forcing on Atlantic–Pacific interactions

Autori: Soufiane Karmouche, Evgenia Galytska, Gerald A. Meehl, Jakob Runge, Katja Weigel, Veronika Eyring
Pubblicato in: Earth System Dynamics, Numero 15, 2024, Pagina/e 689-715, ISSN 2190-4987
Editore: Copernicus GmbH
DOI: 10.5194/esd-15-689-2024

Physics-aware nonparametric regression models for Earth data analysis

Autori: Jordi Cortés-Andrés; Gustau Camps-Valls; Sebastian Sippel; Enikő Székely; Dino Sejdinovic; Emiliano Diaz; Adrián Pérez-Suay; Zhu Li; Miguel Mahecha; Markus Reichstein
Pubblicato in: Environmental Research Letters, 17 (5), Numero 17489326, 2022, ISSN 1748-9326
Editore: Institute of Physics Publishing
DOI: 10.3929/ethz-b-000546957

Identifying compound weather drivers of forest biomass loss with generative deep learning

Autori: Mohit Anand, Friedrich J. Bohn, Gustau Camps-Valls, Rico Fischer, Andreas Huth, Lily-belle Sweet, Jakob Zscheischler
Pubblicato in: Environmental Data Science, Numero 3, 2024, ISSN 2634-4602
Editore: Cambridge Press
DOI: 10.1017/eds.2024.2

Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning

Autori: Laura Martínez-Ferrer, Álvaro Moreno-Martínez, Manuel Campos-Taberner, Francisco Javier García-Haro, Jordi Muñoz-Marí, Steven W. Running, John Kimball, Nicholas Clinton, Gustau Camps-Valls
Pubblicato in: Remote Sensing of Environment, Numero 280, 2022, Pagina/e 113199, ISSN 0034-4257
Editore: Elsevier BV
DOI: 10.1016/j.rse.2022.113199

Contrasting drought legacy effects on gross primary productivity in a mixed versus pure beech forest

Autori: Yu, X. and Orth, R. and Reichstein, M. and Bahn, M. and Klosterhalfen, A. and Knohl, A. and Koebsch, F. and Migliavacca, M. and Mund, M. and Nelson, J. A. and Stocker, B. D. and Walther, S. and Bastos, A.
Pubblicato in: Biogeosciences, Numero 17264189, 2022, ISSN 1726-4189
Editore: Copernicus Publications
DOI: 10.5194/bg-19-4315-2022

ClimateBench v1.0: A Benchmark for Data-Driven Climate Projections

Autori: Watson-Parris, D., Rao, Y., Olivié, D., Seland, Ø., Nowack, P., Camps-Valls, G., Stier, P., Bouabid, S., Dewey, M., Fons, E., Gonzalez, J., Harder, P., Jeggle, K., Lenhardt, J., Manshausen, P., Novitasari, M., Ricard, L., Roesch, C.
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero 14 (10) :e2021MS002954, 2022, ISSN 1942-2466
Editore: American Geophysical Union

Biogeophysical Radiative Forcings of Large‐Scale Afforestation in Europe Are Highly Localized and Dominated by Surface Albedo Change

Autori: Ryan M. Bright, Luca Caporaso, Gregory Duveiller, Matteo Piccardo, Alessandro Cescatti
Pubblicato in: Geophysical Research Letters, Numero 52, 2025, ISSN 0094-8276
Editore: American Geophysical Union
DOI: 10.1029/2024gl112739

ClimateBench v1.0: A Benchmark for Data‐Driven Climate Projections

Autori: D. Watson‐Parris, Y. Rao, D. Olivié, Ø. Seland, P. Nowack, G. Camps‐Valls, P. Stier, S. Bouabid, M. Dewey, E. Fons, J. Gonzalez, P. Harder, K. Jeggle, J. Lenhardt, P. Manshausen, M. Novitasari, L. Ricard, C. Roesch
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero 14, 2023, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2021ms002954

The AIDE Toolbox: Artificial intelligence for disentangling extreme events [Software and Data Sets]

Autori: Maria Gonzalez-Calabuig, Jordi Cortés-Andrés, Tristan Keith Ellis Williams, Mengxue Zhang, Oscar Jose Pellicer-Valero, Miguel-Ángel Fernández-Torres, Gustau Camps-Valls
Pubblicato in: IEEE Geoscience and Remote Sensing Magazine, Numero 12, 2024, Pagina/e 113-118, ISSN 2168-6831
Editore: IEEE Geosciene and Remote Sensing Society
DOI: 10.1109/mgrs.2024.3382544

Discovering causal relations and equations from data

Autori: Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, Jakob Runge
Pubblicato in: Physics Reports, Numero 1044, 2024, Pagina/e 1-68, ISSN 0370-1573
Editore: Elsevier BV
DOI: 10.1016/j.physrep.2023.10.005

Soil moisture–atmosphere feedback dominates land carbon uptake variability

Autori: Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M., Seneviratne, S.I. and Frankenberg, C.
Pubblicato in: Nature, Numero 14764687, 2021, ISSN 1476-4687
Editore: Springer Nature
DOI: 10.1038/s41586-021-03325-5

AI-empowered next-generation multiscale climate modelling for mitigation and adaptation

Autori: Veronika Eyring, Pierre Gentine, Gustau Camps-Valls, David M. Lawrence, Markus Reichstein
Pubblicato in: Nature Geoscience, Numero 17, 2024, Pagina/e 963-971, ISSN 1752-0894
Editore: Nature Publishing Group
DOI: 10.1038/s41561-024-01527-w

Groundwater rivals aridity in determining global photosynthesis

Autori: Francesco Giardina, Sonia I. Seneviratne, Jiangong Liu, Benjamin D. Stocker, Pierre Gentine
Pubblicato in: Nature Geo submitted, 2024, ISSN 1758-678X
Editore: Nature Publishing Group
DOI: 10.21203/rs.3.rs-3793488/v1

Data‐Driven Equation Discovery of a Cloud Cover Parameterization

Autori: Arthur Grundner, Tom Beucler, Pierre Gentine, Veronika Eyring
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero 16, 2024, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2023ms003763

Analyzing climate scenarios using dynamic mode decomposition with control

Autori: Nathan Mankovich, Shahine Bouabid, Peer Nowack, Deborah Bassotto, Gustau Camps-Valls
Pubblicato in: Environmental Data Science, Numero 4, 2025, ISSN 2634-4602
Editore: Cambridge Press
DOI: 10.1017/eds.2025.8

Causal discovery reveals complex patterns of drought-induced displacement

Autori: Jose María Tárraga, Eva Sevillano-Marco, Jordi Muñoz-Marí, María Piles, Vasileios Sitokonstantinou, Michele Ronco, María Teresa Miranda, Jordi Cerdà, Gustau Camps-Valls
Pubblicato in: iScience, Numero 27, 2025, Pagina/e 110628, ISSN 2589-0042
Editore: Elsevier
DOI: 10.1016/j.isci.2024.110628

Invertible Neural Networks for Probabilistic Aerosol Optical Depth Retrieval

Autori: Paolo Pelucchi, Jorge Vicent Servera, Philip Stier, Gustau Camps-Valls
Pubblicato in: IEEE Transactions on Geoscience and Remote Sensing, Numero 63, 2025, Pagina/e 1-13, ISSN 0196-2892
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tgrs.2025.3540173

Implicit learning of convective organization explains precipitation stochasticity

Autori: Sara Shamekh, Kara D. Lamb, Yu Huang, Pierre Gentine
Pubblicato in: Proceedings of the National Academy of Sciences, Numero 120, 2023, ISSN 0027-8424
Editore: National Academy of Sciences
DOI: 10.1073/pnas.2216158120

Satellite remote sensing reveals the footprint of biodiversity on multiple ecosystem functions across the NEON eddy covariance network

Autori: Ulisse Gomarasca, Gregory Duveiller, Javier Pacheco-Labrador, Guido Ceccherini, Alessandro Cescatti, Marco Girardello, Jacob A Nelson, Markus Reichstein, Christian Wirth, Mirco Migliavacca
Pubblicato in: Environmental Research: Ecology, Numero 3, 2024, Pagina/e 045003, ISSN 2752-664X
Editore: IOP Publishing Ltd
DOI: 10.1088/2752-664x/ad87f9

Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data.

Autori: Panwar, A., Migliavacca, M., Nelson, J.A., Cortés, J., Bastos, A., Forkel, M., Winkler, A.J.
Pubblicato in: Sci Rep, Numero 13, 13885, 2023, ISSN 2045-2322
Editore: Nature Publishing Group
DOI: 10.1038/s41598-023-41048-x

Estimating the CO<sub>2</sub> Fertilization Effect on Extratropical Forest Productivity From Flux‐Tower Observations

Autori: Chunhui Zhan, René Orth, Hui Yang, Markus Reichstein, Sönke Zaehle, Martin G. De Kauwe, Anja Rammig, Alexander J. Winkler
Pubblicato in: Journal of Geophysical Research: Biogeosciences, Numero 129, 2024, Pagina/e e2023JG007910, ISSN 2169-8953
Editore: Wiley Online Library
DOI: 10.1029/2023jg007910

Towards hybrid modeling of the global hydrological cycle

Autori: "Kraft, B. and Jung, M. and K\""orner, M. and Koirala, S. and Reichstein, M."
Pubblicato in: Hydrology and Earth System Sciences, Numero 16077938, 2022, ISSN 1607-7938
Editore: Copernicus Publications
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Estimation of vegetation traits with kernel NDVI.

Autori: Qiang Wang and Álvaro Moreno-Martínez and Jordi Muñoz-Marí and Manuel Campos-Taberner and Gustau Camps-Valls
Pubblicato in: ISPRS Journal of Photogrammetry and Remote Sensing, Numero 195, 2023, Pagina/e 408-417, ISSN 0031-8663
Editore: Elsevier
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Towards a Collective Agenda on AI for Earth Science Data Analysis

Autori: Tuia D, Roscher R, Wegner, JD, Jacobs, N, Zhu XX, Camps-Valls, G
Pubblicato in: IEEE Geoscience and Remote Sensing Magazine, Numero 9, 2021, Pagina/e 88-104, ISSN 2168-6831
Editore: IEEE Geosciene and Remote Sensing Society
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A new class of generative classifiers based on staged tree models

Autori: Federico Carli, Manuele Leonelli, Gherardo Varando
Pubblicato in: Knowledge-Based Systems, Numero 268, 2024, Pagina/e 110488, ISSN 0950-7051
Editore: Elsevier BV
DOI: 10.1016/j.knosys.2023.110488

Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis

Autori: J. Emmanuel Johnson; Valero Laparra; Maria Piles; Gustau Camps-Valls
Pubblicato in: IEEE Geoscience and Remote Sensing Magazine, Numero 21686831, 2021, ISSN 2168-6831
Editore: IEEE Geosciene and Remote Sensing Society
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Exploring Optimal Complexity for Water Stress Representation in Terrestrial Carbon Models: A Hybrid-Machine Learning Model Approach

Autori: Jianing Fang, Pierre Gentine
Pubblicato in: Journal of Advances in Modeling Earth Systems, 2024, ISSN 2662-4435
Editore: NA

Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning

Autori: ElGhawi, R., Kraft, B., Reimers, C., Reichstein, M., Körner, M., Gentine, P., Winkler, A.J.,
Pubblicato in: Environ. Res. Lett., Numero 18, 034039, 2023, ISSN 1748-9326
Editore: Institute of Physics Publishing
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Retrieval of Physical Parameters With Deep Structured Kernel Regression

Autori: G. Camps-Valls, M. Campos-Taberner, V. Laparra, L. Martino and J. Muñoz-Marí
Pubblicato in: IEEE Transactions on Geoscience and Remote Sensing, Numero 60, 2022, Pagina/e 1-10, ISSN 0196-2892
Editore: Institute of Electrical and Electronics Engineers
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Emergence of the physiological effects of elevated CO2 on land–atmosphere exchange of carbon and water

Autori: Zhan, Chunhui and Orth, René and Migliavacca, Mirco and Zaehle, Sönke and Reichstein, Markus and Engel, Jan and Rammig, Anja and Winkler, Alexander J.
Pubblicato in: Global Change Biology, Numero 13652486, 2022, ISSN 1365-2486
Editore: Wiley Online Library
DOI: 10.1111/gcb.16397

Constraining biospheric carbon dioxide fluxes by combined top-down and bottom-up approaches

Autori: Samuel Upton, Markus Reichstein, Fabian Gans, Wouter Peters, Basil Kraft, Ana Bastos
Pubblicato in: Atmospheric Chemistry and Physics, Numero 24, 2024, Pagina/e 2555-2582, ISSN 1680-7324
Editore: Copernicus Publications
DOI: 10.5194/acp-24-2555-2024

Global patterns of tree wood density

Autori: Hui Yang, Siyuan Wang, Rackhun Son, Hoontaek Lee, Vitus Benson, Weijie Zhang, Yahai Zhang, Yuzhen Zhang, Jens Kattge, Gerhard Boenisch, Dmitry Schepaschenko, Zbigniew Karaszewski, Krzysztof Stereńczak, Álvaro Moreno‐Martínez, Cristina Nabais, Philippe Birnbaum, Ghislain Vieilledent, Ulrich Weber, Nuno Carvalhais
Pubblicato in: Global Change Biology, Numero 30, 2024, ISSN 1354-1013
Editore: Blackwell Publishing Inc.
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Regime-oriented causal model evaluation of Atlantic-Pacific teleconnections in CMIP6

Autori: Karmouche, S., Galytska, E., Runge, J., Meehl, G. A., Phillips, A. S., Weigel, K., Eyring, V.
Pubblicato in: Earth System Dynamics, Numero Volume 14; Numero 2; 21.03.2023, 2023, Pagina/e 309-344, ISSN 2190-4979
Editore: Copernicus Gesellschaft mbH
DOI: 10.5194/esd-14-309-2023

Non-Linear Dimensionality Reduction With a Variational Encoder Decoder to Understand Convective Processes in Climate Models

Autori: G. Behrens, T. Beucler, P. Gentine, F. Iglesias-Suarez, M. Pritchard, V. Eyring
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero 19422466, 2022, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2022ms003130

Evaluating causal Arctic-midlatitude teleconnections in CMIP6

Autori: Galytska, E., Weigel, K., Handorf, D., Jaiser, R., Köhler, R., Runge, J., Eyring, V.
Pubblicato in: Journal of Geophysical Research: Atmospheres, Numero Volume 128; Numero 17; 09.09.2023, 2023, Pagina/e e2022JD037978, ISSN 2169-897X
Editore: ADVANCING EARTH AND SPACE SCIENCES
DOI: 10.1029/2022jd037978

Enhanced global carbon cycle sensitivity to tropical temperature linked to internal climate variability

Autori: Na Li, Sebastian Sippel, Nora Linscheid, Christian Rödenbeck, Alexander J. Winkler, Markus Reichstein, Miguel D. Mahecha, Ana Bastos
Pubblicato in: Science Advances, Numero 10, 2024, Pagina/e eadl6155, ISSN 2375-2548
Editore: American Association for the Advancement of Science
DOI: 10.1126/sciadv.adl6155

DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology

Autori: Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, Alexander J. Winkler
Pubblicato in: Geoscientific Model Development, Numero 17, 2024, Pagina/e 6683-6701, ISSN 1991-9603
Editore: Copernicus Publications
DOI: 10.5194/gmd-17-6683-2024

Multioutput Feature Selection for Emulation and Sensitivity Analysis

Autori: Jorge Vicent Servera, Luca Martino, Jochem Verrelst, Juan Pablo Rivera-Caicedo, Gustau Camps-Valls
Pubblicato in: IEEE Transactions on Geoscience and Remote Sensing, Numero 62, 2024, Pagina/e 1-11, ISSN 0196-2892
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tgrs.2024.3358231

Estimating Information Theoretic Measures via Multidimensional Gaussianization

Autori: Valero Laparra, Juan Emmanuel Johnson, Gustau Camps-Valls, Raúl Santos-Rodríguez, Jesús Malo
Pubblicato in: IEEE Transactions on Pattern Analysis and Machine Intelligence, Numero 47, 2025, Pagina/e 1293-1308, ISSN 0162-8828
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpami.2024.3495827

Large language models for causal hypothesis generation in science

Autori: Kai-Hendrik Cohrs, Emiliano Diaz, Vasileios Sitokonstantinou, Gherardo Varando, Gustau Camps-Valls
Pubblicato in: Machine Learning: Science and Technology, Numero 6, 2025, Pagina/e 013001, ISSN 2632-2153
Editore: IOP Science
DOI: 10.1088/2632-2153/ada47f

Differentiable Land Model Retrieves Global Environmental Controls of Ecological Parameters

Autori: Jianing Fang, Kevin Bownan, Wenli Zhao, Xu Lian, Pierre Gentine
Pubblicato in: Nature (submitted), 2024, ISSN 2662-4435
Editore: NA

Inference over radiative transfer models using variational and expectation maximization methods

Autori: Svendsen, Daniel Heestermans, Daniel Hernandez-Lobato, Luca Martino, Valero Laparra, Alvaro Moreno-Martinez, and Gustau Camps-Valls
Pubblicato in: Machine Learning, 2021, Pagina/e 1-17, ISSN 0885-6125
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10994-021-05999-4

Integration of a Deep‐Learning‐Based Fire Model Into a Global Land Surface Model

Autori: Rackhun Son, Tobias Stacke, Veronika Gayler, Julia E. M. S. Nabel, Reiner Schnur, Lazaro Alonso, Christian Requena‐Mesa, Alexander J. Winkler, Stijn Hantson, Sönke Zaehle, Ulrich Weber, Nuno Carvalhais
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero 16, 2024, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2023ms003710

A Data-Driven Approach to Partitioning Net Ecosystem Exchange using a Deep State Space Model

Autori: Trifunov, V., T., Shadaydeh, M., Runge, J., Reichstein, M., and Denzler, J. A.
Pubblicato in: IEEE Access, Numero 24732001, 2021, ISSN 2473-2001
Editore: IEEE
DOI: 10.1109/access.2021.3101129

Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy

Autori: Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts
Pubblicato in: Earth System Dynamics, Numero 15, 2024, Pagina/e 1319-1351, ISSN 2190-4987
Editore: Copernicus GmbH
DOI: 10.5194/esd-15-1319-2024

Leaf-level coordination principles propagate to the ecosystem scale.

Autori: Gomarasca, Ulisse, Mirco Migliavacca, Jens Kattge, Jacob A. Nelson, Ülo Niinemets, Christian Wirth, Alessandro Cescatti et al.
Pubblicato in: Nature communications, Numero 14, no. 1, 3948, 2023, ISSN 2041-1723
Editore: Nature Publishing Group

Domain knowledge-driven variational recurrent networks for drought monitoring

Autori: Mengxue Zhang, Miguel-Ángel Fernández-Torres, Gustau Camps-Valls
Pubblicato in: Remote Sensing of Environment, Numero 311, 2024, Pagina/e 114252, ISSN 0034-4257
Editore: Elsevier BV
DOI: 10.1016/j.rse.2024.114252

Getting the leaves right matters for estimating temperature extremes

Autori: Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, Alessandro Cescatti
Pubblicato in: Geoscientific Model Development, Numero 16, 2023, Pagina/e 7357-7373, ISSN 1991-9603
Editore: Copernicus Publications
DOI: 10.5194/gmd-16-7357-2023

Emulation of Forward Modeled Top-of-Atmosphere MODIS-Based Spectral Channels Using Machine Learning

Autori: Jessenia Gonzalez, Sudhakar Dipu, Odran Sourdeval, Alexandre Siméon, Gustau Camps-Valls, Johannes Quaas
Pubblicato in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Numero 18, 2024, Pagina/e 1896-1911, ISSN 1939-1404
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/jstars.2024.3507692

Soil and vegetation water content identify the main terrestrial ecosystem changes

Autori: Diego Bueso, Maria Piles, Philippe Ciais, Jean-Pierre Wigneron, Alvaro Moreno-Martínez, Gustau Camps-Valls
Pubblicato in: National Science Review, Numero Volume 10, Numero 5, May 2023, nwad026, 2023, ISSN 2095-5138
Editore: Oxford Press
DOI: 10.1093/nsr/nwad026

Artificial intelligence for modeling and understanding extreme weather and climate events

Autori: Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Adrian Höhl, Andrea Castelletti, Aytac Pacal, Claire Robin, Francesco Martinuzzi, Ioannis Papoutsis, Ioannis Prapas, Jorge Pérez-Aracil, Katja Weigel, Maria Gonzalez-Calabuig, Markus Reichstein, Martin Rabel, Matteo Giuliani, Miguel D. Mahecha, Oana-Iuliana Popescu, Oscar J. Pellicer-Valero, Said Ouala, Sancho Salcedo-Sanz,
Pubblicato in: Nature Communications, Numero 16, 2025, ISSN 2041-1723
Editore: Nature Publishing Group
DOI: 10.1038/s41467-025-56573-8

Multifidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models

Autori: Vicent, J. and Martino, L. and Verrelst, L. and Camps-Valls, G
Pubblicato in: IEEE Transactions on Geoscience and Remote Sensing, Numero 61, 2023, Pagina/e 1-10, ISSN 0196-2892
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tgrs.2023.3300460

The R Package stagedtrees for Structural Learning of Stratified Staged Trees

Autori: Carli, F., Leonelli, M., Riccomagno, E., & Varando, G
Pubblicato in: Journal of Statistical Software, 2022, ISSN 1548-7660
Editore: University of California at Los Angeles

A Scalable Unsupervised Feature Selection With Orthogonal Graph Representation for Hyperspectral Images

Autori: Gulsen Taskin; E. Fatih Yetkin; Gustau Camps-Valls
Pubblicato in: IEEE Transactions on Geoscience and Remote Sensing, Numero 18, 2023, ISSN 0196-2892
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tgrs.2023.3284475

Machine learning-based parametrizations for the ICON model

Autori: Fernando Iglesias-Suarez, Arthur Grundner, Gunnar Behrens, Tom Beucler, Pierre Gentine, Marco Giorgetta, Michael Pritchard, and Veronika Eyring
Pubblicato in: AGU (American Geophysical Union) Fall Meeting 2021, 2021
Editore: AGU

Simulating Atmospheric Processes in ESMs and Quantifying Uncertainties with Deep Learning Multi-Member and Stochastic Parameterizations

Autori: Behrens, Gunnar und Beucler, Tom und Iglesias-Suarez, Fernando und Yu, Sungduk und Gentine, Pierre und Pritchard, Michael und Schwabe, Mierk und Eyring, Veronika
Pubblicato in: Oxford Workshop on Model Uncertainty, 2024
Editore: Oxford Workshop on Model Uncertainty

Machine learning-based parametrizations for the ICON model

Autori: Arthur Grundner, Fernando Iglesias-Suarez, Veronika Eyring, Pierre Gentine, Tom Beucler, and Marco Giorgetta
Pubblicato in: 3rd NOAA Workshop on Leveraging AI in Environmental Sciences, 2021
Editore: NOAA

Non-Linear Dimensionality Reduction With a Variational Encoder Decoder (VED) to Understand Convective Processes in Climate Models

Autori: Behrens, Gunnar und Beucler, Tom und Gentine, Pierre und Iglesias-Suarez, Fernando und Pritchard, Michael und Eyring, Veronika
Pubblicato in: American Geophysical Union Fall Meeting 2022, 2022
Editore: American Geophysical Union

Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality

Autori: M Morata, D Bueso, M Piles, G Camps-Valls
Pubblicato in: NeurIPS AI for Earth Sciences, 2020
Editore: Neurips

Carbon fluxes estimation with aleatoric and epistemic uncertainties at high spatial resolution over large areas

Autori: Laura Martínez-Ferrer, Álvaro Moreno-Martínez, John S. Kimball, Steven W. Running, Nicholas Clinton, and Gustau Camps-Valls
Pubblicato in: EGU General Assembly 2022, 2022
Editore: EGU General Assembly 2022

Causally-informed neural nets to improve convection in climate models

Autori: F. Iglesias-Suarez, V. Eyring, P. Gentine, T. Beucler, M. Pritchard, J. Runge, and B. Solino-Fernandez
Pubblicato in: 2nd ELLIS workshop on Machine Learning for Earth and Climate sciences, 2022
Editore: ELLIS

Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data

Autori: Allen, M., Dorr, F., Gallego-Mejia, J.A., Martínez-Ferrer, L., Jungbluth, A., Kalaitzis, F., Ramos-Pollán, R.
Pubblicato in: NeurIPS 2023 - Workshop on Tackling Climate Change with Machine Learning., 2023
Editore: NeuIPS

On the Generalization of Agricultural Drought Classification from Climate Data

Autori: J. Gottfriedsen, M. Berrendorf, P. Gentine, B. Hassler, M. Reichstein, K. Weigel, V. Eyring
Pubblicato in: NeurIPS, 2021, ISSN 2331-8422
Editore: Climate Change AI
DOI: 10.48550/arxiv.2111.15452

Machine learning-based parameterizations for the ICON model

Autori: Arthur Grundner, Fernando Iglesias-Suarez, Tom Beucler, Pierre Gentine, Marco Giorgetta, and Veronika Eyring
Pubblicato in: ICCARUS, 2022
Editore: ICCARUS

Causally-informed neural nets to improve subgrid processes in climate models

Autori: F. Iglesias-Suarez, V. Eyring, P. Gentine, T. Beucler, M. Pritchard, J. Runge, and B. Solino-Fernandez
Pubblicato in: II ELLIS Doctoral Symposium 2022, 2022
Editore: ELLIS

Interpretable multiscale Machine Learning-Based Parameterizations of Convection for ICON

Autori: Helge Heuer, Mierk Schwabe, Pierre Gentine, Marco A. Giorgetta, Veronika Eyring
Pubblicato in: EGU General Assembly 2024, 2024
Editore: EGU
DOI: 10.5194/egusphere-egu24-10325

Physics-Aware Machine Learning For Geosciences And Remote Sensing

Autori: G Camps-Valls, DH Svendsen, J Cortés, A Moreno-Martínez, A Pérez-Suay, J Adsuara, I Martin, M Piles, J Muñoz-Marí, L Martino
Pubblicato in: IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium 2021, 2021, ISBN 978-1-6654-0369-6
Editore: IEEE
DOI: 10.1109/igarss47720.2021.9554521

Bootstrap aggregation and confidence measures to improve time series causal discovery

Autori: Kevin Debeire; Andreas Gerhardus; Jakob Runge; Veronika Eyring
Pubblicato in: Proceedings of Machine Learning Research, Numero 236, 2024
Editore: PMLR

Location-Aware Convolutional Encoder-Decoder for Drought Detection in Europe

Autori: J. Cortés-Andrés, M.Á. Fernández-Torres, G. Camps-Valls
Pubblicato in: AGU 2021, 2021
Editore: AGU 2021

Upscaling plant traits to ecosystem level: blending local biodiversity, global traits databases, and remote sensing data.

Autori: Álvaro Moreno-Martínez, Jose E Adsuara, Jordi Muñoz-Marí, Emma Izquierdo-Verdiguier, Jens Katge, Nuno Carvalhais, Markus Reichstein, Steven W Running, Gustau Camps-Valls
Pubblicato in: EGU General Assembly 2021, 2021
Editore: EGU

M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data.

Autori: Allen, M., Dorr, F., Gallego-Mejia, J.A., Martínez-Ferrer, L., Jungbluth, A., Kalaitzis, F., Ramos-Pollán, R.
Pubblicato in: Part of Advances in Neural Information Processing Systems Datasets and Benchmarks Track 37 (NeurIPS 2024) (pp. 104694-104723), 2024
Editore: NeuIPS

Learning Granger Causal Feature Representations

Autori: G Varando, MA Fernández-Torres, G Camps-Valls
Pubblicato in: ICML 2021 - International Conference on Machine Learning - Workshop on Tackling Climate Change with Machine Learning, 2021
Editore: ICML

Fewshot learning on global multimodal embeddings for earth observation tasks,

Autori: Allen, M., Dorr, F., Gallego-Mejia, J.A., Martínez-Ferrer, L., Jungbluth, A., Kalaitzis, F., Ramos-Pollán, R
Pubblicato in: NeurIPS 2023 - R0-FoMo: Workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models, 2023
Editore: NeurIPS

Towards Inference in Hybrid Earth System Models

Autori: Kai-Hendrik Cohrs
Pubblicato in: AAAI 2024 Bridge on Knowledge-Guided Machine Learning, Numero Invited talk, 2024
Editore: AAAI

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Autori: Sungduk Yu, Zeyuan Hu, Akshay Subramaniam, Walter Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius J. M. Busecke, Nora Loose, Charles I. Stern, Tom Beucler, Bryce Harrop, Helge Heuer, Benjamin R. Hillman, Andrea Jenney, Nana Liu, Alistair White, Tian Zheng, Zhiming Kuang, Fiaz Ahmed, Elizabeth Barnes, Noah D. Brenowitz, Chris
Pubblicato in: 37th Conference on Neural Information Processing Systems, NeurIPS 2023, 2023
Editore: NeurIPS
DOI: 10.48550/arxiv.2306.08754

Context-Specific Causal Discovery for Categorical Data Using Staged Trees

Autori: Leonelli, M., Varando, G.
Pubblicato in: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR, 206: 8871–8888, 2023
Editore: PMLR

Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning

Autori: Trifunov, V., T., Shadaydeh, M., Barz, B., and Denzler, J. A.
Pubblicato in: ICMLA, 2021
Editore: IEEE
DOI: 10.1109/icmla52953.2021.00033

Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions

Autori: Violeta-Teodora Trifunov, Maha Shadaydeh, Joachim Denzler
Pubblicato in: A causal view on dynamical systems, NeurIPS 2022 workshop, 2022
Editore: NeurIPS

Causal model evaluation

Autori: Katja Weigel, Kevin Debeire, Evgenia Galytska, Soufiane Karmouche, Jakob Runge, and Veronika Eyring
Pubblicato in: Exploring the Frontiers in Earth System Modeling with Machine Learning and Big Data, 2022
Editore: AGCI

A two-stage machine learning framework using global satellite data of cloud classes for process-oriented model evaluation

Autori: Kaps, A., Lauer, A., Camps-Valls,G., Gentine, P., Gómez-Chova,L., Eyring, V.
Pubblicato in: Living Planet Symposium 2022, 2022
Editore: EESA

Learning Granger Causal Feature Representations

Autori: G. Varando, M.Á. Fernández-Torres, G. Camps-Valls
Pubblicato in: AGU (American Geophysical Union) Fall Meeting 2021, 2022
Editore: AGU

Causally-informed neural nets to improve subgrid processes in climate models

Autori: F. Iglesias-Suarez, V. Eyring, P. Gentine, T. Beucler, M. Pritchard, J. Runge, and B. Solino-Fernandez
Pubblicato in: Exploring the Frontiers in Earth System Modeling with Machine Learning and Big Data, 2022
Editore: AGCI

Graphs in State-Space Models for Granger Causality in Climate Science

Autori: Elvira, V., Chouzenoux, É., Cerdà-Bautista, J., Camps-Valls, G.
Pubblicato in: CausalStats23: When Causal Inference Meets Statistical Analysis, 2023
Editore: CNRS

Development of a Machine Learning Based Parameterization of Convection for ICON

Autori: H. Heuer, M. Schwabe, P. Gentine, V. Eyring
Pubblicato in: Atmospheric Physics: Experiment meets Modelling, 2022
Editore: Bad Honnef Physics School

Identifying the Causes of Pyrocumulonimbus (PyroCb)

Autori: Díaz Salas-Porras, E., Tazi, K., Braude, A., Okoh, D., Lamb, K.D., Watson-Parris, D., Harder, P., Meinert, N.
Pubblicato in: NeurIPS 2022 Workshop - Causality for Real-world Impact, 2022
Editore: Neurips

Long-time record andcontinuous high resolution gross primary productivity estimates at continental scales.

Autori: Alvaro Moreno, Laura Martínez-Ferrer, John Kimball, Martin Jung, Markus Reichstein, Steven W Running, Nicholas Clinton and Gustau Camps-Valls
Pubblicato in: AGU (American Geophysical Union) Fall Meeting 2021, 2021
Editore: AGU

Machine Learning-Based Parameterizations of Convection for ICON

Autori: Heuer, Helge Gustav Helmut; Schwabe, Mierk; Gentine, Pierre; Giorgetta, Marco A.; Eyring, Veronika
Pubblicato in: AGU Fall Meeting 2023, 2023
Editore: AGU

Investigation on the biophysical and biogeochemical responses of ecosystem conductance to water stress using eddy-covariance data

Autori: Han J., Zhan W., Gentine P.
Pubblicato in: American Geophysical Union Fall 2021, 2021
Editore: AGU

Machine learning-based parametrizations for ICON and evaluation with satellite data using the ESMValTool

Autori: Fernando Iglesias-Suarez, Arthur Grundner, Manuel Schlund, Mierk Schwabe, Tom Beucler, Pierre Gentine, Marco A. Giorgetta, and Veronika Eyring
Pubblicato in: Living Planet Symposium 2022, 2022
Editore: EESA

Recent Trends, Challenges and Limitations of Explainable AI in Remote Sensing

Autori: Höhl, A., Obadic, I., Fernández-Torres, M. Á., Oliveira, D., & Zhu, X. X.
Pubblicato in: . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8199-8205)., 2024
Editore: CVF

Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction

Autori: Martínez-Ferrer, L., Jungbluth, A., Gallego-Mejia, J.A., Allen, M., Dorr, F., Kalaitzis, F., Ramos-Pollán, R.
Pubblicato in: NeurIPS 2023 - Workshop on Distribution Shifts: New Frontiers with Foundation Models, 2023
Editore: Neurips

Semiparametric Inference and Equation Discovery with the Bayesian Machine Scientist

Autori: Cohrs, K-H. and Varando, G. and Guimerà, R. and Sales-Pardo, M. and Camps-Valls, G
Pubblicato in: AI for Differential Equations in Science Workshop in ICLR 2024, 2024
Editore: ICLR

Double Machine Learning for Earth and Climate Sciences

Autori: Kai Hendrik Cohrs, Gherardo Varando, Nuno Carvalhais, Markus Reichstein, Gustau Camps-Valls
Pubblicato in: Nordic Probabilistic AI Summer School 2022, 2022
Editore: Nordic Probabilistic AI Summer School 2022

Exploiting Spatial and Temporal Information with ConvLSTM Networks for Cloud Detection over Landmarks

Autori: D. López-Puigdollers, A. Pérez-Suay, G. Mateo-García, L. Gómez-Chova
Pubblicato in: Living Planet Symposium 2022, 2022
Editore: Living Planet Symposium 2022 - ESA

Towards Physically Consistent Deep Learning For Climate Model Parameterizations

Autori: Birgit Kühbacher; Fernando Iglesias-Suarez; Niki Kilbertus; Veronika Eyring
Pubblicato in: ICMLA 2024, 2024
Editore: ICMLA
DOI: 10.48550/arxiv.2406.03920

Analyzing Climate Scenarios Using Dynamic Mode Decomposition With Control

Autori: Mankovich M., Bouabid S. and Camps-Valls G.
Pubblicato in: Dynamics, Data and Deep Learning workshop, Bristol, UK 2024, 2024
Editore: Bristol

Physics-informed Machine Learning-Based Cloud Microphysics Parameterization for Earth System Models

Autori: Sarauer, E.; Schwabe, M.; Lauer, A.; Stier, P.; Weiss, P.; Eyring, V.
Pubblicato in: Tackling Climate Change with Machine Learning workshop at ICLR 2024, 2024
Editore: ICLR

Characterizing the Earth complex dynamical system through spectral decomposition of kernel transfer operators

Autori: J. Adsuara, G. Varando, A. Pérez-Suay, K. Cohrs, E. Díaz, D. Bueso, G. Camps-Valls
Pubblicato in: Living Planet Symposium 2022, 2022
Editore: ESA

Development of a Machine Learning Based Parameterization of Convection for ICON

Autori: H. Heuer, M. Schwabe, P. Gentine, V. Eyring
Pubblicato in: Summer School on Land-Atmosphere Interaction Processes and Convection, 2022
Editore: CHESS

The Kernelized Taylor Diagram

Autori: Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, and Robert Jenssen
Pubblicato in: Symposium of the Norwegian AI Society, 31 May-1 June 2022, Oslo, Norway 2022, 2022
Editore: Norwegian AI Society

Recent Advances in Deep Learning for Spatio-Temporal Drought Monitoring, Forecasting and Model Understanding

Autori: J. Cortés-Andrés, M.Á. Fernández-Torres, G. Camps-Valls
Pubblicato in: EGU 2021, 2021
Editore: EGU

Learning Causal Representations with Granger PCA

Autori: Varando, G., Fernández-Torres, M.Á., Muñoz-Marí, J., Camps-Valls, G.
Pubblicato in: Causal Representation Learning Workshop @ UAI'22,, 2022
Editore: UAI

Interpretable AI – two examples

Autori: M. Schwabe, G. Behrens, T. Beucler, F. Iglesias-Suarez, P. Gentine, M. Giorgetta, A. Grundner, M. Pritchard, V. Eyring
Pubblicato in: 2nd ELLIS workshop on Machine Learning for Earth and Climate sciences, 2022
Editore: ELLIS

Causal discovery on climate models data

Autori: Kevin Debeire, Jakob Runge, and Veronika Eyring
Pubblicato in: ML for Earth System Modelling and Analytics workshop 2021, 2021
Editore: ML for Earth System Modelling and Analytics workshop

Causal evaluation of Arctic-midlatitude processes in CMIP6 model simulations

Autori: Evgenia Galytska, Katja Weigel, Jakob Runge, Dörthe Handorf, Ralf Jaiser, Raphael Köhler, and Veronika Eyring
Pubblicato in: EGU General Assembly 2022, 2022
Editore: EGU
DOI: 10.5194/egusphere-egu22-870

Intercomparing Global Foliar Trait and Canopy Height Maps: Upscaling Approaches and Spatial Patterns

Autori: Benjamin Dechant, Ryan Pavlick, Jens Kattge, Fabian D Schneider, Ethan E Butler, Alvaro Moreno
Pubblicato in: AGU (American Geophysical Union) Fall Meeting 2021, 2021
Editore: AGU (American Geophysical Union) Fall Meeting 2021

Learning Causal Representations with Granger PCA

Autori: G. Varando, M.Á. Fernández-Torres, J. Muñoz-Marí, G. Camps-Valls
Pubblicato in: Causal Representation Learning workshop at the 38th Conference on Uncertainty in Artificial Intelligence (UAI CRL 2022), 2022
Editore: UAI

Proof-of-concept: Using ChatGPT to Translate and Modernize an Earth System Model from Fortran to Python/JAX

Autori: Anthony Zhou, Linnia Hawkins, Pierre Gentine
Pubblicato in: NeurIPS, 2024
Editore: NeurIPS

Epistemic and aleatoric uncertainty maps in high resolution biophysical parameter retrieval

Autori: Laura Martínez-Ferrer, Álvaro Moreno-Martínez, Jordi Muñoz-Marí,Emma Izquierdo-Verdiguier, Manuel Campos-Taberner, Javier García-Haro, MarcoManeta, Nathaniel Robinson, Nicholas Clinton, John Kimball, Steven W. Running, andGustau Camps-Valls
Pubblicato in: EGU General Assembly 2021, 2021
Editore: EGU General Assembly 2021

A two-stage machine learning framework using global satellite data of cloud classes for process-oriented model evaluation

Autori: Kaps, A., Lauer, A., Camps-Valls,G., Gentine, P., Gómez-Chova,L., Eyring, V.
Pubblicato in: EGU General Assembly 2022, 2022
Editore: EGU
DOI: 10.5194/egusphere-egu22-676

Carbon fluxes estimation at scale: long-term, continuous, high spatial resolution with uncertainties at continental scales

Autori: Laura Martínez-Ferrer, Alvaro Moreno‑Martínez, John S. Kimball, Steven W. Running, Nicholas Clinton, Gustau Camps-Valls
Pubblicato in: Living Planet Symposium 2022, 2022
Editore: Living Planet Symposium 2022

Machine learning for improved understanding and projections of climate change

Autori: Schwabe, Mierk; Eyring, Veronika
Pubblicato in: TRR 165/181 Conference, 2023
Editore: TRR 165/181 Conference

Large Language Models for Constrained-Based Causal Discovery.

Autori: Cohrs, K., Diaz, E., Sitokonstantinou, V., Varando G. and Camps-Valls, G.
Pubblicato in: AAAI 2024 Workshop on 'Are Large Language Models Simply Causal Parrots?, 2024
Editore: AAAI

Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests

Autori: Reimers, C.; Hafezi Rachti, D.; Liu, G.; Winkler, A.
Pubblicato in: arXiv, Numero 9, 2023
Editore: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning
DOI: 10.48550/arxiv.2401.03960

Learning ENSO-related Principal Modes of Vegetation via a Granger-Causal Variational Autoencoder

Autori: G. Varando, M.Á. Fernández-Torres, G. Camps-Valls
Pubblicato in: EGU 2022, 2022
Editore: EGU

Highly Efficient Structural Learning of Sparse Staged Trees

Autori: M. Leonelli, G. Varando
Pubblicato in: The 11th International Conference on Probabilistic Graphical Models, 2022
Editore: PMLR press

Machine learning based gravity wave parametrizations for ICON

Autori: Schwabe, M., Grundner, A., Gentine, P., Giorgetta, M. A., Rapp, M., Eyring, V.
Pubblicato in: SPARC Gravity Wave Symposium 2022, 2022
Editore: SPARC

Spatio-Temporal Gaussianization Flows for Extreme Event Detection

Autori: M.Á. Fernández-Torres, J.E. Johnson, M. Piles, G. Camps-Valls
Pubblicato in: EGU, 2021
Editore: EGU

Deep learning based cloud cover parameterization for ICON

Autori: Grundner, A., Beucler, T., Gentine, P., Iglesias-Suarez, F., Giorgetta, M. A., Eyring, V.
Pubblicato in: Journal of Advances in Modeling Earth Systems, Numero Volume 14; Numero 12; 14.12.2022, 2021, Pagina/e e2021MS002959, ISSN 1942-2466
Editore: American Geophysical Union
DOI: 10.1029/2021ms002959

Causal model evaluation of Arctic-midlatitude teleconnections in CMIP6

Autori: E. Galytska, K. Weigel, D. Handorf, R. Jaiser, R.H. Köhler, J. Runge, and V. Eyring
Pubblicato in: Earth and Space Science Open Archive, Numero 23335084, 2022, ISSN 2333-5084
Editore: Earth and Space Science Open Archive
DOI: 10.1002/essoar.10512569.1

Climate-Invariant Machine Learning

Autori: Beucler, T., Pritchard, M., Yuval, J., Gupta, A., Peng, L., Rasp, S., Ahmed, F., O'Gorman, P.A., Neelin, J.D., Lutsko, N.J. and Gentine, P.
Pubblicato in: arXiv, Numero 23318422, 2021, ISSN 2331-8422
Editore: arXiv
DOI: 10.48550/arxiv.2112.08440

Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions

Autori: Svendsen, D. H., Piles, M., Muñoz-Marí, J., Luengo, D., Martino, L, Camps-Valls G.
Pubblicato in: IEEE Transactions on Geoscience and Remote Sensing, Numero 60, 2022, Pagina/e 1-15, ISSN 0196-2892
Editore: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tgrs.2021.3059550

Bringing it all together: Science and modelling priorities to support international climate policy

Autori: Colin Gareth Jones, Fanny Adloff, Ben Booth, Peter Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene Hewitt, Hazel Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm Roberts, Benjamin Sanderson, Roland Séférian, Samuel Somot, Pier-Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Ju
Pubblicato in: EGUsphere, 2024, ISSN 2190-4987
Editore: Copernicus GmbH
DOI: 10.5194/egusphere-2024-453

Machine-Learned Cloud Classes From Satellite Datafor Process-Oriented Climate Model Evaluation

Autori: Kaps, A., Lauer, A., Camps-Valls, G., Gentine, P., Gómez-Chova, L., Eyring, V.
Pubblicato in: TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, Numero Volume 61; 31.01.2023, 2022, ISSN 2331-8422
Editore: IEEE
DOI: 10.1109/tgrs.2023.3237008

Two for one: Partitioning CO2 fluxes and understanding the relationship between solar-induced chlorophyll fluorescence and gross primary productivity using machine learning

Autori: Weiwei Zhan, Xi Yang, Youngryel Ryu, Benjamin Dechant, Yu Huang, Yves Goulas, Minseok Kang, Pierre Gentine
Pubblicato in: Agricultural and Forest Meteorology, Numero 321, 2023, Pagina/e 108980, ISSN 0168-1923
Editore: Elsevier BV
DOI: 10.1016/j.agrformet.2022.108980

Evaluation of Native Earth System Model Output with ESMValTool v2.6.0

Autori: M., Schlund, B., Hassler, A., Lauer, B., Andela, P., Jöckel, R., Kazeroni, S. L., Tomas, B., Medeiros, V., Predoi, S., Sénési, J., Servonnat, T., Stacke, J., Vegas-Regidor, K., Zimmermann, V., Eyring
Pubblicato in: Geoscientific Model Development Discussions, Numero Volume 16; 11.01.2023, 2022, Pagina/e 315-333, ISSN 1991-962X
Editore: Geoscientific Model Development Discussions
DOI: 10.5194/gmd-2022-205

Physics-aware Machine Learning for Earth Observation

Autori: Camps-Valls, G.
Pubblicato in: NeurIPS 2022 - Workshop on Tackling Climate Change with Machine Learning, Numero Keynote talk, 2022
Editore: Neurips

Learning Causal Representations with Granger Rotated PCA

Autori: Gherardo Varando
Pubblicato in: 16th International Conference of the ERCIM WG on Computational and Methodological Statistics, Numero Invited talk, 2023
Editore: CMS

Intercomparison of global foliar trait maps reveals fundamental differences and limitations of upscaling approaches.

Autori: Benjamin Dechant , Jens Kattge, Ryan Pavlick, Fabian Schneider, Francesco Sabatini, Alvaro Moreno-Martinez, Ethan Butler, Peter van Bodegom, Helena Vallicrosa, Teja Kattenborn, Coline Boonman, Nima Madani, Ian Wright, Ning Dong, Hannes Feilhauer, Josep Penuelas, Jordi Sardans, Jesus Aguirre-Gutierrez, Peter Reich, Pedro Leitao, Jeannine Cavender-Bares, Isla H. Myers-Smith , Sandra Duran, Holly Cro
Pubblicato in: 2023
Editore: EarthArchiv

Geological carbon cycle constraints on the terrestrial hydrological response to higher atmospheric CO2.

Autori: Rugenstein, J.K.C., Winkler, A.J.
Pubblicato in: ESSOAR, 2022
Editore: ESSOAR
DOI: 10.1002/essoar.10512683.1

Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water

Autori: Chunhui Zhan, René Orth, Mirco Migliavacca, Sönke Zaehle, Markus Reichstein, Jan Engel, Anja Rammig, Alexander J. Winkler
Pubblicato in: Earth and Space Science Open Archive, 2022
Editore: Earth and Space Science Open Archive
DOI: 10.1002/essoar.10510955.1

The biogeophysical effects of idealized land cover and land management changes in Earth System Models

Autori: Steven Johan De Hertog, Felix Havermann, Inne Vanderkelen, Suqi Guo, Fei Luo, Iris Manola, Dim Coumou, Edouard Léopold Davin, Gregory Duveiller, Quentin Lejeune, Julia Pongratz, Carl-Friedrich Schleussner, Sonia Isabelle Seneviratne, Wim Thiery
Pubblicato in: Earth System Dynamcis Discussions, 2022
Editore: Copernicus GmbH
DOI: 10.5194/esd-2022-5

Better Better — machine learning for improved climate models and projections

Autori: Eyring V. and Gentine P.
Pubblicato in: 2021
Editore: UN

Interannual global carbon cycle variations linked to atmospheric circulation variability

Autori: Na Li, Sebastian Sippel, Alexander J. Winkler, Miguel D. Mahecha, Markus Reichstein, Ana Bastos
Pubblicato in: Earth System Dynamics Discussion, 2022
Editore: Copernicus GmbH
DOI: 10.5194/egusphere-2022-96

Extreme Event Monitoring, Everywhere, All at Once: Challenges and Strategies

Autori: Fernández-Torres, M.A.
Pubblicato in: ICS Theoretical Astrophysics & Computational Science Seminar, Universität Zürich, Zurich, Switzerland, Numero Invited talk, 2023
Editore: ETH

Generative Adversarial Networks in the Geosciences

Autori: Mateo-Garc\'\ia, Gonzalo and Laparra, Valero and Requena-Mesa, Christian and G\'omez-Chova, Luis
Pubblicato in: Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences. Wiley & Sons 2021, 2021
Editore: Wiley and sons

Combining system modeling and machine learning into hybrid ecosystem modeling. Science-guided Machine Learning: Emerging Trends in Combining Scientific Knowledge with Data-driven Methods, Data Mining and Knowledge Discovery Series. CRC Press 2021

Autori: Reichstein, M. and Ahrens, B. and Kraft, B. and Camps-Valls, G. and Carvalhais, N. and Gans, F. and Gentine, P. and Winkler, A.J.
Pubblicato in: 2022
Editore: Chapman and Hall/CRC
DOI: 10.1201/9781003143376

Perspective on Deep Learning for Earth Sciences

Autori: Gustau Camps-Valls
Pubblicato in: Generalization with Deep Learning, 2021, ISSN 1013-2511
Editore: World Scientific
DOI: 10.1142/9789811218842_0007

Spatio-temporal Autoencoders in Weather and Climate Research

Autori: Xavier-Andoni Tibau, Christian Reimers, Christian Requena-Mesa, and Jakob Runge
Pubblicato in: 2021, ISBN 9781119646181
Editore: John Wiley & Sons, Ltd
DOI: 10.1002/9781119646181.ch13

Deep Learning for the Parametrization of Subgrid Processes in Climate Models

Autori: P. Gentine, V. Eyring and T. Beucler
Pubblicato in: Chapter 21 In Book: Deep Learning for the Earth Sciences, Eds G. Camps-Valls, D. Tuia, X. X. Zhu, M. Reichstein, 2021
Editore: John Wiley & Sons Ltd
DOI: 10.1002/9781119646181.ch21

Learning Unsupervised Feature Representations of Remote Sensing Data with Sparse Convolutional Networks

Autori: Adsuara, Jose E and Campos-Taberner, Manuel and Garc\'\ia-Haro, Javier and Gatta, Carlo and Romero, Adriana and Camps-Valls, Gustau
Pubblicato in: Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences. Wiley & Sons 2021, 2021
Editore: Wiley and sons

Introduction to Deep Learning for the Earth Sciences

Autori: Gustau Camps-Valls, Xiao Xiang Zhu, Devis Tuia, and Markus Reichstein
Pubblicato in: Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences. Wiley & Sons 2021, 2021
Editore: Wiley and sons

Outloook to Deep Learning for the Earth Sciences

Autori: Markus Reichstein, Gustau Camps-Valls, Devis Tuia, and Xiao Xiang Zhu
Pubblicato in: Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences. Wiley & Sons 2021, 2021
Editore: Wiley and sons
DOI: 10.1002/9781119646181.ch23

Emulating Ecological Memory with Recurrent Neural Networks

Autori: Basil Kraft, Simon Besnard, and Sujan Koirala
Pubblicato in: 2021
Editore: Wiley Online Library
DOI: 10.1002/9781119646181.ch18

Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences

Autori: Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein
Pubblicato in: Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences, 2021, ISBN 9781119646181
Editore: Wiley and sons
DOI: 10.1002/9781119646181

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