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The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events.
Achieving conservation of energy in neural network emulators for climate modeling, 2019
T. Beucler, S. Rasp, M. Pritchard, and P. Gentine · 1906
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Investigating two super-resolution methods for downscaling precipitation: ESRGAN and CAR, 2020
C. D. Watson, C. Wang, T. Lynar, and K. Weldemariam · 2012
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Conditional generative adversarial nets, 2014
M. Mirza and S. Osindero · 2014
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Image super-resolution using deep convolutional networks
C. Dong, C. C. Loy, K. He, and X. Tang · 2015
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Photo-realistic single image super-resolution using a generative adversarial network, 2016
C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
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Enhanced deep residual networks for single image super-resolution
B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee · 2017
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Video frame synthesis using deep voxel flow
Z. Liu, R. A. Yeh, X. Tang, Y. Liu, and A. Agarwala · 2017
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Deepsd: Generating high resolution climate change projections through single image super-resolution
T. Vandal, E. Kodra, S. Ganguly, A. Michaelis, R. Nemani, and A. R. Ganguly · 2017
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Statistical Downscaling and Bias Correction for Climate Research
D. Maraun and M. Widmann · 2018
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Online dynamical downscaling of temperature and precipitation within the
A. Quiquet, D. M. Roche, C. Dumas, and D. Paillard · 2018
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Deep learning and process understanding for data-driven earth system science
M. Reichstein, G. Camps-Valls, B. Stevens, M. Jung, J. Denzler, N. Carvalhais, and Prabhat · 2019
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Configuration and intercomparison of deep learning neural models for statistical downscaling
J. Baño Medina, R. Manzanas, and J. M. Gutiérrez · 2020
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Cligan: A structurally sensitive convolutional neural network model for statistical downscaling of precipitation from multi-model ensembles
C. Chaudhuri and C. Robertson · 2020
Cited alongside, same era.
Climalign: Unsupervised statistical downscaling of climate variables via normalizing flows
B. Groenke, L. Madaus, and C. Monteleoni · 2020
Cited alongside, same era.
The ongoing need for high-resolution regional climate models: Process understanding and stakeholder information
W. J. Gutowski, P. A. Ullrich, A. Hall, L. R. Leung, T. A. O’Brien, C. M. Patricola, R. W. Arritt, M. S. Bukovsky, K. V. Calvin, Z. Feng, A. D. Jones, G. J. Kooperman, E. Monier, M. S. Pritchard, S. C. Pryor, Y. Qian, A. M. Rhoades, A. F. Roberts, K. Sakaguchi, N. Urban, and C. Zarzycki · 2020
Cited alongside, same era.
The era5 global reanalysis
H. Hersbach, B. Bell, P. Berrisford, S. Hirahara, A. Horányi, J. Muñoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, A. Simmons, C. Soci, S. Abdalla, X. Abellan, G. Balsamo, P. Bechtold, G. Biavati, J. Bidlot, M. Bonavita, G. De Chiara, P. Dahlgren, D. Dee, M. Diamantakis, R. Dragani, J. Flemming, R. Forbes, M. Fuentes, A. Geer, L. Haimberger, S. Healy, R. J. Hogan, E. Hólm, M. Janisková, S. Keeley, P. Laloyaux, P. Lopez, C. Lupu, G. Radnoti, P. de Rosnay, I. Rozum, F. Vamborg, S. Villaume, and J.-N. Thépaut · 2020
Cited alongside, same era.
Enforcing analytic constraints in neural networks emulating physical systems
T. Beucler, M. Pritchard, S. Rasp, J. Ott, P. Baldi, and P. Gentine · 2021
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Single image super-resolution with uncertainty estimation for lunar satellite images
J. Delgano-Centeno, P. Harder, B. Moseley, V. Bickel, S. Ganju, F. Kalaitzis, and M. Olivares-Mendez · 2021
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Dc3: A learning method for optimization with hard constraints
P. Donti, D. Rolnick, and J. Z. Kolter · 2021
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Physics-informed learning of aerosol microphysics
P. Harder, D. Watson-Parris, D. Strassel, N. Gauger, P. Stier, and J. Keuper · 2021
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Augmented convolutional lstms for generation of high-resolution climate change projections
N. Harilal, M. Singh, and U. Bhatia · 2021
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Meshfreeflownet: A physics-constrained deep continuous space-time super-resolution framework
C. M. Jiang, S. Esmaeilzadeh, K. Azizzadenesheli, K. Kashinath, M. Mustafa, H. A. Tchelepi, P. Marcus, Prabhat, and A. Anandkumar · 2020
Cited alongside, same era.
Stochastic super-resolution for downscaling time-evolving atmospheric fields with a generative adversarial network
J. Leinonen, D. Nerini, and A. Berne · 2020
Cited alongside, same era.
Srflow: Learning the super-resolution space with normalizing flow
A. Lugmayr, M. Danelljan, L. Van Gool, and R. Timofte · 2020
Cited alongside, same era.
A fortran-keras deep learning bridge for scientific computing
J. Ott, M. Pritchard, N. Best, E. Linstead, M. Curcic, and P. Baldi · 2020
Cited alongside, same era.
Overview of the norwegian earth system model (noresm2) and key climate response of cmip6 deck, historical, and scenario simulations
Ø. Seland, M. Bentsen, D. Olivié, T. Toniazzo, A. Gjermundsen, L. S. Graff, J. B. Debernard, A. K. Gupta, Y.-C. He, A. Kirkevåg, J. Schwinger, J. Tjiputra, K. S. Aas, I. Bethke, Y. Fan, J. Griesfeller, A. Grini, C. Guo, M. Ilicak, I. H. H. Karset, O. Landgren, J. Liakka, K. O. Moseid, A. Nummelin, C. Spensberger, H. Tang, Z. Zhang, C. Heinze, T. Iversen, and M. Schulz · 2020
Cited alongside, same era.
Adversarial super-resolution of climatological wind and solar data
K. Stengel, A. Glaws, D. Hettinger, and R. N. King · 2020
Cited alongside, same era.
Learning texture transformer network for image super-resolution
F. Yang, H. Yang, J. Fu, H. Lu, and B. Guo · 2020
Cited alongside, same era.
Data-driven equation discovery of ocean mesoscale closures
L. Zanna and T. Bolton · 2020
Cited alongside, same era.
R. Kurinchi-Vendhan, B. Lütjens, R. Gupta, L. Werner, and D. Newman · 2021
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Spatio-temporal downscaling of climate data using convolutional and error-predicting neural networks
A. Serifi, T. Günther, and N. Ban · 2021
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Fast and accurate learned multiresolution dynamical downscaling for precipitation
J. Wang, Z. Liu, I. Foster, W. Chang, R. Kettimuthu, and V. R. Kotamarthi · 2021
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Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models , chapter 20, pages 298–306
L. Zanna and T. Bolton · 2021
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Downscaling atmospheric chemistry simulations with physically consistent deep learning
A. Geiss, S. Silva, and J. Hardin · 2022
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Physics-informed learning of aerosol microphysics, 2022
P. Harder, D. Watson-Parris, P. Stier, D. Strassel, N. R. Gauger, and J. Keuper · 2022
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Physically constrained generative adversarial networks for improving precipitation fields from earth system models
P. Hess, M. Drüke, S. Petri, F. M. Strnad, and N. Boers · 2022
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Strictly enforcing invertibility and conservation in cnn-based super resolution for scientific datasets
A. Geiss and J. C. Hardin · 2023
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