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Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6677-2022
https://doi.org/10.5194/gmd-15-6677-2022
Development and technical paper
 | 
05 Sep 2022
Development and technical paper |  | 05 Sep 2022

Downscaling atmospheric chemistry simulations with physically consistent deep learning

Andrew Geiss, Sam J. Silva, and Joseph C. Hardin

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Abdal, R., Qin, Y., and Wonka, P.: Image 2 Style-GAN: How to embed images into the Style-GAN latent space?, International Conference on Computer Vision (ICCV), 27 October 2019–2 November 2019, Seoul, Korea, https://doi.org/10.1109/ICCV.2019.00453, 2019. a
Anh, D. T., Van, S. P., Dang, T. D., and Hoang, L. P.: Downscaling rainfall using deep learning long short-term memory and feedforward neural network, Int. J. Climatol., 39, 4170–4188, https://doi.org/10.1002/joc.6066, 2019. a
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020. a
Bastidas, A. A. and Tang, H.: Channel Attention Networks, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 16–17 June 2019, Long Beach, CA, USA, 881–888, https://doi.org/10.1109/CVPRW.2019.00117, 2019. a
Bedia, J., Baño-Medina, J., Legasa, M. N., Iturbide, M., Manzanas, R., Herrera, S., Casanueva, A., San-Martín, D., Cofiño, A. S., and Gutiérrez, J. M.: Statistical downscaling with the downscaleR package (v3.1.0): contribution to the VALUE intercomparison experiment, Geosci. Model Dev., 13, 1711–1735, https://doi.org/10.5194/gmd-13-1711-2020, 2020. a
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This work demonstrates the use of modern machine learning techniques to enhance the resolution...
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