On the modern deep learning approaches for precipitation downscaling-Reference-Cited by-同舟云学术

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On the modern deep learning approaches for precipitation downscaling

Published:2023-03-03 Issue:2 Volume:16 Page:1459-1472
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Kumar Bipin,Atey Kaustubh,Singh Bhupendra Bahadur,Chattopadhyay Rajib,Acharya Nachiketa,Singh Manmeet,Nanjundiah Ravi S.,Rao Suryachandra A.

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-023-00970-4.pdf

Reference34 articles.

1. Baño-Medina J, Manzanas R, Gutiérrez JM (2020) 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

2. Cheng J, Kuang Q, Shen C, Liu J, Tan X, Liu W (2020) ResLap: generating high-resolution climate prediction through image super-resolution. IEEE Access 8:39623–39634. https://doi.org/10.1109/ACCESS.2020.2974785

3. Dong C, Loy CC, He K, Tang X (2015) Image super-resolution using deep convolutional networks. CoRR abs/1501.00092

4. Dueben PD, Bauer P (2018) Challenges and design choices for global weather and climate models based on machine learning. Geosci Model Dev 11:3999–4009. https://doi.org/10.5194/gmd-11-3999-2018

5. Gagne DJ, Christensen HM, Subramanian AC, Monahan AH (2020) Machine learning for stochastic parameterization: generative adversarial networks in the Lorenz ’96 Model. J Adv Model Earth Syst 12:e2019MS001896. https://doi.org/10.1029/2019MS001896

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