Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review-Reference-Cited by-同舟云学术

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Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review

Published:2024-02 Issue: Volume:208 Page:14-38
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Sun Yongjian^ORCID,Deng Kefeng,Ren Kaijun^ORCID,Liu Jia,Deng Chongjiu,Jin Yongjun

Publisher

Elsevier BV

Reference242 articles.

1. Development of gridded surface meteorological data for ecological applications and modelling;Abatzoglou;Int. J. Climatol.,2013

2. A review of uncertainty quantification in deep learning: Techniques, applications and challenges;Abdar;Inf. Fusion,2021

3. Methodology of the constraint condition in dynamical downscaling for regional climate evaluation: A review;Adachi;J. Geophys. Res. Atmos.,2020

4. TRU-NET: a deep learning approach to high resolution prediction of rainfall;Adewoyin;Mach. Learn.,2021

5. Convolutional Neural Networks;Aghdam,2017

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