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1. CLDASSD: Reconstructing Fine Textures of the Temperature Field Using Super-Resolution Technology.

Author

Tie, Ruian, Shi, Chunxiang, Wan, Gang, Hu, Xingjie, Kang, Lihua, and Ge, Lingling

Subjects
DOWNSCALING (Climatology), STANDARD deviations, DEEP learning, TEXTURES, MACHINE learning
Abstract

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2022
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2. A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts.

Author

Han, Lei, Chen, Mingxuan, Chen, Kangkai, Chen, Haonan, Zhang, Yanbiao, Lu, Bing, Song, Linye, and Qin, Rui

Subjects
WIND forecasting, DEEP learning, NUMERICAL weather forecasting, STANDARD deviations, GLOBAL modeling systems, CONVOLUTIONAL neural networks
Abstract

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2021
Full Text
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