Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 14, issue 2
Articles | Volume 14, issue 2
https://doi.org/10.5194/essd-14-795-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-795-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 2
Data description paper
 | 
21 Feb 2022
Data description paper |  | 21 Feb 2022

Reconstruction of a daily gridded snow water equivalent product for the land region above 45° N based on a ridge regression machine learning approach

Donghang Shao, Hongyi Li, Jian Wang, Xiaohua Hao, Tao Che, and Wenzheng Ji

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Latest update: 13 Dec 2024
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Short summary
The temporal series and spatial distribution discontinuity of the existing snow water equivalent (SWE) products in the pan-Arctic region severely restricts the use of SWE data in cryosphere change and climate change studies. Using a ridge regression machine learning algorithm, this study developed a set of spatiotemporally seamless and high-precision SWE products. This product could contribute to the study of cryosphere change and climate change at large spatial scales.
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Extreme environment datasets for the three poles
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