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

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

doi:https://doi.org/10.5194/essd-14-795-2022

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:

Extreme environment datasets for the three poles

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

Data sets

Arctic Snow Water Equivalent Grid Dataset (1979-2019) Hongyi Li, Donghang Shao, Haojie Li, Weiguo Wang, Yuan Ma, and Huajin Lei http://dx.doi.org/10.11888/Snow.tpdc.271556

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.