A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products

Liu, Xinyan; He, Tao; Liang, Shunlin; Li, Ruibo; Xiao, Xiongxin; Ma, Rui; Ma, Yichuan

doi:https://doi.org/10.5194/essd-15-3641-2023

Articles | Volume 15, issue 8

https://doi.org/10.5194/essd-15-3641-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-15-3641-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 8

Data description paper

|

15 Aug 2023

Data description paper |

| 15 Aug 2023

A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products

Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, and Yichuan Ma

Data sets

A long-term monthly dataset of cloud fraction over the Arctic based on multiple satellite products using cumulative distribution function matching and Bayesian maximum entropy Xinyan Liu and Tao He https://doi.org/10.5281/zenodo.7619104

Short summary

We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.