Articles | Volume 26, issue 5
https://doi.org/10.5194/acp-26-3321-2026
https://doi.org/10.5194/acp-26-3321-2026
Research article
 | 
04 Mar 2026
Research article |  | 04 Mar 2026

Improving Arctic surface radiation estimation using a nonlinear perturbation model with a fused multi-satellite cloud fraction dataset

Yueming Zheng, Tao He, Yichuan Ma, and Xinyan Liu

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Cited articles

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Short summary
Estimating surface radiation in the Arctic is difficult because cloud conditions are not well captured. We used an advanced learning-based method and a more accurate cloud dataset to correct radiation estimates that are biased by cloud fraction underestimation. The improved results greatly reduce long-standing errors and provide a new and more reliable dataset. This helps researchers better understand Arctic climate change and energy balance.
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