13 Feb 2023
 | 13 Feb 2023
Status: this preprint is currently under review for the journal ESSD.

A monthly 1-degree resolution dataset of 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

Abstract. The low accuracy of satellite cloud fraction (CF) data over the Arctic seriously restricts the accurate assessment of the regional and global radiative energy balance under a changing climate. Previous studies have reported that no individual satellite CF product could satisfy the needs of accuracy and spatio-temporal coverage simultaneously for long-term applications over the Arctic. Merging multiple CF products with complementary properties can provide an effective way to produce a spatiotemporally complete CF data record with higher accuracy. This study proposed a spatiotemporal statistical data fusion framework based on cumulative distribution function (CDF) matching and the Bayesian maximum entropy (BME) method to produce a synthetic 1°×1° CF dataset in the Arctic during 2000–2020. The CDF matching was employed to remove the systematic biases among multiple passive sensor datasets through the constraint of using CF from an active sensor. The BME method was employed to combine adjusted satellite CF products to produce a spatiotemporally complete and accurate CF product. The advantages of the presented fusing framework are that it not only uses the spatiotemporal autocorrelations but also explicitly incorporates the uncertainties of passive sensor products benchmarked with reference data, i.e., active sensor product and ground-based observations. The inconsistencies of Arctic CF between passive sensor products and the reference data were reduced by about 10–20 % after fusing. Compared with ground-based observations, R2 increased by about 0.20–0.48 and the root mean square error (RMSE) and bias reductions averaged about 6.09 % and 4.04 % for land regions, respectively; these metrics for ocean regions were about 0.05–0.31, 2.85 %, and 3.15 %, respectively. Compared with active sensor data, R2 increased by nearly 0.16, and RMSE and bias declined by about 3.77 % and 4.31 %, respectively, in land; meanwhile, improvements in ocean regions were about 0.3 for R2, 4.46 % for RMSE and, 3.92 % for bias. The comparison with the ERA5 reanalysis and CMIP6 CF datasets shows that the proposed fusion algorithm effectively corrected the CF data with differences greater than 30 %. Moreover, the fused product effectively supplements the temporal gaps of AVHRR-based products caused by satellite faults and the data missing from MODIS-based products prior to the launch of Aqua, and extends the temporal range better than the active product; it addresses the spatial insufficiency of the active sensor data and the AVHRR-based products acquired at latitudes greater than 82.5° N. A continuous monthly 1-degree CF product covering the entire Arctic during 2000–2020 was generated and is freely available to the public at (Liu et al., 2022). This is of great importance for reducing the uncertainty in the estimation of surface radiation parameters and thus helps researchers to better understand the earth’s energy imbalance.

Xinyan Liu et al.

Status: open (until 28 Apr 2023)

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  • RC1: 'Comment on essd-2023-10', Anonymous Referee #1, 22 Mar 2023 reply

Xinyan Liu et al.

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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

Xinyan Liu et al.


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