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

• Main Authors: Liu, Xinyan, He, Tao, Liang, Shunlin, Li, Ruibo, Xiao, Xiongxin, Ma, Rui, Ma, Yichuan
• Format: Text
• Language: English
• Published: 2023
• Subjects: Arctic
• Online Access: https://doi.org/10.5194/essd-2023-10; https://essd.copernicus.org/preprints/essd-2023-10/

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, R 2 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, R 2 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 R 2 , 4.46 % for RMSE and, 3.92 % for bias. The ...