A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products
Authors: Liu Xinyan , He Tao , Liang Shunlin , Li Ruibo , Xiao Xiongxin , Ma Rui , Ma Yichuan 
Published: August 2023

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 spatiotemporal 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, with particularly noticeable improvements in the
vicinity of Greenland. 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 results of the comparison with ERA5 and the Meteorological Research Institute – Atmospheric General Circulation model version 3.2S (MRI-AGCM3-2-S)
climate model suggest an obvious improvement in the consistency between the
satellite-observed CF and the reanalysis and model data after fusion. This
serves as a promising indication that the fused CF results hold the
potential to deliver reliable satellite observations for modeling and
reanalysis data. Moreover, the fused product effectively supplements the
temporal gaps of Advanced Very High Resolution Radiometer (AVHRR)-based products caused by satellite faults and the
data missing from MODIS-based products prior to the launch of Aqua, and it
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∘ CF product covering the entire Arctic during 2000–2020 was
generated and is freely available to the public at
https://doi.org/10.5281/zenodo.7624605 (Liu and He, 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.