Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,National Satellite Meteorological Center,China Meteorological Administration
the National Key R&D Program of China (2018 YFC1407200,2018YFC1407204) and Chinese Natural Science Foundation (61531019).
Satellite is one of important observation data sources in Arctic. Due to the complex characteristics of atmospheric and underlying surface, it is extremely challenging for developing the cloud mask algorithm and the derived product accuracy in Arctic by satellite passive remote sensing. A summer cloud detection model in Arctic was studied based on FY-3D/MERSI-II (FengYun-3D/Medium Resolution Spectral Imager-II). Combined with the observations infrared cloud detection tests for the Arctic summer were proposed. The probability distribution density analysis method was adopted, and the loss rate was introduced to optimize the relevant thresholds. Meanwhile, the relevant thresholds were optimized to develop a cloud detection model based on the confidence levels. The validation results reveal that the cloud detection results are highly consistency with the matched CALIPSO observations. The high confidence levels basically represent the cloudy pixels, while the low values correspond to the clear ones. The case study shows that the ratio of cloudy pixels is 100% with confidence level higher than 0.8. When the confidence level is lower than 0.2, 10.15% of the cloudy pixels are still misjudged as clear pixels, which are primarily single-layer clouds with the cloud top heights between 4 and 6km. This may be caused Pby cirrus clouds and still need further study.