Cloud Cover in the Australian Region: Development and Validation of a Cloud Masking, Classification and Optical Depth Retrieval Algorithm for the Advanced Himawari Imager - Qin, Yi; Steven, Andrew D. L.; Schroeder, Thomas; McVicar, Tim R.; Huang, Jing; Cope, Martin; Zhou, Shangzhi (Frontiers in Environmental Science, 2019)
This paper presents a cloud masking, classification and optical depth retrieval algorithm using visible, near-infrared and thermal-infrared bands. A time-series-based approach was developed for cloud masking with quantitative validation against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). An overall hit rate (the proportion of pixels identified by both sensors as either clear or cloudy) of 87% was found. However, analysis revealed that, under partially cloudy conditions, the small footprint of CALIOP had a major impact on the hit rate. When partially cloudy pixels are excluded a hit rate of 98% was found, even for thin clouds with optical depth <0.25. A two-way confidence index for the cloud mask was developed allowing for post-processing reclassification, either biasing towards clearness or cloudiness. On the basis of cloud masking, classification and optical depth retrieval was performed based on radiative transfer modelling. Small modelling error was found, and inspection of typical examples showed consistency between cloud classification, texture and cloud-top-temperatures. While difficult to validate retrieved cloud properties directly, an indirect quantitative validation was performed by comparing surface-level solar flux computed from retrieved cloud properties with in-situ measurements at 11 sites across Australia for up to three years. Excellent agreement between calculated and measured solar flux was found, with a mean monthly bias of 2.96 W/m2 and RMSE of 8.91 W/m2, and r>0.98 at all sites. Further assessment was conducted by comparing seasonal and annual cloud fraction with that of ISCCP (International Satellite Cloud Climatology Project) over Australia and surrounding region. It showed high degree of resemblance between the two datasets in their total cloud fraction. The geographical distribution of cloud classes also showed broad resemblance, though detailed differences exist, especially for high clouds, probably due to the use of different cloud classification systems in the two datasets. The products generated from this study are being used in several applications including ocean colour remote sensing, solar energy, vegetation monitoring and detection of smoke for the study of their health impacts, and aerosol and land surface bidirectional reflectance distribution function (BRDF) retrieval. The method developed herein can be applied to other geostationary sensors.