Abstract
Cloud identification methods of passive sensors are usually on the basis of different thresholds at different wavelengths. However, the high pollution levels may contribute to the misidentification of cloud mask of Advanced Himawari Imager (AHI) carried on Himawari-8. This study comprehensively analyses and demonstrates this possibility by comparing the AHI cloud-masks and space-based lidar observations based on surface observations of air-polluted loadings from January 1, 2016, to December 31, 2019. Therefore, this study comprehensively explores this impact by comparing the AHI cloud-masks and space-based lidar observations by using surface observations of air-polluted loadings from January 1, 2016, to December 31, 2019. Case studies that compare the two sensors indicate that the performance of AHI cloud detection is degenerative during aerosol events. Long-term statistical analysis demonstrates that the average hit ratio of clear (cloud) between the two sensors during the period is 79% (63%) and the consistency (hit rate) of cloud-mask between AHI and CALIOP decreases with increasing pollution levels. On the contrary, the low uncertainty ratios with 15% of cloud and 3% of clear exist in low PM2.5 levels (lower than 40 μg/m3), while the high uncertainty ratios with 47% of cloud and 15% of clear exist in high PM2.5 levels (higher than 130 μg/m3). Therefore, results demonstrate that the reliability of AHI cloud-mask is weakened by high air-polluted levels. Further improvement of AHI cloud-mask algorithm is desired because AHI products with high temporal resolution are vital in several related fields, such as climate change, aerosol-cloud interaction, and air-polluted mapping.
Highlights
Clouds have a significant influence on the radiation balance of the Earth by reflecting the short-wave radiation from the sun, absorbing and emitting infrared radiation, and mainly affect regional precipitation and other environmental conditions [1, 2]. erefore, developing advanced tools to monitor spatiotemporal evolution of cloud accurately and effectively around the Earth is important [3–5].Satellite-based sensors break through the spatial limitations of site-based observation; they can derive the cloud parameters on a large spatial scale, such as MODIS (Moderate Resolution Imaging Spectroradiometer) [2, 6]
Advanced Himawari Image (AHI), a new generation of high-performance sensor carried by meteorological satellite Himawari-8 launched by Japan in October 2014 [7], can detect cloud parameters with 10-minute resolutions in East Asia [8, 9]
This study aims to explore the impact of aerosol on the reliability of its Himawari-8/AHI cloud-mask by an active sensor with an excellent cloud identification ability, namely, Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) carried by Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) [16, 17]
Summary
Erefore, developing advanced tools to monitor spatiotemporal evolution of cloud accurately and effectively around the Earth is important [3–5]. Satellite-based sensors break through the spatial limitations of site-based observation; they can derive the cloud parameters on a large spatial scale, such as MODIS (Moderate Resolution Imaging Spectroradiometer) [2, 6]. In recent years, owing to the development of geostationary satellites, cloud layers are monitored with high temporal resolution for a large-scale region. Advanced Himawari Image (AHI), a new generation of high-performance sensor carried by meteorological satellite Himawari-8 launched by Japan in October 2014 [7], can detect cloud parameters with 10-minute resolutions in East Asia [8, 9]. Himawari-8 cloud product is important to the research on weather forecast and environmental monitoring [10, 11]; it provides a key parameter for aerosol retrievals [1, 12, 13]. Some studies point out that the performances of AHI aerosol retrievals from cloud edge are lower than those from clear pixels. is result implies that the AHI cloud-mask product should be evaluated prior to application in scientific research and be improved as quickly as possible
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