High Pollution Loadings Influence the Reliability of Himawari-8 Cloud-Mask in Comparison with Space-Based Lidar and Surface Observations
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 anal...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2022/2492567 |
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| author | Wei Wang Pengfei Tong Huihui Feng Weiwei Xu |
| author_facet | Wei Wang Pengfei Tong Huihui Feng Weiwei Xu |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8fddb8142c5e44fab05b4a6abb826e16 |
| institution | Kabale University |
| issn | 1687-9317 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
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| series | Advances in Meteorology |
| spelling | doaj-art-8fddb8142c5e44fab05b4a6abb826e162025-08-20T03:38:08ZengWileyAdvances in Meteorology1687-93172022-01-01202210.1155/2022/2492567High Pollution Loadings Influence the Reliability of Himawari-8 Cloud-Mask in Comparison with Space-Based Lidar and Surface ObservationsWei Wang0Pengfei Tong1Huihui Feng2Weiwei Xu3School of Geosciences and Info-PhysicsSchool of Geosciences and Info-PhysicsSchool of Geosciences and Info-PhysicsSchool of Remote Sensing and Information EngineeringCloud 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.http://dx.doi.org/10.1155/2022/2492567 |
| spellingShingle | Wei Wang Pengfei Tong Huihui Feng Weiwei Xu High Pollution Loadings Influence the Reliability of Himawari-8 Cloud-Mask in Comparison with Space-Based Lidar and Surface Observations Advances in Meteorology |
| title | High Pollution Loadings Influence the Reliability of Himawari-8 Cloud-Mask in Comparison with Space-Based Lidar and Surface Observations |
| title_full | High Pollution Loadings Influence the Reliability of Himawari-8 Cloud-Mask in Comparison with Space-Based Lidar and Surface Observations |
| title_fullStr | High Pollution Loadings Influence the Reliability of Himawari-8 Cloud-Mask in Comparison with Space-Based Lidar and Surface Observations |
| title_full_unstemmed | High Pollution Loadings Influence the Reliability of Himawari-8 Cloud-Mask in Comparison with Space-Based Lidar and Surface Observations |
| title_short | High Pollution Loadings Influence the Reliability of Himawari-8 Cloud-Mask in Comparison with Space-Based Lidar and Surface Observations |
| title_sort | high pollution loadings influence the reliability of himawari 8 cloud mask in comparison with space based lidar and surface observations |
| url | http://dx.doi.org/10.1155/2022/2492567 |
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