| Discrimination and Validation of Clouds and Dust Aerosol Layers over the Sahara Desert with Combined CALIOP and IIR Measurements |
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| 作者姓名： | LIU Jingjing CHEN Bin HUANG Jianping |
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| 作者单位： | Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000;Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000;Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 |
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| 基金项目： | Supported by the National (Key) Basic Research and Development (973) Program of China (2012CB955301), Fundamental Research Funds for the Central Universities (LZUJBKY-2013-104 and LZUJBKY-2009-k03), Development Program of Changjiang Scholarship and Research Team (IRT1018), and China Meteorological Administration Special Public Welfare Research Fund (GYHY201206009). |
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| 摘 要： | This study validates a method for discriminating between daytime clouds and dust aerosol layers over the Sahara Desert that uses a combination of active CALIOP（Cloud-Aerosol Lidar with Orthogonal Polarization） and passive IIR（Infrared Imaging Radiometer） measurements;hereafter,the CLIM method.The CLIM method reduces misclassification of dense dust aerosol layers in the Sahara region relative to other techniques.When evaluated against a suite of simultaneous measurements from CALIPSO（Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations）,CloudSat,and the MODIS（Moderate-resolution Imaging Spectroradiometer）,the misclassification rate for dust using the CLIM technique is 1.16%during boreal spring 2007.This rate is lower than the misclassification rates for dust using the cloud aerosol discriminations performed for version 2（V2-CAD;16.39%） or version 3（V3-CAD;2.01%） of the CALIPSO data processing algorithm.The total identification errors for data from in spring 2007 are 13.46%for V2-CAD,3.39%for V3-CAD,and 1.99%for CLIM.These results indicate that CLIM and V3-CAD are both significantly better than V2-CAD for discriminating between clouds and dust aerosol layers.Misclassifications by CLIM in this region are mainly limited to mixed cloud-dust aerosol layers.V3-CAD sometimes misidentifies low-level aerosol layers adjacent to the surface as thin clouds,and sometimes fails to detect thin clouds entirely.The CLIM method is both simple and fast,and may be useful as a reference for testing or validating other discrimination techniques and methods.
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| 关 键 词： | 撒哈拉地区 同时测量 IIR 胶层 沙尘 验证 沙漠 中分辨率成像光谱仪 |
| 收稿时间： | 2013/10/12 0:00:00 |
| 修稿时间： | 2013/12/13 0:00:00 |
Discrimination and Validation of Clouds and Dust Aerosol Layers over the Sahara Desert with Combined CALIOP and IIR Measurements |
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| LIU Jingjing,CHEN Bin,HUANG Jianping.Discrimination and Validation of Clouds and Dust Aerosol Layers over the Sahara Desert with Combined CALIOP and IIR Measurements[J].Acta Meteorologica Sinica,2014,28(2):185-198. |
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| Authors: | LIU Jingjing CHEN Bin and HUANG Jianping |
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| Institution: | 1. Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
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| Abstract: | This study validates a method for discriminating between daytime clouds and dust aerosol layers over the Sahara Desert that uses a combination of active CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) and passive IIR (Infrared Imaging Radiometer) measurements; hereafter, the CLIM method. The CLIM method reduces misclassification of dense dust aerosol layers in the Sahara region relative to other techniques. When evaluated against a suite of simultaneous measurements from CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), CloudSat, and the MODIS (Moderate-resolution Imaging Spectroradiometer), the misclassification rate for dust using the CLIM technique is 1.16% during boreal spring 2007. This rate is lower than the misclassification rates for dust using the cloud aerosol discriminations performed for version 2 (V2-CAD; 16.39%) or version 3 (V3-CAD; 2.01%) of the CALIPSO data processing algorithm. The total identification errors for data from in spring 2007 are 13.46% for V2-CAD, 3.39% for V3-CAD, and 1.99% for CLIM. These results indicate that CLIM and V3-CAD are both significantly better than V2-CAD for discriminating between clouds and dust aerosol layers. Misclassifications by CLIM in this region are mainly limited to mixed cloud-dust aerosol layers. V3-CAD sometimes misidentifies low-level aerosol layers adjacent to the surface as thin clouds, and sometimes fails to detect thin clouds entirely. The CLIM method is both simple and fast, and may be useful as a reference for testing or validating other discrimination techniques and methods. |
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| Keywords: | CALIPSO CLIM dust detection |
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