中文摘要：

这研究为在使用活跃 CALIOP (有直角的极化的云喷雾器激光雷达) 和被动 IIR (红外线的成像辐射计) 的联合的撒哈拉沙漠沙漠上在白天云和灰尘喷雾器层之间区别验证一个方法大小；此后， CLIM 方法。CLIM 方法相对另外的技术在撒哈拉沙漠区域减少稠密的灰尘喷雾器层的错误分类。当从 CALIPSO (云喷雾器激光雷达和红外线的开拓者卫星观察) 与同时的大小的一间套房作比较时， CloudSat，和 MODIS (中等决定的成像分光辐射函数) ，为用 CLIM 技术的灰尘的错误分类率在北方的春天期间是 1.16% 2007。这率比错误分类用辨别为版本 2 执行了的云喷雾器为灰尘评价的低(V2CAD；16.39%) 或版本 3 (V3CAD；2.01%) CALIPSO 数据处理算法。为从在春天 2007 的数据的全部的鉴定错误为 V2CAD 是 13.46% ， 3.39% 为 V3CAD，并且 1.99% 为 CLIM。这些结果为在云和灰尘喷雾器层之间区别比 V2CAD 更好显示 CLIM 和 V3CAD 显著地是两个。由在这个区域的 CLIM 的错误分类主要被限制到混合云灰尘喷雾器层。V3CAD 有时作为薄云识别邻近表面的低级喷雾器层，并且有时没能完全检测薄云。CLIM 方法简单、快，并且可能作为为测试或验证另外的辨别技术和方法的一本参考书有用。

英文摘要：

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.