On the Misclassification of Dust as Cloud at an AERONET Site in the Sonoran Desert
On the Misclassification of Dust as Cloud at an AERONET Site in the Sonoran Desert
复制标题
索诺兰沙漠 AERONET 站点将尘埃错误分类为云
DOI：
10.1175/jtech-d-21-0114.1
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发表时间：
2022
影响因子：
2.2
通讯作者：
Frouin, Robert
中科院分区：
文献类型：
--
作者：
Evan, Amato;Walkowiak, Blake;Frouin, Robert
在这里，我们提出的气溶胶光学厚度τ的反演气溶胶机器人网络（AERONET）站在东南角的加州，一个地区的沙尘暴频繁。通过将AERONET数据与同位云高仪测量、相机图像和卫星数据相结合，我们发现，在重大沙尘暴期间，AERONET云筛选算法经常将沙尘测量结果分类为云污染，从而将其从气溶胶记录中删除。在沙尘暴期间，我们估计约有85%的τ的所有尘埃检索和超过95%的检索时，τ> 0.1被拒绝，导致沙尘暴平均τ的因子2减少。我们记录的具体组成部分的筛选算法负责的错误分类。我们发现，这些尘埃测量的损失的一个主要原因是高的时间变化τ在沙尘暴通过的网站，这本身是有关的网站的排放位置的接近度。我们描述了一种方法来恢复这些尘土飞扬的测量，是基于并置云高仪测量。这些结果表明，AERONET网站，位于靠近沙尘源区可能需要辅助测量，以帮助识别dust.Significance StatementIn这项研究中，我们证明，在沙尘暴期间，太阳光度计在AERONET网站在西部索诺兰沙漠的测量经常被归类为云污染的网络的处理算法。我们确定了导致错误分类的各种算法测试，并讨论了灰尘通常无法通过这些测试的物理原因。然后，我们提出了一种方法来恢复这些数据，利用测量从一个并置云高仪。这项工作强调了在靠近空气尘埃来源的地区运营AERONET站点所面临的挑战和一种解决方案。
Here we present retrievals of aerosol optical depthτfrom an Aerosol Robotic Network (AERONET) station in the southeastern corner of California, an area where dust storms are frequent. By combining AERONET data with collocated ceilometer measurements, camera imagery, and satellite data, we show that during significant dust outbreaks the AERONET cloud-screening algorithm oftentimes classifies dusty measurements as cloud contaminated, thus removing them from the aerosol record. During dust storms we estimate that approximately 85% of all dusty retrievals ofτand more than 95% of retrievals whenτ> 0.1 are rejected, resulting in a factor-of-2 reduction in dust-storm averagedτ. We document the specific components in the screening algorithm responsible for the misclassification. We find that a major reason for the loss of these dusty measurements is the high temporal variability inτduring the passage of dust storms over the site, which itself is related to the proximity of the site to the locations of emission. We describe a method to recover these dusty measurements that is based on collocated ceilometer measurements. These results suggest that AERONET sites that are located close to dust source regions may require ancillary measurements to aid in the identification of dust.Significance StatementIn this study we demonstrate that, during dust storms, measurements made with a sun photometer at an AERONET site in the western Sonoran Desert are frequently classified as cloud contaminated by the network’s processing algorithm. We identify the various algorithmic tests that result in the misclassification and discuss the physical reasons why dust typically fails those tests. We then present a method to restore these data that utilizes measurements from a collocated ceilometer. This work highlights the challenges, and one solution, to operating an AERONET site in a region that is close to the sources of airborne dust.