Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

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使用机器学习算法和天基激光雷达数据进行气溶胶和云探测

云和气溶胶在确定总体大气辐射预算中起着重要作用，但在理解和预测未来气候系统方面仍然存在关键的不确定性。除对地球气候系统的影响外，火山喷发，野火，人为污染事件和沙尘暴产生的气溶胶还对航空安全和人类健康构成危害。天基激光雷达系统提供有关云和气溶胶垂直分布的重要信息，可极大地增进我们对气候系统的了解。但是，来自反向散射激光雷达的白天数据，例如国际空间站（ISS）上的云气溶胶传输系统（CATS），必须在科学处理期间以空间分辨率为代价对平均值进行平均，以获得足够的信噪比（SNR），以准确检测大气特征。例如，白天运行的CATS数据产品中报告的所有大气特征的50％需要平均检测到60 km。此外，CATS主操作模式的单波长性质使得准确键入这些功能在复杂场景中具有挑战性。本文介绍了机器学习（ML）技术，该技术应用于CATS数据时，（1）将1064 nm SNR提高了75％，（2）将检测到的层数（任何分辨率）提高了30％，（3）与白天CATS作战数据产品通常需要的60 km的水平分辨率相比，在白天的操作中，水平分辨率为5 km，可以检测到40％以上的大气特征。使用CATS标准数据产品训练的卷积神经网络（CNN）也证明了与白天用于云边缘和复杂近地面景的可操作CATS算法相比，改进了云气溶胶判别的潜力。