We present a new approach to determine aerosol properties from radiometrically calibrated images provided by an all-sky camera. It is designed to be used regardless of the sky conditions. However, we especially focus on partially cloudy scenes, which is the main novelty of this work. Our methodology is based on using a small sector of the image that contains the principal plane of the Sun. The RGB principal plane radiances are associated to the aerosol optical depth (AOD) and Angstrom exponent (AE) AERONET observations through a Gaussian Process Regression (GPR) machine learning (ML) model. We identify the cloudy points within our working sector and the principal plane signal for the RGB radiances is averaged and smoothed. Then, we use the Pérez model to synthesize the principal plane signal in the cloudy spots. Finally, 2-year dataset has been used to test the method considering different atmospheric conditions related to the presence of clouds and aerosols, according to their amount and type. In addition, we have developed a method to evaluate the quality of predictions based on the standard deviation of the GPR. This quality assurance method may be fine-tuned according to the desired accuracy based on the application for which it is intended. Our AOD and AE predictions show an excellent overall agreement with AERONET measurements that substantially improves when our quality assurance method is applied. In that case, we obtain a high degree of correlation ( ¿ 0.97) and an overall MAE lower than the nominal uncertainty of AERONET measurements (0.006 and 0.05 for AOD and AE, respectively). Moreover, more than 83% and 77% of the predictions fall within the nominal uncertainty associated with AERONET measurements for AOD and AE, respectively. A comprehensive sensitivity analysis of the factors affecting the performance of the proposed methodology confirms that our method is stable and not very sensitive to external and methodological factors, especially when we apply quality assurance criteria. All this supports that our methodology is a reliable alternative to retrieve the optical properties of aerosols independently of the cloud conditions. Our results may contribute to the operational use of all-sky cameras, which may be an interesting complement regarding the study of aerosol-cloud interactions in partially cloud scenarios.

中文翻译：

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从全天相机图像确定气溶胶特性的改进方法：对部分云场景的敏感性

我们提出了一种新方法，可以根据全天相机提供的辐射校准图像来确定气溶胶特性。它的设计目的是无论天空条件如何都可以使用。然而，我们特别关注部分阴天的场景，这是这项工作的主要新颖之处。我们的方法基于使用包含太阳主平面的图像的一小部分。 RGB 主平面辐射率通过高斯过程回归 (GPR) 机器学习 (ML) 模型与气溶胶光学深度 (AOD) 和埃罗姆指数 (AE) AERONET 观测结果相关联。我们识别工作区域内的浑浊点，并对 RGB 辐射率的主平面信号进行平均和平滑处理。然后，我们使用Pérez模型来合成阴斑处的主平面信号。最后，根据云和气溶胶的数量和类型，考虑了与云和气溶胶的存在相关的不同大气条件，使用两年数据集来测试该方法。此外，我们还开发了一种基于探地雷达标准差来评估预测质量的方法。这种质量保证方法可以根据基于其预期应用的所需精度进行微调。我们的 AOD 和 AE 预测与 AERONET 测量结果显示出良好的总体一致性，当应用我们的质量保证方法时，这种一致性会得到显着改善。在这种情况下，我们获得了高度相关性 (¿ 0.97)，并且总体 MAE 低于 AERONET 测量的标称不确定度（AOD 和 AE 分别为 0.006 和 0.05）。此外，超过 83% 和 77% 的预测分别落在与 AOD 和 AE 的 AERONET 测量相关的名义不确定性范围内。对影响所提出方法学性能的因素进行全面的敏感性分析证实，我们的方法是稳定的，并且对外部和方法论因素不太敏感，特别是当我们应用质量保证标准时。所有这些都表明我们的方法是独立于云条件检索气溶胶光学特性的可靠替代方案。我们的结果可能有助于全天摄像机的操作使用，这可能是对部分云场景中气溶胶-云相互作用研究的有趣补充。