Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78-0.81; mean RMSE = 0.013-0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications.

中文翻译：

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使用深度学习和降尺度对 MAIAC AOD 进行时空插补

气溶胶对健康有不利影响，并且对气候也起着重要作用。大气校正的多角度实施 (MAIAC) 提供高时间（每日）和空间（1 公里）分辨率的气溶胶光学深度 (AOD)，使其特别有助于在精细空间尺度上推断和表征气溶胶的时空变化，以进行暴露评估和健康研究。然而，云层和高表面反射率条件会导致 MAIAC AOD 丢失很大一部分。为了填补这些空白，我们提出了一种使用深度学习和缩小尺度的插补方法。使用基线自动编码器，我们利用深度神经网络中的残差连接来促进学习和参数共享以减少过度拟合，并进行装袋以减少插补中的误差方差。通过类似的基于自动编码器的深度残差网络进行缩小，现代时代研究和应用回顾分析第 2 版 (MERRA-2) GMI 重放模拟 (M2GMI) 数据被引入网络，作为一项重要的间隙填充功能，该功能在用于缺失插补的空间。估算 2000 年至 2016 年加利福尼亚州这一具有相当大地理异质性的州的每周 MAIAC AOD，我们的完整（非完整）残差网络在一项独立测试中实现了平均值 R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)]，显示出相当大的差异比常规神经网络或非线性广义加性模型具有更好的性能（平均 R2 = 0.78-0.81；平均 RMSE = 0.013-0.015）。调整后的估算以及综合估算和观察到的 MAIAC AOD 显示出与气溶胶机器人网络 (AERONET) AOD 的强相关性（R = 0.83；R2 = 0.69，RMSE = 0.04）。我们的结果表明，我们可以通过深度学习方法对缺失的 AOD 进行可靠的估算，并具有重要的下游空气质量建模应用。