Abstract. The Yonsei AErosol Retrieval (YAER) algorithm for the Geostationary Ocean Color Imager (GOCI) retrieves aerosol optical properties only over dark surfaces, so it is important to mask pixels with bright surfaces. The Advanced Himawari Imager (AHI) is equipped with three shortwave-infrared and nine infrared channels, which is advantageous for bright-pixel masking. In addition, multiple visible and near-infrared channels provide a great advantage in aerosol property retrieval from the AHI and GOCI. By applying the YAER algorithm to 10 minutes AHI or 1 hour GOCI data at 6 km × 6 km resolution, diurnal variations and aerosol transport can be observed, which has not previously been possible from low-earth-orbit satellites. This study attempted to estimate the optimal aerosol optical depth (AOD) for East Asia by data fusion, taking into account satellite retrieval uncertainty. The data fusion involved two steps: (1) analysis of error characteristics of each retrieved result with respect to the ground-based Aerosol Robotic Network (AERONET), and bias correction based on normalized difference vegetation indexes; and (2) estimation of the fused product using ensemble-mean and maximum-likelihood estimation methods. Fused results show a better statistics in terms of fraction within the expected error, correlation coefficient, root-mean-square error, median bias error than the retrieved result for each product.

中文翻译：

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在2016年KORUS-AQ和2018年EMeRGe活动中整合GOCI和AHI Yonsei气溶胶光学深度产品

摘要。对地静止海洋彩色成像仪（GOCI）的Yonsei AErosol Retrieval（YAER）算法仅在深色表面上检索气溶胶光学特性，因此，对具有明亮表面的像素进行掩膜很重要。先进的Himawari成像仪（AHI）配备了三个短波红外和九个红外通道，这对于明亮像素遮罩是非常有利的。另外，多个可见和近红外通道在从AHI和GOCI的气溶胶特性检索中提供了很大的优势。通过将YAER算法应用于分辨率为6 km×6 km的10分钟AHI或1小时GOCI数据，可以观察到昼夜变化和气溶胶传输，这是低地球轨道卫星以前不可能做到的。这项研究试图通过数据融合来估算东亚的最佳气溶胶光学深度（AOD），考虑到卫星检索的不确定性。数据融合涉及两个步骤：（1）相对于地面气溶胶机器人网络（AERONET）分析每个检索结果的误差特征，以及基于归一化植被指数的偏差校正。（2）使用集合平均和最大似然估计方法估计融合产品。融合的结果显示，与每种产品的检索结果相比，预期误差，相关系数，均方根误差，中位偏差误差中的分数要好。（2）使用集成平均和最大似然估计方法估计融合产品。融合的结果显示，与每种产品的检索结果相比，预期误差，相关系数，均方根误差，中位偏差误差中的分数要好。（2）使用集成平均和最大似然估计方法估计融合产品。融合的结果显示，与每种产品的检索结果相比，预期误差，相关系数，均方根误差，中位偏差误差中的分数要好。