Abstract. While the use and data assimilation (DA) of operational Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace, MODIS is scheduled to sunset in the next year. For data continuity, focus has turned to the development of next generation aerosol products and sensors such as those associated with the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and NOAA-20. Like MODIS algorithms, products from these sensors require their own set of extensive error characterization and correction exercises. This is particularly true in the context of monitoring significant aerosol events that tax an algorithm’s ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar orbiting satellite algorithms to monitor and characterize significant events a Level 3 (L3) product has been developed, using a consistent aggregation methodology, for four years of observations (2016–2019). Included in this product is AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. These MODIS “baseline algorithms” are compared to NASA’s recently released NASA Deep Blue algorithm for use with VIIRS. Using this new dataset, the relative performance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher percentile AOD regions of the world by product, identified those with the highest measured AODs, and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range-in particular for higher AOD events. Most notable are differences in Boreal biomass burning and Saharan dust. Significant percentile biases that must be accounted for when data is used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions on lower boundary condition and aerosol optical models.

中文翻译：

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NASA MODIS 和 VIIRS 气溶胶产品用于数据同化和气候连续性的严重气溶胶事件评估

摘要。虽然中分辨率成像光谱仪 (MODIS) 气溶胶数据的使用和数据同化 (DA) 已司空见惯，但 MODIS 计划在明年停止使用。为了数据连续性，重点已转向开发下一代气溶胶产品和传感器，例如与 Suomi NPOESS 准备项目 (S-NPP) 和 NOAA-20 上的可见红外成像辐射计套件 (VIIRS) 相关的产品和传感器。与 MODIS 算法一样，来自这些传感器的产品需要自己的一套广泛的错误表征和校正练习。在监测显着气溶胶事件的情况下尤其如此，这些事件对算法从气溶胶中分离云的能力提出了要求，并解释了由于气溶胶光学特性的不确定性而加剧的多个散射相关误差。为了研究极轨卫星算法在监测和表征重大事件方面的性能，我们使用一致的聚合方法开发了 3 级 (L3) 产品，用于为期四年（2016-2019 年）的观测。该产品包括 AErosol RObotic NETwork (AERONET)、MODIS 暗目标、深蓝和大气校正 (MAIAC) 算法的多角度实现。这些 MODIS“基线算法”与 NASA 最近发布的用于 VIIRS 的 NASA Deep Blue 算法进行了比较。使用这个新数据集，研究了陆地和海洋算法的相对性能，重点是检测严重事件的相对技能和使用 AERONET 检索的准确性。按产品划分的世界上百分位 AOD 地区的地图，确定了 AOD 测量值最高的那些，并确定了当地标准的高值。虽然 AOD 中的模式在产品之间匹配并且中值到中等 AOD 值匹配良好，但基于采样、算法差异和 AOD 范围的产品之间存在区域相关偏差——尤其是对于较高 AOD 事件。最值得注意的是北方生物质燃烧和撒哈拉尘埃的差异。在趋势研究、数据同化或逆向建模中使用数据时必须考虑的显着百分位偏差。这些偏差因气溶胶状态而异，可能是由于对下边界条件和气溶胶光学模型的检索假设。基于采样、算法差异和 AOD 范围的产品之间存在区域相关的偏差——尤其是对于更高的 AOD 事件。最值得注意的是北方生物质燃烧和撒哈拉尘埃的差异。在趋势研究、数据同化或逆向建模中使用数据时必须考虑的显着百分位偏差。这些偏差因气溶胶状态而异，可能是由于对下边界条件和气溶胶光学模型的检索假设。基于采样、算法差异和 AOD 范围的产品之间存在区域相关的偏差——尤其是对于更高的 AOD 事件。最值得注意的是北方生物质燃烧和撒哈拉尘埃的差异。在趋势研究、数据同化或逆向建模中使用数据时必须考虑的显着百分位偏差。这些偏差因气溶胶状态而异，可能是由于对下边界条件和气溶胶光学模型的检索假设。