Abstract.As an update to our previous use of the collection 4 Moderate Resolution Imaging Spectroradiometer (MODIS) over-ocean aerosol optical depth (AOD) data, we examined ten years of Terra and eight years of Aqua collection 5 data for its potential usage in aerosol assimilation. Uncertainties in the over-ocean MODIS AOD were studied as functions of observing conditions, such as surface characteristics, aerosol optical properties, and cloud artifacts. Empirical corrections and quality assurance procedures were developed and compared to collection 4 data. After applying these procedures, the Root-Mean-Square-Error (RMSE) in the MODIS Terra and Aqua AOD are reduced by 30% and 10-20%, respectively, with respect to AERONET data. Ten years of Terra and eight years of Aqua quality-assured level 3 MODIS over-ocean aerosol products were produced. The newly developed MODIS over-ocean aerosol products will be used in operational aerosol assimilation and aerosol climatology studies, as well as other research based on MODIS products.

Abstract. While stand alone satellite and model aerosol products see wide utilization, there is a significant need in numerous atmospheric and climate applications for a fused product on a regular grid. Aerosol data assimilation is an operational reality at numerous centers, and like meteorological reanalyses, aerosol reanalyses will see significant use in the near future. Here we present a standardized 2003–2013 global 1 × 1° and 6-hourly modal aerosol optical thickness (AOT) reanalysis product. This data set can be applied to basic and applied Earth system science studies of significant aerosol events, aerosol impacts on numerical weather prediction, and electro-optical propagation and sensor performance, among other uses. This paper describes the science of how to develop and score an aerosol reanalysis product. This reanalysis utilizes a modified Navy Aerosol Analysis and Prediction System (NAAPS) at its core and assimilates quality controlled retrievals of AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Multi-angle Imaging SpectroRadiometer (MISR) on Terra. The aerosol source functions, including dust and smoke, were regionally tuned to obtain the best match between the model fine- and coarse-mode AOTs and the Aerosol Robotic Network (AERONET) AOTs. Other model processes, including deposition, were tuned to minimize the AOT difference between the model and satellite AOT. Aerosol wet deposition in the tropics is driven with satellite-retrieved precipitation, rather than the model field. The final reanalyzed fine- and coarse-mode AOT at 550 nm is shown to have good agreement with AERONET observations, with global mean root mean square error around 0.1 for both fine- and coarse-mode AOTs. This paper includes a discussion of issues particular to aerosol reanalyses that make them distinct from standard meteorological reanalyses, considerations for extending such a reanalysis outside of the NASA A-Train era, and examples of how the aerosol reanalysis can be applied or fused with other model or remote sensing products. Finally, the reanalysis is evaluated in comparison with other available studies of aerosol trends, and the implications of this comparison are discussed.

[1] A band of enhanced aerosol optical depth (AOD) over the mid-to-high latitude Southern Oceans exists in some passive satellite-based aerosol data sets, including Moderate Resolution Imaging Spectroradiometer (MODIS) products. Past studies suggest several potential causes contributing to this phenomenon, including signal uncertainty, retrieval bias, and cloud contamination. In this paper, quality-assured Aqua MODIS aerosol products in this zonal band are investigated to assess cloud contamination as a cause. Spatially and temporally collocated cloud and aerosol products produced by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) project relative to Aqua MODIS AOD in this region are considered. Maritime Aerosol Network (MAN) and Aerosol Robotic Network (AERONET) AOD data are also collocated with Aqua MODIS retrievals for surface context. The results of this study indicate that the high Aqua MODIS AOD are not seen in the CALIOP aerosol products, cannot be screened using active profiling of collocated observations for cloud presence, and are not detected by ground-based observations such as MAN and AERONET. Enhanced AOD values are attributable primarily to stratocumulus and low broken cumulus cloud contamination, as identified with CALIOP products. But these clouds explain only about 30-40% of the total anomaly. Cirrus cloud contamination is also a factor. However, in contrast to the rest of the globe, they contribute less overall, relative to low-level liquid water clouds, which are considered likely the result of misidentification of relatively warm cloud tops compared with surrounding open seas.

Abstract. AErosol RObotic NETwork (AERONET) data are the primary benchmark for evaluating satellite-retrieved aerosol properties. However, despite its extensive coverage, the representativeness of the AERONET data is rarely discussed. Indeed, many studies have shown that satellite retrieval biases have a significant degree of spatial correlation that may be problematic for higher-level processes or inverse-emissions-modeling studies. To consider these issues and evaluate relative performance in regions of few surface observations, cross-comparisons between the Aerosol Optical Depth (AOD) products of operational MODIS Collection 5.1 Dark Target (DT) and operational MODIS Collection 5.1 Deep Blue (DB) with MISR version 22 were conducted. Through such comparisons, we can observe coherent spatial features of the AOD bias while sidestepping the full analysis required for determining when or where either retrieval is more correct. We identify regions where MODIS to MISR AOD ratios were found to be above 1.4 and below 0.7. Regions where lower boundary condition uncertainty is likely to be a dominant factor include portions of Western North America, the Andes mountains, Saharan Africa, the Arabian Peninsula, and Central Asia. Similarly, microphysical biases may be an issue in South America, and specific parts of Southern Africa, India Asia, East Asia, and Indonesia. These results help identify high-priority locations for possible future deployments of both in situ and ground based remote sensing measurements. The Supplement includes a kml file.

Abstract. Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) collection 5.1 (c5.1) aerosol optical depth (AOD) data were analyzed and evaluated for the first time from an independent research group using eight years of Terra (2000–2007) and Aqua (2002–2009). Uncertainties in the DB AOD were identified and studied, and our results show that the performance of DB c5.1 is strongly dependent on surface albedo and aerosol microphysics. Using data with only "very good" quality assurance, the root-mean-square error (RMSE) of the DB Terra (Aqua) AOD is 0.24 (0.19) when validated against AERONET. Expanding upon the uncertainty analysis, the potential of applying the DB products for aerosol assimilation was explored. Empirical corrections and quality assurance procedures were developed for North Africa and the Arabian Peninsula to create a data assimilation (DA)-quality DB product. After applying those procedures, the RMSE is reduced by 18.1% (18.2%) for Terra (Aqua) DB data. Prognostic error models of 0.069 + 0.175 × AODTerra_DB with no noise floor and 0.048 + 0.182 × AODAqua_DB with a noise floor of 0.104 were found for DA-quality Terra and Aqua DB data, respectively. These procedures were also applied to two months of DB collection 6 (c6) AOD data, and reductions in RMSE were found, indicating that the algorithms developed for c5.1 data are applicable to c6 data to some extent.