## 2 VIIRS Satellite Data
### Overview of the Two Retrieval Algorithms over Land  
The VIIRS_EDR and VIIRS_ML algorithms are similar in many ways. Both algorithms start with the satellite measurements of spectral reflectance at the top-of-atmosphere (TOA), and are compared to a look-up table (LUT) to determine the most plausible solutions for aerosol and surface properties. The measured reflectance at the TOA is a summation of scattering events from the surface and the atmosphere. In both algorithms, the aerosol optical properties of the aerosol models are essential for radiative transfer computing to generate the atmospheric LUT that is needed for AOD retrievals. However, there are also differences. The VIIRS_EDR algorithm has the heritage from the MODIS atmospheric correction algorithm for land surface reflectance, in which the expected surface reflectance ratio at different wavelengths are used as a constraint in retrieving AOD [20; 21]. In contrast, VIIRS_ML has the heritage from the MODIS Collection-6 aerosol algorithm, in which surface reflectance ratios at different wavelengths are prescribed and the TOA reflectance is used as the most important constraints [8; 22]. Aerosol model type assignment in the two algorithms is also different. In VIIRS_EDR algorithm, the aerosol model type is selected at each pixel for each inversion by using extra blue wavelengths to constrain the aerosol type, while in the VIIRS_ML algorithm, the aerosol model type is assigned to each region and each season prior to retrieval based on the past cluster analysis of AERONET inversions [22; 23]. In our assessment of the VIIRS AOD in the NCP region, we will also use aerosol properties from CE318 inversions to evaluate the aerosol model types and associated optical properties in the VIIRS_EDR and VIIRS_ML algorithms, and thereby, analyze one likely source for AOD retrieval uncertainties. Based on the climatology of AERONET inversion data, the VIIRS_EDR algorithm defines a set of five microphysical aerosol model types. These five models are denoted as dust (for example, observed at Cape Verde), high absorption smoke (African savanna, Zambia), low absorption smoke (Amazonian forest, Brazil), clean urban (Goddard Space Flight Center, Greenbelt, MD, USA), and polluted urban (Mexico City, Mexico) aerosols. All model types have size distributions defined by bimodal lognormaldistributions of spherical particles [6]. As explained by [PERSON] et al. [6], the retrieval LUT is created by starting with a Mie scattering code, for which aerosol inputs include a real part and an imaginary part of refractive indices and size parameters of aerosol fine and coarse mode (i.e., volume mean radius, standard deviation and volume concentration). During the retrieval, the algorithm selects the aerosol models with the lowest residual which is computed based on deviations between the 412 nm, 445 nm, 488 nm, and 2250 nm surface reflectances predicted from the 672 nm surface reflectance and the computed surface reflectances using the retrieved AOD for that model type. For the VIIRS_ML, aerosol model types are also derived from AERONET inversion climatology. However, the retrieval algorithm uses that information in a different way. [PERSON] et al. clustered and classified AERONET retrieval products into statistics that represented the most likely aerosol conditions for a particular region and season [23]. These aerosol model types are separated into fine-mode dominated (fine model) and coarse-mode dominated (coarse model), and the fine model is further separated into being strongly absorbing, moderately absorbing and weakly absorbing aerosol models. In the classification, the moderately absorbing aerosol model is set as the default, overwritten only if clear dominance of one of the other two aerosol model types is observed. By clustering, it is shown that the single scattering albedo (SSA) values at 670 nm of three fine models is \(\sim\)0.85 for strongly absorbing, \(\sim\)0.90 for moderately absorbing and \(\sim\)0.95 for weakly absorbing and \(\sim\)0.95 for the coarse model.