# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 3 Results
### Application of Synthetic Observations to Study the Synergy between TEMPO and GOES-R
#### 3.2.1 Overview of the VIIRS Aerosol Detection Algorithm  
The aerosol detection algorithm used in the case study was initially developed for the Visible Infrared Imaging Radiometer Suite (VIIRS), a key instrument on-board the Suomi National Polar-Orbiting Partnership (SNPP) spacecraft [58]. In the algorithm, the presence of absorbing aerosol is detected by a decrease in the spectral contrast between light reflected at two neighboring deep-blue wavelengths observed by VIIRS (412 nm and 440 nm) relative to a Rayleigh scattering only atmosphere. This spectral contrast is quantified by the Absorbing Aerosol Index (AAI), defined as:  
\[AAI=-100[\log_{10}\frac{R_{412}}{R_{440}}-\log_{10}\frac{R^{{}^{\prime}}_{412 }}{R^{{}^{\prime}}_{440}}] \tag{23}\]  
where \(R_{412}\) and \(R_{440}\) are the observed TOA reflectance at 412 and 440 nm, respectively, and \(R^{{}^{\prime}}_{412}\) and \(R^{{}^{\prime}}_{440}\) are the TOA reflectance for a Rayleigh scattering atmosphere at 412 and 440 nm calculated with the 6S RTM [59] for the given location and satellite viewing geometry. Observations with large values of AAI indicate the presence of enhanced aerosol absorption.  
Because of the large particle size, dust scatters radiation in the shortwave IR. This can be used to distinguish dust from other types of absorbing aerosol (i.e., smoke), which have small particle size and are mostly transparent in this spectral range. The dust smoke discrimination index (DSDI), defined in Equation (24), was developed to aid in separating the dust from smoke, and describes the spectral contrast between TOA reflectance at 412 nm (\(R_{412}\)) and 2250 nm (\(R_{2250}\)). Observations with large values of both AAI and DSDI indicate enhanced absorption and scattering by dust.  
\[DSDI=-10[\log_{10}\frac{R_{412}}{R_{2250}}] \tag{24}\]Optimal thresholds for AAI and DSDI that discriminate between cases of enhanced AOD of dust and smoke from clean atmosphere were determined by analyzing MODIS Aqua observations from 2002-2012 in which smoke and dust events could be identified (i.e., supervised training) [58]. Using these thresholds, the VIIRS aerosol detection algorithm flags pixels as smoke, dust, or clear/undetermined. The algorithm was validated with (1) the CALIOP Vertical Feature Mask product [60], which identifies aerosol types (including smoke and dust), and (2) AERONET inversion data, which provides Angstrom Exponent observations, from which aerosols with small particle size (smoke) and aerosols with large particle size (dust) aerosols can be identified [61; 62]. The validation indicated that smoke and dust with AOD > 0.2 can be detected by the algorithm to within an accuracy of 80 and 70%, respectively [58].  
The GOES-R Series instruments do not have observations in the deep-blue, as the shortest channel is 470 nm, precluding absorbing aerosol detection using the AAI. However, TEMPO will have measurements in the UV-Vis, though it will lack the short-wave IR observation needed to discriminate dust from smoke. Thus, a joint TEMPO/GOES-R aerosol type detection algorithm will be needed. The simulated observations derived from the G5 NR simulation provide a unique dataset to study and demonstrate this potential synergy between GOES-R and TEMPO observations. For this end, we applied the VIIRS aerosol detection algorithm to the 412 nm and 440 nm simulated observations from TEMPO, and the 2250 nm observation from GOES-R. No modifications were made to the algorithm, which assumes that surface reflectance is Lambertian. In the next section we will show that this can lead to lower algorithm accuracy for the viewing geometries of TEMPO and GOES-R.