# 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
#### 3.2.2 Case Study 1: Smoke Detection  
The aerosol detection algorithm code was run using the 27 July 2006 simulated TOA reflectance data as inputs. The 412 nm and 440 nm simulated observations were derived for the TEMPO domain and viewing geometry, while the 2250 nm simulated observations were derived for the GOES-R domain and viewing geometry, assuming 137\({}^{\circ}\) W as the sub-satellite longitude. The GOES-R observations falling within each TEMPO pixel were averaged, such that the TOA reflectance at all three channels were on the same grid. Figure 12 shows aerosol detection results for the simulated data on 27 July 2006 at 20 UTC, corresponding to afternoon for the western US. The shading in Figure 12a indicates the simulated AOD at 550 nm, and the purple curves outline the areas that were detected as smoke. Please note that only pixels with 550 nm AOD greater than 0.2 are shaded in the figures because this is the established detection limit for the aerosol detection algorithm [58]. The shading in Figure 12b indicates the contribution of smoke AOD (defined as the sum of BC and OC AOD) to the total model AOD. The figures show that the VIIRS aerosol detection algorithm identifies most areas of simulated dense smoke. This indicates that (1) the optical properties of the smoke aerosols in the G5 NR simulation are consistent with the MODIS observations from which AAI thresholds were derived for the aerosol detection algorithm, and (2) combining TEMPO and GOES-R observations to characterize smoke aerosol is a viable strategy. However, Figure 13a shows that some missed detections of dense smoke, such as in northeast Nevada, occur even in areas with AAI greater than 5, the AAI threshold for identifying smoke. This is because the DSDI is above the threshold to distinguish dust from smoke (DSDI must be less than \(-3\)) (Figure 14a). Figure 14b shows that if the DSDI were calculated with hypothetical 2250 nm observations from the TEMPO viewpoint, i.e., the same viewing geometry as 412 nm reflectance, consistent with how the algorithm was developed, the DSDI would be below the threshold to identify smoke in most of the missed detection areas. The lower 2250 nm reflectance at the TEMPO geometry is strongly driven by reduced bidirectional surface reflectance in the simulations when going from the GOES-R to TEMPO viewing geometries (Figure 15). This effect is more significant where there is elevated surface reflectance (Figure 13b), as less of the TOA reflectance comes from aerosol scattering. For greater accuracy, re-training the algorithm thresholds, especially angular dependent thresholds, for the combined GOES-R and TEMPO viewing geometries is recommended. Figure 12: (**a**) Simulated 550 nm AOD, (**b**) fraction of simulated 550 nm AOD coming from smoke for 27 July 2006 at 21 UTC in the GSNR simulation. The purple contours represent the areas that were detected as smoke by the VIIRS aerosol detection algorithm. Figure 13: **(a)** Simulated Aerosol Absorbing Index (AAI), **(b)** TEMPO 412 nm surface reflectance used for AAI and DSDI calculations for 27 July 2006 at 21 UTC in the G5 NR simulation. The purple contours represent the areas that were detected as smoke by the VIIRS aerosol detection algorithm. Figure 14: (**a**) Simulated Dust-Smoke Discrimination Index (DSDI) using combined TEMPO 412 nm reflectance and GOES-R 2250 nm reflectance, (**b**) DSDI using only TEMPO 412 nm and 2250 nm reflectance for 27 July 2006 at 21 UTC in the GSNR simulation. The purple contours represent the areas that were detected as smoke by the VIIRS aerosol detection algorithm. Figure 15: (**a**) Simulated GOES-R 2250 nm surface reflectance re-gridded to the TEMPO resolution, and (**b**) TEMPO 2250 nm surface reflectance for 27 July 2006 at 21 UTC in the G5 NR simulation. The purple contours represent the areas that were detected as smoke by the VIIRS aerosol detection algorithm.