Abstract. Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51 % of VIIRS Environmental Data Record (EDR) AOD, 37 % of GOCI AOD, 33 % of VIIRS Intermediate Product (IP) AOD, 26 % of Terra MODIS C6 3 km AOD, and 16 % of Aqua MODIS C6 3 km AOD fell within the reference expected error (EE) envelope (±0.05 ± 0.15 AOD). Comparing against AERONET AOD over the Japan-South Korea region, 64 % of EDR, 37 % of IP, 61 % of GOCI, 39 % of Terra MODIS, and 56 % of Aqua MODIS C6 3 km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases.

Abstract. Aerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosols and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified MultiAngle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) in the channel 1 of GOES is proportional to seasonal average MODIS BRDF in the 2.1 µm channel. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of AOD and surface reflectance retrievals are evaluated through comparisons against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US with correlation coefficients ranges from 0.71 to 0.85 at five out of six sites. At the two western sites Railroad Valley and UCSB, the MAIAC AOD retrievals have Correspondence to: H. Zhang (hazhang@umbc.edu) correlations of 0.8 and 0.85 with AERONET AOD, and are more accurate than GASP retrievals, which have correlations of 0.7 and 0.74 with AERONET AOD. At the three eastern sites, the correlations with AERONET AOD are from 0.71 to 0.81, comparable to the GASP retrievals. In the western US where surface reflectance is higher than 0.15, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.

Abstract. Aerosol optical depth (AOD) in the western United States is observed independently by both the (Geostationary Operational Environmental Satellites) GOES-East and GOES-West imagers. The GASP (GOES Aerosol/Smoke Product) aerosol optical depth retrieval algorithm treats each satellite as a unique sensor and thus obtains two separate aerosol optical depth values at the same time for the same location. The TOA (the top of the atmosphere) radiances and the associated derived optical depths can be quite different due to the different viewing geometries with large difference in solar-scattering angles. In order to fully exploit the simultaneous observations and generate consistent AOD retrievals from the two satellites, the authors develop a new "hybrid" aerosol optical depth retrieval algorithm that uses data from both satellites. The algorithm uses both GOES-East and GOES-West visible channel TOA reflectance and daily average AOD from GOES Multi-Angle Implementation of Atmospheric Correction (GOES-MAIAC) on low AOD days (AOD less than 0.3), when diurnal variation of AOD is low, to retrieve surface BRDF (Bidirectional Reflectance Distribution Function). The known BRDF shape is applied on subsequent days to retrieve BRDF and AOD. The algorithm is validated at three AERONET sites over the western US. The AOD retrieval accuracy from the "hybrid" technique using the two satellites is similar to that from one satellite over UCSB (University of California Santa Barbara) and Railroad Valley, Nevada. Improvement of the accuracy is observed at Boulder, Colorado. The correlation coefficients between the GOES AOD and AERONET AOD are in the range of 0.67 to 0.81. More than 74% of AOD retrievals are within the error of ±(0.05 + 0.15 τ) compared to AERONET AOD. The hybrid algorithm has more data coverage compared to the single satellite retrievals over surfaces with high surface reflectance. For single observation areas the number of valid AOD data increases from the use of two-single satellite algorithms by 5–80% for the three sites. With the application of the new algorithm, consistent AOD retrievals and better retrieval coverages can be obtained using the data from the two GOES satellite imagers.

Abstract. An Observing Systems Simulation Experiment (OSSE) for GOES-R Advanced Baseline Imager (ABI) aerosol products has been carried out. The generation of simulated data involves prediction of aerosol chemical composition fields at one-hour resolution and 12 km × 12 km spacing. These data are then fed to a radiative transfer model to simulate the on-orbit radiances that the GOES-R ABI will see in six channels. This allows the ABI aerosol algorithm to be tested to produce products that will be available after launch. In cooperation with a user group of 40+ state and local air quality forecasters, the system has been tested in real-time experiments where the results mimic what the forecasters will see after 2016 when GOES-R launches. Feedback from this group has allowed refinement of the web display system for the ABI aerosol products and has creatively called for new products that were not envisaged by the satellite team.