Critical evaluation of the MODIS Deep Blue aerosol optical depth product for data assimilation over North Africa

Abstract

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.

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

“…The bias of land AOD is ∼ 0.05 when the CF is close to zero, approaches to zero for 0.05 < CF < 0.25, and increases up to 0.05 as the CF goes to 0.4. This positive bias under high-CF conditions is similar to that of MODIS DT and DB AOD (Hyer et al, 2011;Shi et al, 2013). However, the positive bias of land AOD at CF = 0, which is not observed for MODIS DT and DB AOD, may be due to surface reflectance underestimations over bright surface in GOCI.…”

“…The "diagnostic" expected error (DEE) is based on AERONET AOD, which is more accurate than satellite AOD and is thus more useful in quantitatively evaluating the algorithm, though it is restricted to only the AERONET pixels. Alternatively, the "prognostic" expected error (PEE), a function of retrieved satellite AOD, can be calculated over all retrieved pixels, making it more appropriate for certain applications, such as data assimilation with air-quality forecasting models Shi et al, 2013). A common characteristic Table 4 This study Table 4 This study of EE is that it increases linearly with AOD.…”

Estimating the global abundance of ground level presence of particulate matter (PM2.5)

“…The picture is further complicated by the biases present in satellite AOD products (e.g. Lary et al, 2009;Hyer et al, 2011;Shi et al, 2012;Reid et al, 2013), the difference in spatial scales of the in-situ point PM 2.5 observations and remote sensing data (several km per pixel) and, finally, the sharp PM 2.5 gradients that can exist in and around cities. presented a comprehensive study for the ten Environmental Protection Agency (EPA) regions across USA using multi-linear regression between the PM 2.5 abundance observed by the EPA monitoring sites and the Moderate Resolution Imaging Spectroradiometer (MODIS), AOD and a set of meteorological parameters.…”

Spatio-Temporal Distribution of Aerosol and Cloud Properties over Sindh Using MODIS Satellite Data and a HYSPLIT Model

“…In order to minimize the cloud contamination problem, DB uses AOD spatial variance method to the radiance at 412 nm at every 3 × 3 pixel spatial domain (Hsu et al, 2004). The uncertainty of DB AOD retrievals is expected as ± 0.05 ± 20% × AOD AERONET (Hsu et al, 2006, Huang et al, 2011, Shi et al, 2013. MODIS detailed information on algorithms for the retrieval of aerosol data is available at http://modisatmos.gsfc.nasa.gov.…”