Accuracy Assessment of Aqua-MODIS Aerosol Optical Depth over Coastal Regions: Importance of Quality Flag and Sea Surface Wind Speed

Using data collected from 62 coastal stations worldwide from the Aerosol Robotic Network (AERONET) from 2002-2011, accuracy assessments are made for coastal aerosol optical depth (AOD) retrieved from MODIS aboard the Aqua satellite. It is found that coastal AODs (at 550 nm) characterized respectively by the MODIS Dark Land (Land) surface algorithm, the Open Ocean (Ocean) algorithm, and AERONET all exhibit a log-normal distribution. After filtering by quality flags, the coastal MODIS AODs retrieved from the Land and Ocean algorithms are highly correlated with AERONET (with R 2 ≈0.8), but only the Land algorithm AODs fall within the expected error envelope greater than 66% of the time. Furthermore, the MODIS AODs from the Land algorithm, Ocean algorithm, and combined Land_And_Ocean product show statistically significant discrepancies from their AERONET counterparts in terms of mean, probability density function, and cumulative density function, which suggest a need for future improvement. Without filtering with quality flag, the MODIS Land_And_Ocean AOD dataset can be degraded by 30-50% in terms of mean bias. Overall, the MODIS Ocean algorithm overestimates (underestimates) the coastal AOD by 0.021 (0.029) for AOD < 0.25 (> 0.25), which is shown to be related to the ocean surface wind speed and cloud contamination. The Modern Era Retrospective-Analysis for Research and Applications (MERRA) reveals that wind speeds over the global coastal region (with a mean and median value of 2.94 m s-1 and 2.66 m s-1 respectively) are often slower than the constant 6 m s-1 assumed in the MODIS Ocean algorithm. As a result of high correlation (R 2 >0.98) between the bias in binned MODIS AOD and the corresponding binned wind speed over the coastal sea surface, an empirical scheme for correcting the bias of AOD retrieved from the MODIS Ocean algorithm is formulated and is shown to be effective over the majority of the coastal AERONET stations, and hence can be used in future analysis of AOD trend and MODIS AOD data assimilation.