Improving GOES Advanced Baseline Imager (ABI) Aerosol Optical Depth (AOD) Retrievals using an Empirical Bias Correction Algorithm

• Main Authors: Zhang, Hai, Kondragunta, Shobha, Laszlo, Istvan, Zhou, Mi
• Format: Text
• Language: English
• Published: 2020
• Subjects: Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/amt-2020-115; https://amt.copernicus.org/preprints/amt-2020-115/

The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multi-band algorithm similar to those of polar-orbiting satellites’ sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Therefore, ABI AOD is expected to have accuracy and precision comparable to MODIS AOD and VIIRS AOD. However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to errors in the land surface reflectance relationship between the bands used in the ABI AOD retrieval algorithm, which vary with respect to the Sun-satellite geometry. To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30-day period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period August 6 to December 31, 2018 are used to validate the bias correction algorithm. For the top 2 qualities ABI AOD, after bias correction, the correlation between ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root mean square error (RMSE) improves from 0.09 to 0.05. These results for the bias corrected top 2 qualities ABI AOD are comparable to those of the uncorrected high-quality ABI AOD. Thus, by using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the area coverage of ABI AOD is substantially increased without loss of data accuracy.