SeaWiFS Ocean Aerosol Retrieval (SOAR): Algorithm, validation, and comparison with other data sets

Date

2012-02-15

Department

Program

Citation of Original Publication

Sayer, A. M., N. C. Hsu, C. Bettenhausen, Z. Ahmad, B. N. Holben, A. Smirnov, G. E. Thomas, and J. Zhang. “SeaWiFS Ocean Aerosol Retrieval (SOAR): Algorithm, Validation, and Comparison with Other Data Sets.” Journal of Geophysical Research: Atmospheres 117, no. D3 (2012). https://doi.org/10.1029/2011JD016599.

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain

Abstract

The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) provides a well-calibrated 13-year (1997–2010) record of top-of-atmosphere radiance, suitable for use in retrieval of atmospheric aerosol optical depth (AOD). This paper presents and validates a SeaWiFS Ocean Aerosol Retrieval (SOAR) algorithm, which retrieves the AOD at 550 nm and the partition of aerosol particle volume between fine and coarse modes. The algorithm has been applied over water to the whole SeaWiFS record. The data set includes quality flags to identify those retrievals suitable for quantitative use. SOAR has been validated against Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) data and found to compare well (correlation 0.86 at 550 nm and 0.88 at 870 nm for AERONET, and 0.87 at 550 nm and 0.85 at 870 nm for MAN, using recommended quality control settings). These comparisons are used to identify the typical level of uncertainty on the AOD, estimated as 0.03 + 15% at 550 nm and 0.03 + 10% at 870 nm. The data set also includes the Ångström exponent, although as expected this is noisy for low aerosol loadings (correlation 0.50; 0.78 for points where the AOD at 550 nm is 0.3 or more). Retrieved AOD is compared with colocated observations from other satellite sensors; regional and seasonal patterns are found to be common between all data sets, and differences generally linked to factors such as cloud screening and retrieval assumptions.