Publications

Reid, JS; Gumber, A; Zhang, JL; Holz, RE; Rubin, JI; Xian, P; Smirnov, A; Eck, TF; O'Neill, NT; Levy, RC; Reid, EA; Colarco, PR; Benedetti, A; Tanaka, T (2022). A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode. REMOTE SENSING, 14(13), 2978.

Abstract
Although satellite retrievals and data assimilation have progressed to where there is a good skill for monitoring maritime Aerosol Optical Depth (AOD), there remains uncertainty in achieving further degrees of freedom, such as distinguishing fine and coarse mode dominated species in maritime environments (e.g., coarse mode sea salt and dust versus fine mode terrestrial anthropogenic emissions, biomass burning, and maritime secondary production). For the years 2016 through 2019, we performed an analysis of 550 nm total AOD(550), fine mode AOD (FAOD(550); also known as FM AOD in the literature), coarse mode AOD (CAOD(550)), and fine mode fraction (eta(550)) between Moderate Resolution Spectral Imaging Radiometer (MODIS) V6.1 MOD/MYD04 dark target aerosol retrievals and the International Cooperative for Aerosol Prediction (ICAP) core four multi-model consensus (C4C) of analyses/short term forecasts that assimilate total MODIS AOD(550). Differences were adjudicated by the global shipboard Maritime Aerosol Network (MAN) and selected island AERONET sun photometer observations with the application of the spectral deconvolution algorithm (SDA). Through a series of conditional and regional analyses, we found divergence included regions of terrestrial influence and latitudinal dependencies in the remote oceans. Notably, MODIS and the C4C and its members, while having good correlations overall, have a persistent +0.04 to +0.02 biases relative to MAN and AERONET for typical AOD(550) values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD(550) are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD(550) and FAOD(550) but perform better in characterizing the CAOD(550). Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved angstrom ngstrom Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD(550) and FAOD(550) high bias, whereas for the C4C, it is likely from secondary overproduction and perhaps numerical diffusion. Fourth, while there is no wind-speed-dependent bias for surface winds <12 m s(-1), the C4C and MODIS AOD(550)s also overestimate CAOD(550) and FAOD(550), respectively, for wind speeds above 12 m/s. Finally, sampling bias inherent in MAN, as well as other circumstantial evidence, suggests biases in MODIS are likely even larger than what was diagnosed here. We conclude with a discussion on how MODIS and the C4C products have their own strengths and challenges for a given climate application and discuss needed research.

DOI:
10.3390/rs14132978

ISSN:
2072-4292