A Coupled Evaluation of Operational MODIS and Model Aerosol Products for Maritime Environments Using Sun Photometry: Evaluation of the Fine and Coarse Mode

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 AOD550, fine mode AOD (FAOD550; also known as FM AOD in the literature), coarse mode AOD (CAOD550), and fine mode fraction (η550) 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 AOD550. 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 AOD550 values (84th% < 0.28), with the C4C underestimating significant events thereafter. Second, high biases in AOD550 are largely associated with the attribution of the fine mode in satellites and models alike. Thus, both MODIS and C4C members are systematically overestimating AOD550 and FAOD550 but perform better in characterizing the CAOD550. Third, for MODIS, findings are consistent with previous reports of a high bias in the retrieved Ångström Exponent, and we diagnosed both the optical model and cloud masking as likely causal factors for the AOD550 and FAOD550 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 AOD550s also overestimate CAOD550 and FAOD550, 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.

Intercomparison of Aerosol Optical Depths from four reanalyses and their multi-reanalysis-consensus

“…Shown also in Figure 2 are the total and speciated AOD differences between the individual RA and the MRC. For total AOD, CAMSRA is apparently higher than the other three RAs over the ocean, which is consistent with the findings on its operational counterpart of high biased FM AOD verified with Maritime Aerosol Network over the ocean in Reid et al (2022). This high bias is attributed to its universally higher OA/smoke AOD compared to other RAs, and suggests that CAMSRA may have higher BB emissions and/or less efficient removals compared to the other RAs.…”

“…It is also noted that all the RAs have slightly higher AOD (on the order of 0.02) over the ocean than MODIS QAed product here. MODIS AOD products are well known to low bias significant aerosol events (e.g., Reid et al, 2022;Gumber et al, 2023), which could lower the mean state of AOD. The slightly lower MODIS AOD compared to the RAs could also be related to clear-sky and contextual bias (Zhang et al, 2009), as MODIS AOD retrieval is only available under clear-sky conditions, while all the RAs include all-sky conditions.…”

“…Those 6-hour-averaged MODIS AOD data were then binned into monthly means. Note that MODIS AOD products are well known to low bias significant aerosol events (Reid et al, 2022;Gumber et al, 2023), which could result in a slightly low AOD climatology, especially in source regions.…”

“…In recent years, global aerosol reanalyses have been developed by major operational and research centers, owing to the availability of long-record satellite remote sensing aerosol products and advancements in aerosol data assimilation and modeling. These reanalyses are based on their operational counterparts that are included in the "Core Four" members of the International Cooperative for Aerosol Prediction Multi Model Ensemble (ICAP-MME C4C; Sessions et al, 2016;Xian et al, 2019;Reid et al, 2022). The reanalyses include the Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA; Inness et al, 2019) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF); the Japanese Reanalysis for Aerosol (JRAero) (Yumimoto et al, 2017) developed by the Japan Meteorological Agency (JMA); the NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2; Randles et al, 2017); and the Navy Aerosol Analysis and Prediction System reanalysis (NAAPS-RA; Lynch et al, 2016) developed by the U.S.…”

Assessment of Severe Aerosol Events from NASA MODIS and VIIRS Aerosol Products for Data Assimilation and Climate Continuity

“…However, since the most-severe aerosol events occur near their terrestrial sources over land, statistics are developed that define severe events locally. While many aerosol product evaluation studies have been conducted (Remer et al, 2008;Levy et al, 2010;Hyer et 120 al., 2011;Sayer et al, 2013;Li et al, 2014;Bilal et al, 2018;Lyapustin et al, 2018;Wei et al, 2019;Reid et al, 2022), this study deviates in that it does not strictly compare products against AERONET, or even between each other, but rather an assessment of differences in the probability distribution functions of AOD with a focus on the relative differences between the aerosol retrieval algorithms. Section 2 describes each of the satellite data products, the ground based AERONET observation, and the software used to grid and aggregate the data.…”