The role of cloud contamination, aerosol layer height and aerosol model in the assessment of the OMI near-UV retrievals over the ocean

Abstract

Retrievals of aerosol optical depth (AOD) at 388 nm over the ocean from the Ozone Monitoring Instrument (OMI) two-channel near-UV algorithm (OMAERUV) have been compared with independent AOD measurements. The analysis was carried out over the open ocean (OMI and MODerate-resolution Imaging Spectrometer (MODIS) AOD comparisons) and over coastal and island sites (OMI and AERONET, the AErosol RObotic NETwork). Additionally, a research version of the retrieval algorithm (using MODIS and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) information as constraints) was utilized to evaluate the sensitivity of the retrieval to different assumed aerosol properties.Overall, the comparison resulted in differences (OMI minus independent measurements) within the expected levels of uncertainty for the OMI AOD retrievals (0.1 for AOD < 0.3, 30 % for AOD > 0.3). Using examples from case studies with outliers, the reasons that led to the observed differences were examined with specific purpose to determine whether they are related to instrument limitations (i.e., pixel size, calibration) or algorithm assumptions (such as aerosol shape, aerosol height).The analysis confirms that OMAERUV does an adequate job at rejecting cloudy scenes within the instrument's capabilities. There is a residual cloud contamination in OMI pixels with quality flag 0 (the best conditions for aerosol retrieval according to the algorithm), resulting in a bias towards high AODs in OMAERUV. This bias is more pronounced at low concentrations of absorbing aerosols (AOD 388 nm ∼ < 0.5). For higher aerosol loadings, the bias remains within OMI's AOD uncertainties.In pixels where OMAERUV assigned a dust aerosol model, a fraction of them (< 20 %) had retrieved AODs significantly lower than AERONET and MODIS AODs. In a case study, a detailed examination of the aerosol height from CALIOP and the AODs from MODIS, along with sensitivity tests, was carried out by varying the different assumed parameters in the retrieval (imaginary index of refraction, size distribution, aerosol height, particle shape). It was found that the spherical shape assumption for dust in the current retrieval is the main cause of the underestimate. In addition, it is demonstrated in an example how an incorrect assumption of the aerosol height can lead to an underestimate. Nevertheless, this is not as significant as the effect of particle shape. These findings will be incorporated in a future version of the retrieval algorithm.

The Ozone Monitoring Instrument: overview of 14 years in space

“…Schoeberl et al (2007) further used a wind trajectory mapping technique of MLS ozone profiles and used potential vorticity mapping to obtain better signal-to-noise ratio and horizontal coverage for the OMI/MLS tropospheric column ozone. Wargan et al (2015) discusses an OMI/MLS ozone profile product derived using data assimilation; it is noted that current MERRA-2 (Gelaro et al, 2017;Wargan et al, 2017) analyses include ozone profiles determined similarly via data assimilation of Aura MLS and OMI ozone. A comparison of several OMI/MLS tropospheric column ozone products (data assimilation, trajectory mapping, and profile retrieval methods) is described by Ziemke et al (2014).…”

“…For example, in the OMAERUV aerosol product, data from the CALIOP have been used to constrain the aerosol layer heights, and carbon monoxide (CO) data from AIRS have been used to help distinguish different types of absorbing aerosol, i.e., smoke from dust (Torres et al, 2013). MODIS data (OMMYDCLD) have been used to evaluate the effect of subpixel cloud contamination (Gassó and Torres, 2016). AOTs from MODIS have also been combined with OMI measurements to estimate aerosol layer height (Satheesh et al, 2009;Chimot et al, 2017).…”

Impact of the Ozone Monitoring Instrument Row Anomaly on the Long-term Record of Aerosol Products

“…Desert dust aerosols are known to be irregularly shaped large particles whose phase function may deviate significantly from that of a spherical model at scattering angles larger than about 80° for non-10 absorbing particles. The role of particle shape assumption in the retrieval of desert dust properties was recently identified as an important source of uncertainty in the OMAERUV algorithm [Gassó and Torres, 2016]. An analysis of the uncertainty in retrieved AOD and SSA associated with the spherical shape assumption of desert dust particles is presented next.…”

“…OMAERUV quantitative aerosol products have been evaluated by comparison to independent ground-based observations 10 [Torres et al, 2007;Ahn et al, 2014;Jethva et al, 2014, Zhang et al, 2016, airborne measurements [Livingston et al, 2009] as well as to other satellite measurements [Ahn et al, 2008;Gassó and Torres, 2016].…”

Detection of Absorbing Aerosol Using Single Near-UV Radiance Measurements from a Cloud and Aerosol Imager

“…Yi et al [35] simulated the uncertainties from the particle shape and refractive index with the Ping Yang database, and pointed out that the particle shape effect is found to be related to the dust optical depth and the surface albedo can be an important uncertainty source in radiative transfer simulation. Even though near-UV measurement is insensitive to aerosol phase function effects, as discussed by Torres et al [10], the research done by Gasso et al [38] showed that the dust LUT applied by the T-matrix code resulted in an increasing retrieval of AOD. However, a non-spherical model did not affect to SSA retrieved results, as noted by Kroktov et al [39] and Dubovik et al [26].…”