An analysis of global aerosol type as retrieved by MISR

Kahn, Ralph A.; Gaitley, B. J.

doi:10.1002/2015jd023322

Cited by 215 publications

(237 citation statements)

References 76 publications

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“…The MISR particle types and mixtures are designed to represent several compositional categories typically found in the atmosphere, such as sea spray, sulfate, nitrate, mineral dust, carbonaceous, and urban soot aerosols. Recently, Kahn and Gaitley (2015) provided a thorough verification of MISR's aerosol type retrieval capabilities and Lee et al (2016) demonstrated that the MISR aerosol particle climatologies regionally showed good agreement with the results of chemical transport models. The current 74 mixture set, however, is not complete and has some documented deficiencies Kalashnikova et al, 2013;Limbacher and Kahn, 2014).…”

Section: Previous Misr V2dark Water Algorithmmentioning

confidence: 99%

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New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water

et al. 2018

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Abstract.A new method for retrieving aerosol optical depth (AOD) and its uncertainty from Multi-angle Imaging SpectroRadiometer (MISR) observations over dark water is outlined. MISR's aerosol retrieval algorithm calculates cost functions between observed and pre-simulated radiances for a range of AODs (from 0.0 to 3.0) and a prescribed set of aerosol mixtures. The previous version 22 (V22) operational algorithm considered only the AOD that minimized the cost function for each aerosol mixture and then used a combination of these values to compute the final, "best estimate" AOD and associated uncertainty. The new approach considers the entire range of cost functions associated with each aerosol mixture. The uncertainty of the reported AOD depends on a combination of (a) the absolute values of the cost functions for each aerosol mixture, (b) the widths of the cost function distributions as a function of AOD, and (c) the spread of the cost function distributions among the ensemble of mixtures. A key benefit of the new approach is that, unlike the V22 algorithm, it does not rely on empirical thresholds imposed on the cost function to determine the success or failure of a particular mixture. Furthermore, a new aerosol retrieval confidence index (ARCI) is established that can be used to screen high-AOD retrieval blunders caused by cloud contamination or other factors. Requiring ARCI ≥ 0.15 as a condition for retrieval success is supported through statistical analysis and outperforms the thresholds used in the V22 algorithm. The described changes to the MISR dark water algorithm will become operational in the new MISR aerosol product (V23), planned for release in 2017.

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Section: Previous Misr V2dark Water Algorithmmentioning

confidence: 99%

“…The V22 operational algorithm defines 74 aerosol mixtures, each of which consists of up to three unique particle types having prescribed optical and microphysical properties Kahn and Gaitley, 2015). The 74 mixtures consist of combinations of eight primary particle types.…”

Section: Previous Misr V2dark Water Algorithmmentioning

confidence: 99%

New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water

et al. 2018

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“…The best-fitting mixture out of the 74 candidate mixtures is reported as the "RegLowestResidMixture", but the AOD and information about which mixtures were considered passing are retained in the fields "OptDepthPerMixture" and "AerRetrSuccFlagPerMixture", respectively, for further evaluation. Validation of the Level 2 Version 22 product against ground-based observations from the Aerosol Robotic Network (AERONET) (Holben et al, 1998), including assessments of particle type retrievals, can be found in Kahn et al (2010) and Kahn and Gaitley (2015). The mixture type information in the MISR Version 22 aerosol product has been exploited by Li et al (2013) and Li et al (2015), for example, who used CCM in-formation to select from the complete set of passing mixtures to improve agreement with AERONET in the continental United States.…”

Section: Introductionmentioning

confidence: 99%

Climatology of the aerosol optical depth by components from the Multi-angle Imaging SpectroRadiometer (MISR) and chemistry transport models

et al. 2016

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Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) Joint Aerosol (JOINT_AS) Level 3 product has provided a global, descriptive summary of MISR Level 2 aerosol optical depth (AOD) and aerosol type information for each month over 16+ years since March 2000. Using Version 1 of JOINT_AS, which is based on the operational (Version 22) MISR Level 2 aerosol product, this study analyzes, for the first time, characteristics of observed and simulated distributions of AOD for three broad classes of aerosols: spherical nonabsorbing, spherical absorbing, and nonspherical -near or downwind of their major source regions. The statistical moments (means, standard deviations, and skewnesses) and distributions of AOD by components derived from the JOINT_AS are compared with results from two chemistry transport models (CTMs), the Goddard Chemistry Aerosol Radiation and Transport (GOCART) and SPectral RadIatioN-TrAnSport (SPRINTARS). Overall, the AOD distributions retrieved from MISR and modeled by GOCART and SPRINTARS agree with each other in a qualitative sense. Marginal distributions of AOD for each aerosol type in both MISR and models show considerable high positive skewness, which indicates the importance of including extreme AOD events when comparing satellite retrievals with models. The MISR JOINT_AS product will greatly facilitate comparisons between satellite observations and model simulations of aerosols by type.

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“…However, neither one of these instruments were specifically designed to retrieve aerosol properties. The more recent dedicated aerosol sounders, such as ATSR (The Along Track Scanning Radiometer 2, LlewellynJones and Remedios, 2012), MODIS (Moderate Resolution Imaging Spectroradiometer, Levy et al, 2010), VIISR (Visible Infrared Imaging Radiometer Suite, Jackson et al, 2013) and MISR (Multi-angle Imaging SpectroRadiometer, Kahn and Gaitley, 2015) offer data from 1995, 2000 and 2002 onwards respectively. It is therefore apparent that neither sun photometer nor satellite records of AOD are available for all the decades where industrialization has had a significant effect on the aerosol load.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

et al. 2016

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Abstract. In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the lookup table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during the observation period.

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An analysis of global aerosol type as retrieved by MISR

Cited by 215 publications

References 76 publications

New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water

New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water

Climatology of the aerosol optical depth by components from the Multi-angle Imaging SpectroRadiometer (MISR) and chemistry transport models

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

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