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    Generalized ocean color inversion model for retrieving marine inherent optical properties

    200339_96169_Applied.pdf (2.552Mb)
    Access Status
    Open access
    Authors
    Werdell, P.
    Franz, B.
    Bailey, S.
    Feldman, G.
    Boss, E.
    Brando, V.
    Brando, V.
    Dowell, M.
    Hirata, T.
    Lavender, S.
    Lee, Z.
    Loisel, H.
    Maritorena, S.
    Melin, F.
    Moore, T.
    Smyth, T.
    Antoine, David
    Devred, E.
    d’Andon, O.
    Mangin, A.
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Werdell, P. and Franz, B. and Bailey, S. and Feldman, G. and Boss, E. and Brando, V. and Brando, V. et al. 2013. Generalized ocean color inversion model for retrieving marine inherent optical properties. Applied Optics. 52 (10): pp. 2019-2037.
    Source Title
    Applied Optics
    DOI
    10.1364/AO.52.002019
    ISSN
    0003-6935
    Remarks

    This paper was published in Applied Optics and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://www.opticsinfobase.org/ao/abstract.cfm?uri=ao-52-10-2019. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.

    URI
    http://hdl.handle.net/20.500.11937/22634
    Collection
    • Curtin Research Publications
    Abstract

    Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensembe inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future ensemble applications.

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