Title: Generalized ocean color inversion for retrieving marine inherent optical properties
Authors: WERDELL P.j.FRANZ BryanBAILEY SeanFELDMAN GeneBOSS EmmanuelBRANDO VittorioDOWELL MarkHIRATA TakafumiLAVENDER SamanthaLEE ZhongpingLOISEL HubertMARITORENA StephaneMELIN FredericMOORE TimothySMYTH TimANTOINE DavidDEVRED EmmanuelHEMBISE FANTON D'ANDON OdileMANGIN A.
Citation: APPLIED OPTICS vol. 52 no. 10 p. 2019-2037
Publisher: OPTICAL SOC AMER
Publication Year: 2013
JRC N°: JRC80865
ISSN: 1559-128X
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC80865
DOI: 10.1364/AO.52.002019
Type: Articles in periodicals and books
Abstract: Ocean color measured from satellites provides daily, global observations 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 progressed towards 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 run-time by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of new SAAs, development of regionally tuned SAAs, and execution of ensemble 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.
JRC Directorate:Sustainable Resources

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