Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment

Jolliff, Jason K.; Kindle, John C.; Shulman, Igor; Penta, Bradley; Friedrichs, Marjorie A. M.; Helber, Robert W.; Arnone, Robert

doi:10.1016/j.jmarsys.2008.05.014

Cited by 334 publications

(294 citation statements)

References 25 publications

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“…Each of the 12 instances included a single alternate parameterization: (1,2) S dg 33% ( 0.012 and 0.024 nm −1 ), (3) S dg dynamically calculated using Lee et al [7], (4,5) S bp from Lee et al [7] 33%; (6,7) OC-derived C a 33% prior to input into Bricaud et al [14]; (8) a ϕ λ from Bricaud et al [14] with C a fixed at 0.18 mg m −3 ; (9) a ϕ λ from Ciotti and Bricaud [17] with a size fraction of 0.5; (10) G λ from Morel et al [22], (11) optimization using linear matrix inversion, and (12) optimization considering only 400 ≤ λ ≤ 600 nm. We quantified spectral changes in modeled IOPs for each alternate parameterization (relative to the baseline GIOP-DC configuration) using Type II regression statistics, estimates of ΔIOP, and Taylor [36] and Target [37] summary diagrams. The latter provide convenient means for simultaneously considering magnitudes of deviations, correlations, and biases between the alternate runs and GIOP-DC.…”

Section: F Sensitivity Analysesmentioning

confidence: 99%

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

373

319

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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 ensemble inversion modeling. 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 emsemble applications.

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Section: F Sensitivity Analysesmentioning

confidence: 99%

Generalized ocean color inversion model for retrieving marine inherent optical properties

Werdell¹,

Franz²,

Bailey³

et al. 2013

Appl. Opt.

373

319

View full text Add to dashboard Cite

show abstract

“…For evaluating the success of the physical model, we used Taylor & Target diagrams (Jolli et al, 2009). …”

Section: Skill Assessment Metricsmentioning

confidence: 99%

Modelling the plankton groups of the deep, peri-alpine Lake Bourget

Kerimoglu

Jacquet²,

Vinçon‐Leite

et al. 2017

Ecological Modelling

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Predicting phytoplankton succession and variability in natural systems remains to be a grand challenge in aquatic ecosystems research. In this study, we identi ed six major plankton groups in Lake Bourget (France), based on cell size, taxonomic properties, food-web interactions and occurrence patterns: cyanobacterium Planktothrix rubescens, small and large phytoplankton, mixotrophs, herbivorous and carnivorous zooplankton. We then developed a deterministic dynamic model that describes the dynamics of these groups in terms of carbon and phosphorus uxes, as well as of particulate organic phosphorus and dissolved inorganic provide evidence for the hypothesis that the abundance of this species before the onset of strati cation is critical for its success later in the growing season, pointing thereby to a priority e ect.

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“…The relative difference plot (Figure 9,right) Figure 12) demonstrated that for the DW region, both GIOP and QAA-derived a t (443), b bp (443) and K d (488) were in good agreement with those of SWIM, evidenced by normalized biases and unbiased root mean squared differences (uRMSD) that were less than 1.0. Based on Jolliff et al [2009], we considered Target plot data points that fell outside a radius of 1.0 from the origin (depicted as black circles, bottom row of Figure 12) as points that did not suitably agree with SWIM. We hence refer to the area encompassed by a circle of radius 5 1.0 about the origin in a Target plot as the ''performance marker region.…”

Section: Deep Water Regionmentioning

confidence: 99%

A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization

et al. 2015

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A semianalytical ocean color inversion algorithm was developed for improving retrievals of inherent optical properties (IOPs) in optically shallow waters. In clear, geometrically shallow waters, light reflected off the seafloor can contribute to the water-leaving radiance signal. This can have a confounding effect on ocean color algorithms developed for optically deep waters, leading to an overestimation of IOPs. The algorithm described here, the Shallow Water Inversion Model (SWIM), uses pre-existing knowledge of bathymetry and benthic substrate brightness to account for optically shallow effects. SWIM was incorporated into the NASA Ocean Biology Processing Group's L2GEN code and tested in waters of the Great Barrier Reef, Australia, using the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua time series (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013). SWIM-derived values of the total non-water absorption coefficient at 443 nm, a t (443), the particulate backscattering coefficient at 443 nm, b bp (443), and the diffuse attenuation coefficient at 488 nm, K d (488), were compared with values derived using the Generalized Inherent Optical Properties algorithm (GIOP) and the Quasi-Analytical Algorithm (QAA). The results indicated that in clear, optically shallow waters SWIM-derived values of a t (443), b bp (443), and K d (443) were realistically lower than values derived using GIOP and QAA, in agreement with radiative transfer modeling. This signified that the benthic reflectance correction was performing as expected. However, in more optically complex waters, SWIM had difficulty converging to a solution, a likely consequence of internal IOP parameterizations. Whilst a comprehensive study of the SWIM algorithm's behavior was conducted, further work is needed to validate the algorithm using in situ data.

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Summary diagrams for coupled hydrodynamic-ecosystem model skill assessment

Cited by 334 publications

References 25 publications

Generalized ocean color inversion model for retrieving marine inherent optical properties

Generalized ocean color inversion model for retrieving marine inherent optical properties

Modelling the plankton groups of the deep, peri-alpine Lake Bourget

A semianalytical ocean color inversion algorithm with explicit water column depth and substrate reflectance parameterization

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