The Mediterranean Ocean Colour Observing System: system development and product validation

Volpe, Gianluca; Colella, Simone; Forneris, V.; Tronconi, C.; Santoleri, Rosalia

doi:10.5194/osd-9-1349-2012

Cited by 8 publications

(14 citation statements)

References 17 publications

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“…The regionally tuned satellite PFT algorithms were applied to a Mediterranean multisensor (Medium‐Resolution Imaging Spectrometer(MERIS), Moderate Resolution Imaging Spectroradiometer‐Aqua(MODIS), and Sea‐viewing Wide Field‐of‐view Sensor(SeaWIFS) chlorophyll a satellite time series (from September 1997 to August 2015) available from Copernicus Marine Environment Monitoring Service (CMEMS, OCEANCOLOUR_MED_CHL_L3_REP_OBSERVATIONS_009_073 product). These chlorophyll data are produced by the CMEMS OC TAC (Ocean Color Thematic Assembling Centre) using an ad hoc configuration of the ESA OC‐CCI (European Space Agency‐Ocean Color Climate Change Initiative, http://www.esa-oceancolour-cci.org) processor at high resolution in the Italian National Research Council Mediterranean processing chain (Volpe et al, , ). The chlorophyll product used in Di Cicco et al () is a merged Case I–Case II product, in which the different optical properties of inshore and offshore waters are taken into account by applying two different regional algorithms: (i) the MedOC4 (Volpe et al, ) specialized for open ocean waters and (ii) the AD4 (D'Alimonte & Zibordi, ) for coastal complex water.…”

Section: Methodsmentioning

confidence: 99%

Ecoregions in the Mediterranean Sea Through the Reanalysis of Phytoplankton Functional Types and Carbon Fluxes

et al. 2019

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In this work we produced a long‐term reanalysis of the phytoplankton community structure in the Mediterranean Sea and used it to define ecoregions. These were based on the spatial variability of the phytoplankton type fractions and their influence on selected carbon fluxes. A regional ocean color product of four phytoplankton functional types (PFTs; diatoms, dinoflagellates, nanophytoplankton, and picophytoplankton) was assimilated into a coupled physical‐biogeochemical model of the Mediterranean Sea (Proudman Oceanographic Laboratory Coastal Ocean Modelling System‐European Regional Seas Ecosystem Model, POLCOMS–ERSEM) by using a 100‐member ensemble Kalman filter, in a reanalysis simulation for years 1998–2014. The reanalysis outperformed the reference simulation in representing the assimilated ocean color PFT fractions to total chlorophyll, although the skill for the ocean color PFT concentrations was not improved significantly. The reanalysis did not impact noticeably the reference simulation of not assimilated in situ observations, with the exception of a slight bias reduction for the situ PFT concentrations, and a deterioration of the phosphate simulation. We found that the Mediterranean Sea can be subdivided in three PFT‐based ecoregions, derived from the spatial variability of the PFT fraction dominance or relevance. Picophytoplankton dominates the largest part of open ocean waters; microphytoplankton dominates in a few, highly productive coastal spots near large‐river mouths; nanophytoplankton is relevant in intermediate‐productive coastal and Atlantic‐influenced waters. The trophic and carbon sedimentation efficiencies are highest in the microphytoplankton ecoregion and lowest in the picophytoplankton and nanophytoplankton ecoregions. The reanalysis and regionalization offer new perspectives on the variability of the structure and functioning of the phytoplankton community and related biogeochemical fluxes, with foreseeable applications in Blue Growth of the Mediterranean Sea.

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Section: Methodsmentioning

confidence: 99%

Ecoregions in the Mediterranean Sea Through the Reanalysis of Phytoplankton Functional Types and Carbon Fluxes

et al. 2019

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“…The surface chlorophyll maps are provided at a daily frequency and a spatial resolution of 1 km. Since 2009 the observations have been available as an operational product of the MyOcean project [ Volpe et al ., ]. The satellite chlorophyll data are spatially interpolated to the Mediterranean model resolution (1/8°) by means of a bilinear interpolation, and a 5 day average centered on the assimilation dates is performed, using the data provided as log‐transformed chlorophyll concentrations [ Volpe et al ., ].…”

Section: Satellite Data and Observation Errormentioning

confidence: 99%

A 3‐D variational assimilation scheme in coupled transport‐biogeochemical models: Forecast of Mediterranean biogeochemical properties

et al. 2014

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[1] Increasing attention is dedicated to the implementation of suitable marine forecast systems for the estimate of the state of the ocean. Within the framework of the European MyOcean infrastructure, the pre-existing short-term Mediterranean Sea biogeochemistry operational forecast system has been upgraded by assimilating remotely sensed ocean color data in the coupled transport-biogeochemical model OPATM-BFM using a 3-D variational data assimilation (3D-VAR) procedure. In the present work, the 3D-VAR scheme is used to correct the four phytoplankton functional groups included in the OPATM-BFM in the period July 2007 to September 2008. The 3D-VAR scheme decomposes the error covariance matrix using a sequence of different operators that account separately for vertical covariance, horizontal covariance, and covariance among biogeochemical variables. The assimilation solution is found in a reduced dimensional space, and the innovation for the biogeochemical variables is obtained by the sequential application of the covariance operators. Results show a general improvement in the forecast skill, providing a correction of the basin-scale bias of surface chlorophyll concentration and of the local-scale spatial and temporal dynamics of typical bloom events. Further, analysis of the assimilation skill provides insights into the functioning of the model. The computational costs of the assimilation scheme adopted are low compared to other assimilation techniques, and its modular structure facilitates further developments. The 3D-VAR scheme results especially suitable for implementation within a biogeochemistry operational forecast system.

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“…Landsat retrieved CDOM map, because of the higher GSD, moreover, correctly depicts the detrital contribution of all the minor rivers' discharges occurring in the North and West Adriatic coasts, as well as the Venice Lagoon subbasins. However, it is important to remark that MODIS Rrs are recursively generated as standard product [68,69], while Landsat Rrs were retrieved by applying the following commonly used Rrs equation using as input Landsat radiance data.…”

Section: Discussionmentioning

confidence: 99%

“…MODIS is an ocean colour sensor aboard the polar-orbiting Aqua (MODIS-A) and Terra (MODIS-T) satellite platforms with 36 spectral bands and three spatial resolutions of 250 m, 500 m and 1 km. For the Mediterranean Sea, R rs and diffuse attenuation coefficient of light at 490 nm is operationally produced and uploaded on the CMEMS catalogue by the Group for Satellite Oceanography (GOS-ISAC) of the Italian National Research Council, in Rome, for near real time data from MODIS-A and NPP-VIIRS sensors [1,68,69].…”

Section: Satellite Data and Processingmentioning

confidence: 99%

An Empirical Ocean Colour Algorithm for Estimating the Contribution of Coloured Dissolved Organic Matter in North-Central Western Adriatic Sea

et al. 2017

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Abstract:The performance of empirical band ratio models were evaluated for the estimation of Coloured Dissolved Organic Matter (CDOM) using MODIS ocean colour sensor images and data collected on the North-Central Western Adriatic Sea (Mediterranean Sea). Relationships between in situ measurements (2013-2016) of CDOM absorption coefficients at 355 nm (a CDOM 355) with several MODIS satellite band ratios were evaluated on a test data set. The prediction capability of the different linear models was assessed on a validation data set. Based on some statistical diagnostic parameters (R 2 , APD and RMSE), the best MODIS band ratio performance in retrieving CDOM was obtained by a simple linear model of the transformed dependent variable using the remote sensing reflectance band ratio R rs (667)/R rs (488) as the only independent variable. The best-retrieved CDOM algorithm provides very good results for the complex coastal area along the North-Central Western Adriatic Sea where the Po River outflow is the main driving force in CDOM and nutrient circulation, which in winter mostly remains confined to a coastal boundary layer, whereas in summer it spreads to the open sea as well.

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The Mediterranean Ocean Colour Observing System: system development and product validation

Cited by 8 publications

References 17 publications

Ecoregions in the Mediterranean Sea Through the Reanalysis of Phytoplankton Functional Types and Carbon Fluxes

Ecoregions in the Mediterranean Sea Through the Reanalysis of Phytoplankton Functional Types and Carbon Fluxes

A 3‐D variational assimilation scheme in coupled transport‐biogeochemical models: Forecast of Mediterranean biogeochemical properties

An Empirical Ocean Colour Algorithm for Estimating the Contribution of Coloured Dissolved Organic Matter in North-Central Western Adriatic Sea

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