4fe71812-8c66-44d3-93f3-cb4f28e9d92a Copernicus Marine Service product specification CMEMS servicedesk.cmems@mercator-ocean.eu http://marine.copernicus.eu/ 2022-06-29T09:04:27.204Z ISO 19139, MyOcean profile 0.2 2 1 1 false WGS 84 (EPSG:4326) EPSG [ARCHIVE] Mediterranean Sea Monthly and Daily Reprocessed Surface Chlorophyll Concentration from Multi Satellite observations + SeaWiFS daily climatology OCEANCOLOUR_MED_CHL_L4_REP_OBSERVATIONS_009_078 2012-08-26 2.1 2021-04-26 4fe71812-8c66-44d3-93f3-cb4f28e9d92a '''This product has been archived''' For operationnal and online products, please visit https://marine.copernicus.eu '''Short description:''' The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR), in Rome, distributes Level-4 product including the daily interpolated chlorophyll field with no data voids starting from the multi-sensor (MODIS-Aqua, NOAA-20-VIIRS, NPP-VIIRS, Sentinel3A-OLCI), the monthly averaged chlorophyll concentration for the multi-sensor and climatological fields, all at 1 km resolution. Chlorophyll field are obtained by means of the Mediterranean regional algorithms: an updated version of the MedOC4 (Case 1 waters, Volpe et al., 2019, with new coefficients) and AD4 (Case 2 waters, Berthon and Zibordi, 2004). Discrimination between the two water types is performed by comparing the satellite spectrum with the average water type spectral signature from in situ measurements for both water types. Reference insitu dataset is MedBiOp (Volpe et al., 2019) where pure Case II spectra are selected using a k-mean cluster analysis (Melin et al., 2015). Merging of Case I and Case II information is performed estimating the Mahalanobis distance between the observed and reference spectra and using it as weight for the final merged value. The interpolated gap-free Level-4 Chl concentration is estimated by means of a modified version of the DINEOF algorithm by GOS (Volpe et al., 2018). DINEOF is an iterative procedure in which EOF are used to reconstruct the entire field domain. As first guess, it uses the SeaWiFS-derived daily climatological values at missing pixels and satellite observations at valid pixels. Monthly Level-4 dataset is the time averages of the L3 fields and includes the standard deviation and the number of observations in the monthly period of integration. SeaWiFS daily climatology provides reference for the calculation of Quality Indices (QI) for Chl observations. '''Processing information:''' Multi-sensor product is constituted by MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS and Sentinel3A-OLCI. For consistency with NASA L2 dataset, BRDF correction was applied to Sentinel3A-OLCI prior to band shifting and multi sensor merging. Single sensor NASA Level-2 data are destriped and then all Level-2 data are remapped at 1 km spatial resolution using cylindrical equirectangular projection. Afterwards, single sensor Rrs fields are band-shifted, over the SeaWiFS native bands (using the QAAv6 model, Lee et al., 2002) and merged with a technique aimed at smoothing the differences among different sensors. This technique is developed by The Global Ocean Satellite monitoring and marine ecosystem study group (GOS) of the Italian National Research Council (CNR, Rome). Then geophysical fields (i.e. chlorophyll) are estimated via state-of-the-art algorithms for better product quality. Level-4 includes both monthly time averages and the daily-interpolated fields. Time averages are computed on the delayed-time data. The interpolated product starts from the L3 products at 1 km resolution. At the first iteration, DINEOF procedure uses, as first guess for each of the missing pixels the relative daily climatological pixel. A procedure to smooth out spurious spatial gradients is applied to the daily merged image (observation and climatology). From the second iteration, the procedure uses, as input for the next one, the field obtained by the EOF calculation, using only a number of modes: that is, at the second round, only the first two modes, at the third only the first three, and so on. At each iteration, the same smoothing procedure is applied between EOF output and initial observations. The procedure stops when the variance explained by the current EOF mode exceeds that of noise. The entire data set is consistent and processed in one-shot mode (with an unique software version and identical configurations). For the climatology Rrs data are derived from the latest SeaWiFS NASA reprocessing (R2018.0) and converted to chlorophyll concentrations with the same algorithm as the one used for other L4 and/or L3 products. '''Description of observation methods/instruments:''' Ocean color technique exploits the emerging electromagnetic radiation from the sea surface in different wavelengths. The spectral variability of this signal defines the so called ocean color which is affected by the presence of phytoplankton. '''Quality / Accuracy / Calibration information:''' A detailed description of both the cal/val and a more in depth view of the method is given in QUID-009-038to045-071-073-078-079-095-096.pdf over Copernicus web portal. '''Suitability, Expected type of users / uses:''' This product is meant for use for educational purposes and for the managing of the marine safety, marine resources, marine and coastal environment and for climate and seasonal studies. '''Dataset names:''' *dataset-oc-med-chl-multi-l4-chl_1km_monthly-rep-v02 *dataset-oc-med-chl-multi-l4-interp_1km_daily-rep *dataset-oc-med-chl-seawifs-l4-chl_1km_daily-climatology-v02 '''Files format:''' *CF-1.4 *INSPIRE compliant. '''DOI (product) :''' https://doi.org/10.48670/moi-00114 E.U. Copernicus Marine Service Information Rosalia SANTOLERI OC-CNR-ROMA-IT rosalia.santoleri@cnr.it Gianluca VOLPE OC-CNR-ROMA-IT gianluca.volpe@cnr.it Emanuele BÖHM OC-CNR-ROMA-IT gsdk@isac.cnr.it Simone COLELLA OC-CNR-ROMA-IT simone.colella@cnr.it Javier CONCHA OC-CNR-ROMA-IT gsdk@isac.cnr.it P0M0D0H/P0M0D0H N/A https://catalogue.marine.copernicus.eu/documents/IMG/OCEANCOLOUR_MED_CHL_L4_REP_OBSERVATIONS_009_078.png OCEANCOLOUR_MED_CHL_L4_REP_OBSERVATIONS_009_078 Oceanographic geographical features GEMET - INSPIRE themes, version 1.0 2009-09-22 satellite-observation Discipline 2017-04-25 geonetwork.thesaurus.local.discipline.myocean.discipline mass_concentration_of_chlorophyll_a_in_sea_water Climate and Forecast Standard Names 2017-04-04 geonetwork.thesaurus.external.parameter.myocean.ocean-variables multi-year Temporal scale 2009-06-12 geonetwork.thesaurus.local.temporal-scale.myocean.temporal-scale marine-resources coastal-marine-environment weather-climate-and-seasonal-forecasting marine-safety Area of benefit 2009-09-09 geonetwork.thesaurus.local.area-of-benefit.myocean.area-of-benefit mediterranean-sea Reference Geographical Areas 2009-09-09 geonetwork.thesaurus.local.reference-geographical-area.myocean.reference-geographical-area Level 4 Processing level 2021-07-26 geonetwork.thesaurus.local.processing-level.myocean.processing-level Not Applicable Model assimilation 2016-02-03 geonetwork.thesaurus.external.theme.myocean.model-assimilation See Copernicus Marine Environment Monitoring Service Data commitments and licence at: http://marine.copernicus.eu/web/27-service-commitments-and-licence.php No limitations on public access 7bdc830b-6321-4ffb-ae76-8707256c6d03 e0f8a482-d960-4c72-8976-8c63873ea4de Berthon, J.-F., Zibordi, G.: Bio-optical relationships for the northern Adriatic Sea. Int. J. Remote Sens., 25, 1527-1532, 2004 2019-05-08 Lee, Z. P., et al. (2002), Deriving inherent optical properties from water color: A multi- band quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 5755-5772. 2019-05-08 Mélin, F., & Vantrepotte, V. (2015). How optically diverse is the coastal ocean?. Remote Sensing of Environment, 160, 235-251 2019-05-08 Volpe, G., Buongiorno Nardelli, B., Colella, S., Pisano, A. and Santoleri, R. (2018). An Operational Interpolated Ocean Colour Product in the Mediterranean Sea, in New Frontiers in Operational Oceanography, edited by E. P. Chassignet, A. Pascual, J. Tintorè, and J. Verron, pp. 227–244. 2019-05-08 Volpe, G., Colella, S., Brando, V. E., Forneris, V., Padula, F. L., Cicco, A. D., ... & Santoleri, R. (2019). Mediterranean ocean colour Level 3 operational multi-sensor processing. Ocean Science, 15(1), 127-146. 2019-05-08 eng oceans bounding box -6.00 36.50 30.00 46.00 monthly-mean;daily-interpolated;daily-climatology 1997-09-04 0 0.0 display priority: 11750 observation vertical level number: 1 temporal resolution: monthly mean temporal resolution: daily-interpolated temporal resolution: pluri-annual climatology mean false Grid NetCDF-4 Classic model COMMISSION REGULATION (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 See the referenced specification The myOcean products depends on other products for production or validation. The detailed list of dependencies is given in ISO19115's aggregationInfo (ISO19139 Xpath = "gmd:MD_Metadata/gmd:identificationInfo/gmd:aggregationInfo[./gmd:MD_AggregateInformation/gmd:initiativeType/gmd:DS_InitiativeTypeCode/@codeListValue='upstream-validation' or 'upstream-production']")