chore: update external product types reference

[github-actions]

Update external product-types reference from daily fetch. See Python API User Guide / Collections discovery

Changed file

eodag/resources/ext_product_types.json

commit 4a6394ad05fc5e4d12f11752b6046606c1158637

---

Note: Detailed diffs are available in the job summary.

fedeo_ceda - product_types:

+ TROPOMI_HCHO_L3_V2.0_MONTHLY

---

Changes grouped by JSON paths:

abstract
11 product_type(s) affected (cop_marine)

Too many product_types to list here. See detailed breakdown in job summary for the complete list.

---

abstract
doi
1 product_type(s) affected (cop_marine)

Click to expand for detailed diffs

cop_marine - product_types_config - WAVE_GLO_PHY_SPC_L3_NRT_014_009

--- old
+++ new
@@ -1,6 +1,6 @@
 {
-    "abstract": "Near Real-Time mono-mission satellite-based 2D full wave spectral product. These very complete products enable to characterise spectrally the direction, wave length and multiple sea Sates along CFOSAT track (in boxes of 70km/90km left and right from the nadir pointing). The data format are 2D directionnal matrices. They also include integrated parameters (Hs, direction, wavelength) from the spectrum with and without partitions. \n\n**DOI (product):**   \nN/A",
-    "doi": null,
+    "abstract": "Near Real-Time mono-mission satellite-based 2D full wave spectral product. These very complete products enable to characterise spectrally the direction, wave length and multiple sea Sates along CFOSAT track (in boxes of 70km/90km left and right from the nadir pointing). The data format are 2D directionnal matrices. They also include integrated parameters (Hs, direction, wavelength) from the spectrum with and without partitions. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00382",
+    "doi": "10.48670/mds-00382",
     "instrument": null,
     "keywords": "arctic-ocean,baltic-sea,black-sea,global-ocean,iberian-biscay-irish-seas,level-3,mediterranean-sea,north-west-shelf-seas,oceanographic-geographical-features,satellite-observation,sea-surface-wave-from-direction-at-variance-spectral-density-maximum,sea-surface-wave-period-at-variance-spectral-density-maximum,sea-surface-wave-significant-height,wave-glo-phy-spc-l3-nrt-014-009,wave-spectrum",
     "license": "proprietary",

---

abstract
doi
keywords
1 product_type(s) affected (cop_marine)

Click to expand for detailed diffs

cop_marine - product_types_config - MULTIOBS_GLO_PHY_UVW_3D_MYNRT_015_007

--- old
+++ new
@@ -1,8 +1,8 @@
 {
-    "abstract": "You can find here the OMEGA3D observation-based  quasi-geostrophic vertical and horizontal ocean currents developed by the Consiglio Nazionale delle RIcerche. The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 ) and ERA5 surface fluxes. \n\n**Citation**: \nBuongiorno Nardelli, B. (2020). CNR global observation-based OMEGA3D quasi-geostrophic vertical and horizontal ocean currents (1993-2018) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service (CMEMS). https://doi.org/10.25423/CMCC/MULTIOBS_GLO_PHY_W_REP_015_007\n\n**DOI (product):** \nhttps://doi.org/10.25423/cmcc/multiobs_glo_phy_w_rep_015_007\n\n**References:**\n\n* Buongiorno Nardelli, B. A Multi-Year Timeseries of Observation-Based 3D Horizontal and Vertical Quasi-Geostrophic Global Ocean Currents. Earth Syst. Sci. Data 2020, No. 12, 1711\u20131723. https://doi.org/10.5194/essd-12-1711-2020.\n",
-    "doi": "10.25423/cmcc/multiobs_glo_phy_w_rep_015_007",
+    "abstract": "The data are provided weekly over a regular grid at 1/4\u00b0 horizontal resolution, from the surface to 1500 m depth (representative of each Wednesday). The velocities are obtained by solving a diabatic formulation of the Omega equation, starting from ARMOR3D data (MULTIOBS_GLO_PHY_TSUV_3D_MYNRT_015_012 ) and ERA5 surface fluxes. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00053\n\n**References:**\n\n* Buongiorno Nardelli, B. A Multi-Year Timeseries of Observation-Based 3D Horizontal and Vertical Quasi-Geostrophic Global Ocean Currents. Earth Syst. Sci. Data 2020, No. 12, 1711\u20131723. https://doi.org/10.5194/essd-12-1711-2020.\n",
+    "doi": "10.48670/moi-00053",
     "instrument": null,
-    "keywords": "ageostrophic-eastward-sea-water-velocity,ageostrophic-northward-sea-water-velocity,coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-uvw-3d-mynrt-015-007,northward-sea-water-velocity,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting",
+    "keywords": "coastal-marine-environment,eastward-sea-water-velocity,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-phy-uvw-3d-mynrt-015-007,northward-sea-water-velocity,numerical-model,oceanographic-geographical-features,satellite-observation,upward-sea-water-velocity,weather-climate-and-seasonal-forecasting",
     "license": "proprietary",
     "missionStartDate": "1993-01-06T00:00:00Z",
     "platform": null,

---

abstract
doi
keywords
license
processingLevel
providers[0].name
providers[0].roles[0]
providers[1].name
providers[1].roles[0]
providers[1].roles[1]
providers[1].url
title
5 product_type(s) affected (cop_marine)

Click to expand for detailed diffs

cop_marine - product_types_config - MULTIOBS_GLO_BGC_CARBON_SURFACE_MYNRT_015_008

--- old
+++ new
@@ -1,13 +1,28 @@
 {
-    "abstract": null,
-    "doi": null,
+    "abstract": "This product corresponds to a L4 time series of monthly global reconstructed surface ocean pCO2, air-sea fluxes of CO2, pH, total alkalinity, dissolved inorganic carbon, saturation state with respect to calcite and aragonite, and associated uncertainties on a 0.25\u00b0 x 0.25\u00b0 regular grid. The product is obtained from an ensemble-based forward feed neural network approach mapping situ data for surface ocean fugacity (SOCAT data base, Bakker et al.  2016, https://www.socat.info/) and sea surface salinity, temperature, sea surface height, chlorophyll a, mixed layer depth and atmospheric CO2 mole fraction. Sea-air flux fields are computed from the air-sea gradient of pCO2 and the dependence on wind speed of Wanninkhof (2014). Surface ocean pH on total scale, dissolved inorganic carbon, and saturation states are then computed from surface ocean pCO2 and reconstructed surface ocean alkalinity using the CO2sys speciation software.\n\n**DOI (product):**\nhttps://doi.org/10.48670/moi-00047\n\n**References:**\n\n* Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air\u2013sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087\u20131109, https://doi.org/10.5194/bg-19-1087-2022, 2022.\n* Chau, T.-T.-T., Chevallier, F., & Gehlen, M. (2024). Global analysis of surface ocean CO2 fugacity and air-sea fluxes with low latency. Geophysical Research Letters, 51, e2023GL106670. https://doi.org/10.1029/2023GL106670\n* Chau, T.-T.-T., Gehlen, M., Metzl, N., and Chevallier, F.: CMEMS-LSCE: a global, 0.25\u00b0, monthly reconstruction of the surface ocean carbonate system, Earth Syst. Sci. Data, 16, 121\u2013160, https://doi.org/10.5194/essd-16-121-2024, 2024.\n",
+    "doi": "10.48670/moi-00047",
     "instrument": null,
-    "keywords": ",multiobs-glo-bgc-carbon-surface-mynrt-015-008",
-    "license": null,
+    "keywords": "aragonite-saturation-state-in-sea-water,calcite-saturation-state-in-sea-water,coastal-marine-environment,dissolved-inorganic-carbon-in-sea-water,global-ocean,in-situ-observation,level-4,marine-resources,marine-safety,multi-year,multiobs-glo-bgc-carbon-surface-mynrt-015-008,none,oceanographic-geographical-features,sea-water-ph-reported-on-total-scale,surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,surface-partial-pressure-of-carbon-dioxide-in-sea-water,total-alkalinity-in-sea-water,uncertainty-aragonite-saturation-state-in-sea-water,uncertainty-calcite-saturation-state-in-sea-water,uncertainty-dissolved-inorganic-carbon-in-sea-water,uncertainty-sea-water-ph-reported-on-total-scale,uncertainty-surface-downward-mass-flux-of-carbon-dioxide-expressed-as-carbon,uncertainty-surface-partial-pressure-of-carbon-dioxide-in-sea-water,uncertainty-total-alkalinity-in-sea-water,weather-climate-and-seasonal-forecasting",
+    "license": "proprietary",
     "missionStartDate": "1985-01-01T00:00:00Z",
     "platform": null,
     "platformSerialIdentifier": null,
-    "processingLevel": null,
-    "providers": [],
-    "title": null
+    "processingLevel": "Level 4",
+    "providers": [
+        {
+            "name": "LSCE (France)",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "Copernicus Marine Service",
+            "roles": [
+                "host",
+                "processor"
+            ],
+            "url": "https://marine.copernicus.eu"
+        }
+    ],
+    "title": "Surface ocean carbon fields"
 }

cop_marine - product_types_config - OMI_CIRCULATION_BOUNDARY_ATLANTIC_gulf_stream_destabilization_point

--- old
+++ new
@@ -1,13 +1,28 @@
 {
-    "abstract": null,
-    "doi": null,
+    "abstract": "**DEFINITION**\n\nThe destabilization point of the Gulf Stream marks the longitude where the current transitions  from a boundary-following current to an open-ocean jet. It is identified using the 25 cm SSH contour, from detrended Absolute Dynamic Topography (ADT), a standard method validated in multiple studies (Lillibridge & Mariano, 2013; Rossby et al., 2014; Andres, 2016; Chi et al., 2021; Guo et al., 2023). \n\nMonthly Gulf Stream paths from 1993 to  present are divided into 0.5\u00b0 longitude bins, and the northernmost latitude of the SSH contour is used to compute the latitudinal variance in each bin. The destabilization point is defined as the first longitude where this variance reaches 0.42\u00b0\u00b2, which represents half the maximum variance observed across the full time series ( S\u00e1nchez-Rom\u00e1n et al., 2024). \n\nA 95% confidence interval is computed from monthly estimates, and a 5-year running mean is applied to smooth high-frequency fluctuations. Longitudinal shifts of the destabilization point can exceed 1400 km, influenced by climate modes such as the North Atlantic Oscillation (NAO) (S\u00e1nchez-Rom\u00e1n et al., 2024). \n\n \n\n**CONTEXT**\n\nThe Gulf Stream is a major western boundary current which forms the northward flowing path of the Atlantic Meridional Overturning Circulation (AMOC). It carries near-surface warm waters from the Gulf of Mexico to the subpolar North Atlantic, playing an important role in its climate variability and change. Interannual lateral displacements in the Gulf Stream\u2019s position impact associated water transport to subpolar regions, altering the global climate system (Guo et al., 2023).  \n\nTracking the destabilization point provides an early indicator of structural changes in the North Atlantic circulation system. Its position directly reflects the length of the stable jet segment and the onset region of meanders. The observed shifts indicate modifications in the jet\u2019s structure, its stability, and its interaction with oceanic variability. Its evolution reveals a significant westward migration followed by an eastward shift over the past three decades. \n\n\n**KEY FINDINGS**\n\nThe results reveal  a low-frequency westward and southward displacement of the Gulf Stream destabilization point between 1995 and 2012, followed by a previously unreported reversed migration starting in 2013. These changes affect the intensity of mesoscale variability and the transport of waters toward the subpolar North Atlantic. They appear to be correlated with the dynamics of the North Atlantic Oscillation, whose successive phases modulate the position of the current. \n\n\n**DOI (product):**\nhttps://doi.org/10.48670/mds-00377\n\n**References:**\n\n* Andres, M. (2016), On the recent destabilization of the Gulf Stream path downstream of Cape Hatteras, Geophys. Res. Lett., 43, 9836\u20139842, doi:10.1002/2016GL069966.\n* Chi, L., Wolfe, C. L. P., and Hameed, S.: Has the Gulf Stream slowed or shifted in the altimetry era?, Geophys. Res. Lett., 48, e2021GL093113, https://doi.org/10.1029/2021GL093113, 2021.\u2002\n* Guo, Y., Bishop, S., Bryan, F., and Bachman, S.: Mesoscale variability linked to interannual displacement of Gulf Stream, Geophys. Res. Lett., 50, e2022GL102549, https://doi.org/10.1029/2022GL102549, 2023.\u2002\n* Lillibridge, J. L. and Mariano, A.J.: A statistical analysis of Gulf Stream variability from 18+ years of altimetry data, Deep-Sea Res. Pt. II, 85, 127\u2013146, https://doi.org/10.1016/j.dsr2.2012.07.034, 2013. \u2002\n* Rossby, H., Flagg, C., Donohue, K., Sanchez-Franks, A., Lillibridge, J.: On the long-term stability of Gulf Stream transport based on 20 years of direct measurements, Geophys. Res. Lett., 41, 114\u2013120, https://doi.org/10.1002/2013GL058636, 2014.\u2002\n* S\u00e1nchez-Rom\u00e1n, A., Gues, F., Bourdalle-Badie, R., Pujol, M.-I., Pascual, A., & Dr\u00e9villon, M. (2024, September 30). Changes in the Gulf Stream path over the last 3 decades. State of the Planet. Copernicus GmbH. http://doi.org/10.5194/sp-4-osr8-4-2024\n",
+    "doi": "10.48670/mds-00377",
     "instrument": null,
-    "keywords": ",omi-circulation-boundary-atlantic-gulf-stream-destabilization-point",
-    "license": null,
+    "keywords": "coastal-marine-environment,global-ocean,latitude,level-2,longitude,marine-resources,marine-safety,multi-year,oceanographic-geographical-features,omi-circulation-boundary-atlantic-gulf-stream-destabilization-point,satellite-observation,time,weather-climate-and-seasonal-forecasting",
+    "license": "proprietary",
     "missionStartDate": "1993-01-01T00:00:00Z",
     "platform": null,
     "platformSerialIdentifier": null,
-    "processingLevel": null,
-    "providers": [],
-    "title": null
+    "processingLevel": "Level 2",
+    "providers": [
+        {
+            "name": "CLS (France)",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "Copernicus Marine Service",
+            "roles": [
+                "host",
+                "processor"
+            ],
+            "url": "https://marine.copernicus.eu"
+        }
+    ],
+    "title": "Gulf Stream destabilization point"
 }

cop_marine - product_types_config - OMI_CLIMATE_SI_ARCTIC_transport

--- old
+++ new
@@ -1,13 +1,28 @@
 {
-    "abstract": null,
-    "doi": null,
+    "abstract": "**DEFINITION**\n\nNet sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79\u00b0N, 20\u00b0W - 10\u00b0E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80\u00b0N, 27\u00b0E - 60\u00b0E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60\u00b0E, 76\u00b0N - 80\u00b0N (positive westward). For further details, see Lien et al. (2021).\n\n**CONTEXT**\n\nThe Arctic Ocean contains a large amount of freshwater, and the freshwater export from the Arctic to the North Atlantic influence the stratification, and, the Atlantic Meridional Overturning Circulation (e.g., Aagaard et al., 1985). The Fram Strait represents the major gateway for freshwater transport from the Arctic Ocean, both as liquid freshwater and as sea ice (e.g., Vinje et al., 1998). The transport of sea ice through the Fram Strait is therefore important for the mass balance of the perennial sea-ice cover in the Arctic as it represents a large export of about 10% of the total sea ice volume every year (e.g., Rampal et al., 2011). Sea ice export through the Fram Strait has been found to explain a major part of the interannual variations in Arctic perennial sea ice volume changes (Ricker et al., 2018). The sea ice and associated freshwater transport to the Barents Sea has been suggested to be a driving mechanism for the presence of Arctic Water in the northern Barents Sea, and, hence, the presence of the Barents Sea Polar Front dividing the Barents Sea into a boreal and an Arctic part (Lind et al., 2018). In recent decades, the Arctic part of the Barents Sea has been giving way to an increasing boreal part, with large implications for the marine ecosystem and harvestable resources (e.g., Fossheim et al., 2015).\n\n**CMEMS KEY FINDINGS**\n\nThe sea-ice transport through the Fram Strait shows a distinct seasonal cycle in both sea ice area and volume transport, with a maximum in winter. There is a slight positive trend in the volume transport over the last two and a half decades. In the Barents Sea, a strong reduction of nearly 90% in average sea-ice thickness has diminished the sea-ice import from the Polar Basin (Lien et al., 2021). In both areas, the Fram Strait and the Barents Sea, the winds governed by the regional patterns of atmospheric pressure is an important driving force of temporal variations in sea-ice transport (e.g., Aaboe et al., 2021; Lien et al., 2021).\n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00192\n\n**References:**\n\n* Aaboe S, Lind S, Hendricks S, Down E, Lavergne T, Ricker R. 2021. Sea-ice and ocean conditions surprisingly normal in the Svalbard-Barents Sea region after large sea-ice inflows in 2019. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 140-148\n* Aagaard K, Swift JH, Carmack EC. 1985. Thermohaline circulation in the Arctic Mediterranean seas. J Geophys Res. 90(C7), 4833-4846\n* Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. 2015. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Clim Change. doi:10.1038/nclimate2647\n* Lien VS, Raj RP, Chatterjee S. 2021. Modelled sea-ice volume and area transport from the Arctic Ocean to the Nordic and Barents seas. In: Copernicus Marine Environment Monitoring Service Ocean State Report, issue 5, J Oper Oceanogr. 14, sup1, 10-17\n* Lind S, Ingvaldsen RB, Furevik T. 2018. Arctic warming hotspot in the northern Barents Sea linked to declining sea ice import. Nature Clim Change. doi:10.1038/s41558-018-0205-y\n* Rampal P, Weiss J, Dubois C, Campin J-M. 2011. IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline. J Geophys Res. 116, C00D07. https://doi.org/10.1029/2011JC007110\n* Ricker R, Girard-Ardhuin F, Krumpen T, Lique C. 2018. Satellite-derived sea ice export and its impact on Arctic ice mass balance. Cryosphere. 12, 3017-3032\n* Vinje T, Nordlund N, Kvambekk \u00c5. 1998. Monitoring ice thickness in Fram Strait. J Geophys Res. 103(C5), 10437-10449\n",
+    "doi": "10.48670/moi-00192",
     "instrument": null,
-    "keywords": ",omi-climate-si-arctic-transport",
-    "license": null,
+    "keywords": "arctic-ocean,coastal-marine-environment,level-4,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-si-arctic-transport,sea-ice-concentration-and/or-thickness,sea-ice-transport-across-line,weather-climate-and-seasonal-forecasting",
+    "license": "proprietary",
     "missionStartDate": "1991-01-01T00:00:00Z",
     "platform": null,
     "platformSerialIdentifier": null,
-    "processingLevel": null,
-    "providers": [],
-    "title": null
+    "processingLevel": "Level 4",
+    "providers": [
+        {
+            "name": "NERSC (Norway)",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "Copernicus Marine Service",
+            "roles": [
+                "host",
+                "processor"
+            ],
+            "url": "https://marine.copernicus.eu"
+        }
+    ],
+    "title": "Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis"
 }

cop_marine - product_types_config - SEAICE_ARC_PHY_AUTO_L4_MY_011_025

--- old
+++ new
@@ -1,13 +1,28 @@
 {
-    "abstract": null,
-    "doi": null,
+    "abstract": "Daily sea ice age and sea ice age fractions with uncertainties in the period 1991 - 2025. Coverage:  Arctic Ocean. Resolution: 25 km.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/mds-00371",
+    "doi": "10.48670/mds-00371",
     "instrument": null,
-    "keywords": ",seaice-arc-phy-auto-l4-my-011-025",
-    "license": null,
+    "keywords": "age-of-sea-ice,arctic-ocean,level-4,multi-year,oceanographic-geographical-features,satellite-observation,seaice-arc-phy-auto-l4-my-011-025,target-application#seaiceclimate",
+    "license": "proprietary",
     "missionStartDate": "1995-09-15T00:00:00Z",
     "platform": null,
     "platformSerialIdentifier": null,
-    "processingLevel": null,
-    "providers": [],
-    "title": null
+    "processingLevel": "Level 4",
+    "providers": [
+        {
+            "name": "MET Norway",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "Copernicus Marine Service",
+            "roles": [
+                "host",
+                "processor"
+            ],
+            "url": "https://marine.copernicus.eu"
+        }
+    ],
+    "title": "Arctic Sea Ice Age"
 }

cop_marine - product_types_config - SST_GLO_PHY_L4_NRT_010_005

--- old
+++ new
@@ -1,13 +1,28 @@
 {
-    "abstract": null,
-    "doi": null,
+    "abstract": "For The Global Ocean - The GHRSST Multi-Product Ensemble (GMPE) system has been implemented at the Met Office which takes inputs from various analysis production centres on a routine basis and produces ensemble products at 0.25deg.x0.25deg. horizontal resolution.\n \nA large number of sea surface temperature (SST) analyses are produced by various institutes around the world, making use of the SST observations provided by the Global High Resolution SST (GHRSST) project. These are used by a number of groups including: numerical weather prediction centres; ocean forecasting groups; climate monitoring and research groups. There is a requirement to develop international collaboration in this field in order to assess and inter-compare the different analyses, and to provide uncertainty estimates on both the analyses and observational products. The GMPE system has been developed for these purposes and is run on a daily basis at the Met Office, producing global ensemble median and standard deviations for SST on a regular 0.25 degree resolution global grid.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00378\n\n**References:**\n\n* www.ghrsst.org\n* Matthew Martin, Prasanjit Dash, Alexander Ignatov, Viva Banzon, Helen Beggs, Bruce Brasnett, Jean-Francois Cayula, James Cummings, Craig Donlon, Chelle Gentemann, Robert Grumbine, Shiro Ishizaki, Eileen Maturi, Richard W. Reynolds, Jonah Roberts-Jones, Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE), Deep Sea Research Part II: Topical Studies in Oceanography, Volumes 77\u201380, 2012, Pages 21-30, ISSN 0967-0645, https://doi.org/10.1016/j.dsr2.2012.04.013.\n",
+    "doi": "10.48670/mds-00378",
     "instrument": null,
-    "keywords": ",sst-glo-phy-l4-nrt-010-005",
-    "license": null,
+    "keywords": "coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,near-real-time,oceanographic-geographical-features,satellite-observation,sea-surface-temperature,sst-glo-phy-l4-nrt-010-005,weather-climate-and-seasonal-forecasting",
+    "license": "proprietary",
     "missionStartDate": "2026-01-01T00:00:00Z",
     "platform": null,
     "platformSerialIdentifier": null,
-    "processingLevel": null,
-    "providers": [],
-    "title": null
+    "processingLevel": "Level 4",
+    "providers": [
+        {
+            "name": "Met Office (UK)",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "Copernicus Marine Service",
+            "roles": [
+                "host",
+                "processor"
+            ],
+            "url": "https://marine.copernicus.eu"
+        }
+    ],
+    "title": "Global Ocean GMPE Sea Surface Temperature Multi Product Ensemble"
 }

---

abstract
doi
keywords
license
providers[0].name
providers[0].roles[0]
providers[1].name
providers[1].roles[0]
providers[1].roles[1]
providers[1].url
title
1 product_type(s) affected (cop_marine)

Click to expand for detailed diffs

cop_marine - product_types_config - OMI_CLIMATE_TEMPSAL_IBI_extreme_var_mean_and_anomaly

--- old
+++ new
@@ -1,13 +1,28 @@
 {
-    "abstract": null,
-    "doi": null,
+    "abstract": "**DEFINITION**\n\nThe Iberia Biscay Ireland (IBI) Sea Surface Temperature extreme from Reanalysis ocean monitoring indicator (OMI) (OMI_CLIMATE_TEMPSAL_IBI_extreme_var_temp_mean_and_anomaly)  is based on the computation of the annual 99th percentile of Sea Surface Temperature (SST) from model data. Two different Copernicus Marine products are used to compute the indicator: The IBI Reanalysis (IBI_MULTIYEAR_PHY_005_002) and the IBI Analysis product (IBI_ANALYSISFORECAST_PHY_005_001). \n\nTwo parameters have been considered for this OMI: \n\n* **Map of the 99th mean percentile**: It is obtained from the reanalysis product, the annual 99th percentile is computed for each year of the product. The percentiles are temporally averaged over the whole period (1993-2023). \n\n* **Anomaly of the 99th percentile in 2024**: The 99th percentile of the year 2024 is computed from the Analysis product. The anomaly is obtained by subtracting the mean percentile from the 2024 percentile. \n\nThis indicator is aimed at monitoring the extremes of sea surface temperature every year and at checking their variations in space. The use of percentiles instead of annual maxima, makes this extremes study less affected by individual data. This study of extreme variability was first applied to the sea level variable (P\u00e9rez G\u00f3mez et al 2016) and then extended to other essential variables, such as sea surface temperature and significant wave height (P\u00e9rez G\u00f3mez et al 2018 and Alvarez Fanjul et al., 2019). More details and a full scientific evaluation can be found in the CMEMS Ocean State report (Alvarez Fanjul et al., 2019). \n\n**CONTEXT** \n\nThe Sea Surface Temperature (SST) is one of the essential ocean variables, hence the monitoring of this variable is of key importance, since its variations can affect the ocean circulation, marine ecosystems, and ocean-atmosphere exchange processes. As the oceans continuously interact with the atmosphere, trends of sea surface temperature can also have an effect on the global climate. While the global-averaged sea surface temperatures have increased since the beginning of the 20th century (Hartmann et al., 2013) in the North Atlantic, anomalous cold conditions have also been reported since 2014 (Mulet et al., 2018; Dubois et al., 2018). \n\nThe IBI area is a complex dynamic region with a remarkable variety of ocean physical processes and scales involved. The SST field in the region is strongly dependent on latitude, with higher values towards the South (Locarnini et al. 2013). This latitudinal gradient is supported by the presence of the eastern part of the North Atlantic subtropical gyre that transports cool water from the northern latitudes towards the equator. Additionally, the IBI region is under the influence of the Sea Level Pressure dipole established between the Icelandic low and the Bermuda high. Therefore, the interannual and interdecadal variability of the surface temperature field may be influenced by the North Atlantic Oscillation pattern (Czaja and Frankignoul, 2002; Flatau et al., 2003). \n\nUpwelling processes, taking place in the coastal margins, are also relevant in the IBI region. The most referenced one is the eastern boundary coastal upwelling system off the African and western Iberian coast (Sotillo et al., 2016), although other smaller upwelling systems have also been described in the northern coast of the Iberian Peninsula (Alvarez et al., 2011), the south-western Irish coast (Edwars et al., 1996) and the European Continental Slope (Dickson, 1980). \n\n**CMEMS KEY FINDINGS** \n\nIn the IBI region, the 99th mean percentile for 1993-2023 shows a north-south pattern driven by the climatological distribution of temperatures in the North Atlantic. In the coastal regions of Africa and the Iberian Peninsula, the mean values are influenced by the upwelling processes (Sotillo et al., 2016). These results are consistent with the ones presented in \u00c1lvarez Fanjul (2019) for the period 1993-2016. \n\nThe analysis of the 99th percentile SST anomaly for the year 2024 reveals that the northeastern Atlantic region, between latitudes 36\u00b0 N and 48\u00b0 N, experienced thermal anomalies exceeding twice the standard deviation. Similar anomalies are also observed near the northeastern Iberian Peninsula, suggesting that inshore and coastal areas may have been affected as well. In contrast, the upwelling region west of the Iberian Peninsula shows negative anomalies in maximum SST, indicating an intensification of upwelling processes in this area. \n\n**DOI (product):** \nhttps://doi.org/10.48670/moi-00254\n\n**References:**\n\n* Alvarez I, Gomez-Gesteira M, DeCastro M, Lorenzo MN, Crespo AJC, Dias JM., (2011): Comparative analysis of upwelling influence between the western and northern coast of the Iberian Peninsula. Continental Shelf Research, 31(5), 388-399.\n* \u00c1lvarez Fanjul E, Pascual Collar A, P\u00e9rez G\u00f3mez B, De Alfonso M, Garc\u00eda Sotillo M, Staneva J, Clementi E, Grandi A, Zacharioudaki A, Korres G, Ravdas M, Renshaw R, Tinker J, Raudsepp U, Lagemaa P, Maljutenko I, Geyer G, M\u00fcller M, \u00c7a\u011flar Yumruktepe V. Sea level, sea surface temperature and SWH extreme percentiles: combined analysis from model results and in situ observations, Section 2.7, p:31. In: Schuckmann K, Le Traon P-Y, Smith N, Pascual A, Djavidnia S, Gattuso J-P, Gr\u00e9goire M, Nolan G, et al., (2019): Copernicus Marine Service Ocean State Report, Issue 3, Journal of Operational Oceanography, 12:sup1, S1-S123, DOI: 10.1080/1755876X.2019.1633075\n* Czaja A, Frankignoul C., (2002): Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. Journal of Climate, 15(6), 606-623.\n* Dickson RR, Gurbutt PA, Pillai VN. 1980. Satellite evidence of enhanced upwelling along the European continental slope. Journal of Physical Oceanography, 10(5), 813-819.\n* Dubois C, von Schuckmann K, Josey S., (2018): Changes in the North Atlantic. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 2.9, s66\u2013s70, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Edwards A, Jones K, Graham JM, Griffiths CR, MacDougall N, Patching J, Raine R. 1996. Transient coastal upwelling and water circulation in Bantry Bay, a ria on the south-west coast of Ireland. Estuarine, Coastal and Shelf Science, 42(2), 213-230.\n* Flatau MK, Talley L, Niiler PP., (2003): The North Atlantic Oscillation, surface current velocities, and SST changes in the subpolar North Atlantic. Journal of Climate, 16(14), 2355-2369.\n* Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Br\u00f6nnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM., (2013): Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.\n* Locarnini, R.A., Mishonov, A.V., Antonov, J.I., Boyer, T.P., Garcia, H.E., Baranova, O.K., Zweng, M.M., Paver, C.R., Reagan, J.R., Johnson, D.R., et al., (2013). World ocean atlas 2013. In:Levitus S, Mishonov A, editors, technical editors. NOAAatlas NESDIS 73, 40 pp. (Volume 1: Temperature).\n* Mulet S, Nardelli BB, Good S, Pisano A, Greiner E, Monier M., (2018): Ocean temperature and salinity. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 1.1, s5\u2013s13, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* P\u00e9rez-G\u00f3mez B, \u00c1lvarez-Fanjul E, She J, P\u00e9rez-Gonz\u00e1lez I, Manzano F., (2016): Extreme sea level events, Section 4.4, p:300. In: Von Schuckmann K, Le Traon PY, Alvarez-Fanjul E, Axell L, Balmaseda M, Breivik LA, Brewin RJW, Bricaud C, Drevillon M, Drillet Y, Dubois C , Embury O, Etienne H, Garc\u00eda-Sotillo M, Garric G, Gasparin F, Gutknecht E, Guinehut S, Hernandez F, Juza M, Karlson B, Korres G, Legeais JF, Levier B, Lien VS, Morrow R, Notarstefano G, Parent L, Pascual A, P\u00e9rez-G\u00f3mez B, Perruche C, Pinardi N, Pisano A, Poulain PM , Pujol IM, Raj RP, Raudsepp U, Roquet H, Samuelsen A, Sathyendranath S, She J, Simoncelli S, Solidoro C, Tinker J, Tintor\u00e9 J, Viktorsson L, Ablain M, Almroth-Rosell E, Bonaduce A, Clementi E, Cossarini G, Dagneaux Q, Desportes C, Dye S, Fratianni C, Good S, Greiner E, Gourrion J, Hamon M, Holt J, Hyder P, Kennedy J, Manzano-Mu\u00f1oz F, Melet A, Meyssignac B, Mulet S, Nardelli BB, O\u2019Dea E, Olason E, Paulmier A, P\u00e9rez-Gonz\u00e1lez I, Reid R, Racault MF, Raitsos DE, Ramos A, Sykes P, Szekely T, Verbrugge N. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report, Journal of Operational Oceanography. 9 (sup2): 235-320. http://dx.doi.org/10.1080/1755876X.2016.1273446\n* P\u00e9rez G\u00f3mez B., De Alfonso M., Zacharioudaki A., P\u00e9rez Gonz\u00e1lez I., \u00c1lvarez Fanjul E., M\u00fcller M., Marcos M., Manzano F., Korres G., Ravdas M., Tamm S., (2018): Sea level, SST and waves: extremes variability. In: Copernicus Marine Service Ocean State Report, Issue 2, Journal of Operational Oceanography, 11:sup1, Chap. 3.1, s79\u2013s88, DOI: https://doi.org/10.1080/1755876X.2018.1489208\n* Sotillo MG, Levier B, Pascual A, Gonzalez A., (2016): Iberian-Biscay-Irish Sea. In von Schuckmann et al. 2016. The Copernicus Marine Environment Monitoring Service Ocean State Report No.1, Journal of Operational Oceanography, 9:sup2, s235-s320, DOI: 10.1080/1755876X.2016.1273446\n",
+    "doi": "10.48670/moi-00254",
     "instrument": null,
-    "keywords": ",omi-climate-tempsal-ibi-extreme-var-mean-and-anomaly",
-    "license": null,
+    "keywords": "coastal-marine-environment,iberian-biscay-irish-seas,marine-resources,marine-safety,multi-year,numerical-model,oceanographic-geographical-features,omi-climate-tempsal-ibi-extreme-var-mean-and-anomaly,weather-climate-and-seasonal-forecasting",
+    "license": "proprietary",
     "missionStartDate": "1970-01-01T00:00:00.000000Z",
     "platform": null,
     "platformSerialIdentifier": null,
     "processingLevel": null,
-    "providers": [],
-    "title": null
+    "providers": [
+        {
+            "name": "NOW Systems (Spain)",
+            "roles": [
+                "producer"
+            ]
+        },
+        {
+            "name": "Copernicus Marine Service",
+            "roles": [
+                "host",
+                "processor"
+            ],
+            "url": "https://marine.copernicus.eu"
+        }
+    ],
+    "title": "Iberia Biscay Ireland Sea Surface Temperature extreme from Reanalysis"
 }

---

abstract
doi
title
1 product_type(s) affected (cop_marine)

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cop_marine - product_types_config - INSITU_GLO_PHY_TS_OA_MY_013_052

--- old
+++ new
@@ -1,6 +1,6 @@
 {
-    "abstract": "Global Ocean- Gridded objective analysis fields of temperature and salinity using profiles from the reprocessed in-situ global product CORA (INSITU_GLO_TS_REP_OBSERVATIONS_013_001) using the ISAS software. Objective analysis is based on a statistical estimation method that allows presenting a synthesis and a validation of the dataset, providing a validation source for operational models, observing seasonal cycle and inter-annual variability.\n\n**DOI (product):**  \nhttps://doi.org/10.17882/46219",
-    "doi": "10.17882/46219",
+    "abstract": "Global Ocean in situ - Delayed Mode temperature and salinity CORA -objective analysis \nThe global ocean objective analysis (gridded) fields of temperature and salinity are produced from the in situ profiles available in the reprocessed (or multiyear) in-situ CORA product INSITU_GLO_PHY_TS_DISCRETE_MY_013_001, with the exception of moorings, thermosalinographs and surface drifters. The objective analysis is based on the ISAS method (Gaillard et al. 2009), a statistical estimation approach that enables the mapping of ocean in situ profiles onto three-dimensional gridded fields.\nThe resulting gridded product has a spatial resolution of 0.5\u00b0 in latitude and 0.5\u00b0 in longitude at the equator and includes 187 vertical levels. It provides monthly temperature and salinity fields centered the 15th of the month.  It is updated twice a year by a full reprocessing covering  the whole period from 1960 to December of the last year before present time (produced generally in November) and a temporal extension of the first 6 months of the ongoing year (done generally in July). \nA monthly file (data file) gathering the observed profiles used to calculate the analysis, which are interpolated on the vertical grid, is also provided.\n\n**DOI (product):**  \nhttps://doi.org/10.48670/mds-00383\n\n**References:**\n\n* F. Gaillard, E. Autret, V. Thierry, P. Galaup, C. Coatanoan, and T. Loubrieu. Quality control of large argo datasets. Journal of Atmospheric and Oceanic Technology, 26:337\u2013351, 2009. https://doi.org/10.1175/2008JTECHO552.1.270\n",
+    "doi": "10.48670/mds-00383",
     "instrument": null,
     "keywords": "/observational-data/in-situ,baltic-sea,black-sea,cds-coriolis,coastal-marine-environment,global-ocean,iberian-biscay-irish-seas,in-situ-observation,insitu-glo-phy-ts-oa-my-013-052,level-4,marine-resources,marine-safety,mediterranean-sea,multi-year,north-west-shelf-seas,oceanographic-geographical-features,salinity,sea-temperature,sea-water-salinity,sea-water-temperature,weather-climate-and-seasonal-forecasting",
     "license": "proprietary",
@@ -24,5 +24,5 @@
             "url": "https://marine.copernicus.eu"
         }
     ],
-    "title": "Global Ocean- Delayed Mode gridded CORA- In-situ Observations objective analysis in Delayed Mode"
+    "title": "Global Ocean in situ - Delayed Mode temperature and salinity CORA -objective analysis"
 }

---

abstract
keywords
9 product_type(s) affected (cop_marine)

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cop_marine - product_types_config - OCEANCOLOUR_ATL_BGC_L3_NRT_009_111

--- old
+++ new
@@ -1,8 +1,8 @@
 {
-    "abstract": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and  Reflectance (**RRS**).\n\n* Temporal resolutions: **daily**.\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00284\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Druon, JN., H\u00e9laou\u00ebt, P., Beaugrand, G. et al. Satellite-based indicator of zooplankton distribution for global monitoring. Sci Rep 9, 4732 (2019). https://doi.org/10.1038/s41598-019-41212-2.\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127 *Sieburth, J. M.,\n* Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n",
+    "abstract": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n\n* Variables: Chlorophyll-a (**CHL**), Gradient of Chlorophyll-a (**CHL_gradient**), Phytoplankton Functional types and sizes (**PFT**), Suspended Matter (**SPM**), Secchi Transparency Depth (**ZSD**), Diffuse Attenuation (**KD490**), Particulate Backscattering (**BBP**), Absorption Coef. (**CDM**) and Reflectance (**RRS**).\n\n\n\n* Temporal resolutions: **daily**.\n\n* Spatial resolutions: **1 km** and a finer resolution based on olci **300 meters** inputs.\n\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **GlobColour**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00284\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Druon, JN., H\u00e9laou\u00ebt, P., Beaugrand, G. et al. Satellite-based indicator of zooplankton distribution for global monitoring. Sci Rep 9, 4732 (2019). https://doi.org/10.1038/s41598-019-41212-2.\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127 *Sieburth, J. M.,\n* Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Gohin, F.: Annual cycles of chlorophyll-a, non-algal suspended particulate matter, and turbidity observed from space and in situ in coastal waters, Ocean Sci., 7, 705-732, https://doi.org/10.5194/os-7-705-2011, 2011.\n* Doron, M., Babin, M., Mangin, A. and O. Fanton d'Andon. Estimation of light penetration, and horizontal and vertical visibility in oceanic and coastal waters from surface reflectance. Journal of Geophysical Research, volume 112, C06003, https://doi.org/10.1029/2006JC004007, 2006\n* Loisel, H., Stramski, D., Dessailly, D., J amet, C., Li, L., & Reynolds, R. A. (2018). An inverse model for estimating the optical absorption and backscattering coefficients of seawater from remote-sensing reflectance over a broad range of oceanic and coastal marine environments. Journal of Geophysical Research: Oceans, 123, 2141\u20132171, https://doi.org/10.1002/ 2017JC01363\n* Bonelli, A. G., et al. (2021). Colored dissolved organic matter absorption at global scale from ocean color radiometry observation: Spatio-temporal variability and contribution to the absorption budget. Remote Sensing of Environment, 265, 112637. https://doi.org/10.1016/j.rse.2021.112637\n",
     "doi": "10.48670/moi-00284",
     "instrument": null,
-    "keywords": "bbp-pft,cdm,chl,coastal-marine-environment,global-ocean,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-atl-bgc-l3-nrt-009-111,oceanographic-geographical-features,pft,satellite-observation,secchi-depth-of-sea-water,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting",
+    "keywords": "bbp,cdm,chl,coastal-marine-environment,global-ocean,kd490,level-3,magnitude-of-horizontal-gradient-of-mass-concentration-of-chlorophyll-a-in-sea-water,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-suspended-particulate-matter-in-sea-water,near-real-time,oceancolour-atl-bgc-l3-nrt-009-111,oceanographic-geographical-features,pft,rr555,rr560,rrs400,rrs412,rrs443,rrs490,rrs510,rrs620,rrs665,rrs670,rrs674,rrs681,rrs709,satellite-observation,secchi-depth-of-sea-water,spm,surface-ratio-of-upwelling-radiance-emerging-from-sea-water-to-downwelling-radiative-flux-in-air,volume-absorption-coefficient-of-radiative-flux-in-sea-water-due-to-dissolved-organic-matter-and-non-algal-particles,volume-attenuation-coefficient-of-downwelling-radiative-flux-in-sea-water,volume-backwards-scattering-coefficient-of-radiative-flux-in-sea-water-due-to-particles,weather-climate-and-seasonal-forecasting,zsd",
     "license": "proprietary",
     "missionStartDate": "2023-04-21T00:00:00Z",
     "platform": null,

cop_marine - product_types_config - OCEANCOLOUR_ATL_BGC_L4_MY_009_118

--- old
+++ new
@@ -1,8 +1,8 @@
 {
-    "abstract": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: **1 km**.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00289\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Antoine, D., and Morel, A. Oceanic primary production: 1. Adaptation of a spectral light\u2010photosynthesis model in view of application to satellite chlorophyll observations. Global biogeochemical cycles, 10(1), 43-55, https://doi/10.1029/95GB02831, 1996.\n",
+    "abstract": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"cloud free\" product.\n\n* Spatial resolutions: **1 km**.\n\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\n\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **GlobColour**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00289\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Antoine, D., and Morel, A. Oceanic primary production: 1. Adaptation of a spectral light\u2010photosynthesis model in view of application to satellite chlorophyll observations. Global biogeochemical cycles, 10(1), 43-55, https://doi/10.1029/95GB02831, 1996.\n",
     "doi": "10.48670/moi-00289",
     "instrument": null,
-    "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-atl-bgc-l4-my-009-118,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting",
+    "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prokaryotes-expressed-as-chlorophyll-in-sea-water,multi-year,oceancolour-atl-bgc-l4-my-009-118,oceanographic-geographical-features,pft,pp,primary-production-of-biomass-expressed-as-carbon,satellite-observation,weather-climate-and-seasonal-forecasting",
     "license": "proprietary",
     "missionStartDate": "1997-09-01T00:00:00Z",
     "platform": null,

cop_marine - product_types_config - OCEANCOLOUR_ATL_BGC_L4_NRT_009_116

--- old
+++ new
@@ -1,8 +1,8 @@
 {
-    "abstract": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **\"\"multi\"\"** products, and S3A & S3B only for the **\"\"olci\"\"** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a \"\"cloud free\"\" product.\n* Spatial resolutions: **1 km**.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"\"GlobColour\"\"**.\n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00288\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Antoine, D., and Morel, A. Oceanic primary production: 1. Adaptation of a spectral light\u2010photosynthesis model in view of application to satellite chlorophyll observations. Global biogeochemical cycles, 10(1), 43-55, https://doi/10.1029/95GB02831, 1996.\n",
+    "abstract": "For the **Atlantic** Ocean **Satellite Observations**, ACRI-ST company (Sophia Antipolis, France) is providing **Bio-Geo-Chemical (BGC)** products based on the **Copernicus-GlobColour** processor.\n* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the **multi** products, and S3A & S3B only for the **olci** products.\n* Variables: Chlorophyll-a (**CHL**), Phytoplankton Functional types and sizes (**PFT**), Primary Production (**PP**).\n\n* Temporal resolutions: **monthly** plus, for some variables, **daily gap-free** based on a space-time interpolation to provide a **cloud free** product.\n* Spatial resolutions: **1 km**.\n* Recent products are organized in datasets called Near Real Time (**NRT**) and long time-series (from 1997) in datasets called Multi-Years (**MY**).\n\nTo find the **Copernicus-GlobColour** products in the catalogue, use the search keyword **\"GlobColour\"**. \n\n**DOI (product):**   \nhttps://doi.org/10.48670/moi-00288\n\n**References:**\n\n* Gohin, F., Druon, J.N. and Lampert, L.: A five channel chlorophyll concentration algorithm applied to SeaWiFS data processed by SeaDAS in coastal waters. International journal of remote sensing, 23(8), 1639-1661, https://doi.org/10.1080/01431160110071879, 2002.\n* Hu, C., Lee, Z. and Franz, B.: Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three\u2010band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1). https://doi.org/10.1029/2011jc007395, 2012\n* Gons, Herman J.; Rijkeboer, Machteld; Ruddick, Kevin G.; Effect of a waveband shift on chlorophyll retrieval from MERIS imagery of inland and coastal waters, Journal of Plankton Research, 2005, 10.1093/plankt/fbh151\n* Xi H., Losa N. S., Mangin A, Garnesson P., Bretagnon M., Demaria J, Soppa A. M., Hembise Fanton d'Andon O., Bracher A.(2021) Global chlorophyll a concentrations of phytoplankton functional types with detailed uncertainty assessment using multi\u2010sensor ocean color and sea surface temperature satellite products, Journal of Geophysical Research Oceans 126(5), https://doi.org/10.1029/2020JC017127\n* Sieburth, J. M., Smetacek, V., & Lenz, J. (1978). Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1. Limnology and oceanography, 23(6), 1256-1263.\n* Antoine, D., and Morel, A. Oceanic primary production: 1. Adaptation of a spectral light\u2010photosynthesis model in view of application to satellite chlorophyll observations. Global biogeochemical cycles, 10(1), 43-55, https://doi/10.1029/95GB02831, 1996.\n",
     "doi": "10.48670/moi-00288",
     "instrument": null,
-    "keywords": "chl,coastal-marine-environment,global-ocean,level-4,marine-resources,marine-safety,mass-concentration-of-chlorophyll-a-in-sea-water,mass-concentration-of-diatoms-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-dinophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-greenalgae-and-prochlorophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-haptophytes-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-microphytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-nanophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-picophytoplankton-expressed-as-chlorophyll-in-sea-water,mass-concentration-of-prochlorococcus-expressed-as-chlorophyll-in-sea-water,near-real-time,oceancolour-a