AndreaBozzo/ceres-open-data-index huggingface dataset:
Baltic Sea- Sea Surface Temperature Reprocessed

'''Short description:''' 

For the Baltic Sea- The DMI Sea Surface Temperature reprocessed analysis provides daily gap-free sea surface temperature fields, referred as L4 product, at 0.02deg. x 0.02deg. horizontal resolution. It is produced by the DMI Optimal Interpolation (DMIOI) system (Høyer and She, 2007) to provide a high resolution (1/50deg. - approx. 2km grid resolution) daily analysis of the daily average sea surface temperature (SST) at 20 cm depth. It uses satellite data from infra-red radiometers, from the ESA SST_cci v3.0 (Embury et al., 2024) and Copernicus C3S projects, namely L2P data from (A)ATSRs, SLSTR and AVHRR for the period 1982-2021, L3U data from SLSTR and AVHRR for 2022-July 19 2024 and L2P data from SLSTR and AVHRR from July 20 2024 onward. For the Sea Ice Concentration it uses the Baltic high resolution sea ice concentration data from the Copernicus Marine Service SI TAC.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00156
Sea Ice Area/Volume Transport in the Nordic Seas from Reanalysis

'''DEFINITION'''

Net sea-ice volume and area transport through the openings Fram Strait between Spitsbergen and Greenland along 79°N, 20°W - 10°E (positive southward); northern Barents Sea between Svalbard and Franz Josef Land archipelagos along 80°N, 27°E - 60°E (positive southward); eastern Barents Sea between the Novaya Zemlya and Franz Josef Land archipelagos along 60°E, 76°N - 80°N (positive westward). For further details, see Lien et al. (2021).

'''CONTEXT'''

The 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).

'''CMEMS KEY FINDINGS'''

The 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).

'''DOI (product):''' 
https://doi.org/10.48670/moi-00192
Black Sea Biogeochemistry Reanalysis

'''Short description:''' 

The biogeochemical reanalysis for the Black Sea is produced by the MAST/ULiege Production Unit by means of the BAMHBI biogeochemical model. The workflow runs on the CECI hpc infrastructure (Wallonia, Belgium).

'''DOI (product)''':
https://doi.org/10.48670/mds-00372
Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing)

'''Short description: '''

For the '''Global''' Ocean '''Satellite Observations''', ACRI-ST company (Sophia Antipolis, France) is providing '''Bio-Geo-Chemical (BGC)''' products based on the '''Copernicus-GlobColour''' processor.



* Upstreams: SeaWiFS, MODIS, MERIS, VIIRS-SNPP & JPSS1, OLCI-S3A & S3B for the '''multi''' products, and S3A & S3B only for the '''olci''' products.



* Variables: Chlorophyll-a ('''CHL'''), Phytoplankton Functional types and sizes ('''PFT'''), Primary Production ('''PP'''), Suspended Matter ('''SPM'''), Secchi Transparency Depth ('''ZSD'''), Diffuse Attenuation ('''KD490'''), Particulate Backscattering ('''BBP'''), Absorption Coef. ('''CDM''') and Reflectance ('''RRS''').



* Temporal resolutions: '''monthly''' plus, for some variables, '''daily gap-free''' based on a space-time interpolation to provide a "cloud free" product.



* Spatial resolutions: '''4 km''' and a finer resolution based on olci '''300 meters''' inputs.



* Recent products are organized in datasets called Near Real Time ('''NRT''') and long time-series (from 1997) in datasets called Multi-Years ('''MY''').



To find the '''Copernicus-GlobColour''' products in the catalogue, use the search keyword '''GlobColour'''. 

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00281
Mediterranean Sea - High Resolution Diurnal Subskin Sea Surface Temperature Analysis

''' Short description: ''' 

For the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16° (0.0625°) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014).
 
[https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services How to cite]

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00170
GLOBAL OCEAN ALONG-TRACK L3 SEA SURFACE HEIGHTS NRT

'''Short description:'''

Altimeter satellite along-track sea surface heights anomalies (SLA) computed with respect to a twenty-year [1993, 2012] mean with a 1Hz (~7km) and 5Hz (~1km) sampling. It serves in near-real time applications.
This product is processed by the DUACS multimission altimeter data processing system. It processes data from all altimeter missions available (e.g. Sentinel-6A, Jason-3, Sentinel-3A, Sentinel-3B, Saral/AltiKa, Cryosat-2, HY-2B). The system exploits the most recent datasets available based on the enhanced OGDR/NRT+IGDR/STC production. All the missions are homogenized with respect to a reference mission. Part of the processing is fitted to the Global Ocean. (see QUID document or http://duacs.cls.fr [http://duacs.cls.fr] pages for processing details). 
The product gives additional variables (e.g. Mean Dynamic Topography, Dynamic Atmospheric Correction, Ocean Tides, Long Wavelength Errors) that can be used to change the physical content for specific needs (see PUM document for details)

'''Associated products'''
A time invariant product http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033 [http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_NOISE_L4_STATIC_008_033] describing the noise level of along-track measurements is available. It is associated to the sla_filtered variable. It is a gridded product. One file is provided for the global ocean and those values must be applied for Arctic and Europe products. For Mediterranean and Black seas, one value is given in the QUID document.

'''DOI (product)''':
https://doi.org/10.48670/moi-00147
Global Ocean- CORA- In-situ Observations Yearly Delivery in Delayed Mode

'''Short description:'''
For the Global Ocean- In-situ observation yearly delivery in delayed mode. The In Situ delayed mode product designed for reanalysis purposes integrates the best available version of in situ data for temperature and salinity measurements. These data are collected from main global networks (Argo, GOSUD, OceanSITES, World Ocean Database) completed by European data provided by EUROGOOS regional systems and national system by the regional INS TAC components. It is updated on a yearly basis. This version is a merged product between the previous verion of CORA and EN4 distributed by the Met Office  for the period 1950-1990.

'''DOI (product) :'''  
https://doi.org/10.17882/46219
European North West Shelf/Iberia Biscay Irish Seas – High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations

'''Short description:'''

For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.02° resolution grid. It includes observations by polar orbiting and geostationary satellites .  The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases. 3 more datasets are available that only contain "per sensor type" data : Polar InfraRed (PIR), Polar MicroWave (PMW), Geostationary InfraRed (GIR)

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00310
GLOBAL OCEAN GRIDDED NORMALIZED MEASUREMENT NOISE OF SEA LEVEL ANOMALIES

'''Short description:'''

In wavenumber spectra, the 1hz measurement error is the noise level estimated as the mean value of energy at high wavenumbers (below ~20km in term of wavelength). The 1hz noise level spatial distribution follows the instrumental white-noise linked to the Surface Wave Height but also connections with the backscatter coefficient. The full understanding of this hump of spectral energy (Dibarboure et al., 2013, Investigating short wavelength correlated errors on low-resolution mode altimetry, OSTST 2013 presentation) still remain to be achieved and overcome with new retracking, new editing strategy or new technology.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00144
Arctic Ocean - Sea and Ice Surface Temperature

'''Short description:'''

Arctic Sea and Ice surface temperature product based upon observations from the Metop_A AVHRR instrument. The product is a daily interpolated field with a 0.05 degrees resolution, and covers surface temperatures in the ocean, the sea ice and the marginal ice zone.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00130
GLOBAL OCEAN L3 SPECTRAL PARAMETERS FROM NRT SATELLITE MEASUREMENTS

'''Short description:'''

Near-Real-Time mono-mission satellite-based integral parameters derived from the directional wave spectra.
Using linear propagation wave model, only wave observations that can be back-propagated to wave converging regions are considered.
The dataset parameters includes partition significant wave height, partition peak period and partition peak or principal direction given along swell propagation path in space and time at a 3-hour timestep, from source to land. Validity flags are also included for each parameter and indicates the valid time steps along propagation (eg. no propagation for significant wave height close to the storm source or any integral parameter when reaching the land).
The integral parameters at observation point are also available together with a quality flag based on the consistency between each propagated observation and the overall swell field.
This product is processed by the WAVE-TAC multi-mission SAR data processing system.
It processes near-real-time data from the following missions: SAR (Sentinel-1A and Sentinel-1B) and CFOSAT/SWIM.
One file is produced for each mission and is available in two formats depending on the user needs: one gathering in one netcdf file all observations related to the same swell field, and for another all observations available in a 3-hour time range, and for both formats, propagated information from source to land.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00178
Baltic Sea Physics Analysis and Forecast

'''Short description:'''

This Baltic Sea physical model product provides forecasts for the physical conditions in the Baltic Sea. The Baltic forecast is updated twice daily from a 00Z production proving a 10 days forecast and from a 12Z production providing a 6 days forecast. Several datasets are provided: One with hourly instantaneous values, one with daily mean values and one with monthly mean values, all containing these parameters: sea level variations, ice concentration and thickness at the surface, and temperature, salinity and horizontal and vertical velocities for the 3D field. Additionally a dataset with 15 minutes (instantaneous) surface values are provided for the sea level variation and the surface horizontal currents, as well as detided daily values. The product is produced by a Baltic Sea set up of the NEMOv4.2.1 ocean model. This product is provided at the models native grid with a resolution of 1 nautical mile in the horizontal, and with up to 56 vertical depth levels. The area covers the Baltic Sea including the transition area towards the North Sea (i.e. the Danish Belts, the Kattegat and Skagerrak). The ocean model is forced with Stokes drift data from the Baltic Sea Wave forecast product (BALTICSEA_ANALYSISFORECAST_WAV_003_010). Satellite SST, sea ice concentrations and in-situ T and S profiles are assimilated into the model's analysis field.

'''DOI (product) :'''  
https://doi.org/10.48670/moi-00010
Atlantic-Iberian Biscay Irish- Ocean Biogeochemical Analysis and Forecast

'''Short description:'''

The IBI-MFC provides a high-resolution biogeochemical analysis and forecast product covering the European waters, and more specifically the Iberia–Biscay–Ireland (IBI) area. The last 2 years before now (historic best estimates) as well as daily averaged forecasts with a horizon of 10 days (updated on a weekly basis) are available on the catalogue.
To this aim, an online coupled physical-biogeochemical operational system is based on NEMO-PISCES at 1/36° and adapted to the IBI area, being Mercator-Ocean in charge of the model code development. PISCES is a model of intermediate complexity, with 24 prognostic variables. It simulates marine biological productivity of the lower trophic levels and describes the biogeochemical cycles of carbon and of the main nutrients (P, N, Si, Fe).
The product provides daily and monthly averages of the main biogeochemical variables:  chlorophyll, oxygen, nitrate, phosphate, silicate, iron, ammonium, net primary production, euphotic zone depth, phytoplankton carbon, pH, dissolved inorganic carbon, surface partial pressure of carbon dioxide, zooplankton and light attenuation.

'''DOI (Product)''': 
https://doi.org/10.48670/moi-00026
Black Sea Waves Reanalysis

'''Short description''': 

The wave reanalysis for the Black Sea is produced with the third generation spectral wave model WAM Cycle 6. The reanalysis is produced on the HPC at Helmholtz-Zentrum Hereon. The shallow water Black Sea version is implemented on a spherical grid with a spatial resolution of about 2.5 km (1/40° x 1/40°) with 24 directional and 30 frequency bins. The number of active wave model grid points is 74,518. The model takes into account wave breaking and assimilation of Jason satellite wave and wind data. The system provides one-hourly output and the atmospheric forcing is taken from ECMWF ERA5 data. In addition, the product comprises a monthly climatology dataset based on significant wave height and Tm02 wave period as well as an air-sea-flux dataset.

'''Citation''': 
Staneva, J., Ricker, M., & Behrens, A. (2022). Black Sea Waves Reanalysis (CMEMS BS-Waves, EAS4 system) (Version 1) [Data set]. Copernicus Monitoring Environment Marine Service.

'''DOI (Product)''': 
https://doi.org/10.48670/mds-00357
Baltic Sea Multiyear Ocean Colour Plankton, Reflectances and Transparency L3 daily observations

'''Short description:'''

For the '''Baltic Sea''' Ocean '''Satellite Observations''', the Italian National Research Council (CNR – Rome, Italy), is providing multi-years '''Bio-Geo_Chemical (BGC)''' regional datasets:
* '''''plankton''''' with the phytoplankton chlorophyll concentration (CHL) evaluated via region-specific neural network (Brando et al. 2021) and Phytoplankton Functional Types (PFT) evaluated via region-specific algorithm 
* '''''reflectance''''' with the spectral Remote Sensing Reflectance (RRS)
* '''''transparency''''' with the diffuse attenuation coefficient of light at 490 nm (KD490) 
* '''''pp''''' with the Integrated Primary Production (PP).

'''Upstreams''': SeaWiFS, MODIS, MERIS, VIIRS, OLCI-S3A (ESA OC-CCIv6) for the '''""multi""''' products, and OLCI-S3A & S3B for the '''""olci""''' products

'''Temporal resolution''': daily

'''Spatial resolution''': 1 km for '''""multi""''' (4 km for '''""pp""''') and 300 meters for '''""olci""'''

To find this product in the catalogue, use the search keyword '''""OCEANCOLOUR_BAL_BGC_L3_MY""'''.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00296
European North West Shelf/Iberia Biscay Irish Seas – High Resolution ODYSSEA Sea Surface Temperature Multi-sensor L3 Observations Reprocessed

'''Short description:'''

For the NWS/IBI Ocean- Sea Surface Temperature L3 Observations . This product provides daily foundation sea surface temperature from multiple satellite sources. The data are intercalibrated. This product consists in a fusion of sea surface temperature observations from multiple satellite sensors, daily, over a 0.05° resolution grid. It includes observations by polar orbiting from the ESA CCI / C3S archive .  The L3S SST data are produced selecting only the highest quality input data from input L2P/L3P images within a strict temporal window (local nightime), to avoid diurnal cycle and cloud contamination. The observations of each sensor are intercalibrated prior to merging using a bias correction based on a multi-sensor median reference correcting the large-scale cross-sensor biases.

'''DOI (product) :'''   
https://doi.org/10.48670/moi-00311
Baltic Sea Surface Temperature anomaly time series and trend from Observations Reprocessing

'''DEFINITION'''

OMI_CLIMATE_SST_BAL_area_averaged_anomalies product includes time series of monthly mean SST anomalies over the period 1982-2024, relative to the 1991-2020 climatology, averaged for the Baltic Sea. The SST Level 4 analysis products that provide the input to the monthly averages are taken from the reprocessed product SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 with a recent update to include 2023. The product has a spatial resolution of 0.02 in latitude and longitude. 
The OMI time series runs from Jan 1, 1982 to December 31, 2024 and is constructed by calculating monthly averages from the daily level 4 SST analysis fields of the SST_BAL_SST_L4_REP_OBSERVATIONS_010_016 product . The climatology period from 1991 to 2020 (30 years) is selected according to WMO recommendations (WMO, 2017) and the most recent practice from the U.S. National Oceanic and Atmospheric Administration practice (https://wmo.int/media/news/updated-30-year-reference-period-reflects-changing-climate). 
See the Copernicus Marine Service Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018) for more information on the OMI product.

'''CONTEXT'''

Sea Surface Temperature (SST) is an Essential Climate Variable (GCOS) that is an important input for initialising numerical weather prediction models and fundamental for understanding air-sea interactions and monitoring climate change (GCOS 2010). The Baltic Sea is a region that requires special attention regarding the use of satellite SST records and the assessment of climatic variability (Høyer and She 2007; Høyer and Karagali 2016). The Baltic Sea is a semi-enclosed basin with natural variability and it is influenced by large-scale atmospheric processes and by the vicinity of land. In addition, the Baltic Sea is one of the largest brackish seas in the world. When analysing regional-scale climate variability, all these effects have to be considered, which requires dedicated regional and validated SST products. Satellite observations have previously been used to analyse the climatic SST signals in the North Sea and Baltic Sea (BACC II Author Team 2015; Lehmann et al. 2011). Recently, Høyer and Karagali (2016) demonstrated that the Baltic Sea had warmed 1-2 oC from 1982 to 2012 considering all months of the year and 3-5 °C when only July-September months were considered. This was corroborated in the Ocean State Reports (section 1.1 in Von Schuckmann et al., 2016; section 3 in Von Schuckmann et al., 2018).

'''CMEMS KEY FINDINGS'''

The basin-average trend of SST anomalies for Baltic Sea region amounts to 0.039±0.003°C/year over the period 1982-2024 which corresponds to an average warming of 1.68°C. Adding the North Sea area, the average trend amounts to 0.026±0.002°C/year over the same period, which corresponds to an average warming of 1.19°C for the entire region since 1982.

'''DOI (product):''' 
https://doi.org/10.48670/moi-00205
Global Ocean GMPE Sea Surface Temperature Multi Product Ensemble

'''Short description:''' 

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.
 
A 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.

'''DOI (product) :'''  
https://doi.org/10.48670/mds-00378