the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Merging and serving Ocean Observations: a description of Marine Data Aggregators
Abstract. Observations are a fundamental element in ocean predictions: they are crucial not only for monitoring the ocean state, but also for improving the forecasting systems and validating the model outputs. In this framework, it is essential to adequately access, manage, and integrate such information in the ocean value chain. Data providers are in charge of collecting, processing and analyzing these observations, delivering comprehensive datasets that can be used for informed decision making and by forecasters to improve ocean models. In this paper, several examples of data services are discussed – ranging from the Copernicus Marine In-Situ Thematic Assembly Center to European Marine Observation and Data network (EMODnet) to SeaDataNet – recognized as key players in the framework of monitoring and management of the marine resource. The paper offers an outlook on future directions in ocean data integration, particularly on the opportunities offered by the standardization of protocols for data dissemination and the role of cost-effective and citizen-based data collection.
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RC1: 'Comment on sp-2024-23', Jon Turton, 17 Oct 2024
General comments
The paper is essentially a description of the various systems that have been established within the ocean science community to collect and integrate marine data from multiple sources, and make it available to users, so is basically factual. As such it is provides a useful summary of what presently exists.
Technical corrections/typing errors
There are many acronyms used in the paper and numerous instances where they are defined several times, not defined at their first use, or not defined at all. I would encourage the authors to ensure that all acronyms are defined the first time they appear in the paper. (One example, line 16 Copernicus Marine In-Situ Thematic Assembly Centre is referred to where the acronym is used in lines 53, 54, 55 and 61 before being defined in line 68, similarly ‘real-time and delayed mode’ appears in line 43 but RT and DM are used and defined later.)
Line 36, define what OceanOPS is (the GOOS in-situ Ocean Observations Programme Support Centre) as it may not be familiar to many readers.
Line 69 it would be more correct to say ‘..connected to the GOOS global networks..’ rather than ‘..connected to the OceanOPS networks…’ as OceanOPS is a supporting centre.
Line 56. Do you mean ‘other CMEMS data providers’ rather than ‘outside CMEMS data providers’?
Line 56 & 57. What is meant by ‘forcing assimilation’. Normally assimilation of observations data is to define the initial conditions for numerical ocean forecasting models. Would it be better to say ‘..for assimilation into, and validation of, ..’
Line 71 & 72 I wonder whether ‘Everyone’s Gliding Observatories (EGO)’ should be replaced by ‘OceanGliders’ that is the name of the GOOS coordinating body for glider observations (see also reference in Table 1).
I would encourage the users to check all the hyperlinks given in the paper, for example the link https://data-errdap.emodnet-physics.eu appears to be a dead end.
Table 1 spelling. Tsunami not Tsunamy
Citation: https://doi.org/10.5194/sp-2024-23-RC1 -
RC2: 'Comment on sp-2024-23', Mathieu Belbeoch, 17 Dec 2024
Paper provides a good overview of the in situ observing network data flows and clarifies value added of main partners engaged which is needed and welcome.A good diagram and mapping to summarize it would have been a very useful addition.l30-35 could cite the progress made to modernize the WMO data exchange system (GTS to WIS 2.0) leveraging new web technologies and existing infrastructures (GDACs) and formats (netCDF) used in oceanography . WIS 2.0 could become a major upgrade to DAC/GDAC architecture taking benefits of underlying operational components (caching, brokering, messaging, etc).l36The GOOS is coordinated by the Observation Coordination Group, supported by OceanOPS, and GRAs.
The integration between OCG ( main international networks) and National/Regional initiatives (and BioEco) is not done yet thus overall data integration has substantial challenges to overcome.
Can data aggregator play a role here ? it is suggested - to help complete this integration.Authors could place GOOS networks and data in the overall ocean data complexity (national, private, citizen etc) led by IOC and even beyond when its about WMO (ocean, land, etc). GOOS data is for now a subset of ocean (and atmospheric) data.As well noted in the paper, DACs (often national and close or equal to NODCs) play a key role in processing raw data and delivering QCed data to GDACs, first global distribution point to users in the data value chain.The GOOS networks, encouraged through the OCG data strategy (quote/Ref as needed) are setting up global data nodes which gradually improve the overall data delivery and ensure the "GOOS quality" within the wider ocean data lake.p2Figure overlooks XBTs providing repeated subsurface temperature on defined sections globally.EMODnet role could be placed as the European part of IODE/ODIS ?Also it may have a DAC or GDAC role for emerging contributions (private, citizens, ad hoc) not organized under GOOS networks.This growing importance of data diversity is well highlighted by the authors, but paper doesn't tell much on how to get prepared for this new era of massively diverse ocean data.It seems investing in marine data scientists and IT engineers (to process raw data according to international standards) is anticipated. NODCs (and DACs) can't all absorb this diversity while many data are there ready to be shared.Can Emodnet and partners get organized - on a decentralized way - to respond to this growing question.
The conclusion of the paper insists on the ackowledgement of the data source. It would have been interesting to explore recommendations (metadata) here on how to value GOOS data and implementers accross the data value chain.
An interrogation on how the current data offer meet user needs (and which users ?) would be interesting to explore as well.
Suspecting the answer is "not fully", how are we getting prepared for an ambitious data offer to a wide range of users. digital twin ?Citation: https://doi.org/10.5194/sp-2024-23-RC2
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