⇒ ARGO: an international programme using autonomous floats ...https://repository.oceanbestpractices.org/handle/11329/4532024-03-28T14:45:18Z2024-03-28T14:45:18ZBest practices for Core Argo floats: Getting started, physical handling, metadata, and data considerations. Version 1. [GOOS ENDORSED PRACTICE]Morris, TamarynScanderbeg, MeganWest-Mack, DeborahGourcuff, ClairePoffa, NoéUdaya Bhaskar, TVSHanstein, CraigDiggs, SteveTalley, LynneTurpin, VictorLiu, ZenghongOwens, Breckhttps://repository.oceanbestpractices.org/handle/11329/23872024-01-15T13:40:10Z2023-01-01T00:00:00ZBest practices for Core Argo floats: Getting started, physical handling, metadata, and data considerations. Version 1. [GOOS ENDORSED PRACTICE]
Morris, Tamaryn; Scanderbeg, Megan; West-Mack, Deborah; Gourcuff, Claire; Poffa, Noé; Udaya Bhaskar, TVS; Hanstein, Craig; Diggs, Steve; Talley, Lynne; Turpin, Victor; Liu, Zenghong; Owens, Breck
Argo floats have been deployed in the global ocean for over 20 years. The Core mission of
the Argo program (Core Argo) has contributed well over 2 million profiles of salinity and
temperature of the upper 2000 m for a variety of operational and scientific applications. Core
Argo floats have evolved such that the program currently consists of more than eight types
of Core Argo float, some of which belong to second or third generation developments, three
unique satellite communication systems and two types of Conductivity, Temperature and
Depth (CTD) sensor systems. Coupled with a well-established data management system,
with delayed mode quality control, makes for a very successful ocean observing network.
Here we present the Best Practices for Core Argo floats in terms of float types, physical
handling and deployments, recommended metadata parameters and the data management
system. The objective is to encourage new and developing scientists, research teams and
institutions to contribute to the OneArgo Program, specifically to the Core Argo mission. Only
by leveraging sustained contributions of current Core Argo float groups with new and
emerging Argo teams and users, can the OneArgo initiative be realised. This paper makes
involvement with the Core Argo mission smoother by providing a framework endorsed by a
wide community for these observations.
2023-01-01T00:00:00ZArgo Quality Control Manual for CTD and Trajectory Data. Version 3.7.Wong, AnnieKeeley, RobertCarval, Thierryhttps://repository.oceanbestpractices.org/handle/11329/879.22023-08-11T22:01:11Z2023-01-01T00:00:00ZArgo Quality Control Manual for CTD and Trajectory Data. Version 3.7.
Wong, Annie; Keeley, Robert; Carval, Thierry
versus pressure. Trajectory data involve positions and time. This document is the Argo
Quality Control Manual for CTD and Trajectory data. It describes two levels of quality
control and adjustment procedures:
• The first level is the real-time system that performs a set of automatic checks and
adjustments.
• The second level is the delayed-mode system that consists of evaluation and
adjustment of the data by experts.
These quality control and adjustment procedures are applied to the Argo parameters:
<PARAM> = JULD, LATITUDE, LONGITUDE, PRES, TEMP, PSAL. For biogeochemical
parameters, please refer to "Argo Quality Control Manual for Biogeochemical Data",
http://dx.doi.org/10.13155/40879.
2023-01-01T00:00:00ZDMQC Cookbook for Core Argo parameters.Cabanes, CecileAngel-Benavides, IngridBuck, JustinCoatanoan, ChristineDobler, DelphineHerbert, GaelleKlein, BirgitMaze, GuillaumeNotarstefano, GuilioOwens, BreckThierry, VirginieWalicka, KamilaWong, Anniehttps://repository.oceanbestpractices.org/handle/11329/23552023-08-11T21:31:52Z2021-01-01T00:00:00ZDMQC Cookbook for Core Argo parameters.
Cabanes, Cecile; Angel-Benavides, Ingrid; Buck, Justin; Coatanoan, Christine; Dobler, Delphine; Herbert, Gaelle; Klein, Birgit; Maze, Guillaume; Notarstefano, Guilio; Owens, Breck; Thierry, Virginie; Walicka, Kamila; Wong, Annie
This cookbook is to document the end-to-end processing chain of Delayed Mode Quality Control (DMQC) of Core Argo parameters. It provides guidlines on existing manuals, and explains best practices through case studies. This document was initiated after the 1st EU DMQC workshop held in Brest in April 2018, under the MOCCA project. Lately, this work has been undertaken under EuroArgo RISE project.
The document is organized as follows.The first part gives some general information (e.g.: How to check quality indicators in delayed mode? What are the reference databases? How to correct pressure? How to use the OWC software to correct salinity? What are the common failures? etc.). The second part gives more specific information for the regional analysis (specific difficulties encountered, reference data available in regional seas, configuration parameters usually used, etc...). The regions covered so far are: the sub-polar Atlantic zone, the Nordic Seas, the Mediterranean and Black Seas, and the Southern Ocean.The third part of the cookbook presents detailled examples of delayed-mode processing for float data in these regions.
2021-01-01T00:00:00ZQuantifying observational errors in Biogeochemical‐Argo oxygen, nitrate, and chlorophyll a concentrations.Mignot, A.D'Ortenzio, F.Taillandier, V.Cossarini, G.Salon, S.https://repository.oceanbestpractices.org/handle/11329/21482023-03-02T20:31:42Z2019-01-01T00:00:00ZQuantifying observational errors in Biogeochemical‐Argo oxygen, nitrate, and chlorophyll a concentrations.
Mignot, A.; D'Ortenzio, F.; Taillandier, V.; Cossarini, G.; Salon, S.
Biogeochemical (BGC)‐Argo floats observations are becoming a major data source for
assimilation into and constraining of ocean biogeochemical models. An important prerequisite for a
successful synthesis between models and observations is the characterization of the observational
errors in BGC‐Argo float data. The root‐mean‐square error and multiplicative and additive biases in
quality‐controlled data sets of oxygen, nitrate, and chlorophyll a concentrations collected with 17 BGC‐Argo
floats in the Mediterranean Sea between 2013 and 2017 are assessed using the triple collocation analysis.
The analysis suggests that BGC‐Argo float oxygen, nitrate and chlorophyll a concentrations data have an
additive bias of 2.9 ± 5.5 μmol/kg, 0.46 ± 0.07 μmol/kg, and −0.06 ± 0.02 mg/m3, respectively. The
root‐mean‐square error is evaluated at 5.1 ± 0.8 μmol/kg, 0.25 ± 0.07 μmol/kg, and 0.03 ± 0.01 mg/m3.
Additional studies should determine whether these values are applicable to the global ocean.
Plain Language Summary The Biogeochemical‐Argo program is a network of ocean robots
whose sensors monitor oxygen, nitrate, and chlorophyll a concentrations, information that is needed to
detect decadal changes in biological carbon production, ocean acidification, ocean carbon uptake, and
hypoxia in the world ocean. One of the goals of the Biogeochemical‐Argo program is to incorporate these
observations into ocean models to understand and forecast the changing state of the carbon cycle. The
successful integration of the float data into numerical models, however, requires the specification of the
observational errors. This study provides, for the first time, the biases and errors of the three cores variables
of the Biogeochemical‐Argo floats network: oxygen, nitrate, and chlorophyll a concentrations.
2019-01-01T00:00:00ZargoFloats: an R Package for Analyzing Argo Data.Kelley, Dan E.Harbin, JaimieRichards, Clarkhttps://repository.oceanbestpractices.org/handle/11329/15582021-05-04T18:48:36Z2021-01-01T00:00:00ZargoFloats: an R Package for Analyzing Argo Data.
Kelley, Dan E.; Harbin, Jaimie; Richards, Clark
An R package named argoFloats has been developed to facilitate identifying, downloading, caching, and analyzing oceanographic data collected by Argo profiling floats. The analysis phase benefits from close connections between argoFloats and the oce package, which is likely to be familiar to those who already use R for the analysis of oceanographic data of other kinds. This paper outlines how to use argoFloats to accomplish some everyday tasks that are particular to Argo data, ranging from downloading data and finding subsets to handling quality control and producing a variety of diagnostic plots. The benefits of the R environment are sketched in the examples, and also in some notes on the future of the argoFloats package.
2021-01-01T00:00:00ZBiogeochemical Argo Cheat Sheets: Data distribution; Quality control and GDAC; Chlorophyll-a; Optical backscatter; pH; Irradiance; Oxygen; Nitrate.Baldry, KimberleeSauzède, RaphaëlleCornec, Marinhttps://repository.oceanbestpractices.org/handle/11329/14792021-01-07T16:12:34Z2021-01-01T00:00:00ZBiogeochemical Argo Cheat Sheets: Data distribution; Quality control and GDAC; Chlorophyll-a; Optical backscatter; pH; Irradiance; Oxygen; Nitrate.
Baldry, Kimberlee; Sauzède, Raphaëlle; Cornec, Marin
Baldry, Kimberlee
Eight cheat sheets for users of Biogeochemical Argo data. The sheets describe data distribution, quality control in the Global Data Acquisition Center and the six core Biogeochemical Argo variables (chlorophyll-a, optical backscatter, pH, Irradiance, oxygen and nitrate). The cheat sheets aim to guide users by displaying information on data processing, quality control and sensor performance for education purposes.
2021-01-01T00:00:00ZOperating Cabled Underwater Observatories in Rough Shelf-Sea Environments: A Technological Challenge.Fischer, PhilippBrix, HolgerBaschek, BurkardKraberg, AlexandraBrand, MarkusCisewski, BorisRiethmüller, RolfBreitbach, GisbertMöller, Klas OveGattuso, Jean-PierreAlliouane, Samirvan de Poll, Willem H.Witbaard, Robhttps://repository.oceanbestpractices.org/handle/11329/14622020-12-07T11:48:53Z2020-01-01T00:00:00ZOperating Cabled Underwater Observatories in Rough Shelf-Sea Environments: A Technological Challenge.
Fischer, Philipp; Brix, Holger; Baschek, Burkard; Kraberg, Alexandra; Brand, Markus; Cisewski, Boris; Riethmüller, Rolf; Breitbach, Gisbert; Möller, Klas Ove; Gattuso, Jean-Pierre; Alliouane, Samir; van de Poll, Willem H.; Witbaard, Rob
Cabled coastal observatories are often seen as future-oriented marine technology that enables science to conduct observational and experimental studies under water year-round, independent of physical accessibility to the target area. Additionally, the availability of (unrestricted) electricity and an Internet connection under water allows the operation of complex experimental setups and sensor systems for longer periods of time, thus creating a kind of laboratory beneath the water. After successful operation for several decades in the terrestrial and atmospheric research field, remote controlled observatory technology finally also enables marine scientists to take advantage of the rapidly developing communication technology. The continuous operation of tw ocabled observatories in the southern North Sea and off the Svalbard coast since 2012 shows that even highly complex sensor systems, such as stereo-optical cameras, video plankton recorders or systems for measuring the marine carbonate system, can be successfully operated remotely year-round facilitating continuous scientific access to areas that are difficult to reach, such as the polar seas or the North Sea. Experience also shows, however, that the challenges of operating a cabled coastal observatory go far beyond the provision of electricity and network connection under water. In this manuscript, the essential developmental stages of the “COSYNA Shallow WaterUnderwater Node” system are presented, and the difficulties and solutions that have arisen in the course of operation since 2012 are addressed with regard to technical,organizational and scientific aspects
2020-01-01T00:00:00ZArgo User’s Manual Version 3.3, 22 November 2019.https://repository.oceanbestpractices.org/handle/11329/12402020-03-26T11:48:50Z2019-01-01T00:00:00ZArgo User’s Manual Version 3.3, 22 November 2019.
This document is the Argo data user’s manual. It contains the description of the formats and files produced by the Argo Data Assembly Centres (DACs).
2019-01-01T00:00:00ZNovel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts.Salon, StefanoCossarini, GianpieroBolzon, GiorgioFeudale, LauraLazzari, PaoloTeruzzi, AnnaSolidoro, CosimoCrise, Alessandrohttps://repository.oceanbestpractices.org/handle/11329/11822019-12-23T08:48:28Z2019-01-01T00:00:00ZNovel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts.
Salon, Stefano; Cossarini, Gianpiero; Bolzon, Giorgio; Feudale, Laura; Lazzari, Paolo; Teruzzi, Anna; Solidoro, Cosimo; Crise, Alessandro
The quality of the upgraded version of the Copernicus Marine Environment Monitoring Service (CMEMS) biogeochemical operational system of the Mediterranean Sea (MedBFM) is assessed in terms of consistency and forecast skill, following a mixed validation protocol that exploits different reference data from satellite, oceanographic databases, Biogeochemical Argo floats, and literature. We show that the quality of the MedBFM system has been improved in the previous 10 years. We demonstrate that a set of metrics based on the GODAE (Global Ocean Data Assimilation Experiment) paradigm can be efficiently applied to validate an operational model system for biogeochemical and ecosystem forecasts. The accuracy of the CMEMS biogeochemical products for the Mediterranean Sea can be achieved from basin-wide and seasonal scales to mesoscale and weekly scales, and its level depends on the specific variable and the availability of reference data, the latter being an important prerequisite to build robust statistics. In particular, the use of the Biogeochemical Argo floats data proved to significantly enhance the validation framework of operational biogeochemical models. New skill metrics, aimed to assess key biogeochemical processes and dynamics (e.g. deep chlorophyll maximum depth, nitracline depth), can be easily implemented to routinely monitor the quality of the products and highlight possible anomalies through the comparison of near-real-time (NRT) forecasts skill with pre-operationally defined seasonal benchmarks. Feedbacks to the observing autonomous systems in terms of quality control and deployment strategy are also discussed.
2019-01-01T00:00:00ZBGC-Argo synthetic profile file processing and format on Coriolis GDAC. Version 1.1Bittig, HenryWong, AnniePlant, Joshhttps://repository.oceanbestpractices.org/handle/11329/10322019-09-03T07:48:33Z2019-01-01T00:00:00ZBGC-Argo synthetic profile file processing and format on Coriolis GDAC. Version 1.1
Bittig, Henry; Wong, Annie; Plant, Josh
The current V3.1 Argo netCDF format that produces a pair of core- and b- profile files per cycle, with N_PROF > 1, allows storage of all profile information returned from BGC floats, in a manner that is as close to float output as possible. These can include multiple full-depth profiles with different pressure levels, multiple shallow profiles with different pressure levels, and recording of spatial and/or temporal delays between the CTD and various BGC sensor outputs. The advantage of this data management approach is that float outputs are faithfully recorded, so that any reprocessing demands that require access to the raw data can be met with ease.
However, when measurements from multiple sensors are not aligned during onboard processing by the floats, they are recorded in their raw pressure locations. This makes it difficult to study these BGC parameters as co-located measurements, since some data manipulation to align them needs to be done before scientific studies can be carried out. Moreover, because the V3.1 format requires that all parameters have dimensions (N_PROF, N_LEVELS), where N_LEVELS = maximum number of vertical levels, the files are large in file size and are mostly filled with white space.
The goal of a simplified synthetic profile is to co-locate as many BGC observations as possible while preserving the character of the sampling pattern, i.e., sample interval, number of samples, and approximate pressure locations. Data come from the single-cycle c- and b-files. Only c- and b- parameters are included (with all subfields), which means no intermediate BGC parameters (i-argo params) are included. The synthetic pressure axis is constructed from the BGC sampling levels from each cycle. This means that there is no fixed vertical grid for all floats and all cycles. At the end, each single-cycle synthetic profile will have dimension N_PROF = 1. The co-location takes different vertical attachments of BGC sensors into account by displacing the pressure location (based on the config parameter vertical_pressure_offset), which is not the case in core- or b- profile files.
This document details the processing steps used to generate synthetic profile data from Argo profile data. It also describes the format of the NetCDF files produced by the Coriolis GDAC to store the synthetic profile data.
2019-01-01T00:00:00Z