Processing traits of European catfish (Silurus glanis Linnaeus, 1758) from outdoor flow-through and indoor recycling aquaculture units

The quality of fish cultured using recycling units may differ from that of fish from outdoor farming units due to a range of deviating environmental determinants. This applies not only to flesh quality but also to morphological (processing) traits. This study evaluates processing yields of sibling fish cultured in two different farming units: (i) an outdoor pond aquaculture system with a flow-through regime (24.6 ± 0.2°C), and (ii) indoor tanks using a recirculation aquaculture system (RAS; 26.0 ± 1.0°C). Clear differences were observed in the most important processing traits, i.e. skinned trunk and fillet yields, which were both significantly higher (P < 0.01) in RAS fish due to significantly smaller (P < 0.05) head weight in fish of the flow-through system. Skin represented a significantly higher (P < 0.01) proportion of total weight in both RAS males and females. The most obvious difference was in the deposited fat weight, which was significantly higher (P < 0.01) in RAS fish. Visceral fat deposits were significantly higher (P < 0.01) in females and ventral and dorsal fat deposits higher (P > 0.05) in males.

Processing Bio-Argo particle backscattering at the DAC level

This document does NOT address the issue of particle backscattering quality control (either real-time or delayed mode). As a preliminary step towards that goal, this document seeks to ensure that all countries deploying floats equipped with backscattering sensors document the data and metadata related to these floats properly. We produced this document in response to action item 9 from the first Bio-Argo Data Management meeting in Hyderabad (November 12-13, 2012). If the recommendations contained herein are followed, we will end up with a more uniform set of particle backscattering data within the Bio-Argo data system, allowing users to begin analyzing not only their own particle backscattering data, but also those of others, in the true spirit of Argo data sharing.

Processing bio-Argo chlorophyll-A concentration at the DAC level

This document does NOT address the issue of chlorophyll-a quality control (either real-time or delayed mode). As a preliminary step towards that goal, this document seeks to ensure that all countries deploying floats equipped with chlorophyll-a sensors document the data and metadata related to these floats properly. We produced this document in response to action item 3 from the first Bio-Argo Data Management meeting in Hyderabad (November 12-13, 2012). If the recommendations contained herein are followed, we will end up with a more uniform set of chlorophyll-a data within the Bio-Argo data system, allowing users to begin analyzing not only their own chlorophyll-a data, but also those of others, in the true spirit of Argo data sharing.

Processing Argo oxygen data at the DAC level cookbook

This document does NOT address the issue of oxygen data quality control (either real-time or delayed mode). As a preliminary step towards that goal, this document seeks to ensure that all countries deploying floats equipped with oxygen sensors document the data and metadata related to these floats properly. We produced this document in response to action item 14 from the AST-10 meeting in Hangzhou (March 22-23, 2009). Action item 14: Denis Gilbert to work with Taiyo Kobayashi and Virginie Thierry to ensure DACs are processing oxygen data according to recommendations. If the recommendations contained herein are followed, we will end up with a more uniform set of oxygen data within the Argo data system, allowing users to begin analysing not only their own oxygen data, but also those of others, in the true spirit of Argo data sharing. Indications provided in this document are valid as of the date of writing this document. It is very likely that changes in sensors, calibrations and conversions equations will occur in the future. Please contact V. Thierry (vthierry@ifremer.fr) for any inconsistencies or missing information. A dedicated webpage on the Argo Data Management website (www) contains all information regarding Argo oxygen data management : current and previous version of this cookbook, oxygen sensor manuals, calibration sheet examples, examples of matlab code to process oxygen data, test data, etc..

FluxEngine: A flexible processing system for calculating atmosphere-ocean carbon dioxide gas fluxes and climatologies

The air-sea flux of greenhouse gases (e.g. carbon dioxide, CO2) is a critical part of the climate system and a major factor in the biogeochemical development of the oceans. More accurate and higher resolution calculations of these gas fluxes are required if we are to fully understand and predict our future climate. Satellite Earth observation is able to provide large spatial scale datasets that can be used to study gas fluxes. However, the large storage requirements needed to host such data can restrict its use by the scientific community. Fortunately, the development of cloud-computing can provide a solution. Here we describe an open source air-sea CO2 flux processing toolbox called the ‘FluxEngine’, designed for use on a cloud-computing infrastructure. The toolbox allows users to easily generate global and regional air-sea CO2 flux data from model, in situ and Earth observation data, and its air-sea gas flux calculation is user configurable. Its current installation on the Nephalae cloud allows users to easily exploit more than 8 terabytes of climate-quality Earth observation data for the derivation of gas fluxes. The resultant NetCDF data output files contain >20 data layers containing the various stages of the flux calculation along with process indicator layers to aid interpretation of the data. This paper describes the toolbox design, the verification of the air-sea CO2 flux calculations, demonstrates the use of the tools for studying global and shelf-sea air-sea fluxes and describes future developments.

Processing Bio-Argo nitrate concentration at the DAC Level

The only method used to date to measure dissolved nitrate concentration (NITRATE) with sensors mounted on profiling floats is based on the absorption of light at ultraviolet wavelengths by nitrate ion (Johnson and Coletti, 2002; Johnson et al., 2010; 2013; D’Ortenzio et al., 2012). Nitrate has a modest UV absorption band with a peak near 210 nm, which overlaps with the stronger absorption band of bromide, which has a peak near 200 nm. In addition, there is a much weaker absorption due to dissolved organic matter and light scattering by particles (Ogura and Hanya, 1966). The UV spectrum thus consists of three components, bromide, nitrate and a background due to organics and particles. The background also includes thermal effects on the instrument and slow drift. All of these latter effects (organics, particles, thermal effects and drift) tend to be smooth spectra that combine to form an absorption spectrum that is linear in wavelength over relatively short wavelength spans. If the light absorption spectrum is measured in the wavelength range around 217 to 240 nm (the exact range is a bit of a decision by the operator), then the nitrate concentration can be determined. Two different instruments based on the same optical principles are in use for this purpose. The In Situ Ultraviolet Spectrophotometer (ISUS) built at MBARI or at Satlantic has been mounted inside the pressure hull of a Teledyne/Webb Research APEX and NKE Provor profiling floats and the optics penetrate through the upper end cap into the water. The Satlantic Submersible Ultraviolet Nitrate Analyzer (SUNA) is placed on the outside of APEX, Provor, and Navis profiling floats in its own pressure housing and is connected to the float through an underwater cable that provides power and communications. Power, communications between the float controller and the sensor, and data processing requirements are essentially the same for both ISUS and SUNA. There are several possible algorithms that can be used for the deconvolution of nitrate concentration from the observed UV absorption spectrum (Johnson and Coletti, 2002; Arai et al., 2008; Sakamoto et al., 2009; Zielinski et al., 2011). In addition, the default algorithm that is available in Satlantic sensors is a proprietary approach, but this is not generally used on profiling floats. There are some tradeoffs in every approach. To date almost all nitrate sensors on profiling floats have used the Temperature Compensated Salinity Subtracted (TCSS) algorithm developed by Sakamoto et al. (2009), and this document focuses on that method. It is likely that there will be further algorithm development and it is necessary that the data systems clearly identify the algorithm that is used. It is also desirable that the data system allow for recalculation of prior data sets using new algorithms. To accomplish this, the float must report not just the computed nitrate, but the observed light intensity. Then, the rule to obtain only one NITRATE parameter is, if the spectrum is present then, the NITRATE should be recalculated from the spectrum while the computation of nitrate concentration can also generate useful diagnostics of data quality.

DORIS: Processing and management of Sound Velocity Profiles for echosounding applications

If a bathymetric echosounder is the essential device to carry on hydrographic surveys, other external sensors are absolutely also necessary (positioning system, motion unit or sound velocity profiler). And because sound doesn‛t go straight away into the whole bathymetric swath its measurement and processing are very sensitive for all the water column. DORIS is the very answer for an operational sound velocity profile processing.