Abstract
Glider vehicles are now perhaps some of the most prolific providers of real-time and near real-time operational oceanographic data. However the data from these vehicles can and should be considered to have a long term legacy value capable of playing a critical role in understanding and separating inter-annual, inter-decadal and long term global change. To achieve this we have to go further than simply assuming the manufacturers calibrations; and field correct glider data in a more traditional way, for example, by careful comparison to water bottle calibrated lowered CTD datasets and/or ‘gold’ standard recent climatologies. In this manuscript we bring into the 21st century an historical technique that has been used manually by oceanographers for many years/decades for field correction, thermal lag correction and adjustment for biological fouling. The technique has now been made semi-automatic for machine processing of oceanographic glider data, although it’s future and indeed its origins have far wider scope. The subject of this manuscript is drawn from the original Description of Work (DoW) for a key task in the recently completed JERICO-NEXT (Joint European Research Infrastructure Network for Coastal observatories) EU funded program, but goes on to consider future application and the suitability for integration with machine learning.
Highlights
Ocean observing “Glider” vehicles constitute an essential component of coastal and open ocean observing systems (Testor et al, 2019) for a number of reasons
Field Correction by Whitespace Maximization the data collected by glider vehicles have an immensely important role in what is generically termed operational oceanography; to give them a legacy value for long-term historical climatologies, we must find a means to inter-calibrate their data, and we discuss this further
Some gliders have been externally fitted with UV absorption nitrate sensors (Thomsen et al, 2019), and turbulence/microstructure instruments (Fer et al, 2014). While in this manuscript we focus on the inter-calibration/field correction of derived salinity data, the technique presented is and can be extended to other datasets and we address this in the concluding discussion
Summary
Ocean observing “Glider” vehicles constitute an essential component of coastal and open ocean observing systems (Testor et al, 2019) for a number of reasons. Stable and well-defined water masses and mixing lines are not common in near-surface or shallow waters, where high-frequency air–sea interaction processes can effect water temperature changes and freshwater input independently and lead to considerable variability in θ/S space For these applications, the operator may not be able to do better than accept the error in manufacturer’s instrument calibration and instrument drift, unless they have access to their own calibration facilities or a mechanism for taking bottle samples very close to the glider vehicle. The new metadata variables added for the DM corrected conductivity and salinity variables, “Conductivity_corr” and “Salinity_corr”, respectively, are as follows: FIGURE 11 | A potential temperature/oxygen (θ/O) diagram, showing the typical size of correction necessary for DM field correction of a glider mission (blue) to bottle calibrated ship CTD survey data (red, yellow, green). Residual_salinity_differences_std_background_data: The estimated confidence level for the DM reference datasets—copied from the metadata for the reference datasets
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