Poster Session 2, Tuesday,  October  4, 10:40–12:40

Poster 46

Monitor the operational status of hyperspectral underwater light field based on BGC-Argo floats in optically simple to complex waters bodies

Observational coverage from Argo floats far exceeds that of conventional observation methods (e.g., ships, moorings, aircraft), with continuous, rapid, and depth-resolved measurements that enable retrospective and historical analysis. The addition of optical and biogeochemical sensors (i.e., BGC-Argo) is fueling the tracking a variety of ocean phenomena. BGC-Argo floats are providing an on-demand capability for observing optically complex regions like the Baltic Sea, but observations are limited by the spectral capabilities of the sensors. Current field exercises in the Baltic, Labrador and Mediterranean seas are evaluating the addition of hyperspectral radiometers (TriOS RAMSES), which come in two variants to measure the downward irradiance ( E d ), covering different parts of the ultraviolet and visible spectrum, along with the legacy BGC-Argo products. Generally, all Argo data are supposed to be reviewed in real time through dedicated quality control processes. In the current state of the system, radiometric Argo profiles are at risk of influences due to weather and sea conditions, sensor drift and/or malfunction during the mission. As a pivotal process, these potentially anomalies should be identified and eliminated or at least flagged before data is used for further scientific analysis. Especially hyperspectral radiometry is a newly introduced sensor that requires a robust data evaluation procedure. Herein, we present a quality evaluation process to monitor the operational status of the hyperspectral underwater light field. Application to oceanic and complex water bodies exhibits the applicability of our procedure to wide range of water bodies. 

*Ahlem Jemai, The Carl von Ossietzky University of Oldenburg, Germany, [email protected]

Hendrik Bünger, Carl von Ossietzky University Oldenburg, [email protected] 

Jochen Wollschläger, Carl von Ossietzky University Oldenburg, [email protected] 

Daniela Voß, Carl von Ossietzky University Oldenburg, [email protected]

Oliver Zielinski, Carl von Ossietzky University Oldenburg and German Research Center for Artificial Intelligence, [email protected] 

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