CAMPUS is working to improve the exchange of data between model systems and observational networks to inform an improved strategy for the deployment of the UK's high-cost marine observing capability. In particular utilising mathematical and statistical models to develop and test "smart" autonomy - autonomous systems that are enabled to selectively search and monitor explicit features within the marine system.
By developing data assimilation techniques to utilise autonomous data, our model systems will be able to better characterise episodic events such as the spring bloom, harmful algal blooms and oxygen depletion, which are currently not well captured and are key to understanding ecosystem variability and therefore quantifying good environmental status. In doing so CAMPUS will provide a step change in the combined use of observation and modelling technologies, delivered through a combination of autonomous technologies (gliders), other observations and shelf-wide numerical models. This will provide improved analysis of key ocean variables, better predictions of episodic events, and 'smart' observing systems in order to improve the evidence base for compliance with European directives and support the UK industrial strategy.
Marine autonomous vehicles (MAVs) are a rapidly maturing technology and are now routinely deployed both in support of research and as a component of an ocean observing system. When used in conjunction with fixed point observatories, ships of opportunity and satellite remote sensing, the strategic deployment of MAVs offers the prospect of substantial improvement in our observing network. Marine gliders in particular have the capability to provide depth resolved data sets of high resolution from deployments that can endure several months and cover hundreds of kilometres, allowing the collection of sufficient information to be useful for assimilation into models.