A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts

Ford, David A.; Grossberg, Shenan; Rinaldi, Gianmario; Menon, Prathyush P.; Palmer, Matthew R.; Skákala, Jozef; Smyth, Tim; Williams, Charlotte A. J.; Lorenzo Lopez, Alvaro; Ciavatta, Stefano. 2022 A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts. Frontiers in Marine Science, 9. https://doi.org/10.3389/fmars.2022.1067174

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Abstract/Summary

This study presents a proof-of-concept for a fully automated and adaptive observing system for coastal ocean ecosystems. Such systems present a viable future observational framework for oceanography, reducing the cost and carbon footprint of marine research. An autonomous ocean robot (an ocean glider) was deployed for 11 weeks in the western English Channel and navigated by exchanging information with operational forecasting models. It aimed to track the onset and development of the spring phytoplankton bloom in 2021. A stochastic prediction model combined the real-time glider data with forecasts from an operational numerical model, which in turn assimilated the glider observations and other environmental data, to create high-resolution probabilistic predictions of phytoplankton and its chlorophyll signature. A series of waypoints were calculated at regular time intervals, to navigate the glider to where the phytoplankton bloom was most likely to be found. The glider successfully tracked the spring bloom at unprecedented temporal resolution, and the adaptive sampling strategy was shown to be feasible in an operational context. Assimilating the real-time glider data clearly improved operational biogeochemical forecasts when validated against independent observations at a nearby time series station, with a smaller impact at a more distant neighboring station. Remaining issues to be addressed were identified, for instance relating to quality control of near-real time data, accounting for differences between remote sensing and in situ observations, and extension to larger geographic domains. Based on these, recommendations are made for the development of future smart observing systems.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3389/fmars.2022.1067174
ISSN:	2296-7745
Date made live:	19 Dec 2022 11:43 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533741

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