A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts
dc.contributor.author | Ford, DA | |
dc.contributor.author | Grossberg, S | |
dc.contributor.author | Rinaldi, G | |
dc.contributor.author | Menon, PP | |
dc.contributor.author | Palmer, MR | |
dc.contributor.author | Skákala, J | |
dc.contributor.author | Smyth, T | |
dc.contributor.author | Williams, CAJ | |
dc.contributor.author | Lopez, AL | |
dc.contributor.author | Ciavatta, S | |
dc.date.accessioned | 2023-07-19T15:04:36Z | |
dc.date.issued | 2022-12-19 | |
dc.date.updated | 2023-07-19T13:38:49Z | |
dc.description.abstract | 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. | en_GB |
dc.description.sponsorship | Natural Environment Research Council (NERC) | en_GB |
dc.description.sponsorship | Natural Environment Research Council (NERC) | en_GB |
dc.description.sponsorship | European Union Horizon 2020 project SEAMLESS | en_GB |
dc.description.sponsorship | National Centre for Earth Observation | en_GB |
dc.description.sponsorship | Royal Navy’s Defence Oceanography Programme | en_GB |
dc.format.extent | 1067174- | |
dc.identifier.citation | Vol. 9, article 1067174 | en_GB |
dc.identifier.doi | https://doi.org/10.3389/fmars.2022.1067174 | |
dc.identifier.grantnumber | NE/R006849/1 | en_GB |
dc.identifier.grantnumber | NE/P013902/1 | en_GB |
dc.identifier.grantnumber | 101004032 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/133619 | |
dc.identifier | ORCID: 0000-0001-9149-7304 (Grossberg, Shenan) | |
dc.identifier | ORCID: 0000-0003-2021-5458 (Rinaldi, Gianmario) | |
dc.identifier | ScopusID: 57195318992 (Rinaldi, Gianmario) | |
dc.identifier | ORCID: 0000-0003-3804-9291 (Menon, Prathyush P) | |
dc.language.iso | en | en_GB |
dc.publisher | Frontiers Media | en_GB |
dc.rights | © 2022 Ford, Grossberg, Rinaldi, Menon, Palmer, Skákala, Smyth, Williams, Lorenzo Lopez and Ciavatta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | en_GB |
dc.subject | autonomous observations | en_GB |
dc.subject | operational forecasting | en_GB |
dc.subject | ocean gliders | en_GB |
dc.subject | data assimilation | en_GB |
dc.subject | phytoplankton bloom | en_GB |
dc.subject | smart observing system | en_GB |
dc.subject | adaptive sampling | en_GB |
dc.subject | path planning | en_GB |
dc.title | A solution for autonomous, adaptive monitoring of coastal ocean ecosystems: Integrating ocean robots and operational forecasts | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2023-07-19T15:04:36Z | |
dc.identifier.issn | 2296-7745 | |
exeter.article-number | ARTN 1067174 | |
dc.description | This is the final version. Available from Frontiers Media via the DOI in this record. | en_GB |
dc.description | Data availability statement: The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. | en_GB |
dc.identifier.journal | Frontiers in Marine Science | en_GB |
dc.relation.ispartof | Frontiers in Marine Science, 9 | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2022-11-28 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2022-12-19 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2023-07-19T14:57:24Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2023-07-19T15:05:17Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2022-12-19 |
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Except where otherwise noted, this item's licence is described as © 2022 Ford, Grossberg, Rinaldi, Menon, Palmer, Skákala, Smyth, Williams, Lorenzo Lopez and Ciavatta. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.