| Feb. 13, 2024, 4:19 p.m. |
Created
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[{"model": "core.projectfund", "pk": 61435, "fields": {"project": 9617, "organisation": 5, "amount": 186767, "start_date": "2019-11-01", "end_date": "2022-03-31", "raw_data": 174301}}]
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| Jan. 30, 2024, 4:24 p.m. |
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[{"model": "core.projectfund", "pk": 54286, "fields": {"project": 9617, "organisation": 5, "amount": 186767, "start_date": "2019-11-01", "end_date": "2022-03-31", "raw_data": 148146}}]
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| Jan. 2, 2024, 4:15 p.m. |
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[{"model": "core.projectfund", "pk": 47074, "fields": {"project": 9617, "organisation": 5, "amount": 186767, "start_date": "2019-11-01", "end_date": "2022-03-31", "raw_data": 129341}}]
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| Dec. 5, 2023, 4:23 p.m. |
Created
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[{"model": "core.projectfund", "pk": 39827, "fields": {"project": 9617, "organisation": 5, "amount": 186767, "start_date": "2019-11-01", "end_date": "2022-03-30", "raw_data": 91973}}]
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| Nov. 27, 2023, 2:13 p.m. |
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{"external_links": []}
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| Nov. 21, 2023, 4:36 p.m. |
Created
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[{"model": "core.projectfund", "pk": 32523, "fields": {"project": 9617, "organisation": 5, "amount": 186767, "start_date": "2019-11-01", "end_date": "2022-03-30", "raw_data": 50958}}]
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| Nov. 21, 2023, 4:36 p.m. |
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[{"model": "core.projectorganisation", "pk": 93041, "fields": {"project": 9617, "organisation": 11850, "role": "LEAD_ORG"}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
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[{"model": "core.projectperson", "pk": 58425, "fields": {"project": 9617, "person": 13935, "role": "RESEARCH_COI_PER"}}]
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| Nov. 21, 2023, 4:36 p.m. |
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[{"model": "core.projectperson", "pk": 58424, "fields": {"project": 9617, "person": 13934, "role": "RESEARCH_COI_PER"}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
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[{"model": "core.projectperson", "pk": 58423, "fields": {"project": 9617, "person": 13689, "role": "COI_PER"}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
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[{"model": "core.projectperson", "pk": 58422, "fields": {"project": 9617, "person": 13690, "role": "COI_PER"}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
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[{"model": "core.projectperson", "pk": 58421, "fields": {"project": 9617, "person": 13936, "role": "COI_PER"}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
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[{"model": "core.projectperson", "pk": 58420, "fields": {"project": 9617, "person": 13937, "role": "COI_PER"}}]
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| Nov. 21, 2023, 4:36 p.m. |
Created
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[{"model": "core.projectperson", "pk": 58419, "fields": {"project": 9617, "person": 13691, "role": "PI_PER"}}]
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| Nov. 20, 2023, 2:03 p.m. |
Updated
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{"title": ["", "CAMPUS (Combining Autonomous observations and Models for Predicting and Understanding Shelf seas)"], "description": ["", "\nShelf seas are of major societal importance providing a diverse range of goods (e.g. fisheries, renewable energy, transport) and services (e.g. carbon and nutrient cycling and biodiversity). Managing UK seas to maintain clean, healthy, safe, productive and biologically diverse oceans and seas is a key governmental objective, as evidenced by the obligations to obtain Good Environmental Status (GES) under the UK Marine Strategy Framework, the Convention on Biological Diversity and ratification of the Oslo-Paris Convention (OSPAR) .. The delivery of these obligations requires comprehensive information about the state of our seas which in turn requires a combination of numerical models and observational programs. \n\nComputer modelling of marine ecosystems allows us to explore the recent past and predict future states of physical, chemical and biological properties of the sea, and how they vary in 3D space and time. In an analogous manner to the weather forecast, the Met Office runs a marine operational forecast system providing both short term forecast and multi-decadal historical data products. The quality of these forecasts is improved by using data assimilation; the process of predicting the most accurate ocean state using observations to nudge model simulations, producing a combined observation and model product. \n\nMarine 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 100s kms, allowing the collection of sufficient information to be useful for assimilation into models. \n\nWe will 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 we will utilise 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 GES.\nIn 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.\n\n"], "extra_text": ["", "\n\nPotential Impact:\nThis project will be of interest and benefit to a wide community, ranging from policy makers to end users of operational forecasts, and industry, underpinning the development of strategies to achieve a healthy marine environment, rendering ecosystems more resilient to climate change and variability, and assisting sustainable exploitation of marine resources.\nPolicy and Marine Management: Defra, Marine Scotland and Agri-Food and Biosciences Institute AFBI are responsible for implementation of the Marine Strategy Framework Directive in the UK and the establishment of clear environmental targets and monitoring programmes. They will benefit from improved knowledge and predictive skill for key indicators of the state of the marine environment. We will inform the assessment of Good Environmental Status, through demonstrating that data assimilated model products can provide a complete and contiguous information resource. This work will also inform the OSPAR assessment and future advances in the indicators used. Other policy areas that will benefit are marine spatial planning, fisheries policy and environmental assessment for off-shore operations. We will also engage with relevant international actors, e.g. International Council for the Exploration of the Sea; and non-governmental organisations (e.g. WWF), who develop science and advice to support the sustainable use of the oceans. CAMPUS will seek to engage with policy makers, via briefing notes, POSTnotes, or via the Parliamentary & Scientific Committee. \nMarine Monitoring strategies: Optimising the use of the UK's high-cost marine observing capability requires integrated network design and cost benefit analysis: CAMPUS will provide understanding of the scales and variability of the shelf sea system in the context of GES indicators and thus advise on the optimal observation network design for monitoring. This will assist the UK Integrated Marine Observing Network (UK-IMON); UK Marine Monitoring and Assessment Strategy (UKMMAS); the UK Marine Science Co-ordination Committee (MSCC). Key stakeholders include Defra, Marine Scotland, UKMO and AFBI. \nOperational Ocean forecast: The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference and monitoring information on the state of the oceans and regional seas, for both policy applications, such as MSFD, businesses and marine operators to exploit commercially. CAMPUS will integrate improved model-data systems into existing CMEMS operational oceanographic simulation and data delivery systems, further providing a template as to how this might be effected in other regions. Key stakeholders include MERCATOR and the UKMO\nIndustry: Dynamic sampling and truly autonomous vehicles open up the potential for a) new technology developments in the AV industry b) wider applications for monitoring in the energy / offshore industry and c) risk assessment for the aquaculture industry. Key stakeholders include DSTL, the MASSMO consortium and the Scottish aquaculture industry. \nData delivery: A key challenge for encouraging the use of model data products is the ability to make them easily accessible to the full range of stakeholders (e.g. policy, NGO, business, academics, general public). Model and data products will be made accessible via working with BODC, the Marine Environmental Data and Information Network (MEDIN) and international data infrastructures provided by ICES and EMODnet. A public facing web-based GIS data portal will allow users to browse and map data.\nOverseas Development Assistance: CAMPUS will provide a template for closely aligning models and observations in the ODA context, for example using models to aid the design of monitoring effort and inform model system configuration. We will explore how this would work in practice in a short desk study, focusing on Living Marine resources in the Bay of Bengal. This project will contribute in particular to UN Sustainable Development Goals (UNSDG14)\n\n\n"], "status": ["", "Closed"]}
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| Nov. 20, 2023, 2:03 p.m. |
Added
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{"external_links": [39335]}
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| Nov. 20, 2023, 2:03 p.m. |
Created
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[{"model": "core.project", "pk": 9617, "fields": {"owner": null, "is_locked": false, "coped_id": "08647c5e-aff7-4ee2-a14c-7df9b774921a", "title": "", "description": "", "extra_text": "", "status": "", "start": null, "end": null, "raw_data": 50941, "created": "2023-11-20T13:29:13.358Z", "modified": "2023-11-20T13:29:13.358Z", "external_links": []}}]
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