Articles | Volume 17, issue 4
https://doi.org/10.5194/os-17-891-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-17-891-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jul 2021
Research article |
| 06 Jul 2021
| 06 Jul 2021
High-resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics
Georgy I. Shapiro
CORRESPONDING AUTHOR
School of Biological and Marine Sciences, University of Plymouth,
Plymouth, PL4 8AA, UK
Jose M. Gonzalez-Ondina
University of Plymouth Enterprise LTD, Plymouth, PL4 8AA, UK
Vladimir N. Belokopytov
Marine Hydrophysical Institute, Russian Academy of Sciences,
Sevastopol, 299011, Russia
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In this paper we review marine data assimilation (MDA) in the UK, its stakeholders, needs, past and present developments in different areas of UK MDA, and offer a vision for their longer future. The specific areas covered are ocean physics and sea ice, marine biogeochemistry, coupled MDA, MDA informing observing network design and MDA theory. We also discuss future vision for MDA resources: observations, software, hardware and people skills.
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An effective method is developed for data assimilation in a high-resolution (child) ocean model in the case when the output from a coarse-resolution data-assimilating model (parent) is available. The basic idea is to assimilate data from the coarser model instead of actual observations. The method named Data Assimilation with Stochastic-Deterministic Downscaling (SDDA) does not allow the child model to drift away from reality as it is indirectly controlled by observations via the parent model.
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In this paper we review marine data assimilation (MDA) in the UK, its stakeholders, needs, past and present developments in different areas of UK MDA, and offer a vision for their longer future. The specific areas covered are ocean physics and sea ice, marine biogeochemistry, coupled MDA, MDA informing observing network design and MDA theory. We also discuss future vision for MDA resources: observations, software, hardware and people skills.
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Preprint withdrawn
Short summary
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An effective method is developed for data assimilation in a high-resolution (child) ocean model in the case when the output from a coarse-resolution data-assimilating model (parent) is available. The basic idea is to assimilate data from the coarser model instead of actual observations. The method named Data Assimilation with Stochastic-Deterministic Downscaling (SDDA) does not allow the child model to drift away from reality as it is indirectly controlled by observations via the parent model.
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This study is focused on water exchange between the Sea of Azov and the Black Sea. The Sea of Azov is a small freshened sea that receives a large freshwater discharge and, therefore, can be regarded as a large river estuary connected by narrow Kerch Strait with the Black Sea. In this work we show that water transport through the Kerch Strait is governed by wind forcing and does not depend on the river discharge rate to the Sea of Azov on an intra-annual timescale.
F. Wobus, G. I. Shapiro, J. M. Huthnance, M. A. M. Maqueda, and Y. Aksenov
Ocean Sci., 9, 885–899, https://doi.org/10.5194/os-9-885-2013, https://doi.org/10.5194/os-9-885-2013, 2013
G. Shapiro, M. Luneva, J. Pickering, and D. Storkey
Ocean Sci., 9, 377–390, https://doi.org/10.5194/os-9-377-2013, https://doi.org/10.5194/os-9-377-2013, 2013
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Short summary
This paper presents an efficient method for high-resolution ocean modelling based on a combination of the deterministic and stochastic approaches. The method utilises mathematical tools similar to those developed for data assimilation in ocean modelling. The main difference is that instead of assimilating a relatively small number of observations, the SDD method assimilates all the data produced by a parent model. The method is applied to create an operational Stochastic Model of the Red Sea.
This paper presents an efficient method for high-resolution ocean modelling based on a...
