Articles | Volume 15, issue 2
https://doi.org/10.5194/os-15-291-2019
© Author(s) 2019. 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-15-291-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Mar 2019
Research article |
| 22 Mar 2019
| 22 Mar 2019
Upscaling of a local model into a larger-scale model
seamod.ro, Jailoo srl, Salatrucu, Romania
MAST, Université de Liège, Liège, Belgium
Alexander Barth
GHER, Université de Liège, Liège, Belgium
Related authors
Lauranne Alaerts, Jonathan Lambrechts, Ny Riana Randresihaja, Luc Vandenbulcke, Olivier Gourgue, Emmanuel Hanert, and Marilaure Grégoire
Earth Syst. Sci. Data, 17, 3125–3140, https://doi.org/10.5194/essd-17-3125-2025, https://doi.org/10.5194/essd-17-3125-2025, 2025
Short summary
Short summary
We created the first comprehensive, high-resolution, and easily accessible bathymetry dataset for the three main branches of the Danube Delta. By combining four data sources, we obtained a detailed representation of the riverbed, with resolutions ranging from 2 to 100 m. This dataset will support future studies on water and nutrient exchanges between the Danube and the Black Sea and provide insights into the delta's buffer role within the understudied Danube–Black Sea continuum.
Loïc Macé, Luc Vandenbulcke, Jean-Michel Brankart, Pierre Brasseur, and Marilaure Grégoire
EGUsphere, https://doi.org/10.5194/egusphere-2024-3682, https://doi.org/10.5194/egusphere-2024-3682, 2024
Short summary
Short summary
The amount of light found in seawater influences water temperature and primary production and must be finely modelled in systems that aim at representing marine biogeochemical environments. We analyse results from a radiative transfer model accounting for absorption and scattering of light in the ocean and compare them with in situ and remote-sensed data, along with the associated uncertainties. We also highlight the benefits of using advanced representations of light in modelling frameworks.
Arthur Capet, Luc Vandenbulcke, and Marilaure Grégoire
Biogeosciences, 17, 6507–6525, https://doi.org/10.5194/bg-17-6507-2020, https://doi.org/10.5194/bg-17-6507-2020, 2020
Short summary
Short summary
The Black Sea is 2000 m deep, but, due to limited ventilation, only about the upper 100 m contains enough oxygen to support marine life such as fish. This oxygenation depth has been shown to be decreasing (1955–2019). Here, we evidence that atmospheric warming induced a clear shift in an important ventilation mechanism. We highlight the impact of this shift on oxygenation. There are important implications for marine life and carbon and nutrient cycling if this new ventilation regime persists.
A. Barth, J.-M. Beckers, C. Troupin, A. Alvera-Azcárate, and L. Vandenbulcke
Geosci. Model Dev., 7, 225–241, https://doi.org/10.5194/gmd-7-225-2014, https://doi.org/10.5194/gmd-7-225-2014, 2014
Lauranne Alaerts, Jonathan Lambrechts, Ny Riana Randresihaja, Luc Vandenbulcke, Olivier Gourgue, Emmanuel Hanert, and Marilaure Grégoire
Earth Syst. Sci. Data, 17, 3125–3140, https://doi.org/10.5194/essd-17-3125-2025, https://doi.org/10.5194/essd-17-3125-2025, 2025
Short summary
Short summary
We created the first comprehensive, high-resolution, and easily accessible bathymetry dataset for the three main branches of the Danube Delta. By combining four data sources, we obtained a detailed representation of the riverbed, with resolutions ranging from 2 to 100 m. This dataset will support future studies on water and nutrient exchanges between the Danube and the Black Sea and provide insights into the delta's buffer role within the understudied Danube–Black Sea continuum.
Aida Alvera-Azcárate, Dimitry Van der Zande, Alexander Barth, Antoine Dille, Joppe Massant, and Jean-Marie Beckers
Ocean Sci., 21, 787–805, https://doi.org/10.5194/os-21-787-2025, https://doi.org/10.5194/os-21-787-2025, 2025
Short summary
Short summary
This work presents an approach for increasing the spatial resolution of satellite data and interpolating gaps due to cloud cover, using a method called DINEOF (data-interpolating empirical orthogonal functions). The method is tested on turbidity and chlorophyll-a concentration data in the Belgian coastal zone and the North Sea. The results show that we are able to improve the spatial resolution of these data in order to perform analyses of spatial and temporal variability in coastal regions.
Bayoumy Mohamed, Alexander Barth, Dimitry Van der Zande, and Aida Alvera-Azcárate
EGUsphere, https://doi.org/10.5194/egusphere-2025-1578, https://doi.org/10.5194/egusphere-2025-1578, 2025
Short summary
Short summary
We quantified the role of climate change and internal variability on marine heatwaves (MHWs) in the North Sea over more than four decades (1982–2024). A key finding is the 2013 climate shift, which was associated with increased warming and MHWs. Long-term warming accounted for 80 % of the observed trend in MHW frequency. The most intense MHW event in May 2024 was attributed to an anomalous anticyclonic atmospheric circulation. We also explored the impact of MHWs on chlorophyll concentrations.
Cécile Pujol, Alexander Barth, Iván Pérez-Santos, Pamela Muñoz-Linford, and Aida Alvera-Azcárate
EGUsphere, https://doi.org/10.5194/egusphere-2025-1421, https://doi.org/10.5194/egusphere-2025-1421, 2025
Short summary
Short summary
Marine heatwaves and cold spells are periods of extreme sea temperatures. This study focuses on Chilean Northern Patagonia, a fjord region vulnerable due to its aquaculture. It aims to understand these events' distribution and identify the most affected basins. Results show higher intensity in enclosed areas like Reloncaví Sound and Puyuhuapi Fjord. Marine heatwaves are becoming more frequent over time, while cold spells are decreasing.
Ehsan Mehdipour, Hongyan Xi, Alexander Barth, Aida Alvera-Azcárate, Adalbert Wilhelm, and Astrid Bracher
EGUsphere, https://doi.org/10.5194/egusphere-2025-112, https://doi.org/10.5194/egusphere-2025-112, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Phytoplankton are vital for marine ecosystems and nutrient cycling, detectable by optical satellites. Data gaps caused by clouds and other non-optimal conditions limit comprehensive analyses like trend monitoring. This study evaluated DINCAE and DINEOF gap-filling methods for reconstructing chlorophyll-a datasets, including total chlorophyll-a and five major phytoplankton groups. Both methods showed robust reconstruction capabilities, aiding pattern detection and long-term ocean colour analysis.
Matjaž Zupančič Muc, Vitjan Zavrtanik, Alexander Barth, Aida Alvera-Azcarate, Matjaž Ličer, and Matej Kristan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-208, https://doi.org/10.5194/gmd-2024-208, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Accurate sea surface temperature data (SST) is crucial for weather forecasting and climate modeling, but satellite observations are often incomplete. We developed a new method called CRITER, which uses machine learning to fill in the gaps in SST data. Our two-stage approach reconstructs large-scale patterns and refines details. Tested on Mediterranean, Adriatic, and Atlantic seas data, CRITER outperforms current methods, reducing errors by up to 44 %.
Loïc Macé, Luc Vandenbulcke, Jean-Michel Brankart, Pierre Brasseur, and Marilaure Grégoire
EGUsphere, https://doi.org/10.5194/egusphere-2024-3682, https://doi.org/10.5194/egusphere-2024-3682, 2024
Short summary
Short summary
The amount of light found in seawater influences water temperature and primary production and must be finely modelled in systems that aim at representing marine biogeochemical environments. We analyse results from a radiative transfer model accounting for absorption and scattering of light in the ocean and compare them with in situ and remote-sensed data, along with the associated uncertainties. We also highlight the benefits of using advanced representations of light in modelling frameworks.
Alexander Barth, Julien Brajard, Aida Alvera-Azcárate, Bayoumy Mohamed, Charles Troupin, and Jean-Marie Beckers
Ocean Sci., 20, 1567–1584, https://doi.org/10.5194/os-20-1567-2024, https://doi.org/10.5194/os-20-1567-2024, 2024
Short summary
Short summary
Most satellite observations have gaps, for example, due to clouds. This paper presents a method to reconstruct missing data in satellite observations of the chlorophyll a concentration in the Black Sea. Rather than giving a single possible reconstructed field, the discussed method provides an ensemble of possible reconstructions using a generative neural network. The resulting ensemble is validated using techniques from numerical weather prediction and ocean modelling.
Francesca Doglioni, Robert Ricker, Benjamin Rabe, Alexander Barth, Charles Troupin, and Torsten Kanzow
Earth Syst. Sci. Data, 15, 225–263, https://doi.org/10.5194/essd-15-225-2023, https://doi.org/10.5194/essd-15-225-2023, 2023
Short summary
Short summary
This paper presents a new satellite-derived gridded dataset, including 10 years of sea surface height and geostrophic velocity at monthly resolution, over the Arctic ice-covered and ice-free regions, up to 88° N. We assess the dataset by comparison to independent satellite and mooring data. Results correlate well with independent satellite data at monthly timescales, and the geostrophic velocity fields can resolve seasonal to interannual variability of boundary currents wider than about 50 km.
Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers
Geosci. Model Dev., 15, 2183–2196, https://doi.org/10.5194/gmd-15-2183-2022, https://doi.org/10.5194/gmd-15-2183-2022, 2022
Short summary
Short summary
Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.
Malek Belgacem, Katrin Schroeder, Alexander Barth, Charles Troupin, Bruno Pavoni, Patrick Raimbault, Nicole Garcia, Mireno Borghini, and Jacopo Chiggiato
Earth Syst. Sci. Data, 13, 5915–5949, https://doi.org/10.5194/essd-13-5915-2021, https://doi.org/10.5194/essd-13-5915-2021, 2021
Short summary
Short summary
The Mediterranean Sea exhibits an anti-estuarine circulation, responsible for its low productivity. Understanding this peculiar character is still a challenge since there is no exact quantification of nutrient sinks and sources. Because nutrient in situ observations are generally infrequent and scattered in space and time, climatological mapping is often applied to sparse data in order to understand the biogeochemical state of the ocean. The dataset presented here partly addresses these issues.
Arthur Capet, Luc Vandenbulcke, and Marilaure Grégoire
Biogeosciences, 17, 6507–6525, https://doi.org/10.5194/bg-17-6507-2020, https://doi.org/10.5194/bg-17-6507-2020, 2020
Short summary
Short summary
The Black Sea is 2000 m deep, but, due to limited ventilation, only about the upper 100 m contains enough oxygen to support marine life such as fish. This oxygenation depth has been shown to be decreasing (1955–2019). Here, we evidence that atmospheric warming induced a clear shift in an important ventilation mechanism. We highlight the impact of this shift on oxygenation. There are important implications for marine life and carbon and nutrient cycling if this new ventilation regime persists.
Alexander Barth, Aida Alvera-Azcárate, Matjaz Licer, and Jean-Marie Beckers
Geosci. Model Dev., 13, 1609–1622, https://doi.org/10.5194/gmd-13-1609-2020, https://doi.org/10.5194/gmd-13-1609-2020, 2020
Short summary
Short summary
DINCAE is a method for reconstructing missing data in satellite datasets using a neural network. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images.
J.-M. Beckers, A. Barth, I. Tomazic, and A. Alvera-Azcárate
Ocean Sci., 10, 845–862, https://doi.org/10.5194/os-10-845-2014, https://doi.org/10.5194/os-10-845-2014, 2014
J. Marmain, A. Molcard, P. Forget, A. Barth, and Y. Ourmières
Nonlin. Processes Geophys., 21, 659–675, https://doi.org/10.5194/npg-21-659-2014, https://doi.org/10.5194/npg-21-659-2014, 2014
A. Barth, J.-M. Beckers, C. Troupin, A. Alvera-Azcárate, and L. Vandenbulcke
Geosci. Model Dev., 7, 225–241, https://doi.org/10.5194/gmd-7-225-2014, https://doi.org/10.5194/gmd-7-225-2014, 2014
Related subject area
Approach: Data Assimilation | Depth range: All Depths | Geographical range: Mediterranean Sea | Phenomena: Temperature, Salinity and Density Fields
Dense CTD survey versus glider fleet sampling: comparing data assimilation performance in a regional ocean model west of Sardinia
A hybrid variational-ensemble data assimilation scheme with systematic error correction for limited-area ocean models
Design and validation of MEDRYS, a Mediterranean Sea reanalysis over the period 1992–2013
Jaime Hernandez-Lasheras and Baptiste Mourre
Ocean Sci., 14, 1069–1084, https://doi.org/10.5194/os-14-1069-2018, https://doi.org/10.5194/os-14-1069-2018, 2018
Short summary
Short summary
Different sampling strategies have been assessed in order to evaluate the most efficient configuration for the assimilation of high resolution measurements into a regional ocean model. The results show the capability of the model to ingest both large scale and high resolution observations and the improvement of the forecast fields. In particular, the configurations using eight gliders and the one assimilating CTDs show similar results and the give the best performance among all the simulations
Paolo Oddo, Andrea Storto, Srdjan Dobricic, Aniello Russo, Craig Lewis, Reiner Onken, and Emanuel Coelho
Ocean Sci., 12, 1137–1153, https://doi.org/10.5194/os-12-1137-2016, https://doi.org/10.5194/os-12-1137-2016, 2016
Mathieu Hamon, Jonathan Beuvier, Samuel Somot, Jean-Michel Lellouche, Eric Greiner, Gabriel Jordà, Marie-Noëlle Bouin, Thomas Arsouze, Karine Béranger, Florence Sevault, Clotilde Dubois, Marie Drevillon, and Yann Drillet
Ocean Sci., 12, 577–599, https://doi.org/10.5194/os-12-577-2016, https://doi.org/10.5194/os-12-577-2016, 2016
Short summary
Short summary
The paper describes MEDRYS, a MEDiterranean sea ReanalYsiS at high resolution for the period 1992–2013. The NEMOMED12 ocean model is forced at the surface by a new high resolution atmospheric forcing dataset (ALDERA). Altimeter data, satellite SST and temperature and salinity vertical profiles are jointly assimilated. The ability of the reanalysis to represent the sea surface high-frequency variability, water mass characteristics and transports through the Strait of Gibraltar is shown.
Cited articles
Alberola, C., Millot, C., and Font, J.: On the seasonal and mesoscale
variabilites of the Northern Current during the PRIMO-0 experiment in the
western Mediterranean Sea, Oceanol. Acta, 18, 163–192, 1995. a
Alvarez, A., Lopez, C., Riera, M., Hernandez-Garcia, E., and Tintore, J.:
Forecasting the SST space-time variability of the Alboran sea with genetic
algorithms, Geophys. Res. Lett., 27, 2709–2712, https://doi.org/10.1029/1999GL011226, 2000. a
Auclair, F., Casitas, S., and Marsaleix, P.: Application of an inverse method
to coastal modeling, J. Atmos Ocean. Tech., 17, 1368–1391, 2000. a
Auclair, F., Marsaleix, P., and Estournel, C.: The penetration of the Northern
Current over the Gulf of Lions (Mediterranean) as a downscaling problem, Oceanol.
Acta, 24, 529–544, 2001. a
Auclair, F., Marsaleix, P., and De Mey, P.: Space-time structure and dynamics
of the forecast error in a coastal circulation model of the Gulf of Lions,
Dynam. Atmos. Oceans, 36, 309–346, 2003. a
Barth, A. and Vandenbulcke, L.: Ocean Assimilation Kit documentation, Universite
de Liege, GHER, available at: http://modb.oce.ulg.ac.be/mediawiki/index.php/Ocean Assimilation Kit
(last access: 28 December 2018), 2017. a
Barth, A., Alvera-Azcarate, A., Rixen, M., and Beckers, J.: Two-way nested
model of mesoscale circulation features in the Ligurian Sea, Progr. Oceanogr.,
66, 171–189, 2005. a
Barth, A., Alvera-Azcarate, A., Beckers, J.-M., Rixen, M., and Vandenbulcke, L.:
Multigrid state vector for data assimilation in a two-way nested model of the
Ligurian Sea, J. Mar. Syst., 65, 41–59, https://doi.org/10.1016/j.jmarsys.2005.07.006, 2006. a
Barth, A., Alvera-Azcarate, A., and Weisberg, R. H.: Assimilation of
high-frequency radar currents in a nested model of the West Florida shelf,
J. Geophys. Res., 113, C08033, https://doi.org/10.1029/2007JC004585, 2008. a
Barth, A., Alvera-Azcarate, A., Beckers, J.-M., Staneva, J., Stanev, E., and
Schulz-Stellenfleth, J.: Correcting surface winds by assimilating high-frequency
radar surface currents in the German Bight, Ocean Dynam., 61, 599–610, 2011. a
Bishop, C., Etherton, B., and Majumdar, S.: Adaptive sampling with the Ensemble
Transform Kalman Filter part I: the theoretical aspects, Mon. Weather Rev.,
129, 420–436, 2001. a
Clementi, E., Pistoia, J., Fratianni, C., Delrosso, D., Grandi, A., Drudi, M.,
Coppini, G., Lecci, R., and Pinardi, N.: Med analyses at 1/16th, Mediterranean
Sea Analysis and Forecast (CMEMS MED-Currents 2013–2017), Dataset,
https://doi.org/10.25423/MEDSEA_ANALYSIS, 2017. a
Debreu, L., Vouland, C., and Blayo, E.: AGRIF: Adaptive grid refinement in
Fortran, Comput. Geosci., 8, 8–13, 2008. a
Debreu, L., Marchesiello, P., Penven, P., and Cambon, G.: Two-way nesting in
split-explicit ocean models: Algorithms, implementation and validation, Ocean
Model., 49-50, 1–21, https://doi.org/10.1016/j.ocemod.2012.03.003, 2012. a, b
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars,
A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R.,
Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm,
E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally,
A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay,
P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis:
configuration and performance of the data assimilation system, Q. J. Roy.
Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Guinehut, S., Larnicol, G., and Le Traon, P.: Design of an array of profiling
floats in the North Atlantic from model simulations, J. Mar. Syst., 35, 1–9,
https://doi.org/10.1016/S0924-7963(02)00042-8, 2002. a
Guinehut, S., Le Traon, P., Larnicol, G., and Philipps, S.: Combining ARGO and
remote-sensing data to estimate the ocean three-dimensional temperature
fields – A first approach based on simulated observations, J. Mar. Syst.,
46, 85–98, 2004. a
Hunt, B., Kostelich, E., and Szunyogh, I.: Efficient data assimilation for
spatiotemporal chaos: A local ensemble transform Kalman filter, Physica D,
230, 112–126, 2007. a
Le Traon, P.-Y., Antoine, D., Bentamy, A., Bonekamp, H., Breivik, L., Chapron,
B., Corlett, G., Dibarboure, G., DiGiacomo, P., Donlon, C., Faugère, Y.,
Font, J., Girard-Ardhuin, F., Gohin, F., Johannessen, J., Kamachi, M.,
Lagerloef, G., Lambin, J., Larnicol, G., Le Borgne, P., Leuliette, E., Lindstrom,
E., Martin, M., Maturi, E., Miller, L., Mingsen, L., Morrow, R., Reul, N., Rio,
M., Roquet, H., R., S., and Wilkin, J.: Use of satellite observations for
operational oceanography: recent achievements and future prospects, J. Operat.
Oceanogr., 8, s12–s27, https://doi.org/10.1080/1755876X.2015.1022050, 2015. a
Madec, G.: NEMO ocean engine, Note du Pôle de modélisation, No. 27,
Institut Pierre-Simon Laplace (IPSL), France, ISSN 1288-1619, 2008. a
Mason, E., Molemaker, J., Shchepetkin, A. F., Colas, F., McWilliams, J. C., and
Sangrà, P.: Procedures for offline grid nesting in regional ocean models,
Ocean Model., 35, 1–15, https://doi.org/10.1016/j.ocemod.2010.05.007, 2010. a
Millot, C.: Circulation in the Western Mediterranean Sea, J. Mar. Syst.,
20, 423–442, 1999. a
Nerger, L., Janjic, T., Schroter, J., and Hiller, W.: A regulated localization
scheme for ensemble-based Kalman filters, Q. J. Roy. Meteorol. Soc, 138, 802–812, 2012. a
Onken, R., Robinson, A. R., Kantha, L., Lozano, C. J., Haley, P. J., and Carniel,
S.: A rapid response nowcast/forecast system using multiply nested ocean models
and distributed data systems, J. Mar. Syst., 56, 45–66, https://doi.org/10.1016/j.jmarsys.2004.09.010, 2005. a
Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P.,
Simoncelli, S., Tonani, M., Lyubartsev, V., Dobricic, S., and Bonaduce, A.:
Mediterranean Sea large-scale low-frequency ocean variability and water mass
formation rates from 1987 to 2007: A retrospective analysis, Prog. Oceanogr.,
132, 318–332, https://doi.org/10.1016/j.pocean.2013.11.003, 2015. a, b
Simoncelli, A., Fratianni, C., Pinardi, N., Grandi, A., Drudi, M., Oddo, P.,
and Dobricic, S.: Mediterranean Sea physical reanalysis (MEDREA 1987–2015),
Copernicus Monitoring Environment Marine Service (CMEMS), Dataset, https://doi.org/10.25423/medsea_reanalysis_phys_006_004, 2014. a
Simoncelli, S., Pinardi, N., Oddo, P., Mariano, A., Montanari, G., Rinaldi, A.,
and Deserti, M.: Coastal Rapid Environmental Assessment in the Northern Adriatic
Sea, Dynam. Atmos. Oceans, 52, 250–283, https://doi.org/10.1016/j.dynatmoce.2011.04.004, 2011. a
Simoncelli, S., Pinardi, N., Fratianni, C., Dubois, C., and Notarstefano, G.:
Water mass formation processes in the Mediterranean sea over the past 30 years,
in: Copernicus Marine Service Ocean State Report, J. Operat. Oceanogr., 11,
s13–s16, https://doi.org/10.1080/1755876X.2018.1489208, 2018. a
Somot, S., Houpert, L., Sevault, F., Testor, P., Bosse, A., Taupier-Letage, I.,
Bouin, M.-N., Waldman, R., Cassou, C., Sanchez-Gomez, E., Durrieu de Madron,
X., Adloff, F., Nabat, P., and Herrmann, M.: Characterizing, modelling and
understanding the climate variability of the deep water formation in the
North-Western Mediterranean Sea, Clim. Dynam., 51, 1179–1210, https://doi.org/10.1007/s00382-016-3295-0, 2018.
a
Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M., and Whitaker, J.
S.: Ensemble Square Root Filters, Mon. Weather Rev., 131, 1485–1490, 2003. a
Vandenbulcke, L., Barth, A., Rixen, M., Alvera-Azcarate, A., Ben Bouallegue, Z.,
and Beckers, J. M.: Study of the combined effects of data assimilation and grid
nesting in ocean models – application to the Gulf of Lions, Ocean Sci., 2,
213–222, https://doi.org/10.5194/os-2-213-2006, 2006. a
Vandenbulcke, L., Beckers, J., and Barth, A.: Correction of inertial oscillations
by assimilation of HF radar data in a model of the Ligurian Sea, Ocean Dynam.,
67, 117–135, 2017. a
Wang, X., Bishop, C. H., and Julier, S. J.: Which Is Better, an Ensemble of
Positive–Negative Pairs or a Centered Spherical Simplex Ensemble?, Mon. Weather
Rev., 132, 1590–1605, 2004. a
Short summary
In operational oceanography, regional and local models use large-scale models (such as those run by CMEMS) for their initial and/or boundary conditions, but unfortunately there is no feedback that improves the large-scale models. The present study aims at replacing normal two-way nesting by a data assimilation technique. This
upscalingmethod is tried out in the north-western Mediterranean Sea using the NEMO model and shows that the basin-scale model does indeed benefit from the nested model.
In operational oceanography, regional and local models use large-scale models (such as those run...
