José M. González‐Ondina

University of Plymouth

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G. I. Shapiro

University of Plymouth

Xavier Francis

University of Cincinnati

Mauricio González

Universidad de Cantabria

Lewis Sampson

Met Office

Jiada Tu

University of Plymouth

ÍJ

Íñigo J. Losada

Universidad de Cantabria

Raúl Medina

Universidad de Cantabria

V. N. Belokopytov

Marine Hydrophysical Institute

Muhammad Asif

Bahria University

M. Salim

Khalifa University of Science and Technology

Cooperative Institutions

Universidad de Cantabria

Instituto de Física de Cantabria

University of Plymouth

Universidad Nacional Autónoma de México

Met Office

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering

World Bank

Argans (United Kingdom)

Universidade do Vale do Itajaí

Russian Academy of Sciences

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An integrated coastal modeling system for analyzing beach processes and beach restoration projects, SMC

Computers & Geosciences (2007)

Mauricio González Raúl Medina José M. González‐Ondina Andrés F. Osorio Fernando J. Méndez

Beach nourishment

Coastal engineering

10.1016/j.cageo.2006.12.005

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Citations (94)

Generation and seasonal variability of eddy kinetic energy in the central Indian Ocean

G. I. Shapiro José M. González‐Ondina

The breakthrough in our knowledge of ocean eddies came with the results of the POLYGON-67 experiment in the central Indian Ocean carried out in January-April 1967 (see Koshlyakov et al, 2016). It was the first direct and unambiguous observation that proved an earlier hypothesis by V. B. Shtockman of the existence of mesoscale eddies in open ocean, not only next to strong jet-stream currents. Now it is well known that the currents in open ocean are almost everywhere dominated by meso-scale eddies also known as synoptic eddies (Robinson, 1983). POLYGON-67 experiment covered a rectangle bounded by 10-15&#176;N and 63-66.5&#176;E. The purpose of this work is to analyse the seasonal variability of meso-scale eddy activity in the area covered by POLYGON-67 using a modern and comprehensive data set produced by an operational data assimilation model over a period from 1998 to 2017.The 20-year long eddy resolving reanalysis of velocity fields in the Indian Ocean allows the study of seasonal variability, dynamics and generating mechanisms of eddy kinetic energy (EKE) in the tropical Indian Ocean, including the area covered by the original survey of POLYGON-67. In contrast to some other areas of the World Ocean, the EKE seasonality shows two maxima, the large one in April and the secondary one in October. The main mechanism of EKE generation is the barotropic instability which is evidenced by high correlation between EKE and enstrophy of large-scale currents, representing the strength of horizontal shear. It is found that the main contributor to the EKE variability within POLYGON-67 area is the advection of EKE across the boundaries during January-October, while the local generation has a comparable magnitude during August-December. The direction and strength of surface currents is consistent with the monsoon wind pattern in the area.ReferencesKoshlyakov, M.N., Morozov, E.G., and Neiman, V.G., 2016. Historical findings of the Russian physical oceanographers in the Indian Ocean. Geoscience Letters, 3:19; doi:10.1186/s40562-016-0051-6Robinson, A.R. (Ed), 1983. Eddies in Marine Science. Springer, ISBN 978-3-642-69003-7, 612p.

Eddy

Barotropic fluid

Polygon (computer graphics)

10.5194/egusphere-egu21-2157

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An Efficient Method for Nested High-Resolution Ocean Modelling Incorporating a Data Assimilation Technique

Journal of Marine Science and Engineering (2022)

G. I. Shapiro José M. González‐Ondina

A simple and computationally efficient method is presented for creating a high-resolution regional (child) model nested within a coarse-resolution, good-quality data-assimilating (parent) model. The method, named Nesting with Downscaling and Data Assimilation (NDA), reduces bias and root mean square errors (RMSE) of the child model and does not allow the child model to drift from reality. Usually coarser resolution models, e.g., global scale, are used to provide boundary conditions for the nested child model. The basic idea of the NDA method is to use a complete 3D set of output data from the parent model using a process which is similar to data assimilation of observations into an ocean model. In this way, the child model is physically aware of observations via the parent model. The method allows for avoiding a complex process of assimilating the same observations which were already assimilated into the parent model. The NDA method is illustrated in several simple 2D synthetic cases where the true solution is known. The NDA method reduces the child model bias to the same level as in the parent model and reduces the RMSE, typically by a factor of two to five, occasionally more.

Nested set model

Unified Model

10.3390/jmse10030432

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Citations (5)

A new method for downscaling of forecasting ocean models - the Red Sea case study

EGUGA (2019)

G. I. Shapiro José M. González‐Ondina Xavier Francis

Source

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Citations (0)

High resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics

G. I. Shapiro José M. González‐Ondina V. N. Belokopytov

Abstract. High-resolution modelling of a large ocean domain requires significant computational resources. The main purpose of this study is to develop an efficient tool for downscaling the lower resolution data such as available from Copernicus Marine Environment Monitoring Service (CMEMS). Common methods of downscaling CMEMS ocean models utilize their lower resolution output as boundary conditions for local, higher resolution hydrodynamic ocean models. Such methods reveal greater details of spatial distribution of ocean variables; however, they increase the cost of computations, and often reduce the model skill due to the so called double penalty effect. This effect is a common problem for many high-resolution models where predicted features are displaced in space or time. This paper presents a Stochastic Deterministic Downscaling (SDD) method, which is an efficient tool for downscaling of ocean models based on the combination of deterministic and stochastic approaches. The ability of the SDD method is first demonstrated in an idealised case when the true solution is known a priori. Then the method is applied to create an operational eddy-resolving Stochastic Model of the Red Sea (SMORS) with the parent model being the eddy-permitting Mercator Global Ocean Analysis and Forecast System. The stochastic component is data-driven rather than equation-driven and applied to the areas smaller than the Rossby radius, where distributions of ocean variables are more coherent. The method, based on objective analysis, is similar to what is used for data assimilation in ocean models, and stems from the philosophy of 2D turbulence. The SMORS model produces higher resolution (1/24th degree latitude mesh) oceanographic data using the output from a coarser resolution (1/12th degree mesh) parent model available from CMEMS. The values on the high-resolution mesh are computed under condition of minimisation of the cost function which represents the error between the model and true solution. The SMORS model has been validated against Sea Surface Temperature and ARGO floats observations. Comparisons show that the model and observations are in good agreement and SMORS is not subject to the ‘double penalty’ effect. SMORS is very fast to run on a typical desktop PC and can be relocated to another area of the ocean.

Stochastic modelling

10.5194/os-2020-119

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High-resolution stochastic downscaling method for ocean forecasting models and its application to the Red Sea dynamics

Ocean science (2021)

G. I. Shapiro José M. González‐Ondina V. N. Belokopytov

Abstract. High-resolution modelling of a large ocean domain requires significant computational resources. The main purpose of this study is to develop an efficient tool for downscaling the lower-resolution data such as those available from Copernicus Marine Environment Monitoring Service (CMEMS). Common methods of downscaling CMEMS ocean models utilise their lower-resolution output as boundary conditions for local, higher-resolution hydrodynamic ocean models. Such methods reveal greater details of spatial distribution of ocean variables; however, they increase the cost of computations and often reduce the model skill due to the so called “double penalty” effect. This effect is a common problem for many high-resolution models where predicted features are displaced in space or time. This paper presents a stochastic–deterministic downscaling (SDD) method, which is an efficient tool for downscaling of ocean models based on the combination of deterministic and stochastic approaches. The ability of the SDD method is first demonstrated in an idealised case when the true solution is known a priori. Then the method is applied to create an operational Stochastic Model of the Red Sea (SMORS), with the parent model being the Mercator Global Ocean Analysis and Forecast System at 1/12∘ resolution. The stochastic component of the model is data-driven rather than equation-driven, and it is applied to the areas smaller than the Rossby radius, within which distributions of ocean variables are more coherent than over a larger distance. The method, based on objective analysis, is similar to what is used for data assimilation in ocean models and stems from the philosophy of 2-D turbulence. SMORS produces finer-resolution (1/24∘ latitude mesh) oceanographic data using the output from a coarser-resolution (1/12∘ mesh) parent model available from CMEMS. The values on the fine-resolution mesh are computed under conditions of minimisation of the cost function, which represents the error between the model and true solution. SMORS has been validated against sea surface temperature and ARGO float observations. Comparisons show that the model and observations are in good agreement and SMORS is not subject to the “double penalty” effect. SMORS is very fast to run on a typical desktop PC and can be relocated to another area of the ocean.

Stochastic modelling

10.5194/os-17-891-2021

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Citations (10)

Seasonal Variability of Eddy Kinetic Energy in the Central Indian Ocean: POLYGON-67 Revised

Physical Oceanography (2020)

G. I. Shapiro José M. González‐Ondina

Purpose.The main goal of this study is to analyse the seasonal variability of meso-scale eddy activity in the north tropical Indian Ocean.The selected area coincides with the location of POLYGON-67 (P67) experiment where the mesoscale eddies of the open ocean were first discovered.Methods and results.The variability of mesoscale eddy kinetic energy in surface ocean layer, enstrophy of larger scale circulation, spatial and temporal patterns of surface currents and surface winds are jointly analysed using a 20-year long daily time series of eddy-resolving ocean reanalysis data obtained from EU Copernicus Marine Environment Monitoring Service and climatic wind data from US National Oceanographic and Atmospheric Administration.The fast mesoscale and slow large-scale processes are separated using a Savitsky -Golay filter with the cut-off time of 103 days which corresponds to a local minimum in the full kinetic energy power spectrum.In contrast to other parts of the tropical ocean, the seasonal variability of EKE exhibits 2 maxima -the largest being in April, and the secondary being in October which are related to the maxima in enstrophy of larger scale currents.Conclusions.The double peak variability in EKE corresponds to the seasonal variability of large scale enstrophy and monsoon wind circulation and supports a hypothesis that the main mechanism of EKE generation is barotropic instability of larger scale currents.The EKE variability within P67 is mostly controlled by advection of energy from neighbouring areas, and to a lesser extent by local generation.

Barotropic fluid

Eddy

Enstrophy

Sea-surface height

10.22449/1573-160x-2020-6-573-589

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Citations (2)

Numerical modeling of nonlinear resonance of semi-enclosed water bodies: Description and experimental validation

Coastal Engineering (2007)

Íñigo J. Losada José M. González‐Ondina Gabriel Díaz Hernández Mauricio González

Unstructured grid

Forcing (mathematics)

Shallow water equations

Transient (computer programming)

10.1016/j.coastaleng.2007.06.002

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Citations (65)

Seasonal Variability of Eddy Kinetic Energy in the Central Indian Ocean: POLYGON-67

Morskoy gidrofizicheskiy zhurnal (2020)

G. I. Shapiro José M. González‐Ondina

Purpose. The main goal of this study is to analyze seasonal variability of the meso-scale eddy activity in the north tropical Indian Ocean. The selected area coincides with the POLYGON-67 experiment location where the meso-scale eddies were first discovered in the open ocean. Methods and results. Variability of the meso-scale eddy kinetic energy in the ocean surface layer, enstrophy of the larger scale circulation, spatial and temporal patterns of the surface currents and the surface winds are jointly analyzed using a 20-year long daily time series, containing both the eddy-resolving ocean reanalysis data obtained from the EU Copernicus Marine Environment Monitoring Service, and the climatic wind data – from the US National Oceanographic and Atmospheric Administration. The fast meso-scale and slow large-scale processes are separated using the Savitsky – Golay filter with the cut-off time 103 days that corresponds to the local minimum in the full kinetic energy power spectrum. In contrast to the other parts of the tropical ocean, seasonal variability of the eddy kinetic energy exhibits 2 maxima (the largest – in April and the secondary – in October), which are related to the enstrophy maxima of the larger scale currents. Conclusions. The double variability peak in the eddy kinetic energy corresponds to seasonal variability of the large-scale currents enstrophy and the wind monsoon circulation. This supports the hypothesis that the main mechanism of the eddy kinetic energy generation is barotropic instability of the larger scale currents. The eddy kinetic energy variability within the POLYGON-67 is mostly controlled by energy advection from the neighbouring areas, and to a lesser extent – by local generation.

Eddy

Barotropic fluid

10.22449/0233-7584-2020-6-628-645

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Citations (0)

A new method for computing horizontally anisotropic background error covariance matrices for data assimilation in ocean models.

José M. González‐Ondina Lewis Sampson G. I. Shapiro

Current operational ocean modelling systems often use variational data assimilation (DA) to improve the skill of the ocean predictions by combining the numerical model with observational data. Many modern methods are derivatives of objective (optimal) interpolation techniques developed by L. S. Gandin in the 1950s, which requires computation of the background error covariance matrix (BECM), and much research has been devoted into overcoming the difficulties surrounding its calculation and improving its accuracy. In practice, due to time and memory constraints, the BECM is never fully computed. Instead, a simplified model is used, where the correlation at each point is modelled using a simple function while the variance and length scales are computed using error estimation methods such as the Hollingsworth-Lonnberg&#160; or the NMC (National Meteorological Centre). Usually, the correlation is assumed to be horizontally isotropic, or to have a predefined anisotropy based on latitude. However, observations indicate that horizontal diffusion is sometimes anisotropic, hence this has to be propagated into BECM. It is suggested that including these anisotropies would improve the accuracy of the model predictions.We present a new method to compute the BECM which allows to extract horizontal anisotropic components from observational data. Our method, unlike current techniques, is fundamentally multidimensional and can be applied to 2D or 3D sets of un-binned data. It also works better than other methods when observations are sparse, so there is no penalty when trying to extract the additional anisotropic components from the data.Data Assimilation tools like NEMOVar use a matrix decomposition technique for the BECM in order to minimise the cost function. Our method is well suited to work with this type of decomposition, producing the different components of the decomposition which can be readily used by NEMOVar.We have been able to show the spatial stability of our method to quantify anisotropy in areas of sparse observations. While also demonstrating the importance of including anisotropic representation within the background error. Using the coastal regions of the Arabian Sea, it is possible to analyse where improvements to diffusion can be included. Further extensions of this method could lead to a fully anisotropic diffusion operator for the calculation of BECM in NEMOVar. However further testing and optimization are needed to correctly implement this into operational assimilation systems.

Interpolation

10.5194/egusphere-egu2020-2686

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