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See detailAssimilation of sea surface temperature and sea surface height in a two-way nested primitive equation model of the Ligurian Sea
Barth, Alexander ULiege

Doctoral thesis (2004)

A coarse grid primitive equation model of 1/4° resolution is implemented covering the whole Mediterranean Sea. Within this grid a 1/20° resolution model of the Liguro-Provençal basin and the northern part ... [more ▼]

A coarse grid primitive equation model of 1/4° resolution is implemented covering the whole Mediterranean Sea. Within this grid a 1/20° resolution model of the Liguro-Provençal basin and the northern part of the Tyrrhenian Sea is embedded. A third fine resolution model of 1/60° is nested in the latter one and simulates the dynamics of the Ligurian Sea. Comparisons between one-way and two-way nesting in representing the Northern Current (NC) are made. This system of nested models is coupled with a simplified Kalman-filter based assimilation method. The state vector for the data assimilation is composed by the temperature, salinity and elevation of the three models. The forecast error is estimated by an ensemble run by perturbing initial conditions and atmospheric forcings. The leading empirical orthogonal functions (EOF) of this ensemble are taken as the error covariance of the model forecast. This error covariance is assumed to be constant in time. Sea surface temperature (SST) and sea surface height (SSH) are assimilated in this system. Data assimilation is also used to couple the hydrodynamic model with a statistical predictor of SST in the Ligurian Sea. The forecast improvement of this hybrid modelling system is shown and applications to operational models are highlighted. [less ▲]

Detailed reference viewed: 50 (8 ULiège)
See detailStudy of the impact of satellite data assimilation into a hydrodynamical model of the ligurian sea. Comparison between sst fields and sst satellite-based predicted fields
Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege; Rixen, M. et al

Conference (2004)

The verification of a 3D hydrodynamic model of the Ligurian Sea is presented. Two assimilation experiments have been carried out with this model: the assimilation of real SST, and the assimilation of SST ... [more ▼]

The verification of a 3D hydrodynamic model of the Ligurian Sea is presented. Two assimilation experiments have been carried out with this model: the assimilation of real SST, and the assimilation of SST forecasted by a statistical predictor. The aim of the study is to establish the skill of the model in these two configurations. The assimilation of predicted SST can help to increase the model skill when observations are not available, and preliminary results show that both approaches obtained similar results. The verification is done in a multi-scale approach, by decomposing the model results and the observations into several spatial scales, using 2D discrete wavelet transforms. At each scale the error between the model and the observations is calculated, and the scales where the biggest errors occur can be identified. The variability distribution of the model and the observations is also examined at each scale, to study the impact of the assimilation on the model variability. This methodology provides a scale-dependent insight in the study of the assimilation of SST and predicted SST. The differences between both assimilated data and how these differences affect the model results are examined. [less ▲]

Detailed reference viewed: 16 (2 ULiège)
See detailReconstruction of missing data in geophysical fields. resolution of moving patterns
Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege; Rixen, M. et al

Conference (2004)

A method for the reconstruction of missing data in large data sets is presented. The method, DINEOF (Data INterpolating Empirical Orthogonal Functions), calculates the missing data from an optimal number ... [more ▼]

A method for the reconstruction of missing data in large data sets is presented. The method, DINEOF (Data INterpolating Empirical Orthogonal Functions), calculates the missing data from an optimal number of EOFs determined by cross-validation. A Lanczos method has been used for the EOF decomposition, in order to work with large matrices. DINEOF has been applied to two data sets of sea surface temperature: a set of 105 images in the Adriatic Sea and a set of 216 images in Tanganyika Lake. These data sets present 52% and 37% of missing data respectively, due to cloud coverage. Several validation studies have been carried out: comparison with in situ data and reconstruction of increasing amounts of missing data, from 40% to 80% of the total data, by artificially adding clouds. All tests show that results are robust. DINEOF uses a classical EOF decomposition. In this work we also present a different EOF decomposition, known as Extended EOF (ExEOF) or Singular Spectrum Analysis (SSA). This technique consists in using a lagged version of the matrix being analysed. By taking into account both the spatial and temporal correlation of the data, the ExEOF technique resolves spatio-temporal moving patterns in a more accurate way. Preliminary results show that this technique helps to better reconstruct the missing data in our data sets. [less ▲]

Detailed reference viewed: 19 (3 ULiège)
See detailAssimilation of Sea Surface Temperature predicted by a satellite-based forecasting system in a doubly nested primitive equation model of the Ligurian Sea
Barth, Alexander ULiege; Alvera Azcarate, Aïda ULiege; Alvarez, A. et al

Conference (2004)

Data assimilation is traditionally used to combine model dynamics and observations in a statistical optimal way. Assimilation of observations improves therefore hindcasts and nowcasts of the ocean state ... [more ▼]

Data assimilation is traditionally used to combine model dynamics and observations in a statistical optimal way. Assimilation of observations improves therefore hindcasts and nowcasts of the ocean state than otherwise obtained by the model alone. The observational constraints are necessary to reduce uncertainties and imperfections of the ocean model. Due to the obvious lack of future observations, the model forecast cannot be controlled by observations and the predictive skill degrades as the forecast time lag increases. The error grow is not only caused by the chaotic nature of the system but also by the biases and drifts of the model. The later part can be reduced by considering different models with different imperfections. Data assimilation provides the statistical frame for merging the different model results. A primitive equation model of the Mediterranean Sea (1/4° resolution) has been implemented with two successive grid refinements of the Liguro-Provençal Basin (1/20°) and the Ligurian Sea (1/60°) respectively (Barth et al, 2003). The dependence of the ``parent'' model and the embedded ``child'' model is bi-directional; it involves the exchange of boundary conditions and feedback between the models. Alvarez el al. (2004) developed a statistical predictor for forecasting the SST of the Ligurian Sea with a time lag of 7 days based on the previous remote sensed SST. The degrees of freedom of the SST are reduced by an Empirical Orthogonal Function (EOF) analysis. A genetic algorithm trained by the historical SST evolution in the Ligurian Sea is used to predict the EOF amplitudes. Observed and forecasted SST are assimilated in the hydrodynamic model and the results of this two experiments are compared to the model run without assimilation. The assimilation of the forecasted SST reduces the error of the model by an amount comparable to the assimilation of real SST, showing the potential of skill improvement of combining statistical and hydrodynamic models. [less ▲]

Detailed reference viewed: 18 (1 ULiège)
See detailReconstruction of incomplete satellite images in the Adriatic Sea. Study of an upwelling in the Albanian coast
Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege; Rixen, M. et al

Conference (2004)

Clouds in satellite images are a common problem. When working with these data is often desirable, if not necessary, to have complete fields. A method for the reconstruction of missing data in large data ... [more ▼]

Clouds in satellite images are a common problem. When working with these data is often desirable, if not necessary, to have complete fields. A method for the reconstruction of missing data in large data sets (Beckers and Rixen 2003, Alvera-Azcárate et al, 2004) is presented. The method, called DINEOF (Data INterpolating Empirical Orthogonal Functions), calculates the missing data from an optimal number of EOFs determined by cross-validation. A Lanczos method has been used for the EOF decomposition, in order to work with large matrices. In this work we present the reconstruction of a cloudy set of AVHRR SST satellite images of the Adriatic Sea. The results obtained are robust, as can be seen when comparing them to in situ observations [CTD profiles from MEDAR/Medatlas database (Medar Group, 2002)]. The results of the reconstruction are analysed, in particular a cold water event in the Albanian coast. This kind of event can be due to the action of winds, namely the Bora wind. Temporal EOFs and surface winds from ECMWF 40 years reanalysis are used to study this event. [less ▲]

Detailed reference viewed: 38 (2 ULiège)
See detailRecovering missing data in satellite images. An application to adriatic sst and comparison with in situ data
Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege; Rixen, M. et al

Conference (2004)

Satellite images are very useful for many applications in oceanography and other environmental sciences. They offer a great coverage both in time and space, not attained by in situ measurements. Clouds ... [more ▼]

Satellite images are very useful for many applications in oceanography and other environmental sciences. They offer a great coverage both in time and space, not attained by in situ measurements. Clouds are responsible for missing data on images provided by receptors working in the visible and IR range receptors. In some seasons the cloud coverage can reach an important percentage. Many data analysis techniques do not need a total coverage, although it is always desirable. Some applications, such as Empirical Orthogonal Function (EOF) analysis, or wavelet decomposition need a complete set of data, and a technique for recovering these missing data is indispensable. In this work we present DINEOF (Data INterpolating Empirical Orthogonal Functions), a method for the reconstruction of satellite data, based on an EOF decomposition. DINEOF reconstructs the missing data from an optimal set of EOFs. The optimal number of EOFs is determined by cross-validation. This method has shown to obtain robust results. DINEOF has been applied to a series of 105 AVHRR SST images of the Adriatic Sea, in a period ranging from May to October 1995. The mean cloud coverage of this data set is 52%. The error obtained by the cross-validation is of 0.6°C, and a total of 10 EOFs were necessary to reconstruct the data. A comparison with in situ data obtained form the MEDAR/Medatlas database is made. A total of 452 stations are examined. The RMS error between MEDAR/Medatlas and the reconstructed data is of 0.95°C. The error between MEDAR/Medatlas data and the points that are not missing in the Adriatic data set is of 0.67°C, which can be considered as the inherent error between the in situ and remote sensed data sets. [less ▲]

Detailed reference viewed: 32 (2 ULiège)
See detailData Assimilation in a Nested Model of the Gulf of Lions
Vandenbulcke, Luc ULiege; Barth, Alexander ULiege; Ben Bouallegue, Z. et al

Conference (2004)

Detailed reference viewed: 16 (2 ULiège)
See detailDemonstration of the two-way nesting in the Gulf of Lions
Vandenbulcke, Luc ULiege; Barth, Alexander ULiege; Ben Bouallegue, Z. et al

Conference (2003, September)

Detailed reference viewed: 5 (1 ULiège)
See detailForecast verification using skill scores and wavelets. Application to a two-way nested primitive equation model of the Ligurian Sea.
Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege; Rixen, M. et al

Conference (2003, April)

The verification of a forecast is an important part of a forecasting process. It allows to establish the quality of a model, and to make the pertinent improvements. The verification methodology may be ... [more ▼]

The verification of a forecast is an important part of a forecasting process. It allows to establish the quality of a model, and to make the pertinent improvements. The verification methodology may be designed to detect the errors contained in the model. The verification process consist in the comparison of the model results with a reference system (as sample climatology, or the output of a reference version of the model), in order to establish the accuracy and skill of the first one. The Anomaly Correlation Coefficient, the Brier Skill Score and the Root Mean Square Error are used in the present work to quantify the predictive skill. The disadvantage of this kind of measures is its over simplification. They are very useful, since the comparison between the model and the reference system is reduced to a limited set of numbers to establish the error, but it also results in a great loss of information. The method presented here combines the skill score analysis with a more detailed study. The use of wavelet transforms is shown to be useful, because of their capacity to localize in time and frequency the analysed signal. The signal is decomposed at different spatial scales, where the skill score methods can be applied separately. The information obtained with this method is more detailed, and scales where the largest errors occur can be easily identified. This combination of methods has been applied to a two-way nested primitive equation model of the Ligurian Sea. The scale decomposition allows to better understand the differences between the model and the observed field, to establish the weaknesses and strengths of the model, and to propose the possible improvements that can be done. [less ▲]

Detailed reference viewed: 42 (1 ULiège)
See detailSelf consistent and computationally efficient EOF calculation from incomplete oceanographic data sets
Beckers, Jean-Marie ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege et al

Conference (2003, April)

We present a new self-consistent method to infer missing data from oceanographic data series and to extract the relevant empirical orthogonal functions. As a by-product, the new method allows to detect ... [more ▼]

We present a new self-consistent method to infer missing data from oceanographic data series and to extract the relevant empirical orthogonal functions. As a by-product, the new method allows to detect the number of statistically significant EOFs by a cross-validation procedure for a complete or incomplete data set as well as the noise level and interpolation error. Since for the proposed filling and analysis method there is no need for a priori information about the error covariance structure, the method is self-consistent and parameter free. The method is exemplified on a synthetic data set as well as real satellite data with cloud coverage. [less ▲]

Detailed reference viewed: 15 (2 ULiège)
See detailAlong or across front ocean survey strategy? an operational example at an unstable front and the impact on the estimation of quasi-geostrophic vertical velocities and temperature fluxes
Rixen, M.; Allen, J.; Pollard, R. et al

Conference (2003, April)

We present results of the optimization of near-real time on-board sampling strategy in the Iceland-Faroes oceanic frontal area, based on the outputs of a mesoscale 3D operational data assimilation ... [more ▼]

We present results of the optimization of near-real time on-board sampling strategy in the Iceland-Faroes oceanic frontal area, based on the outputs of a mesoscale 3D operational data assimilation forecasting experiment. By minimizing a root mean square error cost function, we show that in this example an along-front sampling strategy, i.e. with transects parallel to the front, produces smaller errors in temperature, salinity, nitrate, phytoplankton, and zooplankton fields, as a result of a combination of the direction of the sampling of the front and errors associated with the asynopticy of observations (Doppler effect). This is contrary to the classic across-front sampling strategies that are used in most field experiments reported in the literature, i.e. where transects are perpendicular to the front. A control model shows that at these spatio-temporal scales, the along front sampling strategy is optimal when the frontal instability has sufficiently developed. We further examine the impact of optimised sampling strategies on the accuray of derived vertical motion and temperature fluxes. It is shown that the unusual along front sampling strategy may also provide better estimations of vertical velocities and temperature fluxes compared to the classic across front sampling strategy, especially when the front is sufficiently developed. [less ▲]

Detailed reference viewed: 54 (28 ULiège)
See detailAssimilation of Sea Surface Temperature in a doubly, two-way nested primitive equation model of the Ligurian Sea
Barth, Alexander ULiege; Alvera Azcarate, Aïda ULiege; Rixen, M. et al

Conference (2003, April)

The GHER 3D primitive equation model is implemented with three different resolutions: a low resolution model (1/4^o) covering the whole Mediterranean Sea, an intermediate resolution model (1/20^o) of the ... [more ▼]

The GHER 3D primitive equation model is implemented with three different resolutions: a low resolution model (1/4^o) covering the whole Mediterranean Sea, an intermediate resolution model (1/20^o) of the Liguro-Provençal basin and a high resolution model (1/60^o) simulating the fine mesoscale structures in the Ligurian Sea. Boundary conditions and the averaged fields (feedback) are exchanged between two successive nesting levels. The model of the Ligurian Sea is also coupled with the assimilation package SESAM. It allows to assimilate satellite data and in situ observations using the local adaptative SEEK (Singular Evolutive Extended Kalman) filter. Instead of evolving the error space by the numerically expensive Lyapunov equation, a simplified algebraic equation depending on the misfit between observation and model forecast is used. Starting from the 1st January 1998 the low and intermediate resolution models are spun up for 18 months. The initial conditions for the Ligurian Sea are interpolated from the intermediate resolution model. The three models are then integrated until August 1999. During this period AVHRR Sea Surface Temperature of the Ligurian Sea is assimilated. The results are validated by using CTD and XBT profiles of the SIRENA cruise from the SACLANT Center. The overall objective of this study is pre-operational. It should help to identify limitations and weaknesses of forecasting methods and to suggest improvements of existing operational models. [less ▲]

Detailed reference viewed: 16 (1 ULiège)
See detailForecasting Skill Assessment of a Doubly, Two-Way Nested Model of the Ligurian Sea driven by Assimilation of Sea Surface Temperature
Barth, Alexander ULiege; Alvera Azcarate, Aïda ULiege; Rixen, M. et al

Conference (2003)

The GHER 3D primitive equation model is implemented with threedifferent resolutions: a low resolution model (1/4Ž°) coveringthe whole Mediterranean Sea, an intermediate resolution model (1/20Ž°)of the ... [more ▼]

The GHER 3D primitive equation model is implemented with threedifferent resolutions: a low resolution model (1/4Ž°) coveringthe whole Mediterranean Sea, an intermediate resolution model (1/20Ž°)of the Liguro-Provençal basin and a high resolution model (1/60Ž°)simulating the fine mesoscale structures in the Ligurian Sea. Boundaryconditions and the averaged fields (feedback) are exchanged betweentwo successive nesting levels. The model of the Ligurian Sea is also coupled with the assimilationpackage SESAM. It allows to assimilate satellite data and in situobservations using the local adaptative SEEK (Singular EvolutiveExtended Kalman) filter. Instead of evolving the error space by thenumerically expensive Lyapunov equation, a simplified algebraicequation depending on the misfit between observation and modelforecast is used. Starting from the 1st January 1998 the low and intermediate resolutionmodels are spun up for 18 months. The initial conditions for theLigurian Sea are interpolated from the intermediate resolutionmodel. The three models are then integrated until August 1999. Duringthis period AVHRR Sea Surface Temperature of the Ligurian Sea isassimilated. The results are validated by using CTD and XBT profilesof the SIRENA cruise from the SACLANT Center. In a second validation exercise,the AVHRR SST and model forecast %(not yet affected by the SST used for validation) are decomposed at different scalesusing a horizontal wavelet transform.The processes characteristic for each scale are isolated in the model and the SST images.The error statistics calculated on the wavelet amplitudes can thus be related to the ability of the model in forecasting the correspondingmarine processes. In a second validation exercise, the AVHRR SST and model forecast areboth decomposed at different scales using a horizontal wavelet transform.The characteristic processes for each scale are isolated in the modeland the SST images. The error statistics calculated on the waveletamplitudes can thus be related to the ability of the model inforecasting the corresponding marine processes. [less ▲]

Detailed reference viewed: 14 (2 ULiège)
See detailTrace metal dispersion and uptake in the Gulf of Cadiz
Beckers, Jean-Marie ULiege; Barth, Alexander ULiege; Rixen, M. et al

Conference (2002, September)

Detailed reference viewed: 8 (2 ULiège)
See detailTwo-ways, doubly nested primitive equation model of the Ligurian Sea
Barth, Alexander ULiege; Rixen, M.; Alvera Azcarate, Aïda ULiege et al

Conference (2002, April)

In the framework of the SOFT project (Satellite-based Ocean ForecasTing), the GHER 3D primitive equation model is implemented in its double nesting version. A low res- olution model (15 ) simulates the ... [more ▼]

In the framework of the SOFT project (Satellite-based Ocean ForecasTing), the GHER 3D primitive equation model is implemented in its double nesting version. A low res- olution model (15 ) simulates the circulation in the Mediterranean Sea. This model provides the boundary conditions for an intermediate resolution model (3 ) located in the Gulf of Lions and the Ligurian Sea. The next nesting level is a one-minute resolu- tion model centered in the Ligurian Sea. Each "child" model depends on its "parent" model by the boundary conditions. At each time step the mean values of the "child" model are also injected in the "parent" model. The conservation laws require the inter- polation of the boundary values to be consistent with the finite volume discretisation of the model. A method taking also into account the difference of the land-sea mask in the "child" and "parent" model, will be discussed. The models run over a time period of 1 months starting the 1st January 1999. The output of the high resolution model is compared with the Sea Surface Temperature measured by the ATSR-2. The results show that nested and two ways coupled models are a powerful approach to solve open boundary problems and to use different grid resolutions in a numerically efficient way. [less ▲]

Detailed reference viewed: 16 (1 ULiège)
See detailNon-linear neural networks forecasting of sea level anomaly in the Alboran Sea
Rixen, M.; Barth, Alexander ULiege; Beckers, Jean-Marie ULiege

Conference (2002)

Forecasts based on artificial intelligence (AI) concepts exploit past time series of satellite images to infer near future ocean conditions at the surface by feed-forward non-linear neural networks. The ... [more ▼]

Forecasts based on artificial intelligence (AI) concepts exploit past time series of satellite images to infer near future ocean conditions at the surface by feed-forward non-linear neural networks. The size of the AI problem is drastically reduced by splitting the spatio-temporal variability contained in the remote sensing data by using empirical orthogonal function (EOF) decomposition. The problem of forecasting the dynamics of a two-dimensional surface field can thus be reduced by selecting the most relevant empirical modes, and non-linear time series predictors are then applied on the time independent amplitudes only. In the present case study, we use altimetric maps of the Mediterranean Sea and the Alboran Sea, combining TOPEX-POSEIDON and ERS-1/2 data for the period October 1992 to March 2000. The learning procedure is applied to each mode individually. The final forecast is then reconstructed from the EOFs and the forecasted amplitudes, and compared to the real observed field, the persistence and linear forecasts for validation purposes. [less ▲]

Detailed reference viewed: 14 (1 ULiège)