Forecast verification of a 3D model of the Mediterranean Sea. Analysis of model results and observations using wavelets and Empirical Orthogonal Functions.

The quality assessment of the three-dimensional GHER (GeoHydrodynamics and Environmental Research) model of the Mediterranean Sea is presented in this work. The verification of the model results is done in a spatio-temporal approach. Traditional error measures (i.e. correlation, mean error, etc) are very useful to assess the quality of a model, but they do not take into account the high complexity of three-dimensional models. The verification process is thus done in three main parts: first, the model is compared to observations and climatology in a qualitative approach, in order to make a preliminar study about the model behaviour. Then, the error assessment is done, using traditional statistic measures. In order to take into account the complexity of the model and observations, the last step in the verification process consists in a spatio-temporal analysis using wavelets and empirical orthogonal functions. This last analysis will allow us to have an insight about the model quality in a more detailed way. This verification process has been applied to the GHER model. This model is implemented in a two-way nesting approach in the Mediterranean Sea, Liguro-Provençal basin and Ligurian Sea, where the highest resolution is reached. Assimilation of sea surface temperature and sea level anomaly is made during a nine-week experiment. Another test is carried out, to assess the quality of sea surface temperature from the SOFT predictor of the Ligurian Sea. The predicted sea surface temperature is assimilated in the model and the quality of the forecast is compared to the first assimilation experiment. The assimilation of the SOFT statistical predictors can be very useful to force models in a real forecast experiment, where no observations are available

Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF

DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface temperature satellite images. A technique to reduce spurious time variability in DINEOF reconstructions is presented. The reconstruction of these images within a long time series using DINEOF can lead to large discontinuities in the reconstruction. Filtering the temporal covariance matrix allows to reduce this spurious variability and therefore more realistic reconstructions are obtained. The approach is tested in a three years sea surface temperature data set over the Black Sea. The effect of the filter in the temporal EOFs is presented, as well as some examples of the improvement achieved with the filtering in the SST reconstruction, both compared to the DINEOF approach without filtering

Data Interpolating Empirical Orthogonal Functions (DINEOF): a tool for geophysical data analyses

An overview of the technique called DINEOF (Data Interpolating Empirical Orthog- onal Functions) is presented. DINEOF reconstructs missing information in geophys- ical data sets, such as satellite imagery or time series. A summary of the technique is given, with its main characteristics, recent developments and future research di- rections. DINEOF has been applied to a large variety of oceanographic variables in various domains of different sizes. This technique can be applied to a single variable (monovariate approach), or to several variables together (multivariate approach), with no complexity increase in the application of the technique. Error fields can be computed to establish the accuracy of the reconstruction. Examples are given to illustrate the capabilities of the technique. DINEOF is freely offered to download, and help is provided to users in the form of a wiki and through a discussion email list.RECOLOU

Variational data analysis for generating ocean climatologies (DIVA) and web-based distribution of data products (OceanBrowser)

SeaDataNet I

Generation of analysis and consistent error fields using the Data Interpolating Variational Analysis (Diva)

SeaDataNet

Variational data analysis for generating ocean climatologies (DIVA) and web-based distribution of data products (OceanBrowser)

SeaDataNet I

Le logiciel d'Interpolation de Données par Analyse Variationnelle (Diva), un outil pour les mers régionales d'Europe et la production de climatologies de l'Océan

SeaDataNet

Dynamically Constrained Ensemble Perturbations: Application to Tides on the West Florida Shelf

Abstract. A method is presented to create an ensemble of perturbations that satisfies linear dynamical constraints. A cost function is formulated defining the probability of each perturbation. It is shown that the perturbations created with this approach take the land-sea mask into account in a similar way as variational analysis techniques. The impact of the land-sea mask is illustrated with an idealized configuration of a barrier island. Perturbations with a spatially variable correlation length can be also created by this approach. The method is applied to a realistic configuration of the West Florida Shelf to create perturbations of the M2 tidal parameters for elevation and depth-averaged currents. The perturbations are weakly constrained to satisfy the linear shallow-water equations. Despite that the constraint is derived from an idealized assumption, it is shown that this approach is applicable to a non-linear and baroclinic model. The amplitude of spurious transient motions created by constrained perturbations of initial and boundary conditions is significantly lower compared to perturbing the variables independently or to using only the momentum equation to compute the velocity perturbations from the elevation

DINEOF reconstruction of clouded images including error maps. Application to the Sea-Surface Temperature around Corsican Island

We present an extension to the Data INterpolating Empirical Orthogonal Functions (DINEOF) technique which allows not only to fill in clouded images but also to provide an estimation of the error covariance of the reconstruction. This additional information is obtained by an analogy with optimal interpolation. It is shown that the error fields can be obtained with a clever rearrangement of calculations at a cost comparable to that of the interpolation itself. The method is presented on the reconstruction of sea-surface temperature in the Ligurian Sea and around the Corsican Island (Mediterranean Sea), including the calculation of inter-annual variability of average surface values and their expected errors. The application shows that the error fields are not only able to reflect the data-coverage structure but also the covariances of the physical fields