Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology.

in Journal of Sea Research (2011), 65(1), 114-130

Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However ... [more ▼]

Optical remote sensing data is now being used systematically for marine ecosystem applications, such as the forcing of biological models and the operational detection of harmful algae blooms. However, applications are hampered by the incompleteness of imagery and by some quality problems. The Data Interpolating Empirical Orthogonal Functions methodology (DINEOF) allows calculation of missing data in geophysical datasets without requiring a priori knowledge about statistics of the full data set and has previously been applied to SST reconstructions. This study demonstrates the reconstruction of complete space-time information for 4 years of surface chlorophyll a (CHL), total suspended matter (TSM) and sea surface temperature (SST) over the Southern North Sea (SNS) and English Channel (EC). Optimal reconstructions were obtained when synthesising the original signal into 8 modes for MERIS CHL and into 18 modes for MERIS TSM. Despite the very high proportion of missing data (70%), the variability of original signals explained by the EOF synthesis reached 93.5 % for CHL and 97.2 % for TSM. For the MODIS TSM dataset, 97.5 % of the original variability of the signal was synthesised into 14 modes. The MODIS SST dataset could be synthesised into 13 modes explaining 98 % of the input signal variability. Validation of the method is achieved for 3 dates below 2 artificial clouds, by comparing reconstructed data with excluded input information. Complete weekly and monthly averaged climatologies, suitable for use with ecosystem models, were derived from regular daily reconstructions. Error maps associated with every reconstruction were produced according to Beckers et al. (2006) [6]. Embedded in this error calculation scheme, a methodology was implemented to produce maps of outliers, allowing identification of unusual or suspicious data points compared to the global dynamics of the dataset. Various algorithms artefacts were associated with high values in the outlier maps (undetected cloud edges, haze areas, contrails, cloud shadows). With the production of outlier maps, the data reconstruction technique becomes also a very efficient tool for quality control of optical remote sensing data and for change detection within large databases. [less ▲]

Correcting surface winds by assimilating High-Frequency Radar surface currents in the German Bight

in Ocean Dynamics (2011), 61(5), 599-610

Surface winds are crucial for accurately modeling the surface circulation in the coastal ocean. In the present work, high-frequency (HF) radar surface currents are assimilated using an ensemble scheme ... [more ▼]

Surface winds are crucial for accurately modeling the surface circulation in the coastal ocean. In the present work, high-frequency (HF) radar surface currents are assimilated using an ensemble scheme which aims to obtain improved surface winds taking into account ECMWF (European Centre for Medium-Range Weather Forecasts) winds as a first guess and surface current measurements. The objective of this study is to show that wind forcing can be improved using an approach similar to parameter estimation in ensemble data assimilation. Like variational assimilation schemes, the method provides an improved wind field based on surface current measurements. However, the technique does not require an adjoint and it is thus easier to implement. In addition, it does not rely on a linearization of the model dynamics. The method is validated directly by comparing the analyzed wind speed to independent in situ measurements and indirectly by assessing the impact of the corrected winds on model sea surface temperature (SST) relative to satellite SST. [less ▲]

Reconstruction of satellite-derived sea surface temperature of the South China Sea in 2003-2009

Poster (2011)

The South China Sea (SCS) is a large marginal sea in the tropical region where the percentage of missing data of the daily Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) is ... [more ▼]

The South China Sea (SCS) is a large marginal sea in the tropical region where the percentage of missing data of the daily Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature (SST) is very high. Here we use a relatively new technique, DINEOF (Data INterpolating Empirical Orthogonal Functions), to reconstruct the SST of the SCS in 2003-2009. Furthermore, a comparison between the reconstructed data and daily Tropical rainfall Measuring Mission Microwave Imager (TMI) SST is implemented. [less ▲]

A web interface for griding arbitrarily distributed in situ data based on Data-Interpolating Variational Analysis (DIVA)

in Advances in Geosciences (2010), 28(28), 29-37

Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). It is often used to create climatological maps for physical ... [more ▼]

Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). It is often used to create climatological maps for physical, biological or chemical parameters representing e.g. monthly or seasonally averaged fields. Since instantaneous observations can not be directly related to a field representing an average, simple spatial interpolation of observations is in general not acceptable. DIVA (Data-Interpolating Variational Analysis) is an analysis tool which takes the error in the observations and the typical spatial scale of the underlying field into account. Barriers due to the coastline and the topography in general and also currents estimates (if available) are used to propagate the information of a given observation spatially. DIVA is a command-line driven application written in Fortran and Shell Scripts. To make DIVA easier to use, a web interface has been developed (http://gher-diva.phys.ulg.ac.be). Installation and compilation of DIVA is therefore not required. The user can directly upload the data in ASCII format and enter several parameters for the analysis. The analyzed field, location of the observations, and the error mask are presented as different layers using the Web Map Service protocol. They are visualized in the browser using the Javascript library OpenLayers allowing the user to interact with layers (for example zooming and panning). Finally, the results can be downloaded as a NetCDF file, Matlab/Octave file and Keyhole Markup Language (KML) file for visualization in applications such as Google Earth. [less ▲]

Reconstruction of missing data in satellite and in situ data sets with DINEOF (Data Interpolating Empirical Orthogonal Functions)

Conference (2010, July 12)

DINEOF (Data Interpolating Empirical Orthogonal Functions), a method to reconstruct missing data in geophysical data sets, is presented. Based on a truncated Empirical Orthogonal Functions (EOF) basis ... [more ▼]

DINEOF (Data Interpolating Empirical Orthogonal Functions), a method to reconstruct missing data in geophysical data sets, is presented. Based on a truncated Empirical Orthogonal Functions (EOF) basis, DINEOF uses an iterative procedure to calculate the values at the missing locations. A clear advantage of DINEOF is that no aprioriate knowledge about the statistics of the data set being reconstructed is needed (such as covariance or correlation length): the EOF basis is used internally to infer necessary information about the data, so no estimation of those parameters is needed. This characteristic is specially interesting for heterogeneous data distributions for which is difficult to derive this information. Also obtained are estimations of the error covariance of the reconstructed field, and outliers, i.e. data that present anomalous values with respect to the surrounding information in the original data, for which the residuals are larger than the statistically expected misfit calculated during the analysis. When very few data is available, the estimated covariance between two successive images used in the EOF calculation might not sufficiently robust. As a consequence, spikes appear in the temporal EOFs, which result in unrealistic discontinuities in the reconstruction. A temporal filter has been applied to the covariance matrix used to determined the EOFs, which effectively enhance temporal continuity. This has been applied to a SST data set of the Black Sea and the reconstruction error is estimated by cross-validation. On-going work includes the development of a merging capability within DINEOF that will allow to blend data from different platforms (satellite and in situ data). [less ▲]

Multiparametric observations and analysis in the Bay of Calvi (Corsica), an ideal site for studying the human activity effects and climate changes in the Mediterranean Sea; STARESO

Conference (2010, May)

STARESO (Station de REcherche Sous marine et Océanographique) is the marine and oceanographic research station of the University of Liège (Belgium) managed by the French company STARESO S.A.. Constructed ... [more ▼]

STARESO (Station de REcherche Sous marine et Océanographique) is the marine and oceanographic research station of the University of Liège (Belgium) managed by the French company STARESO S.A.. Constructed in 1969, it is located near Calvi (Corsica, Western Mediterranean Sea) in an oligotrophic area chosen for the exceptional quality of its coastal waters STARESO offers to the oceanographers, by diving or with boats, a direct access to the sea. The variety of the accessible ecosystems is remarkable and unique in the Mediterranean basin: -the Bay of Calvi is characterized by healthy and very diverse biocenosis (e.g. Posidonia meadows, rocky and sandy communities, -a steep submarine canyon, with depths greater than 1 000 meters, is accessible in 15 minutes of navigation; -the Liguro-Provençal front, a major hydrologic structure, is situated between 10 and 15 miles of the coast. STARESO is accessible all the year for everybody and is functioning like an oceanographic research vessel. The Station is a platform for all oceanographic disciplines with a scientific expertise widely based on a long tradition of interdisciplinary work, and a direct access to time series of physical, chemical and biological data registered with automated systems and variety of sensors deployed in the Bay of Calvi since 30 years. This platform provides the opportunity to reach coastal, pelagic, benthic, deep systems with a manageable cost and ship requirements in a pristine zone. [less ▲]

High-resolution measurements and modelling of the Cape Ghir upwelling filament during the CAIBEX cruise

Conference (2010, April 26)

A web interface for gridding and visualizing oceanographic data sets

Conference (2010, March)

Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). Diva (Data-Interpolating Variational Analysis) is an analysis tool ... [more ▼]

Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). Diva (Data-Interpolating Variational Analysis) is an analysis tool for gridding oceanographic in situ data. Diva takes the error in the observations and the typical spatial scale of the underlying field into account. Barriers due to the coastline and the topography in general are also used to propagate the information of a given observation spatially. Diva is a command-line driven application. To make Diva easier to use, a web interface has been developed. The user can directly upload his/her data in ASCII format and enter several parameters for the analysis. The analyzed field, location of the observations, and the error mask are then directly visualized in the browser. While this interface allows the user to create his/her own gridded field, a web interface is also developed to visualize pre-computed gridded oceanographic data sets. Those data sets are typically four-dimensional (longitude, latitude, depth and time). The system allows to visualize horizontal sections at a given depth and time to study the horizontal distribution of a given variable. It is also possible to display the results on an arbitrary vertical section. To study the evolution of the variable in time, the horizontal and vertical sections can also be animated. The user can customize the plot by changing the color-map, the range of the color-bar, the type of the plot (linearly interpolated color, simple contours, filled contours) and download the current view as a simple image or as Keyhole Markup Language (KML) file for visualization in applications such as Google Earth. The system is build using a client and server architecture. The server is written in Python using the Web Server Gateway Interface. The server implements version 1.1.1 and 1.3.0 of the Web Map Service (WMS) protocol of the Open Geospatial Consortium. On the server, all oceanographic data sets are stored as NetCDF files organized in folders and sub-folders allowing for a hierarchical presentation of the available variables. The client is build as a web application using the OpenLayers Javascript library. The web interface is accessible at http://gher-diva.phys.ulg.ac.be/. It is currently used for climatologies created in the frame of the SeaDataNet project and will be used for the EMODNET project (chemical lot). Thrid-party data centers can also integrate the web interface of Diva to show an interpolated field of in situ data as an additional WMS layer. A demonstration near-real time cloud-free sea surface temperature (SST) product of the Mediterranean Sea is presented. The reconstruction of the data set missing information (due to clouds, for example) is realised using DINEOF (Data Interpolating Empirical Orthogonal Functions). DINEOF is an EOF-based technique that does no need a priori information about the data set (such as signal to noise ratio, or correlation length) and that has shown to be faster and equally reliable than other widely used techniques for reconstructing missing data, such as optimal interpolation. Here we present a daily reconstruction of the Western Mediterranean SST. Cloudy data are downloaded from the Ifremer Medspiration ftp site. After extracting the data from the study zone, they are added to a data set containing the last 6 months of SST. A first DINEOF reconstruction is performed to identify outliers, i.e. pixels for which the analysis-observation difference (the residuals) are larger than the statistically expected misfit calculated during the analysis. Proximity to a cloud edge and deviation respect to a local median also penalize a pixel in the outlier classification. These outliers are removed from the original data set, and a second DINEOF reconstruction is performed, along with the calculation of error maps. Plots are realised, and the reconstruction of the latest 10 days is shown at http://gher-diva.phys.ulg.ac.be/DINEOF/dineof.html, together with the original data, the error maps and identified outliers. The whole procedure takes less than two hours and has been running automatically for more than 5 months. This product is intended as a demonstration of the capabilities of DINEOF as a near-real time technique to reconstruct missing data in satellite data sets. This procedure can be easily applied to other variables and other geographical zones. [less ▲]

Climatological analysis of irregularly distributed data using Data Interpolating Variational Analysis DIVA

Poster (2010, February 22)

Ensemble smoother for optimizing tidal boundary conditions and wind forcing by assimilation of High-Frequency Radar surface currents measurements of the German Bight

Conference (2010)

An ensemble smoother scheme is presented to assimilate HF radar surface currents to improve tidal boundary conditions and wind forcings of a circulation model of the German Bight. To create an ensemble of ... [more ▼]

An ensemble smoother scheme is presented to assimilate HF radar surface currents to improve tidal boundary conditions and wind forcings of a circulation model of the German Bight. To create an ensemble of dynamically realistic tidal boundary conditions, a cost function is formulated which is directly related to the probability of each perturbation. This cost function ensures that the perturbations are spatially smooth and that the structure of the perturbations satisfies approximately the harmonic linearized shallow water equations. Based on those perturbations an ensemble simulation is carried out using the full three-dimensional General Estuarine Ocean Model (GETM). Optimized boundary values are obtained using all observations within the assimilation period using the covariances of the ensemble simulation. The approach acts like a smoother scheme since all observations are taken into account. Since the scheme aims to derive the optimal perturbation, it might be called Ensemble Perturbation Smoother. The final analysis is obtained by rerunning the model using the optimal perturbation to the boundary conditions. The analyzed model solution satisfies thus the model equations exactly and does not suffer from spurious adjustments often observed with sequential assimilation schemes. Model results are also compared to independent tide gage data. The assimilation did also reduce the model error compared to those sea level observations. The same scheme has also been used to correct surface winds. Surface winds are crucial for accurately modeling the marine circulation in coastal waters. The method is validated directly by comparing the analyzed wind speed to in situ measurements and indirectly by assessing the impact of the corrected winds on sea surface temperature (SST) relative to satellite SST. [less ▲]