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See detailGeneration of analysis and consistent error fields using the Data Interpolating Variational Analysis (Diva)
Troupin, Charles ULg; Barth, Alexander ULg; Sirjacobs, Damien ULg et al

in Ocean Modelling (2012), 52-53

The Data Interpolating Variational Analysis (Diva) is a method designed to interpolate irregularly-spaced, noisy data onto any desired location, in most cases on regular grids. It is the combination of a ... [more ▼]

The Data Interpolating Variational Analysis (Diva) is a method designed to interpolate irregularly-spaced, noisy data onto any desired location, in most cases on regular grids. It is the combination of a particular methodology, based on the minimisation of a cost function, and a numerically efficient method, based on a finite-element solver. The cost function penalises the misfit between the observations and the reconstructed field, as well as the regularity or smoothness of the field. The intrinsic advantages of the method are its natural way to take into account topographic and dynamic constraints (coasts, advection, . . . ) and its capacity to handle large data sets, frequently encountered in oceanography. The method provides gridded fields in two dimensions, usually in horizontal layers. Three-dimension fields are obtained by stacking horizontal layers. In the present work, we summarize the background of the method and describe the possible methods to compute the error field associated to the analysis. In particular, we present new developments leading to a more consistent error estimation, by determining numerically the real covariance function in Diva, which is never formulated explicitly, contrarily to Optimal Interpolation. The real covariance function is obtained by two concurrent executions of Diva, the first providing the covariance for the second. With this improvement, the error field is now perfectly consistent with the inherent background covariance in all cases. A two-dimension application using salinity measurements in the Mediterranean Sea is presented. Applied on these measurements, Optimal Interpolation and Diva provided very similar gridded fields (correlation: 98.6%, RMS of the difference: 0.02). The method using the real covariance produces an error field similar to the one of OI, except in the coastal areas. [less ▲]

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See detailAssessing the impacts of present and future interannual climate variability on European ecosystems using a dynamic vegetation model
Dury, Marie ULg; Hambuckers, Alain ULg; Warnant, Pierre et al

Poster (2011, April)

Climate projections indicate changes in mean climate as well as in climate variability and frequency of extreme events for the end of the 21st century compared to present. Since many biological processes ... [more ▼]

Climate projections indicate changes in mean climate as well as in climate variability and frequency of extreme events for the end of the 21st century compared to present. Since many biological processes reach non-reversible thresholds (loss of ability to germinate, mortality, etc.) at some temperatures or soil water values, changes in climate variability have long-term consequences for ecosystem composition, functioning and carbon storage. The CARAIB dynamic vegetation model is used to evaluate and analyse how future climate variability will affect European ecosystems. We examine the impacts of climate change and associated drought episodes on primary productivity (NPP) as well as on fire intensity. CARAIB is driven by the ARPEGE/Climate model and three regional climate models from the European Union project ENSEMBLES (KNMI-RACMO2, DMI-HIRHAM5 and HC-HadRM3Q0 models) forced with the IPCC A1B emission scenario. We analyse the interannual climate variability simulated by those climate models and compare it with the observed climate variability (CRU TS 3.0 historical climate dataset) over the period 1961-1990. None of these climate models can reproduce accurately the present natural climate variability. Therefore, the present NPP interannual variability simulated by CARAIB using climate outputs from the climate models differs from the one obtained with observed climate. For instance, the NPP interannual variability obtained with the ARPEGE/Climate model is significantly overestimated in some parts of Europe, especially in the Mediterranean region, in France, in northern Germany and northern Poland, in the Balkans and in Ukraine. Since discrepancies between modelled and observed current climate variability may also affect NPP variability calculated for the future as well as the intensity and the frequency of severe drought periods and wildfires, comparing the terrestrial ecosystem evolutions obtained with a range of climate models allows to improve the assessment of climate change impacts on ecosystems in the future. Anyway the trend between the present and the future is expected to be more robust. The NPP interannual variability increases in the future with the four climate models as a result of more frequent and more severe soil water stress episodes in southern and Central Europe. The projected climate changes are also likely to induce increased fire risk in the Mediterranean region but also in Central Europe and Russia. [less ▲]

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See detailAdvanced Data Interpolating Variational Analysis. Application to climatological data
Troupin, Charles ULg; Sirjacobs, Damien ULg; Rixen, Michel et al

Poster (2011, April)

DIVA (Data Interpolating Variational Analysis) is a variational analysis tool designed to interpolate irregularly-spaced, noisy data onto any desired location, in most cases on regular grids. It is the ... [more ▼]

DIVA (Data Interpolating Variational Analysis) is a variational analysis tool designed to interpolate irregularly-spaced, noisy data onto any desired location, in most cases on regular grids. It is the combination of a particular methodology, based on the minimization of a functional, and a numerically efficient resolution method, based on a finite elements solver. The intrinsic advantages of DIVA are its natural way to take into account topographic and dynamic constraints (coasts, advection, ...) and its capacity to handle large data sets, frequently encountered in oceanography. In the present work, we describe various improvements to the variational analysis tool. The most significant advance is the development of a full error calculation, whilst until now, only an approximate error-field estimate was available. The key issue is the numerical determination of the real covariance function in DIVA, which is not formulated explicitly. This is solved by two concurrent executions of two DIVA, one providing the covariance for the other. The new calculation of the error field is now perfectly coherent with the inherent background covariance in all cases. The correlation length, which was previously set uniform over the computational domain, is now allowed to vary spatially. The efficiency of the tools for estimating the signal-to-noise ratio, through generalized cross-validation, has also been improved. Finally, a data quality-control method is implemented and allows one to detect possible outliers, based on statistics of the data-reconstruction misfit. The added value of these features are illustrated in the case of a large data set of salinity measured in the Mediterranean Sea. Several analyses are performed with different parameters in order to demonstrate their influence on the interpolated fields. In particular, we examine the benefits of using the parameter optimization tools and the advection constraint. The results are validated by means of a subset of data set apart for an independent validation. The corresponding errors fields are estimated using different methods and underline the role of the data coverage. [less ▲]

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See detailSeaDataNet regional climatologies: an overview
Troupin, Charles ULg; Ouberdous, Mohamed ULg; Barth, Alexander ULg et al

Poster (2011, April)

In the frame of the SeaDataNet project, a set of regional climatologies for temperature and salinity has been developed by the different regional groups. The data used for these climatologies are ... [more ▼]

In the frame of the SeaDataNet project, a set of regional climatologies for temperature and salinity has been developed by the different regional groups. The data used for these climatologies are distributed by the SeaDataNet data centers. These climatologies have several uses: 1. The detection of outliers by comparison of the in situ data with the climatological fields; 2. The the optimization of locations of new observations; 3. The initialization of numerical hydrodynamic model; 4. The definition of a reference state to identify anomalies and to detect long-term climatic trends. These climatologies are produced with the help of the Data Interpolating Variational Analysis (DIVA) software. Here we present the latest developments in the regional climatologies along with the choice of parameters by the different groups. [less ▲]

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See detailAdvanced Data Interpolating Variational Analysis. Application to climatological data.
Troupin, Charles ULg; Sirjacobs, Damien ULg; Rixen, Michel et al

Poster (2011, March 21)

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See detailResponses of European forest ecosystems to 21(st) century climate: assessing changes in interannual variability and fire intensity
Dury, Marie ULg; Hambuckers, Alain ULg; Warnant, P. et al

in iForest: Biogeosciences and Forestry (2011), 4

Significant climatic changes are currently observed and, according to projections, will be strengthened over the 21(st) century throughout the world with the continuing increase of the atmospheric CO2 ... [more ▼]

Significant climatic changes are currently observed and, according to projections, will be strengthened over the 21(st) century throughout the world with the continuing increase of the atmospheric CO2 concentration. Climate will be generally warmer with notably changes in the seasonality and in the precipitation regime. These changes will have major impacts on the biodiversity and the functioning of natural ecosystems. The CARAIB dynamic vegetation model driven by the ARPEGE/Climate model under forcing from the A2 IPCC emission scenario is used to illustrate and analyse the potential impacts of climate change on forest productivity and distribution as well as fire intensity over Europe. The potential CO2 fertilizing effect is studied throughout transient runs of the vegetation model over the 1961-2100 period assuming constant and increasing atmospheric CO2 concentration. Without fertilisation effect, the net primary productivity (NPP) might increase in high latitudes and altitudes (by up to 40 % or even 60-100 %) while it might decrease in temperate (by up to 50 %) and in warmer regions, e.g., Mediterranean area (by up to 80 %). This strong decrease in NPP is associated with recurrent drought events occurring mostly in summer time. Under rising CO2 concentration, NPP increases all over Europe by as much as 25-75%, but it is not clear whether or not soils might sustain such an increase. The model indicates also that interannual NPP variability might strongly increase in the areas which will undergo recurrent water stress in the future. During the years exhibiting summer drought, the NPP might decrease to values much lower than present-day average NPP even when CO2 fertilization is included. Moreover, years with such events will happen much more frequently than today. Regions with more severe droughts might also be affected by an increase of wildfire frequency and intensity, which may have large impacts on vegetation density and distribution. For instance, in the Mediterranean basin, the area burned by wildfire can be expected to increase by a factor of 3-5 at the end of the 21(st) century compared to present. [less ▲]

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See detailResponse of the European forests to extreme climatic events predicted for the 21st century: sensitivity to climate models and their variability
Dury, Marie ULg; Hambuckers, Alain ULg; Warnant, Pierre et al

Conference (2010, October)

Significant climatic changes are currently observed and, according to projections, will be strengthened over the 21st century throughout the world with the enhanced greenhouse effect. Climate will be ... [more ▼]

Significant climatic changes are currently observed and, according to projections, will be strengthened over the 21st century throughout the world with the enhanced greenhouse effect. Climate will be generally warmer with notably changes in the seasonality and in the precipitation regime. The CARAIB dynamic vegetation model is used to evaluate and analyse the potential impacts of climate change on forests ecosystems in Europe. Changes in the hydrological budget as well as in the intensity and the frequency of wildfires and their effects on forest productivity and distribution are especially assessed. CARAIB is driven by the ARPEGE-Climat model and some other regional climate models from the European Union (EU) project ENSEMBLES forced with IPCC A1B emission scenario. Climate projections indicate changes in variability and frequency of extreme events. Since climate variability governs the response of plant species (e.g. net primary productivity, NPP) to climate change, we analyse the climate variability (seasonal and interannual) given by climate models comparing it with the observed climate variability (CRU TS 3.0 historical climate dataset) over the period 1961-1990. The variability modelled by the ARPEGE-Climat model is notably slightly more pronounced than the observed one, at least for some areas. Since discrepancies between modelled and observed current climate variability may affect NPP variability calculated for the future as well as the intensity and the frequency of severe drought period and wildfires, comparing the forest ecosystem evolutions obtained with a range of climate models allows improving the assessment of climate change impacts on forest in the future. [less ▲]

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See detailA web interface for griding arbitrarily distributed in situ data based on Data-Interpolating Variational Analysis (DIVA)
Barth, Alexander ULg; Alvera Azcarate, Aïda ULg; Troupin, Charles ULg et al

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 ▲]

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See detailHigh-resolution Climatology of the northeast Atlantic using Data-Interpolating Variational Analysis (DIVA)
Troupin, Charles ULg; Machin, Francis; Ouberdous, Mohamed ULg et al

in Journal of Geophysical Research (2010), 115(C08005), 20

Numerous climatologies are available at different resolutions and cover various parts of the global ocean. Most of them have a resolution too low to represent suitably regional processes and the methods ... [more ▼]

Numerous climatologies are available at different resolutions and cover various parts of the global ocean. Most of them have a resolution too low to represent suitably regional processes and the methods for their construction are not able to take into account the influence of physical effects (topographic constraints, boundary conditions, advection, etc.). A high-resolution atlas for temperature and salinity is developed for the northeast Atlantic Ocean on 33 depth levels. The originality of this climatology is twofold: (1) For the data set, data are collected on all major databases and aggregated to lead to an original data collection without duplicates, richer than the World Ocean Database 2005, for the same region of interest. (2) For the method, climatological fields are constructed using the variational method Data-Interpolating Variational Analysis. The formulation of the latter allows the consideration of coastlines and bottom topography, and has a numerical cost almost independent on the number of observations. Moreover, only a few parameters, determined in an objective way, are necessary to perform an analysis. The results show overall good agreement with the widely used World Ocean Atlas, but also reveal significant improvements in coastal areas. Error maps are generated according to different theories and emphasize the importance of data coverage for the creation of such climatological fields. Automatic outlier detection is performed, and its effects on the analysis are examined. The method presented here is very general and not dependent on the region, hence it may be applied for creating other regional atlas in different zones of the global ocean. [less ▲]

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See detailSynthesis of regional product activities JRA4-JRA9
Beckers, Jean-Marie ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Conference (2010, April 01)

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See detailGODIVA: a 4-dimension implementation of DIVA
Troupin, Charles ULg; Ouberdous, Mohamed ULg; Beckers, Jean-Marie ULg

Poster (2010, March 29)

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See detailSeaDataNet regional climatologies: an overview
Troupin, Charles ULg; Ouberdous, Mohamed ULg; Barth, Alexander ULg et al

Poster (2010, March 29)

Detailed reference viewed: 41 (2 ULg)
See detailA web interface for gridding and visualizing oceanographic data sets
Barth, Alexander ULg; Alvera Azcarate, Aïda ULg; Sirjacobs, Damien ULg et al

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 ▲]

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