References of "Barth, Alexander"
     in
Bookmark and Share    
Full Text
Peer Reviewed
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 ▲]

Detailed reference viewed: 281 (47 ULg)
Peer Reviewed
See detailAn EOF-based technique to compute merged high resolution sea surface temperature fields
Alvera Azcarate, Aïda ULg; Troupin, Charles ULg; Barth, Alexander ULg et al

Conference (2012, May 10)

High quality sea surface temperature (SST) data sets are needed for various applications, including numerical weather prediction, ocean forecasting and climate research. The coverage, resolution and ... [more ▼]

High quality sea surface temperature (SST) data sets are needed for various applications, including numerical weather prediction, ocean forecasting and climate research. The coverage, resolution and precision of individual SST satellite observations is not sufficient for these applications, therefore the merging of these complementary data sets is needed to reduce the final data set error. This is usually performed by optimal interpolation (OI).We present an extension of the capabilities of DINEOF (Data INterpolating Empirical Orthogonal Functions) to merge data from different platforms. The analysis is based on the formalism of OI, but the crucial difference is that the error covariance is not parametrized a priori using an analytical expression, but expressed using a spatial EOF basis calculated by DINEOF. This EOF basis represents more realistically the complex variability of SST data sets than the parametric covariance used in most OI applications. An example will be presented using data from a polar-orbiting satellite (AVHRR on MetOp) and a geostationary satellite (SEVIRI on MSG). The high spatial resolution of the polar-orbiting satellite and the high temporal resolution of the geostationary satellite are retained to create a very high spatial and temporal resolution field of the western Mediterranean SST. The results are validated with independent data. [less ▲]

Detailed reference viewed: 8 (2 ULg)
Peer Reviewed
See detailReconstruction of Total Suspended Matter data over the North Sea using DINEOF: use of the Gaussian anamorphosis transformation
Alvera Azcarate, Aïda ULg; Neukermans, Griet; Barth, Alexander ULg et al

Conference (2012, May 10)

Total Suspended Matter (TSM) from the SEVIRI sensor in the North Sea will be analysed using DINEOF (Data INterpolating Empirical Orthogonal Functions), an EOFbased technique to reconstruct missing data ... [more ▼]

Total Suspended Matter (TSM) from the SEVIRI sensor in the North Sea will be analysed using DINEOF (Data INterpolating Empirical Orthogonal Functions), an EOFbased technique to reconstruct missing data. The information needed to reconstruct the missing data is computed internally based on a truncated EOF basis, so no assumptions about the statistics of the data have to be made. DINEOF uses the mean and covariance of the original data to calculate the EOF basis. If the data are normally distributed, then the probability density distribution can be completely described by their mean and the eigenvectors of the covariance matrix (the EOFs). Variables such as TSM, however, do not have a Gaussian distribution, since TSM is never smaller than zero. DINEOF typically does not take this into account. To overcome this, a logarithmic transformation is usually performed to non-Gaussian variables, although the exponential transformation needed to retrieve the original variable units after using DINEOF leads sometimes to unrealistic high values in the reconstruction. An empirical transformation, which allows to obtain a normally distributed variable based solely on the data themselves, will be applied. This procedure, called Gaussian anamorphosis, is sometimes used in data assimilation. A Gaussian anamorphosis transformation will be applied to the TSM data of the North Sea prior to their reconstruction. The high spatial and temporal dynamics of the gapfree geostationary TSM data set will be analysed, focusing on tidal dynamics and sub-daily variability. [less ▲]

Detailed reference viewed: 26 (3 ULg)
Full Text
Peer Reviewed
See detailInterannual variability of Black Sea’s hydrodynamics and connection to atmospheric patterns
Capet, Arthur ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg et al

Conference (2012, May 09)

The long term variability (1962–2000) of the Black Sea physical processes (e.g. temperature, main circulation, cold intermediate layer, sea level) and its relation to atmospheric conditions and large ... [more ▼]

The long term variability (1962–2000) of the Black Sea physical processes (e.g. temperature, main circulation, cold intermediate layer, sea level) and its relation to atmospheric conditions and large scale climate patterns are investigated using an eddy-resolving tridimensional model in combination with statistical tools (e.g. Empirical Orthogonal Functions, Self Organizing Maps). First, the ability of the model to represent the interannual dynamics of the system is assessed by comparing the modeled and satellite sea surface temperature (SST) and sea level anomaly (SLA) decomposed into their dominant Empirical Orthogonal Functions (EOFs). The correlation between the spatial and temporal EOFs modes derived from model and satellite data is usually satisfactory and this gives some confidence in using the model as a tool to investigate not only the SST and SLA dynamics but also the dynamics of connected variables. Then, the long term variability (1962–2000) of the Black Sea hydrodynamics is assessed by decomposing into their dominant EOFs modeled SST, SLA and selected key hydrodynamical variables associated to the main circulation and vertical structure of the water column. Significant correlations between the EOFs associated to these variables are investigated in order to link the variability of surface fields and the internal dynamics of the system. In particular, the intensity of the general cyclonic circulation (the Rim Current) is shown to impact strongly (1) the mean sea level, (2) the SST response to air temperature (AT), (3) the formation of the cold intermediate layer, (4) the meridional repartition of the SST anomaly and (5) the exchanges of heat between the north-western shelf and the open basin. In order to appraise the variability of atmospheric conditions over the Black Sea during 1962–2000 and their role in driving the hydrodynamics, a self-organizing maps technique is used to identify spatial recurrent patterns of atmospheric fields (i.e., AT, wind stress and curl). The impact on these patterns of large scale climatic variability over the north Atlantic and Eurasia (estimated by respectively the north Atlantic oscillation (NAO) and the east Atlantic/west Russia oscillation (EA/WR) indexes) is assessed. Distinct time scales of influence of the large scale teleconnection patterns on the AT are identified: EA/WR drives the short scale (1–5 years) variations of SST, while the long term (4-5 years) trends of the NAO drive the long term SST trends. The drastic changes that have occurred in the Black Sea deep sea ecosystem at the end of the 80s are connected to an intensification of the general circulation that has promoted an export of riverine materials from the eutrophicated north-western shelf to the deep sea. Finally, in the last two decades, we find an increased duration of persistent atmospheric anomalies regime that has the potential to drive the system away from its average state as occurred in the late 80s. If persistent in the future, such long lasting atmospheric anomalies may have a significant impact on the ecosystem functioning. [less ▲]

Detailed reference viewed: 20 (10 ULg)
Full Text
See detailReconstruction of the long-term satellite-derived sea surface temperature in the South China Sea
Huynh, Thi Hong Ngu ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2012, February 24)

The AVHRR (Advanced Very High Resolution Radiometer) sea surface temperature is very useful for researches in oceanography because of its high resolution. An AVHRR limitation is the high missing data ... [more ▼]

The AVHRR (Advanced Very High Resolution Radiometer) sea surface temperature is very useful for researches in oceanography because of its high resolution. An AVHRR limitation is the high missing data percentage due to cloud coverage. In the South China Sea, the average missing data is usually more than 80%, especially more than 95% in the region near the Borneo Island. In this study, we use DINEOF tool to reconstruct a daily night-time AVHRR data set with horizontal resolution of 4km spanning from 1989 to 2009. Besides, a comparison between the results and in situ data is shown. The EOF analysis shows that the first three modes explain about 95% of seasonal variability. [less ▲]

Detailed reference viewed: 22 (5 ULg)
Full Text
See detailViewing through the clouds in satellite images
Troupin, Charles ULg; Barth, Alexander ULg; Alvera Azcarate, Aïda ULg et al

Poster (2012, February 24)

Detailed reference viewed: 15 (6 ULg)
Full Text
Peer Reviewed
See detailInterannual variability of Black Sea’s hydrodynamics and connection to atmospheric patterns
Capet, Arthur ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg et al

in Deep-Sea Research Part II, Topical Studies in Oceanography (2012)

The long term variability (1962–2000) of the Black Sea physical processes (e.g. temperature, main circulation, cold intermediate layer, sea level) and its relation to atmospheric conditions and large ... [more ▼]

The long term variability (1962–2000) of the Black Sea physical processes (e.g. temperature, main circulation, cold intermediate layer, sea level) and its relation to atmospheric conditions and large scale climate patterns are investigated using an eddy-resolving tridimensional model in ombination with statistical tools (e.g. Empirical Orthogonal Functions, Self Organizing Maps). First, the ability of the model to represent the interannual dynamics of the system is assessed by comparing the modeled and satellite sea surface temperature (SST) and sea level anomaly (SLA) decomposed into their dominant Empirical Orthogonal Functions (EOFs). The correlation between the spatial and temporal EOFs modes derived from model and satellite data is usually satisfactory and this gives some confidence in using the model as a tool to investigate not only the SST and SLA dynamics but also the dynamics of connected variables. Then, the long term variability (1962–2000) of the Black Sea hydrodynamics is assessed by decomposing into their dominant EOFs modeled SST, SLA and selected key hydrodynamical variables associated to the main circulation and vertical structure of the water column. Significant correlations between the EOFs associated to these variables are investigated in order to link the variability of surface fields and the internal dynamics of the system. In particular, the intensity of the general cyclonic circulation (the Rim Current) is shown to impact strongly (1) the mean sea level, (2) the SST response to air temperature (AT), (3) the formation of the cold intermediate layer, (4) the meridional repartition of the SST anomaly and (5) the exchanges of heat between the north-western shelf and the open basin. In order to appraise the variability of atmospheric conditions over the Black Sea during 1962–2000 and their role in driving the hydrodynamics, a self-organizing maps technique is used to identify spatial recurrent patterns of atmospheric fields (i.e., AT, wind stress and curl). The impact on these patterns of large scale climatic variability over the north Atlantic, Eurasia and the Pacific Ocean (estimated by respectively the north Atlantic oscillation (NAO), the east Atlantic/west ̃Russia oscillation (EA/WR) and the El Nino southern oscillation (ENSO) indexes) is assessed. Distinct time scales of influence of the large scale teleconnection patterns on the AT are identified: EA/WR drives the short scale (1–5 years) variations of SST, while the long term (4-5 years) trends of the NAO drive the long term SST trends. The drastic changes that have occurred in the Black Sea deep sea ecosystem at the end of the 80s are connected to an intensification of the general circulation that has promoted an export of riverine materials from the eutrophicated north-western shelf to the deep sea. Finally, in the last two decades, we find an increased duration of persistent atmospheric anomalies regime that has the potential to drive the system away from its average state as occurred in the late 80s. If persistent in the future, such long lasting atmospheric anomalies may have a significant impact on the ecosystem functioning. [less ▲]

Detailed reference viewed: 87 (16 ULg)
Full Text
Peer Reviewed
See detailOutlier detection in satellite data using spatial coherence
Alvera Azcarate, Aïda ULg; Sirjacobs, Damien ULg; Barth, Alexander ULg et al

in Remote Sensing of Environment (2012), 119

Satellite data sets often contain outliers (i.e., anomalous values with respect to the surrounding pixels), mostly due to undetected clouds and rain or to atmospheric and land contamination. A methodology ... [more ▼]

Satellite data sets often contain outliers (i.e., anomalous values with respect to the surrounding pixels), mostly due to undetected clouds and rain or to atmospheric and land contamination. A methodology to detect outliers in satellite data sets is presented. The approach uses a truncated Empirical Orthogonal Function (EOF) basis. The information rejected by this EOF basis is used to identify suspect data. A proximity test and a local median test are also performed, and a weighted sum of these three tests is used to accurately detect outliers in a data set. Most satellite data undergo automated quality-check analyses. The approach presented exploits the spatial coherence of the geophysical fields, therefore detecting outliers that would otherwise pass such checks. The methodology is applied to infrared sea surface temperature (SST), microwave SST and chlorophyll-a concentration data over different domains, to show the applicability of the technique to a range of variables and temporal and spatial scales. A series of sensitivity tests and validation with independent data are also conducted. [less ▲]

Detailed reference viewed: 105 (17 ULg)
Full Text
See detailScience based management of coastal waters
Delhez, Eric ULg; Barth, Alexander ULg

in Journal of Marine Systems (2011, October), 88(1),

Detailed reference viewed: 101 (30 ULg)
See detailHiSea: High resolution merged satellite sea surface temperature fields
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Toussaint, Marie-Eve ULg et al

Conference (2011, May 25)

Several satellites measure Sea Surface Temperature (SST), each of these with different technical specificities and error sources. Together with in situ data, they form a highly complementary data set. The ... [more ▼]

Several satellites measure Sea Surface Temperature (SST), each of these with different technical specificities and error sources. Together with in situ data, they form a highly complementary data set. The creation of merged SST products, integrating the strengths of each of its components and minimising their weaknesses, is however not an easy task, but it is certainly a desirable goal that has generated a large amount of research over the last years. The main objectives of this project are, among others: 1.To develop a technology, based on DINEOF (Data Interpolating Empirical Orthogonal Functions), that allows to merge different data sets at very different sampling intervals (in space and time) and create an integrated product at the highest sampling frequency and with the highest quality possible. 2.To provide improved, merged analyses of variables such as SST and chlorophyll. 3.Obtain a better understanding of the diurnal cycle of the studied variables. 4.To better understand the relation between variables (and take advantage of these relationship to improve the analyses). 5.Using the above-mentioned developments, explore the capability of the developed technology to produce SST forecasts based on multi-variate EOFs and model forecasts. We will present the first preliminary results for merging different SST data sets, as well as our plans for future developments and applications. Website of the project: http://www.gher.ulg.ac.be/HiSea/ [less ▲]

Detailed reference viewed: 25 (7 ULg)
Peer Reviewed
See detailMerging satellite and in situ sea surface temperature data using DINEOF
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg

Conference (2011, April 05)

High quality sea surface temperature data sets are needed for various applications, including numerical weather prediction, ocean forecasting and climate research. The coverage, resolution and precision ... [more ▼]

High quality sea surface temperature data sets are needed for various applications, including numerical weather prediction, ocean forecasting and climate research. The coverage, resolution and precision of individual sea surface temperature observations is not sufficient for these applications, therefore merging of complementary data sets is needed to increase the coverage and to reduce the final data set error. DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique to reconstruct missing information -due to clouds, for example in satellite data sets. A new development of this method consists in its capability to merge different data sources into one estimate. This development is tested using AVHRR data of the western Mediterranean Sea and in situ data from various international databases (World Ocean Database (WOD), MEDAR/Medatlas, Coriolis Data Center, International Council for the Exploration of the Sea (ICES) and International Comprehensive Ocean-Atmosphere Data Set (ICOADS)). An error assessment between the satellite and in situ data is performed first, in order to determine the error statistics between these two data sources. The error is calculated by database, platform type (CTD, XBT, drifters, bottles and ships) and depth. This error assessment is used to merge the in situ and satellite data. The impact of the sensor-specific errors on the quality of the final product will be assessed, and compared to the results obtained when the same error estimate is used for all sensors. The benefit of using in situ data in addition to satellite data will be also discussed. Additional information can be found at http://modb.oce.ulg.ac.be/mediawiki/index.php/DINEOF and http://gherdiva.phys.ulg.ac.be/DINEOF/ [less ▲]

Detailed reference viewed: 43 (1 ULg)
See detailEOF analysis of Sea Surface Temperature in the Canary Island - Madeira region
Troupin, Charles ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Conference (2011, April 05)

We analyzed Sea Surface Temperature (SST) images in a region covering the Canary Islands and Madeira archipelagos, with the following objectives 1. The reconstruction of incomplete SST satellite images ... [more ▼]

We analyzed Sea Surface Temperature (SST) images in a region covering the Canary Islands and Madeira archipelagos, with the following objectives 1. The reconstruction of incomplete SST satellite images during the year 2009. 2. The determination of the main spatial and temporal patters in the region. SST images for 2009 are downloaded from the Medspiration project (http://www.medspiration.org). The images consist of combined measurements from several satellite systems. The images with less than 5% of valid pixels (e.g., clouds) were removed, so that out of the 365 initial images, 347 were kept. The method used in this work for the reconstruction of missing data is Data INterpolating Empirical Orthogonal Functions (DINEOF, Alvera-Azcárate et al., 2005). The results show that the first mode is largely dominant, with 87% of the variance explained, and represents the regional seasonal cycle. The second mode accounts for 9% of the variance and depicts a separation between coastal waters and open-ocean waters. The signal of the Cape Ghir upwelling filament is also present in the second mode. The reconstruction allows one to reproduce the characteristic mesoscale features of the region: the coastal upwelling, the island wakes (Gran Canaria, Madeira, ... ), the filament and the eddies in the lee of the main islands. A near-operational version of the reconstruction has been implemented and is available at http://gher-diva.phys.ulg.ac.be/DINEOF/dineof_allCAN.html [less ▲]

Detailed reference viewed: 61 (6 ULg)
Peer Reviewed
See detailSatellite and in situ sea surface temperature comparison and merging in the Mediterranean Sea.
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Beckers, Jean-Marie ULg

Conference (2011, April 03)

A comparison between in situ and satellite sea surface temperature (SST) for the western Mediterranean Sea is presented. Several international databases are used to extract in situ data: World Ocean ... [more ▼]

A comparison between in situ and satellite sea surface temperature (SST) for the western Mediterranean Sea is presented. Several international databases are used to extract in situ data: World Ocean Database (WOD), MEDAR/Medatlas, Coriolis Data Center, International Council for the Exploration of the Sea (ICES) and International Comprehensive Ocean-Atmosphere Data Set (ICOADS). The in situ data are classified into different platforms or sensors (CTD, XBT, drifters, bottles, ships), in order to assess the average difference between these type of data and AVHRR (Advanced Very High Resolution Radiometer) SST satellite data. Attention is given also to the relative accuracy of each database, and advantages and shortcomings on the use of each database will be discussed. The error assessment will be used to merge in situ and satellite SST data using DINEOF (Data Interpolating Empirical Orthogonal Functions), an EOF-based technique. The impact of the sensor-specific errors on the quality of the final product will be assessed, and compared to the results obtained when a more general error estimate is used. [less ▲]

Detailed reference viewed: 26 (2 ULg)
Full Text
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 ▲]

Detailed reference viewed: 86 (17 ULg)
See detailAssimilation of high-frequency radar currents in the Ligurian Sea
Barth, Alexander ULg; Chiggiato, Jacopo; Alvera Azcarate, Aïda ULg et al

Conference (2011, April)

The circulation in the Ligurian Sea is dominated by strong currents, namely the Western Corsican Current and the East Corsican Current, which jointly form the Northern Current. A high mesoscale activity ... [more ▼]

The circulation in the Ligurian Sea is dominated by strong currents, namely the Western Corsican Current and the East Corsican Current, which jointly form the Northern Current. A high mesoscale activity, including meanders and eddy formation, is associated to those energetic currents. The non-linear instability processes and apparently chaotic behavior of this current system make this region a challenging testbed for data assimilation. High-frequency radar surface currents have been measured by the NATO Undersea Research Centre (NURC), La Spezia, Italy from two sites at the Italian Coast (Isola Palmaria and San Rossore). Each of those sites measures the radial currents relative to the position of the radar system. This WERA system captures well the general circulation and mesoscale flow features. The present study shows an application of the assimilation of those measurements in a nested model con- figuration of the Ligurian Sea. It is assumed that the error in the model surface currents comes primarily from uncertainties in the lateral boundary conditions and surface wind fields. The objective of this study is to reduce the uncertainty in these forcing fields by data assimilation. An ensemble of 100 perturbed lateral boundary conditions and surface wind fields is created to take the uncertainty into account. Using an ensemble-smoother technique described in Barth et al, 2010 (Ocean Science) and Barth et al, 2010 (Ocean Dynamics, in press), improved estimates of the wind forcing and boundary conditions are obtained. By rerunning the model with the updated forcing fields, it is verified that the analyzed model solution is closer to the observed HF radar currents. This technique is similar to 4D-Var, but since it is based on the ensemble covariance between forcing fields and observations, it does not require the formulation of an adjoint. [less ▲]

Detailed reference viewed: 26 (2 ULg)
Full Text
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 ▲]

Detailed reference viewed: 20 (2 ULg)
Full Text
See detailAdvanced Data Interpolating Variational Analysis. Application to climatological data.
Troupin, Charles ULg; Sirjacobs, Damien ULg; Rixen, Michel et al

Poster (2011, March 21)

Detailed reference viewed: 17 (4 ULg)