References of "Alvera Azcarate, Aïda"
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See detailCharacteristics of surface chlorophyll-a concentrations in the South China Sea
Huynh, Thi Hong Ngu ULg; Alvera Azcarate, Aida ULg; Barth, Alexander ULg et al

Poster (2017, April 25)

In this study, the spatial and temporal variability of surface chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is investigated, using the cloud-free MODISA Chl-a data set (2003-2016 ... [more ▼]

In this study, the spatial and temporal variability of surface chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is investigated, using the cloud-free MODISA Chl-a data set (2003-2016) reconstructed by the Data Interpolating Empirical Orthogonal Functions technique. EOF analysis on the reconstructed data set presents the characteristics of the surface Chl-a: (1) the first mode presents the high Chl-a concentrations in the coastal regions, except those of the Palawan and Philippines, generally with peaks in summer (June-July) and winter (November-December). (2) the second mode shows the seasonal variability of Chl-a in the whole basin, increasing in winter and decreasing in summer. (3) the third mode highlights the out-of-phase variability of the southern SCS Chl-a between the west and east coasts in winter and summer. The analysis also indicates that the variability of surface Chl-a is influenced by ENSO with a time lag of 5-9 months. [less ▲]

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See detailRelationships between environmental parameters and the microbenthic loop of Posidonia oceanica meadows at small spatial scale
Pete, Dorothée ULg; Alvera Azcarate, Aida ULg; Gobert, Sylvie ULg

in Zoology 2016: 23rd Congress of Zoology (2016, December)

In these times of global change, understanding how carbon flows through ecosystems is of primary importance. In coastal Mediterranean regions, Posidonia oceanica meadows produce and store a lot of carbon ... [more ▼]

In these times of global change, understanding how carbon flows through ecosystems is of primary importance. In coastal Mediterranean regions, Posidonia oceanica meadows produce and store a lot of carbon, but most of it is described as refractory. As a step in the understanding of how organic matter degradation/storage happens in this ecosystem, this study focus on small scale relationships between the microbenthic loop (organic matter, microphytobenthos, bacteria and meiofauna) and environmental parameters (grain size, P. oceanica density, vegetal fibre biomass, pore water nutrient content, sediment total carbon (TC), organic carbon (TOC), nitrogen (TN) and phosphorous (TP) contents, phaeopigments and bacterial production (FDC)). Thus, a 1.25 m x 1.25 m frame was put in a pristine P. oceanica meadow and twelve points were randomly sampled in May 2008 for all the studied parameters. At such a small scale, every component of the microbenthic loop presented a heterogeneity, which was the highest for the microphytobenthos biomass and the lowest for total meiofauna abundance (TMA). No relationship was found between the components of the microbenthic loop but the abundances of Turbellaria and Ciliophora were correlated with total organic matter in the sediment (TOM). None of the environmental parameter was linked with the microphytobenthos biomass, suggesting that the high spatial variability observed did not depend on the measured parameters. Relationships were found between TOM and ammonium, total bacteria abundance and biomass (TBA and TBB) and sediment phaeopigments, TMA and TC, TN and TP. TBB variability was however explained by a combination of FDC and nutrient contents in pore water and sediment. Taking the microbenthic loop as a whole, pore water ammonium and nitrites + nitrates, FDC and phaeopigments were able to explain the observed variability. So, at small scale the variability in the microbenthic loop of a P. oceanica meadow is related with parameters linked with degradation processes and bacteria activities (phaeopigments, FDC, ammonium, nitrites + nitrates), except for meiofauna, which is related with the nutrient content of the sediment, especially TC. Those results underline the importance of the relationship between the microbenthic loop and degradation processes, even at a small scale. [less ▲]

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See detailOceanBrowser: on-line visualization of gridded ocean data and in situ observations
Barth, Alexander ULg; Watelet, Sylvain ULg; Troupin, Charles et al

Conference (2016, October)

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See detailOperational forecasting of the Black sea: merging model simulations and satellite products
Vandenbulcke, Luc ULg; Capet, Arthur ULg; Ivanov, Evgeny ULg et al

Scientific conference (2016, September 29)

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See detailDINEOF analyses of Sea Surface Temperature data in the Black Sea
Alvera Azcarate, Aida ULg; Vandenbulcke, Luc ULg; Barth, Alexander ULg et al

Scientific conference (2016, September 29)

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See detailAnalysis of Ocean in Situ Observations and Web - Based Visualization: From Individual Measurements to an Integrated View
Barth, Alexander ULg; Watelet, Sylvain ULg; Troupin, Charles et al

in Diviacco, Paolo; Leadbetter, Adam; Glaves, Helen (Eds.) Oceanographic and Marine Cross-Domain Data Management for Sustainable Development (2016)

The sparsity of observations poses a challenge common to various ocean disciplines. Even for physical parameters where the spatial and temporal coverage is higher, current observational networks ... [more ▼]

The sparsity of observations poses a challenge common to various ocean disciplines. Even for physical parameters where the spatial and temporal coverage is higher, current observational networks undersample a broad spectrum of scales. This situation is generally more severe for chemical and biological parameters because such sensors are less widely deployed. The present chapter describes the analysis tool DIVA (Data-Interpolating Variational Analysis) which is designed to generate gridded fields from in situ observations. DIVA has been applied to various physical (temperature and salinity), chemical (concentration of nitrate, nitrite and phosphate) and biological parameters (abundance of a species). The chapter also shows the technologies used to visualize the gridded fields. Visualization of analyses from in situ observations provide a unique set of challenges since the accuracy of the analysed field is not spatially uniform as it strongly depends on the location of the observations. In addition, an adequate treatment of the depth and time dimensions is essential. [less ▲]

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See detailAnalysis of SMOS sea surface salinity data using DINEOF
Alvera Azcarate, Aïda ULg; Barth, Alexander ULg; Parard, Gaëlle ULg et al

in Remote Sensing of Environment (2016), 180

n analysis of daily Sea Surface Salinity (SSS) at 0.15 ° × 0.15° spatial resolution from the Soil Moisture and Ocean Salinity (SMOS) satellite mission using DINEOF (Data Interpolating Empirical Orthogonal ... [more ▼]

n analysis of daily Sea Surface Salinity (SSS) at 0.15 ° × 0.15° spatial resolution from the Soil Moisture and Ocean Salinity (SMOS) satellite mission using DINEOF (Data Interpolating Empirical Orthogonal Functions) is presented. DINEOF allows reconstructing missing data using a truncated EOF basis, while reducing the amount of noise and errors in geophysical datasets. This work represents a first application of DINEOF to SMOS SSS. Results show that a reduction of the error and the amount of noise is obtained in the DINEOF SSS data compared to the initial SMOS SSS data. Errors associated to the edge of the swath are detected in 2 EOFs and effectively removed from the final data, avoiding removing the data at the edges of the swath in the initial dataset. The final dataset presents a centered root mean square error of 0.2 in open waters when comparing with thermosalinograph data at their original spatial and temporal resolution. Constant biases present near land masses, large scale biases and latitudinal biases cannot be corrected with DINEOF because persistent signals are retained in high order EOFs, and therefore these need to be corrected separately. The signature of the Douro and Gironde rivers is detected in the DINEOF SSS. The minimum SSS observed in the Gironde plume corresponds to a flood event in June 2013, and the shape and size of the Douro river shows a good agreement with chlorophyll-a satellite data. These examples show the capacity of DINEOF to remove noise and provide a full SSS dataset at a high temporal and spatial resolution with reduced error, and the possibility to retrieve physical signals in zones with high initial errors. [less ▲]

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See detailReconstruction and analysis of long-term satellite-derived sea surface temperature for the South China Sea
Huynh, Thi Hong Ngu ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

in Journal of Oceanography (2016)

Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. Unfortunately, the SST data sources in the South China ... [more ▼]

Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. Unfortunately, the SST data sources in the South China Sea (SCS) are not abundant due to sparse measurements of in situ SST and a high percentage of missing data in the satellite-derived SST. Therefore, SST data sets with low resolution and/or a short-term period have often been used in previous researches. Here we used Data INterpolating Empirical Orthogonal Functions, a self-consistent and parameter-free method for filling in missing data, to reconstruct the daily nighttime 4-km AVHRR Pathfinder SST for the long-term period spanning from 1989 to 2009. In addition to the reconstructed field, we also estimated the local error map for each reconstructed image. Comparisons between the reconstructed and other data sets (satellite-derived microwave and in situ SSTs) show that the results are reliable for use in many different researches, such as validating numerical models, or identifying and tracking meso-scale oceanic features. Moreover, the Empirical Orthogonal Function (EOF) analysis of the reconstructed SST and the reconstructed SST anomalies clearly shows the subseasonal, seasonal, and interannual variability of SST under the influence of monsoon and El Niño-Southern Oscillation (ENSO), as well as reveals some oceanic features that could not be captured well in previous EOF analyses. The SCS SST often lags ENSO by about half a year. However, in this study, we see that the time lag changes with the frequencies of the SST variability, from 1 to 6 months. [less ▲]

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See detailAnalysis of ocean in situ observations and web-based visualization
Barth, Alexander ULg; Watelet, Sylvain ULg; Troupin, Charles ULg et al

Conference (2016)

The sparsity of observations poses a challenge common to various ocean science disciplines. Even for physical parameters where the spatial and temporal coverage is higher, current observational networks ... [more ▼]

The sparsity of observations poses a challenge common to various ocean science disciplines. Even for physical parameters where the spatial and temporal coverage is higher, current observational networks undersample a broad spectrum of scales. The situation is generally more severe for chemical and biological parameters because related sensors are less widely deployed. The analysis tool DIVA (Data-Interpolating Variational Analysis) is designed to generate gridded fields from in situ observations. DIVA has been applied to various physical (temperature and salinity), chemical (concentration of nitrate, nitrite and phosphate) and biological parameters (abundance of a species) in the context of different European projects (SeaDataNet, EMODnet Chemistry and EMODnet Biology). We show the technologies used to visualize the gridded fields based on the Web Map Services standard. Visualization of analyses from in situ observations provides a unique set of challenges since the accuracy of the analysed field is not spatially uniform as it strongly depends on the observations location. In addition, an adequate handling of depth and time dimensions is essential. Beside visualizing the gridded fields, access is also given to the underlying observations. It is thus also possible to view more detailed information about the variability of the observations. The in situ observation visualization service allows one to display vertical profiles and time series and it is built upon OGC standards (the Web Feature Service and Web Processing Services) and following recommendation from the INSPIRE directive. [less ▲]

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See detailDeriving ocean climatologies with multivariate coupling
Barth, Alexander ULg; Alvera Azcarate, Aida ULg; Beckers, Jean-Marie ULg

Conference (2016)

In situ measurements of ocean properties are generally sparsely distributed and thus undersample the ocean variability. Deriving ocean climatologies is a challenging task especially for biological and ... [more ▼]

In situ measurements of ocean properties are generally sparsely distributed and thus undersample the ocean variability. Deriving ocean climatologies is a challenging task especially for biological and chemical parameters where the number of data is, by an order of magnitude, smaller than for physical parameters. However, physical and biogeochemical parameters are related through the ocean dynamics. In particular fronts visible in physical parameters are often related to gradients in biogeochemical parameters. Ocean climatologies are generally derived for different variables independently. For biogeochemical parameters, only the very large-scale variability can be derived for poorly sampled areas. Here we present a method to derive multivariate analysis taking the relationship between physical and biogeochemical variables into account. The benefit of this procedure is showed by using model data for salinity, nitrate and phosphate of the Mediterranean Sea. The model fields are sampled at the locations of true observations (extracted from the World Ocean Database 2013) and the analysed fields are compared to the original model fields. The multivariate analysis result in a reduction of the RMS error and to a better representation of the gradients [less ▲]

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See detailLocal ensemble assimilation scheme with global constraints and conservation
Barth, Alexander ULg; Yan, Yajing ULg; Alvera Azcarate, Aida ULg et al

in Ocean Dynamics (2016), 66

Ensemble assimilation schemes applied in their original, global formulation respect linear conservation properties if the ensemble perturbations are set up accordingly. For realistic ocean systems, only a ... [more ▼]

Ensemble assimilation schemes applied in their original, global formulation respect linear conservation properties if the ensemble perturbations are set up accordingly. For realistic ocean systems, only a relatively small number of ensemble members can be calculated. A localization of the ensemble increment is therefore necessary to filter out spurious long-range correlations. The conservation of the global properties will be lost if the assimilation is performed locally, since the conservation requires a coupling between all model grid points which is removed by the localization. The distribution of ocean observations is often highly inhomogeneous. Systematic errors of the observed parts of the ocean state can lead to spurious adjustment of the non-observed parts via data assimilation and thus to a spurious increase or decrease in long-term simulations of global properties which should be conserved. In this paper, we propose a local assimilation scheme (with different variants and assumptions) which can satisfy global conservation properties. The proposed scheme can also be used for non-local observation operators. Different variants of the proposed scheme are tested in an idealized model and compared to the traditional covariance localization with an ad-hoc step enforcing conservation. It is shown that the inclusion of the conservation property reduces the total RMS error and that the presented stochastic and deterministic schemes avoiding error space rotation provide better results than the traditional covariance localization. [less ▲]

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See detailAssimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean
Barth, Alexander ULg; Canter, Martin ULg; Van Schaeybroeck, Bert et al

in Ocean Modelling (2015), 93

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice ... [more ▼]

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice concentration and sea ice drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using sea ice drift estimated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measurements of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to identify model errors in the Antarctic sea ice area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements. [less ▲]

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See detailAssimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean
Barth, Alexander ULg; Canter, Martin ULg; Van Schaeybroeck, Bert et al

Poster (2015)

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice ... [more ▼]

Current ocean models have relatively large errors and biases in the Southern Ocean. The aim of this study is to provide a reanalysis from 1985 to 2006 assimilating sea surface temperature, sea ice concentration and sea ice drift. In the following it is also shown how surface winds in the Southern Ocean can be improved using sea ice drift es- timated from infrared radiometers. Such satellite observations are available since the late seventies and have the potential to improve the wind forcing before more direct measure- ments of winds over the ocean are available using scatterometry in the late nineties. The model results are compared to the assimilated data and to independent measurements (the World Ocean Database 2009 and the mean dynamic topography based on observations). The overall improvement of the assimilation is quantified, in particular the impact of the assimilation on the representation of the polar front is discussed. Finally a method to iden- tify model errors in the Antarctic sea ice area is proposed based on Model Output Statistics techniques using a series of potential predictors. This approach provides new directions for model improvements. [less ▲]

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See detailLocal ensemble assimilation scheme with global constraints and conservation
Barth, Alexander ULg; Yan, Yajing ULg; Canter, Martin ULg et al

Conference (2015)

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ... [more ▼]

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ocean systems, only a relatively small number of ensemble members can be calculated. A localization of the ensemble increment is thus necessary to filter out spurious long-range correlations. However, the conservation of the global property will be lost if the assimilation is performed locally since the conservation requires a coupling between model grid points, which is filtered out by the localization. In the ocean, the distribution of observations is highly inhomogeneous. System- atic errors of the observed parts of the ocean state can lead to spurious systematic adjust- ments of the non-observed part of the ocean state due to data assimilation. As a result, global properties which should be conserved, increase or decrease in long-term simulations. We propose an assimilation scheme (with stochastic or deterministic analysis steps) which is formulated globally (i.e. for the whole state vector) but where spurious long-range correlations can be filtered out. The scheme can thus be used to enforce global conservation properties and non-local observation operators. Both aspects are indeed linked since one can introduce the global conservation as a weak constraint by using a global ob- servation operator. The conserved property becomes thus an observed value. The proposed scheme is tested with the Kuramoto-Sivashinsky model which is conservative. The benefit compared to the traditional covariance localization scheme (with an ad-hoc step enforcing conservation) where observations are assimilated sequentially is shown. The assimilation scheme is suitable to be implemented on parallel computers where the number of available computing cores is a multiple of the ensemble size. [less ▲]

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See detailAssimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean
Barth, Alexander ULg; Canter; Van Schaeybroeck, Bert et al

Conference (2015)

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See detailAnalysis of high frequency geostationary ocean colour data using DINEOF
Alvera Azcarate, Aïda ULg; Vanhellemont, Quinten; Ruddick, Kevin et al

in Estuarine Coastal & Shelf Science (2015), 159

DINEOF (Data Interpolating Empirical Orthogonal Functions), a technique to reconstruct missing data, is applied to turbidity data obtained through the Spinning Enhanced Visible and Infrared Imager (SEVIRI ... [more ▼]

DINEOF (Data Interpolating Empirical Orthogonal Functions), a technique to reconstruct missing data, is applied to turbidity data obtained through the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation 2. The aim of this work is to assess if the tidal variability of the southern North Sea in 2008 can be accurately reproduced in the reconstructed dataset. Such high frequency data have not previously been analysed with DINEOF and present new challenges, like a strong tidal signal and long night-time gaps. An outlier detection approach that exploits the high temporal resolution (15 min) of the SEVIRI dataset is developed. After removal of outliers, the turbidity dataset is reconstructed with DINEOF. In situ Smartbuoy data are used to assess the accuracy of the reconstruction. Then, a series of tidal cycles are examined at various positions over the southern North Sea. These examples demonstrate the capability of DINEOF to reproduce tidal variability in the reconstructed dataset, and show the high temporal and spatial variability of turbidity in the southern North Sea. An analysis of the main harmonic constituents (annual cycle, daily cycle, M2 and S2 tidal components) is performed, to assess the contribution of each of these modes to the total variability of turbidity. The variability not explained by the harmonic fit, due to the natural processes and satellite processing errors as noise, is also assessed. [less ▲]

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See detailEOF analysis of long-term reconstructed AVHRR Pathfinder SST in the South China Sea
Huynh, Thi Hong Ngu ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2014, May 02)

Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. For recent decades, the AVHRR Pathfinder SST, measured ... [more ▼]

Sea surface temperature (SST) is one of the key variables often used to investigate ocean dynamics, ocean-atmosphere interaction, and climate change. For recent decades, the AVHRR Pathfinder SST, measured by infrared sensors, has been widely used because of its high resolution and long time-series. The disadvantage of the AVHRR Pathfinder SST is high percentage of missing data due to cloud coverage. This becomes more serious in the South China Sea (SCS) because it is located in the tropical region, frequently covered by clouds. In this study, we used the Data INterpolating Empirical Orthogonal Functions (DINEOF) method to reconstruct daily night-time 4 km AVHRR Pathfinder SST spanning from 1989 to 2009 for the whole SCS. In order to better understand the spatial and temporal variability of the SCS SST, an EOF analysis of the reconstructed field is performed in association with surface wind. The first SST mode, accounting for 69% of the variance, presents the cooling (warming) of the basin due to the solar inclination through seasons, water exchange, topography, and monsoon-induced cyclonic circulation. The second SST mode, explaining 24.8% of the variance, shows the advection of cold and warm water from two opposite directions along the southwest-northeast diagonal of the basin. The second SST mode is affected by the atmospheric anticyclone (cyclone) located over the Philippine Sea. Comparing both SST modes with Nino3.0 index, it shows that the interannual variability of the SCS SST is influenced by the moderate and strong ENSO events with a lag of 5-6 months. Moreover, the analysis of the high-resolution reconstructed dataset reveals some oceanic features that could not be captured in previous EOF analyses. [less ▲]

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See detailLocal ensemble assimilation scheme with global constraints and conservation
Barth, Alexander ULg; Yan, Yajing ULg; Canter, Martin ULg et al

Poster (2014, April)

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ... [more ▼]

Ensemble assimilation schemes applied in their original, global formulation have no problem in respecting linear conservation properties if the ensemble perturbations are setup accordingly. For realistic ocean systems, only a relatively small number of ensemble members can be calculated. A localization of the ensemble increment is thus necessary to filter out spurious long-range correlations. However, the conservation of the global property will be lost if the assimilation is performed locally since the conservation requires a coupling between model grid points, which is filtered out by the localization. In the ocean, the distribution of observations is highly inhomogeneous. Systematic errors of the observed parts of the ocean state can lead to spurious systematic adjustments of the non-observed part of the ocean state due to data assimilation. As a result, global properties which should be conserved, increase or decrease in long-term simulations. We propose an assimilation scheme (with stochastic or deterministic analysis steps) which is formulated globally (i.e. for the whole state vector) but where spurious long-range correlations can be filtered out. The scheme can thus be used to enforce global conservation properties and non-local observation operators. Both aspects are indeed linked since one can introduce the global conservation as a weak constraint by using a global observation operator. The conserved property becomes thus an observed value. The proposed scheme is tested with the Kuramoto-Sivashinsky model which is conservative. The benefit compared to the traditional covariance localization scheme (with an ad-hoc step enforcing conservation) where observations are assimilated sequentially is shown. The assimilation scheme is suitable to be implemented on parallel computers where the number of available computing cores is a multiple of the ensemble size. [less ▲]

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See detailWeb-based application for Data INterpolation Empirical Orthogonal Functions (DINEOF) analysis
Tomazic, Igor ULg; Alvera Azcarate, Aïda ULg; Barth, Alexander ULg et al

Poster (2014, April)

DINEOF (Data INterpolating Empirical Orthogonal Functions) is a powerful tool based on EOF decomposition developed at the University of Liege/GHER for the reconstruction of missing data in satellite ... [more ▼]

DINEOF (Data INterpolating Empirical Orthogonal Functions) is a powerful tool based on EOF decomposition developed at the University of Liege/GHER for the reconstruction of missing data in satellite datasets, as well as for the reduction of noise and detection of outliers. DINEOF is openly available as a series of Fortran routines to be compiled by the user, and as binaries (that can be run directly without any compilation) both for Windows and Linux platforms. In order to facilitate the use of DINEOF and increase the number of interested users, we developed a web-based interface for DINEOF with the necessary parameters available to run high-quality DINEOF analysis. This includes choosing variable within selected dataset, defining a domain, time range, filtering criteria based on available variables in the dataset (e.g. quality flag, satellite zenith angle …) and defining necessary DINEOF parameters. Results, including reconstructed data and calculated EOF modes will be disseminated in NetCDF format using OpenDAP and WMS server allowing easy visualisation and analysis. First, we will include several satellite datasets of sea surface temperature and chlorophyll concentration obtained from MyOcean data centre and already remapped to the regular grid (L3C). Later, based on user’s request, we plan to extend number of datasets available for reconstruction. [less ▲]

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See detailApproximate and Efficient Methods to Assess Error Fields in Spatial Gridding with Data Interpolating Variational Analysis (DIVA)
Beckers, Jean-Marie ULg; Barth, Alexander ULg; Troupin, Charles ULg et al

in Journal of Atmospheric & Oceanic Technology (2014), 31(2), 515-530

We present new approximate methods to provide error fields for the spatial analysis tool Diva. It is first shown how to replace the costly analysis of a large number of covariance functions by a single ... [more ▼]

We present new approximate methods to provide error fields for the spatial analysis tool Diva. It is first shown how to replace the costly analysis of a large number of covariance functions by a single analysis for quick error computations. Then another method is presented where the error is only calculated in a small number of locations and from there the spatial error field itself interpolated by the analysis tool. The efficiency of the methods is illustrated on simple schematic test cases and a real application in the Mediterranean Sea. These examples show that with these methods one has the possibility for quick masking of regions void of sufficient data and the production of "exact" error fields at reasonable cost. The error-calculation methods can also be generalized for use with other analysis methods such as 3D-Var and are therefore potentially interesting for other implementations. [less ▲]

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