References of "Tomazic, Igor"
     in
Bookmark and Share    
Full Text
See detailEast African Great Lake Ecosystem Sensitivity to changes final report
Descy, J.-P.; André, L.; Delvaux, C. et al

Report (2015)

Detailed reference viewed: 86 (12 ULiège)
See detailWeb-based application for Data INterpolation Empirical Orthogonal Functions (DINEOF) analysis
Tomazic, Igor ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege 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 ▲]

Detailed reference viewed: 71 (3 ULiège)
Full Text
Peer Reviewed
See detailMulti-scale optimal interpolation: application to DINEOF analysis spiced with a local optimal interpolation
Beckers, Jean-Marie ULiege; Barth, Alexander ULiege; Tomazic, Igor ULiege et al

in Ocean Science Discussions (2014), 11

We present a method in which the optimal interpolation of multi-scale processes can be untangled into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of ... [more ▼]

We present a method in which the optimal interpolation of multi-scale processes can be untangled into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of two processes can be obtained by different mathematical formulations involving iterations and analysis focusing on a single process. From the 5 different mathematical equivalent formulations we then select the most efficient ones by analyzing the behavior of the different possibilities in a simple and well controlled test case. The clear guidelines deduced from this experiment are then applied in a real situation in which we combine large-scale analysis of hourly SEVIRI satellite images using DINEOF with a local optimal interpolation using a Gaussian covariance. It is 10 shown that the optimal combination indeed provides the best reconstruction and can therefore be exploited to extract the maximum amount of useful information from the original data [less ▲]

Detailed reference viewed: 138 (21 ULiège)
See detailEstimating Inter-Sensor Sea Surface Temperature Biases using DINEOF analysis
Tomazic, Igor ULiege; Alvera Azcarate, Aïda ULiege; Troupin, Charles ULiege et al

Poster (2013)

Climate studies need long-term data sets of homogeneous quality, in order to discern trends from other physical signals present in the data and to minimise the contamination of these trends by errors in ... [more ▼]

Climate studies need long-term data sets of homogeneous quality, in order to discern trends from other physical signals present in the data and to minimise the contamination of these trends by errors in the source data. Sea surface temperature (SST), defined as one of essential climatology variables, has been increasingly used in both oceanographical and meteorological operational context where there is a constant need for more accurate measurements. Satellite-derived SST provides an indispensable dataset, with both spatially and temporally high resolutions. However, these data have errors of 0.5 K on a global scale and present inter-sensor and inter-regional differences due to their technical characteristics, algorithm limitations and the changing physical properties of the measured environments. These inter-sensor differences should be taken into account in any research involving more than one sensor (SST analysis, long term climate research . . . ). The error correction for each SST sensor is usually calculated as a difference between the SST data derived from referent sensor (e.g. ENVISAT/AATSR) and from the other sensors (SEVIRI, AVHRR, MODIS). However, these empirical difference (bias) fields show gaps due to the satellite characteristics (e.g. narrow swath in case of AATSR) and to the presence of clouds or other atmospheric contaminations. We present a methodology based on DINEOF (Data INterpolation Empirical Orthogonal Functions) to reconstruct and analyse SST biases with the aim of studying temporal and spatial variability of the SST bias fields both at a large scale (European seas) and at a regional scale (Mediterranean Sea) and to perform the necessary corrections to the original SST fields. Two different approaches were taken: by analysing SST biases based on reconstructed SST differences and based on differences of reconstructed SST fields. Corrected SST fields based on both approaches were validated against independent in situ buoy SST data or with ENVISAT/AATSR SST data for areas without in situ data (e.g. eastern Mediterranean). [less ▲]

Detailed reference viewed: 37 (0 ULiège)
Full Text
See detailDINEOF-based bias correction of SEVIRI sea surface temperature using Metop-A/AVHRR and ENVISAT/AATSR SST
Tomazic, Igor ULiege; Alvera Azcarate, Aïda ULiege; Barth, Alexander ULiege et al

Poster (2013)

Satellite-derived sea surface temperature (SST) show inter-sensor and inter-regional differences (biases) due to their technical characteristics, multispectral algorithm limitations and the changing ... [more ▼]

Satellite-derived sea surface temperature (SST) show inter-sensor and inter-regional differences (biases) due to their technical characteristics, multispectral algorithm limitations and the changing physical properties of the measured environments. The bias correction is usually calculated as a difference between the SST measurements from two sensors where one is defined as the reference (e.g. ENVISAT/AATSR). These empirical bias fields show gaps due to the satellite characteristics (e.g. narrow swath in case of AATSR) and to the presence of clouds or other atmospheric contamination sources. We present a bias correction approach based on DINEOF (Data Interpolating Empirical Orthogonal Functions) for reconstructing missing data. Two different approaches for deriving SST bias fields were used: analysing SST biases based on reconstructed SST differences or based on differences of the reconstructed SST fields. The method is applied at a large scale (European seas) and at a regional scale (e.g. Mediterranean Sea) to correct SEVIRI and Metop-A/AVHRR SST measurements using ENVISAT/AATSR as a corrector. For SEVIRI we additionally used Metop-A/AVHRR SST as a corrector to analyse the impact of ENVISAT/AATSR failure. Corrected SST fields based on both approaches were validated against independent in situ buoy SST data or with ENVISAT/AATSR SST data for areas without in situ data (e.g. eastern Mediterranean). The method is also compared to the operational bias correction method at Meteo-France/CMS that uses a temporal and spatial averaging. Results show that both approaches lead to near-zero biases when compared to AATSR SST measurements, although the differences of reconstructions exhibit much higher standard deviation (> 0.6 K) compared to the reconstruction of differences (< 0.5 K). Comparison with in situ data expectedly depends on the initial comparison between AATSR and in situ SST for specific regions. [less ▲]

Detailed reference viewed: 39 (0 ULiège)
Full Text
See detailDefining optimal brightness temperature simulation adjustment parameters to improve Metop-A/AVHRR SST over the Mediterranean Sea
Tomazic, Igor ULiege; Le Borgne, Pierre; Roquete, Hervé

Conference (2013)

Sea surface temperature (SST) multispectral algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of ... [more ▼]

Sea surface temperature (SST) multispectral algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BT), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High resolution (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night and summer and colder season. Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST, that we adopted as validation reference. We obtained an optimized result across all defined conditions for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, time series analyses showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (< 5 days) and higher spatial smoothing (>10 deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results, leading to more appropriate separate BT adjustment for day and night in areas and seasons of intensive diurnal warming conditions. [less ▲]

Detailed reference viewed: 20 (0 ULiège)
Full Text
Peer Reviewed
See detailAssessing the impact of brightness temperature simulation adjustment conditions in correcting Metop-A SST over the Mediterranean Sea
Tomazic, Igor ULiege; Roquet, Hervé; Le Borgne, Pierre

in Remote Sensing of Environment (2013)

Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of ... [more ▼]

Multispectral sea surface temperature (SST) algorithms applied to infrared (IR) radiometer data exhibit regional biases due to the intrinsic inability of the SST algorithm to cope with the vast range of atmospheric types, mainly influenced by water vapor and temperature profiles. Deriving a SST correction from simulated brightness temperatures (BTs), obtained by applying a Radiative Transfer Model (RTM) to Numerical Weather Prediction (NWP) atmospheric profiles and first guess SST, is one of the solutions to reduce regional biases. This solution is envisaged in the particular case of Metop-A Advanced Very High Resolution Radiometer (AVHRR) derived SST. Simulated BTs show errors, linked to RTM, atmospheric profiles or guess field errors. We investigated the conditions of adjusting simulated to observed BTs in the particular case of the Mediterranean Sea over almost one year. Our study led to define optimal spatio/temporal averaging parameters of the simulation observation differences, both during day and night, summer and colder season and for two simulation modes: operational (with reduced vertical resolution – 15 levels – NWP atmospheric profiles and two days old analysis used as first guess SST) and delayed (full vertical resolution – 91 levels – and concurrent analysis used as first guess SST). Each BT adjustment has been evaluated by comparing the corresponding corrected AVHRR SST to the AATSR SST that we adopted as validation reference. We obtained an optimized result across all defined conditions and modes for a spatial smoothing of 15 deg and a temporal averaging between 3 and 5 days. Specifically, analyses based on 10 day averages showed that a standard deviation based criterion favors spatial smoothing above 10 deg for all temporal averaging, while a bias based criterion favors shorter temporal averaging during daytime (< 5 days) and higher spatial smoothing (> 10 deg) for nighttime. This study has shown also the impact of diurnal warming both in deriving BT adjustment and in validation results. [less ▲]

Detailed reference viewed: 22 (1 ULiège)
See detailAssessing the impact of space and time resolution of brightness temperature simulation conditions in correcting SEVIRI SST over the Adriatic Sea
Tomazic, Igor ULiege; Le Borgne, Pierre; Tudor, Martina et al

Conference (2012)

Majority of operational sea surface temperature (SST) infra-red (IR) products have small overall bias but exhibit higher absolute biases in specific regions. Previous studies [for example Tomazic et al ... [more ▼]

Majority of operational sea surface temperature (SST) infra-red (IR) products have small overall bias but exhibit higher absolute biases in specific regions. Previous studies [for example Tomazic et al., 2011] showed that over Adriatic Sea there is a positive summer bias sometimes exceeding 0.5 K. Methodology to decrease regional biases [Le Borgne et al., 2011], based on using atmospheric profiles, surface SST fields and radiative transfer model to simulate the non-linear split window SST (NLSST) algorithm error, was used to assess the impact of atmospheric profiles with different spatial resolution (ECMWF: 0.125 deg and ALADIN 2 km), different input surface SST fields (OSTIA 6 km and CNR UHR L4 1 km) and different time and space averaging criteria’s in deriving the algorithm correction. SST corrections derived for the NLSST Spinning Enhanced Visible and Infrared Imager (SEVIRI) algorithm for five months (July, October and December 2010 and March and July 2011) are validated with AATSR L2 SST fields and compared to already implemented regional correction procedure at Center de Meteorologie Spatiale (CMS) to assess the optimal combination of space and time averaging criteria’s and input fields. Results show that the best improvement for all available months and for both day and night is obtained when using spatial averaging over the whole domain in combination with time averaging between the last 15 and last 31 days both for day and night time analysis. Using higher resolution ALADIN atmospheric profiles with OSTIA input SST fields didn’t improve the SST correction compared to using combination of coarser ECMWF atmospheric profiles and OSTIA input SST. Small improvement, based only on analysis for two months in 2011, is obtained when using both higher spatial resolution atmospheric profiles (ALADIN) and higher resolution input SST fields (CNR UHR L4 1 km). The best improvement obtained for spatial averaging over the whole domain (Adriatic Sea) suggests that the domain should be more extended (to Mediterranean Sea) to derive optimal spatial averaging, while conclusion of improvement obtained when using both the higher spatial resolution atmospheric profiles and input SST field need extension of analysis on other months in 2010. [less ▲]

Detailed reference viewed: 31 (0 ULiège)