ANALYSE MULTI FRACTALE DES ÉCHOS RADAR PAR LA MÉTHODE DES MAXIMUMS DES MODULES DE LA TRANSFORMÉE EN ONDELETTE (MMTO) 2D POUR LES SITES DE BORDEAUX (FRANCE), SÉTIF (ALGÉRIE) : APPLICATION À L'ÉLIMINATION DES ÉCHOS PARASITES

International audienceIn this work, the 2D-WTMM multifractal approach was applied to analysis the radar echoes, and to identify the unwanted echoes coming from terrestrial surface. With this intention, we considered radar images taken from two areas where different climates and relief prevail. We showed that almost Anaprops are characterized by a monofractal spectrum contrary to the echoes of precipitations which present a multifractal character. Moreover, we showed that the Holder coefficient and the combination of the spectrum mode and density of skeleton per pixel present robust factors to discriminate between the two types of echoes. Indeed, the unwanted echoes are practically eliminated at 98 per cent whereas the echoes of precipitation are almost preserved at 98,2 per cent. Also, we showed that the error between the measured intensity on the ground and the estimated intensity after treatment of the unwanted echoes does not exceed 5% for the Sétif site. Because the computation time is three minutes, the radar images can be processed in real-time.Dans le présent travail, l'approche MMTO-2d est appliquée pour l'analyse multi fractale des échos radar et l'identification des échos parasites en provenance de la surface terrestre. Pour ce faire, nous avons considéré des images radar prises dans deux régions où prévalent des climats et des reliefs différents. Il s'agit des sites de Sétif (Algérie) et Bordeaux (France). Nous avons montré que la plupart des Anaprops sont caractérisés par un spectre monofractal contrairement aux échos de précipitations qui présentent un caractère multi fractal. En outre, nous avons montré que le coefficient d'Holder ou la combinaison mode du spectre et densité de squelette par pixel se présentent comme des facteurs robustes de discrimination entre les deux types d'échos. En effet, les échos parasites sont pratiquement éliminés à 98% alors que les échos de précipitation sont quasiment conservés à 98,2%. Aussi, nous avons montré que l'erreur entre l'intensité mesurée au sol et estimée après traitement des échos parasites ne dépasse pas 5% pour le site de Sétif. Etant donné que le temps de traitement est égal à trois minutes, les images radar peuvent être traitées en temps réel

Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery

Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed

A Multifractal-based Wavefront Phase Estimation Technique for Ground-based Astronomical Observations

International audienceTurbulence in the Earth's atmosphere interferes with the propagation of planar wavefronts from outer space resulting in a phase distorted non-planar wavefront. This phase distortion is responsible for the refractive blurring of images accounting to the loss in spatial resolution power of ground-based telescopes. The technology widely used to remove this phase distortion is Adaptive Optics (AO). In AO, an estimate of the distorted phase is provided by a wavefront sensor (WFS) in the form of low-resolution slope measurements of the wavefront. The estimate is then used to create a corrected wavefront, that (approximately) removes the phase distortion from the incoming wavefronts. Phase reconstruction from WFS measurements is done by solving large linear systems followed by interpolating the low-resolution phase to its desired high-resolution. In this paper, we propose an alternate technique to wavefront phase reconstruction using concepts derived from the Microcanonical Multiscale Formalism (MMF), which is a specific approach to multifractality. We take into account an a priori information of the wavefront phase, provided by the multifractal exponents. Then through the framework of multiresolution analysis and wavelet transform, we address the problem of phase reconstruction from low-resolution WFS measurements. Comparison, in terms of reconstruction quality, with classical techniques in AO proves the superiority of our approach

Removal of nonprecipitation echoes in weather radar using multifractals and intensity

In this paper, we present an algorithm for the automated removal of nonprecipitation related echoes such as atmospheric anomalous propagation (AP) in the lower elevations of meteorological-radar volume scans. The motivation for the development of this technique is the need for an objective quality control algorithm that minimizes human interaction. The algorithm uses both textural and intensity information obtained from the two lower-elevation reflectivity maps. The texture of the reflectivity maps is analyzed with the help of multifractals. Four multifractal exponents are computed for each pixel of the reflectivity maps and are compared to a strict and a soft threshold. Pixels with multifractal exponents larger than the strict threshold are marked as nonrain, and pixels with exponents smaller than the soft threshold are marked as rain. Pixels with all other exponent values are further examined using intensity information. We evaluate our QC procedure by comparison with the Tropical Rainfall Measurement Mission (TRMM) Ground Validation Project quality control algorithm that was developed by TRMM scientists. Comparisons are based on a number of selected cases where nonprecipitation and a variety of rain events are present, and results show that both algorithms are effective in eliminating nonprecipitation related echoes while maintaining the rain pixels. The principal advantage of our algorithm is that it is automated; therefore, it cases the requirements for the training for the QC analysis and it speeds the data reduction process by eliminating the need for labor-intensive human-interactive software

Removal Of Nonprecipitation Echoes In Weather Radar Using Multifractals And Intensity

In this paper, we present an algorithm for the automated removal of nonprecipitation related echoes such as atmospheric anomalous propagation (AP) in the lower elevations of meteorological-radar volume scans. The motivation for the development of this technique is the need for an objective quality control algorithm that minimizes human interaction. The algorithm uses both textural and intensity information obtained from the two lower-elevation reflectivity maps. The texture of the reflectivity maps is analyzed with the help of multifractals. Four multifractal exponents are computed for each pixel of the reflectivity maps and are compared to a strict and a soft threshold. Pixels with multifractal exponents larger than the strict threshold are marked as nonrain and pixels with exponents smaller than the soft threshold are marked as rain. Pixels with all other exponent values are further examined using intensity information. We evaluate our QC procedure by comparison with the Tropical Rainfall Measurement Mission (TRMM) Ground Validation Project quality control algorithm that was developed by TRMM scientists. Comparisons are based on a number of selected cases where nonprecipitation and a variety of rain events are present, and results show that both algorithms are effective in eliminating nonprecipitation related echoes while maintaining the rain pixels. The principal advantage of our algorithm is that it is automated; therefore, it eases the requirements for the training for the QC analysis and it speeds the data reduction process by eliminating the need for labor-intensive human-interactive software

Removal of nonprecipitation echoes in weather radar using multifractals and intensity

In this paper, we present an algorithm for the automated removal of nonprecipitation related echoes such as atmospheric anomalous propagation (AP) in the lower elevations of meteorological-radar volume scans. The motivation for the development of this technique is the need for an objective quality control algorithm that minimizes human interaction. The algorithm uses both textural and intensity information obtained from the two lower-elevation reflectivity maps. The texture of the reflectivity maps is analyzed with the help of multifractals. Four multifractal exponents are computed for each pixel of the reflectivity maps and are compared to a "strict" and a "soft" threshold. Pixels with multifractal exponents larger than the strict threshold are marked as "nonrain" and pixels with exponents smaller than the soft threshold are marked as "rain." Pixels with all other exponent values are further examined using intensity information. We evaluate our QC procedure by comparison with the Tropical Rainfall Measurement Mission (TRMM) Ground Validation Project quality control algorithm that was developed by TRMM scientists. Comparisons are based on a number of selected cases where nonprecipitation and a variety of rain events are present, and results show that both algorithms are effective in eliminating nonprecipitation related echoes while maintaining the rain pixels. The principal advantage of our algorithm is that it is automated; therefore, it eases the requirements for the training for the QC analysis and it speeds the data reduction process by eliminating the need for labor-intensive human-interactive software