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Networks of small, often mobile, polarimetric radars are gaining popularity in the hydrometeorology community due to their rainfall observing capabilities and relative low purchase cost. In recent years, a number of installations have become operational around the globe. The problem of signal attenuation by intervening rainfall has been recognized as the major source of error in rainfall estimation by short-wavelength (C-, X, K-band) radars. The simultaneous observation of precipitation by multiple radars creates new prospects for better and more robust attenuation correction algorithms and, consequently, yields more accurate rainfall estimation. The University of Iowa hydrometeorology group's acquisition of a network of four mobile, polarimetric, X-band radars has resulted in the need for a thoughtful evaluation of the instrument. In this work, we use computer simulations and the data collected by The University of Iowa Polarimetric Radar Network to study the performance of attenuation correction methods in single-radar and network-based arrangements. To support the computer simulations, we developed a comprehensive polarimetric radar network simulator, which replicates the essential aspects of the radar network rainfall observing process. The simulations are based on a series of physics- and stochastic-based simulated rainfall events occurring over the area of interest. The characteristics of the simulated radars are those of The University of Iowa Polarimetric Radar Network. We assess the correction methods by analyzing the errors in reflectivity and rainfall rate over the area of interest covered by the network's radars. To enable the implementation of the attenuation correction methods to the data collected by The University of Iowa Polarimetric Radar Network, we first developed a set of utilities to assist with efficient data collection and analysis. Next, we conducted a series of calibration tests to evaluate the relative calibration and channel balance of the 2 network's radars. Finally, in an attempt to verify the results obtained via computer simulations, we applied the set of attenuation correction algorithms to the data collected by The University of Iowa Polarimetric Radar Network.

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Submitted by Lucienne Costa (lucienneferreira@ufcg.edu.br) on 2018-08-13T17:06:59Z No. of bitstreams: 1 JEFFERSON APARECIDO ARESTIDES DE MELO – DISSERTAÇÃO (PPGMET) 2016.pdf: 4954301 bytes, checksum: 801b98838911a33f335fda02c77f585a (MD5) / Made available in DSpace on 2018-08-13T17:06:59Z (GMT). No. of bitstreams: 1 JEFFERSON APARECIDO ARESTIDES DE MELO – DISSERTAÇÃO (PPGMET) 2016.pdf: 4954301 bytes, checksum: 801b98838911a33f335fda02c77f585a (MD5) Previous issue date: 2016-02-29 / Capes / A linha de instabilidade (LI) Amazônica que atingiu Belém, em 08 de junho de 2008, foi monitorada e analisada por meio de medições de superfície, altitude, satélite e radar. A LI foi identificada, inicialmente, através de imagens do satélite GOES 12. O evento ocorreu durante a campanha de Belém, do Projeto CHUVA, que foi realizada durante o período de 01-30 junho de 2011, durante máxima ocorrência das linhas de instabilidades na região. Através da análise dos dados pluviométricos disponíveis percebe-se que a chuva associada à linha de instabilidade do dia 08 corresponde, aproximadamente, 29% da precipitação acumulada durante todo o experimento. A LI foi monitorada pelo radar meteorológico Banda - XPOL e permitiu a avaliação dinâmica e microfísica do sistema. Esta ultima realizada por meio da classificação de hidrometeoros com as variáveis polarimétricas. As variáveis utilizadas foram: refletividade horizontal (Zh, dBZ), refletividade diferencial (Zdr, dB), fase diferencial específica (Kdp, ° km-1), coeficiente de correlação (ρhv) e, por fim, se realizou a classificação dos hidrometeoros. O sistema apresentou fortes núcleos de refletividade que indicam a região convectiva. Esta parte da LI também é caracterizada por colunas com, relativamente, fortes Zdr e Kdp. A co-localização de colunas de Zh, Zdr e Kdp sugerem que esta é uma zona de elevada concentração de gotas de chuva com um tamanho considerável. A classificação dos hidrometeoros apresentou um resultado bem condizente com o que pode ser observado por outros pesquisadores e com as características microfísicas de outros sistemas convectivos. / A squall line (SL) Amazon, which reached Belém on June 8, 2008, was monitored and analyzed by means of surface measurements, altitude, satellite and radar. The SL was identified initially through the satellite GOES 12 images. The event was during the campaign of Belém, the Chuva Project, which was held during the period 01-30 June 2011, during maximum occurrence of squall line in the region. Through the analysis of available rainfall data we can see that the rain associated with the squall line the day 08 corresponds to approximately 29% of rainfall accumulated during all the experiment. The LI was monitored by weather radar Band - X POL and allowed the dynamic evaluation and microphysics of the system. The latter performed by hydrometeors classification with variables polarimetric. The variables used were: horizontal reflectivity (Zh, dBZ), differential reflectivity (Zdr, dB), specific differential phase (Kdp, ° km -1), correlation coefficient (ρhv) and, finally, was held the classification of hydrometeors. The system showed strong core of reflectivity indicating the convective region. This part of LI is also characterized by columns relatively strong Zdr and Kdp. The co-location columns Zh, Zdr and Kdp suggest that this is an area of high concentration of raindrops with a considerable size. The classification of hydrometeors presented the result well consistent with which can be observed by other researchers and the microphysical characteristics of other convective systems.

Notre recherche vise à évaluer l’apport d’une seule image polarimétrique RSO (Radar à Synthèse d’Ouverture) à haute résolution spatiale pour classifier les surfaces urbaines. Pour cela, nous définissons plusieurs types de toits, de sols et d’objets.Dans un premier temps, nous proposons un inventaire d’attributs statistiques, texturaux et polarimétriques pouvant être utilisés dans un algorithme de classification. Nous étudions les lois statistiques des descripteurs et montrons que la distribution de Fisher est bien adaptée pour la plupart d’entre eux. Dans un second temps, plusieurs algorithmes de classification vectorielle supervisée sont testés et comparés, notamment la classification par maximum de vraisemblance basée sur une distribution gaussienne, ou celle basée sur la distribution de Wishart comme modèle statistique de la matrice de cohérence polarimétrique, ou encore l’approche SVM. Nous proposons alors une variante de l’algorithme par maximum de vraisemblance basée sur une distribution de Fisher, dont nous avons étudié l’adéquation avec l’ensemble de nos attributs. Nous obtenons une nette amélioration de nos résultats avec ce nouvel algorithme mais une limitation apparaît pour reconnaître certains toits. Ainsi, la forme des bâtiments rectangulaires est reconnue par opérations morphologiques à partir de l’image d’amplitude radar. Cette information spatiale est introduite dans le processus de classification comme contrainte. Nous montrons tout l’intérêt de cette information puisqu’elle empêche la confusion de classification entre pixels situés sur des toits plats et des pixels d’arbre. De plus, nous proposons une méthode de sélection des attributs les plus pertinents pour la classification, basée sur l’information mutuelle et une recherche par algorithme génétique. Nos expériences sont menées sur une image polarimétrique avec un pixel de 35 cm, acquise en 2006 par le capteur aéroporté RAMSES de l’ONERA. / In this research, our aim is to assess the potential of a one single look high spatial resolution polarimetric radar image for the classification of urban areas. For that purpose, we concentrate on classes corresponding to different kinds of roofs, objects and ground surfaces.At first, we propose a uni-variate statistical analysis of polarimetric and texture attributes, that can be used in a classification algorithm. We perform a statistical analysis of descriptors and show that the Fisher distribution is suitable for most of them. We then propose a modification of the maximum likelihood algorithm based on a Fisher distribution; we train it with all of our attributes. We obtain a significant improvement in our results with the new algorithm, but a limitation appears to recognize some roofs.Then, the shape of rectangular buildings is recognized by morphological operations from the image of radar amplitude. This spatial information is introduced in a Fisher-based classification process as a constraint term and we show that classification results are improved. In particular, it overcomes classification ambiguities between flat roof pixels and tree pixels.In a second step, some well-known algorithms for supervised classification are used. We deal with Maximum Likelihood based on complex Gaussian distribution (uni-variate) and multivariate Complex Gaussian using coherency matrix. Meanwhile, the support vector machine, as a nonparametric method, is used as classification algorithm. Moreover, a feature selection based on Genetic Algorithm using Mutual Information (GA-MI) is adapted to introduce optimal subset to classification method. To illustrate the efficiency of subset selection based on GA-MI, we perform a comparison experiment of optimal subset with different target decompositions based on different scattering mechanisms, including the Pauli, Krogager, Freeman, Yamaguchi, Barnes, Holm, Huynen and the Cloude decompositions. Our experiments are based on an image of a suburban area, acquired by the airborne RAMSES SAR sensor of ONERA, in 2006, with a spatial spacing of 35 cm. The results highlight the potential of such data to discriminate some urban land cover types.