The Experts below are selected from a list of 9501 Experts worldwide ranked by ideXlab platform
P. A Owolawi - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Effective Transmission Loss Due to Subtropical Hydrometeor Scatters using a 3D Rain Cell Model for Centimeter and Millimeter Wave Applications
Journal of Infrared Millimeter and Terahertz Waves, 2014Co-Authors: P. A OwolawiAbstract:The problem of Hydrometeor scattering on microwave radio communication down links continues to be of interest as the number of the ground and earth space terminals continually grows The interference resulting from the Hydrometeor scattering usually leads to the reduction in the signal-to-noise ratio ( SNR ) at the affected terminal and at worst can even end up in total link outage. In this paper, an attempt has been made to compute the effective transmission loss due to subtropical Hydrometeors on vertically polarized signals in Earth–satellite propagation paths in the Ku, Ka and V band frequencies based on the modified Capsoni 3D rain cell model. The 3D rain cell model has been adopted and modified using the subtropical log-normal distributions of raindrop sizes and introducing the equivalent path length through rain in the estimation of the attenuation instead of the usual specific attenuation in order to account for the attenuation of both wanted and unwanted paths to the receiver. The co-channels, interference at the same frequency is very prone to the higher amount of unwanted signal at the elevation considered. The importance of joint transmission is also considered.
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Estimation of Effective Transmission Loss Due to Subtropical Hydrometeor Scatters using a 3D Rain Cell Model for Centimeter and Millimeter Wave Applications
Journal of Infrared Millimeter and Terahertz Waves, 2014Co-Authors: J. S Ojo, P. A OwolawiAbstract:The problem of Hydrometeor scattering on microwave radio communication down links continues to be of interest as the number of the ground and earth space terminals continually grows The interference resulting from the Hydrometeor scattering usually leads to the reduction in the signal-to-noise ratio ( SNR ) at the affected terminal and at worst can even end up in total link outage. In this paper, an attempt has been made to compute the effective transmission loss due to subtropical Hydrometeors on vertically polarized signals in Earth–satellite propagation paths in the Ku, Ka and V band frequencies based on the modified Capsoni 3D rain cell model. The 3D rain cell model has been adopted and modified using the subtropical log-normal distributions of raindrop sizes and introducing the equivalent path length through rain in the estimation of the attenuation instead of the usual specific attenuation in order to account for the attenuation of both wanted and unwanted paths to the receiver. The co-channels, interference at the same frequency is very prone to the higher amount of unwanted signal at the elevation considered. The importance of joint transmission is also considered.
Alexis Berne - One of the best experts on this subject based on the ideXlab platform.
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Unraveling Hydrometeor mixtures in polarimetric radar measurements
Atmospheric Measurement Techniques, 2018Co-Authors: Nikola Besic, Jacopo Grazioli, Marco Gabella, Urs Germann, Jordi Figueras I Ventura, Josué Gehring, Christophe Praz, Alexis BerneAbstract:Abstract. Radar-based Hydrometeor classification typically comes down to determining the dominant type of Hydrometeor populating a given radar sampling volume. In this paper we address the subsequent problem of inferring the secondary Hydrometeor types present in a volume – the issue of Hydrometeor de-mixing. The present study relies on the semi-supervised Hydrometeor classification proposed by Besic et al. ( 2016 ) but nevertheless results in solutions and conclusions of a more general character and applicability. In the first part, oriented towards synthesis, a bin-based de-mixing approach is proposed, inspired by the conventional coherent and linear decomposition methods widely employed across different remote-sensing disciplines. Intrinsically related to the concept of entropy, introduced in the context of the radar Hydrometeor classification in Besic et al. ( 2016 ) , the proposed method, based on the hypothesis of the reduced random interferences of backscattered signals, estimates the proportions of different Hydrometeor types in a given radar sampling volume, without considering the neighboring spatial context. Plausibility and performances of the method are evaluated using C- and X-band radar measurements, compared with Hydrometeor properties derived from a Multi-Angle Snowflake Camera instrument. In the second part, we examine the influence of the potential residual random interference contribution in the backscattering from different Hydrometeors populating a radar sampling volume. This part consists in adapting and testing the techniques commonly used in conventional incoherent decomposition methods to the context of weather radar polarimetry. The impact of the residual incoherency is found to be limited, justifying the hypothesis of the reduced random interferences even in a case of mixed volumes and confirming the applicability of the proposed bin-based approach, which essentially relies on first-order statistics.
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Unraveling Hydrometeor mixtures in polarimetric radar measurements
2018Co-Authors: Nikola Besic, Jordi Figueras I Ventura, Jacopo Grazioli, Marco Gabella, Urs Germann, Josué Gehring, Christophe Praz, Alexis BerneAbstract:Abstract. Radar-based Hydrometeor classification typically comes down to determining the dominant type of Hydrometeor populating a radar sampling volume. In this paper we address the subsequent problem of inferring the secondary Hydrometeor types present in a volume – the issue of Hydrometeor de-mixing. The presented study relies on the semi-supervised Hydrometeor classification proposed by Besic et al. (2016), but nevertheless results in solutions and conclusions of a more general character and applicability. In the first part, dominantly marked by synthesis, a bin-based de-mixing approach is proposed, inspired by the conventional coherent and linear decomposition methods widely employed across different remote sensing disciplines. Intrinsically related to the concept of entropy, introduced in the context of the radar Hydrometeor classification in Besic et al. (2016), the proposed method, based on the hypothesis of coherency in backscattering, estimates the proportions of different Hydrometeor types in a given radar sampling volume, without considering the wider spatial context. Plausibility and performances of the method are evaluated using C and X band radar measurements, compared with Hydrometeor properties derived from a Multi Angle Snowflake Camera instrument. In the second part, we examine the influence of the potential incoherency in the backscattering from different Hydrometeors populating a radar sampling volume. This part, consists of adapting and testing the techniques commonly used in conventional incoherent decomposition methods to the context of weather radar polarimetry. The impact of the incoherence is found to be limited, justifying the hypothesis of coherency even in a case of mixed volumes, and confirming the applicability of the proposed bin-based approach.
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solid Hydrometeor classification and riming degree estimation from pictures collected with a multi angle snowflake camera
Atmospheric Measurement Techniques, 2017Co-Authors: Christophe Praz, Yvesalain Roulet, Alexis BerneAbstract:Abstract. A new method to automatically classify solid Hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the Hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each Hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a Hydrometeor-type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel) and characterized by a probable error of 5.5 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent.
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Hydrometeor classification through statistical clustering of polarimetric radar measurements a semi supervised approach
Atmospheric Measurement Techniques, 2016Co-Authors: Nikola Besic, Jacopo Grazioli, Marco Gabella, Urs Germann, Jordi Figueras I Ventura, Alexis BerneAbstract:Abstract. Polarimetric radar-based Hydrometeor classification is the procedure of identifying different types of Hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different Hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the Hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each Hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various Hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different Hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.
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Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach
2016Co-Authors: Nikola Besic, Jordi Figueras I Ventura, Jacopo Grazioli, Marco Gabella, Urs Germann, Alexis BerneAbstract:Abstract. Hydrometeor classification is the procedure of identifying different types of Hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behavior of different Hydrometeor types. Namely, the results of classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it eventually lacks the constraints related to the Hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of Hydrometeor classes. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various Hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are finally employed in operational labeling of different Hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on an X-band dataset acquired by a research radar. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, with a particular emphasis on hail detection performances.
Emmanuel Fontaine - One of the best experts on this subject based on the ideXlab platform.
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Statistical analysis of ice microphysical properties in tropical mesoscale convective systems derived from cloud radar and in situ microphysical observations
Atmospheric Chemistry and Physics, 2020Co-Authors: Emmanuel Fontaine, Alfons Schwarzenboeck, Delphine Leroy, Julien Delanoë, Alain Protat, Fabien Dezitter, John Walter Strapp, Lyle Edward LilieAbstract:This study presents a statistical analysis of the properties of ice Hydrometeors in tropical mesoscale convective systems observed during four different aircraft campaigns. Among the instruments on board the aircraft, we focus on the synergy of a 94 GHz cloud radar and 2 optical array probes (OAP; measuring Hydrometeor sizes from 10 µm to about 1 cm). For two campaigns, an accurate simultaneous measurement of the ice water content is available, while for the two others, ice water content is retrieved from the synergy of the radar reflectivity measurements and Hydrometeor size and morphological retrievals from OAP probes. The statistics of ice Hydrometeor properties is calculated as a function of radar reflectivity factor measurement percentiles and temperature. Hence, MCS microphysical properties (ice water content, visible extinction, mass-size relationship coefficients, total concentrations and second and third moment of Hydrometeors size distribution) are sorted in temperature (thus altitude) zones, and subsequently each individual campaign is analysed with respect to median microphysical properties of the global dataset (merging all 4 campaign datasets). The study demonstrates that ice water content, visible extinction, total crystal concentration, and second and third moments of Hydrometeors size distributions are similar in all 4 type of MCS for IWC larger than 0.1 g m−3. Finally, two parameterizations are developed for deep convective systems. The first one concerns the calculation of the visible extinction as a function of temperature and ice water content. The second one concerns the calculation of Hydrometeor size distributions as a function of ice water content and temperature that can be used in numerical weather prediction.
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Variations of Ice Microphysical Properties in Tropical MCS Using Cloud In-Situ Data and Corresponding Radar Reflectivity Profiles
2016Co-Authors: Emmanuel Fontaine, Alfons Schwarzenboeck, Delphine Leroy, Julien Delanoë, Alain Protat, Fabien Dezitter, John Walter Strapp, Pierre Coutris, Alice Calmels, Lyle LilieAbstract:Mesoscale convective systems (MCS) can last several hours or more and affect human societies in different ways. In order to predict and identify hazardous weather (e. g. in view of aviation safety) linked to MCS, numerical weather forecast and active/passive remote sensing products are currently the most useful tools. A major obstacle to increasing the accuracy of those tools is the fact that cumulonimbus clouds in MCS are composed of liquid droplets and ice Hydrometeors (ice crystals); the latter have complex shapes based on diffusional growth, aggregation, and riming, which complicates the interpretation of remote sensing products and increases the uncertainties in numerical weather forecast. In this context, the study presented here investigates the properties of ice Hydrometeors in MCS as a function of temperature and horizontal distance from the convective core. For this purpose, ice Hydrometeor images, bulk TWC, and simultaneously measured radar reflectivity factors from three airborne campaigns in tropical MCS have been used. The underlying scope of producing these results is to improve the retrieval of MCS microphysical properties from satellite products and also improve cloud parameterizations in numerical weather prediction models.
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Simulations of Radar Reflectivity Factors at 94GHz: Ice Crystal Approximation with Oblate Spheroids
2016Co-Authors: Emmanuel Fontaine, Alfons Schwarzenboeck, Delphine Leroy, Julien Delanoë, Alain Protat, Fabien Dezitter, Pierre Coutris, Alice Calmels, J. W. Strapp, Lyle LilieAbstract:This study explores the potential of simulating cloud radar reflectivity factors (RRF) at 94GHz from a combination of cloud particle 2D images from optical array probes and bulk condensed water content, simultaneously measured during the first aircraft field campaign of the High Altitude Ice Crystals (HAIC) / High Ice Water Content (HIWC) international project performed out of Darwin in 2014. Within these simulations, ice crystals are approximated by oblate spheroids without a priori assumptions on mass-size relationships of ice crystals. The method allows calculating time dependent ranges of radar reflectivity factors deduced for given size distributions of Hydrometeors, averaged over 5 seconds. Ice Hydrometeor size distributions and shapes (flattening parameter for oblate spheroids) are deduced from 2D images, and are subsequently used to constrain the ice particle density (thereby matching simulated and corresponding measured radar reflectivity factors). Finally, uncertainties in oblate spheroid approximations are studied in order to demonstrate some limitations of this method.
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Characterization of Hydrometeors in Sahelian convective systems with an X-band radar and comparison with in situ measurements. Part II : a simple brightband method to infer the density of icy Hydrometeors
Journal of Applied Meteorology and Climatology, 2016Co-Authors: Matias Alcoba, Marielle Gosset, Modeste Kacou, Frédéric Cazenave, Emmanuel FontaineAbstract:AbstractA simple scheme that is based on the shape and intensity of the radar bright band is used to infer the density of Hydrometeors just above the freezing level in Sahelian mesoscale convective systems (MCS). Four MCS jointly observed by a ground-based X-band radar and by an instrumented aircraft as part of the Megha-Tropiques algorithm-validation campaign during August 2010 in Niamey, Niger, are analyzed. The instrumented aircraft (with a 94-GHz radar and various optical probes on board) provided mass–diameter laws for the particles sampled during the flights. The mass–diameter laws derived from the ground-radar vertical profile of reflectivity (VPR) for each flight are compared with those derived from the airborne measurements. The density laws derived by both methods are consistent and encourage further use of the simple VPR scheme to quantify Hydrometeor density laws and their variability for various analyses (microphysical processes and icy-Hydrometeor scattering and radiative properties).
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Evaluation and application of Hydrometeor classification algorithm outputs inferred from multi-frequency dual-polarimetric radar observations collected during HyMeX
Quarterly Journal of the Royal Meteorological Society, 2015Co-Authors: J.-f. Ribaud, Olivier Bousquet, Sylvain Coquillat, Dominique Lambert, Veronique Ducrocq, Hassan Al-sakka, Emmanuel FontaineAbstract:A fuzzy logic Hydrometeor classification algorithm (HCA), allowing discrimination between six microphysical species regardless of the radar wavelength is presented and evaluated. The proposed method is based upon combination sets of dual-polarimetric observables (reflectivity at horizontal polarization ZH, differential reflectivity ZDR, specific differential phase KDP, correlation coefficient ρHV) along with temperature data inferred from a numerical weather prediction model output. The performance of the HCA is evaluated using 20 h of multi-frequency dual-polarimetric radar data collected during the first Special Observation Period (SOP1) of the Hydrological Cycle in the Mediterranean Experiment (HyMeX). A new method based upon intercomparisons of retrieved Hydrometeor data deduced from pairs of neighbouring radars (S-band vs. S-band and S-band vs. C-band) over a common sampling area is proposed to evaluate the consistency of hydrometor classification outputs. S-/C-band radar comparisons generally show better consistency than S-/S-band radar comparisons due to issues with the identification of the 0°C isotherm on one of the two S-band radars. Imperfect attenuation correction at C-band may also lead into differences in Hydrometeor fields retrieved from the C- and S-band radars in convective situations, but retrieved Hydrometeor data are globally very consistent from one radar to another. Comparisons against in situ airborne data also confirm the overall good performance of the HCA. In a second experiment, an original method allowing the production of multi-radar three-dimensional (3D) Hydrometeor fields from single-radar 2D Hydrometeor data is tested on a bow-echo convective system observed with C- and S-band radars. The resulting 3D Hydrometeor fields provide a detailed view of the bow-echo microphysical structure and confirm the good performance of both the HCA and interpolation technique.
Nikola Besic - One of the best experts on this subject based on the ideXlab platform.
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Unraveling Hydrometeor mixtures in polarimetric radar measurements
Atmospheric Measurement Techniques, 2018Co-Authors: Nikola Besic, Jacopo Grazioli, Marco Gabella, Urs Germann, Jordi Figueras I Ventura, Josué Gehring, Christophe Praz, Alexis BerneAbstract:Abstract. Radar-based Hydrometeor classification typically comes down to determining the dominant type of Hydrometeor populating a given radar sampling volume. In this paper we address the subsequent problem of inferring the secondary Hydrometeor types present in a volume – the issue of Hydrometeor de-mixing. The present study relies on the semi-supervised Hydrometeor classification proposed by Besic et al. ( 2016 ) but nevertheless results in solutions and conclusions of a more general character and applicability. In the first part, oriented towards synthesis, a bin-based de-mixing approach is proposed, inspired by the conventional coherent and linear decomposition methods widely employed across different remote-sensing disciplines. Intrinsically related to the concept of entropy, introduced in the context of the radar Hydrometeor classification in Besic et al. ( 2016 ) , the proposed method, based on the hypothesis of the reduced random interferences of backscattered signals, estimates the proportions of different Hydrometeor types in a given radar sampling volume, without considering the neighboring spatial context. Plausibility and performances of the method are evaluated using C- and X-band radar measurements, compared with Hydrometeor properties derived from a Multi-Angle Snowflake Camera instrument. In the second part, we examine the influence of the potential residual random interference contribution in the backscattering from different Hydrometeors populating a radar sampling volume. This part consists in adapting and testing the techniques commonly used in conventional incoherent decomposition methods to the context of weather radar polarimetry. The impact of the residual incoherency is found to be limited, justifying the hypothesis of the reduced random interferences even in a case of mixed volumes and confirming the applicability of the proposed bin-based approach, which essentially relies on first-order statistics.
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Unraveling Hydrometeor mixtures in polarimetric radar measurements
2018Co-Authors: Nikola Besic, Jordi Figueras I Ventura, Jacopo Grazioli, Marco Gabella, Urs Germann, Josué Gehring, Christophe Praz, Alexis BerneAbstract:Abstract. Radar-based Hydrometeor classification typically comes down to determining the dominant type of Hydrometeor populating a radar sampling volume. In this paper we address the subsequent problem of inferring the secondary Hydrometeor types present in a volume – the issue of Hydrometeor de-mixing. The presented study relies on the semi-supervised Hydrometeor classification proposed by Besic et al. (2016), but nevertheless results in solutions and conclusions of a more general character and applicability. In the first part, dominantly marked by synthesis, a bin-based de-mixing approach is proposed, inspired by the conventional coherent and linear decomposition methods widely employed across different remote sensing disciplines. Intrinsically related to the concept of entropy, introduced in the context of the radar Hydrometeor classification in Besic et al. (2016), the proposed method, based on the hypothesis of coherency in backscattering, estimates the proportions of different Hydrometeor types in a given radar sampling volume, without considering the wider spatial context. Plausibility and performances of the method are evaluated using C and X band radar measurements, compared with Hydrometeor properties derived from a Multi Angle Snowflake Camera instrument. In the second part, we examine the influence of the potential incoherency in the backscattering from different Hydrometeors populating a radar sampling volume. This part, consists of adapting and testing the techniques commonly used in conventional incoherent decomposition methods to the context of weather radar polarimetry. The impact of the incoherence is found to be limited, justifying the hypothesis of coherency even in a case of mixed volumes, and confirming the applicability of the proposed bin-based approach.
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Hydrometeor classification through statistical clustering of polarimetric radar measurements a semi supervised approach
Atmospheric Measurement Techniques, 2016Co-Authors: Nikola Besic, Jacopo Grazioli, Marco Gabella, Urs Germann, Jordi Figueras I Ventura, Alexis BerneAbstract:Abstract. Polarimetric radar-based Hydrometeor classification is the procedure of identifying different types of Hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different Hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the Hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each Hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various Hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different Hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.
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Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach
2016Co-Authors: Nikola Besic, Jordi Figueras I Ventura, Jacopo Grazioli, Marco Gabella, Urs Germann, Alexis BerneAbstract:Abstract. Hydrometeor classification is the procedure of identifying different types of Hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behavior of different Hydrometeor types. Namely, the results of classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it eventually lacks the constraints related to the Hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of Hydrometeor classes. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various Hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are finally employed in operational labeling of different Hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on an X-band dataset acquired by a research radar. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, with a particular emphasis on hail detection performances.
Alain Protat - One of the best experts on this subject based on the ideXlab platform.
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Statistical analysis of ice microphysical properties in tropical mesoscale convective systems derived from cloud radar and in situ microphysical observations
Atmospheric Chemistry and Physics, 2020Co-Authors: Emmanuel Fontaine, Alfons Schwarzenboeck, Delphine Leroy, Julien Delanoë, Alain Protat, Fabien Dezitter, John Walter Strapp, Lyle Edward LilieAbstract:This study presents a statistical analysis of the properties of ice Hydrometeors in tropical mesoscale convective systems observed during four different aircraft campaigns. Among the instruments on board the aircraft, we focus on the synergy of a 94 GHz cloud radar and 2 optical array probes (OAP; measuring Hydrometeor sizes from 10 µm to about 1 cm). For two campaigns, an accurate simultaneous measurement of the ice water content is available, while for the two others, ice water content is retrieved from the synergy of the radar reflectivity measurements and Hydrometeor size and morphological retrievals from OAP probes. The statistics of ice Hydrometeor properties is calculated as a function of radar reflectivity factor measurement percentiles and temperature. Hence, MCS microphysical properties (ice water content, visible extinction, mass-size relationship coefficients, total concentrations and second and third moment of Hydrometeors size distribution) are sorted in temperature (thus altitude) zones, and subsequently each individual campaign is analysed with respect to median microphysical properties of the global dataset (merging all 4 campaign datasets). The study demonstrates that ice water content, visible extinction, total crystal concentration, and second and third moments of Hydrometeors size distributions are similar in all 4 type of MCS for IWC larger than 0.1 g m−3. Finally, two parameterizations are developed for deep convective systems. The first one concerns the calculation of the visible extinction as a function of temperature and ice water content. The second one concerns the calculation of Hydrometeor size distributions as a function of ice water content and temperature that can be used in numerical weather prediction.
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Cloud Microphysical Properties in Mesoscale Convective Systems: An Intercomparison of Three Tropical Locations
2017Co-Authors: Alfons Schwarzenboeck, Julien Delanoë, Alain Protat, Fabien Dezitter, John Walter Strapp, Pierre Coutris, Alice Grandin, Lyle Edward LilieAbstract:Mesoscale Convective Systems are complex cloud systems which are primarily the result of specific synoptic conditions associated with mesoscale instabilities leading to the development of cumulonimbus type clouds (Houze, 2004). These systems can last several hours and can affect human societies in various ways. In general, weather and climate models use simplistic schemes to describe ice Hydrometeors' properties. However, MCS are complex cloud systems where the dynamic, radiative and precipitation processes depend on spatiotemporal location in the MCS (Houze, 2004). As a consequence, Hydrometeor growth processes in MCS vary in space and time, thereby impacting shape and concentration of ice crystals and finally CWC. As a consequence, differences in the representation of ice properties in models (Li et al., 2007, 2005) lead to significant disagreements in the quantification of ice cloud effects on climate evolution (Intergovernmental Panel on Climate Change Fourth Assessment Report). An accurate estimation of the spatiotemporal CWC distribution is therefore a key parameter for evaluating and improving numerical weather prediction (Stephens et al., 2002). The main purpose of this study is to show ice microphysical properties of MCS observed in three different locations in the tropical atmosphere: West-African continent, Indian Ocean, and Northern Australia. An intercomparison study is performed in order to quantify how similar or different are the ice Hydrometeors' properties in these three regions related to radar reflectivity factors and temperatures observed in respective MCS.
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Variations of Ice Microphysical Properties in Tropical MCS Using Cloud In-Situ Data and Corresponding Radar Reflectivity Profiles
2016Co-Authors: Emmanuel Fontaine, Alfons Schwarzenboeck, Delphine Leroy, Julien Delanoë, Alain Protat, Fabien Dezitter, John Walter Strapp, Pierre Coutris, Alice Calmels, Lyle LilieAbstract:Mesoscale convective systems (MCS) can last several hours or more and affect human societies in different ways. In order to predict and identify hazardous weather (e. g. in view of aviation safety) linked to MCS, numerical weather forecast and active/passive remote sensing products are currently the most useful tools. A major obstacle to increasing the accuracy of those tools is the fact that cumulonimbus clouds in MCS are composed of liquid droplets and ice Hydrometeors (ice crystals); the latter have complex shapes based on diffusional growth, aggregation, and riming, which complicates the interpretation of remote sensing products and increases the uncertainties in numerical weather forecast. In this context, the study presented here investigates the properties of ice Hydrometeors in MCS as a function of temperature and horizontal distance from the convective core. For this purpose, ice Hydrometeor images, bulk TWC, and simultaneously measured radar reflectivity factors from three airborne campaigns in tropical MCS have been used. The underlying scope of producing these results is to improve the retrieval of MCS microphysical properties from satellite products and also improve cloud parameterizations in numerical weather prediction models.
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Simulations of Radar Reflectivity Factors at 94GHz: Ice Crystal Approximation with Oblate Spheroids
2016Co-Authors: Emmanuel Fontaine, Alfons Schwarzenboeck, Delphine Leroy, Julien Delanoë, Alain Protat, Fabien Dezitter, Pierre Coutris, Alice Calmels, J. W. Strapp, Lyle LilieAbstract:This study explores the potential of simulating cloud radar reflectivity factors (RRF) at 94GHz from a combination of cloud particle 2D images from optical array probes and bulk condensed water content, simultaneously measured during the first aircraft field campaign of the High Altitude Ice Crystals (HAIC) / High Ice Water Content (HIWC) international project performed out of Darwin in 2014. Within these simulations, ice crystals are approximated by oblate spheroids without a priori assumptions on mass-size relationships of ice crystals. The method allows calculating time dependent ranges of radar reflectivity factors deduced for given size distributions of Hydrometeors, averaged over 5 seconds. Ice Hydrometeor size distributions and shapes (flattening parameter for oblate spheroids) are deduced from 2D images, and are subsequently used to constrain the ice particle density (thereby matching simulated and corresponding measured radar reflectivity factors). Finally, uncertainties in oblate spheroid approximations are studied in order to demonstrate some limitations of this method.
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Constraining mass-diameter relations from Hydrometeor images and cloud radar reflectivities in tropical continental and oceanic convective anvils
Atmospheric Chemistry and Physics, 2014Co-Authors: E. Fontaine, Alfons Schwarzenboeck, Delphine Leroy, Julien Delanoë, Wolfram Wobrock, R. Dupuy, C. Gourbeyre, Alain ProtatAbstract:In this study the density of Hydrometeors in tropical clouds is derived from a combined analysis of particle images from 2-D-array probes and associated reflectivities measured with a Doppler cloud radar on the same research aircraft. The mass-diameter m(D) relationship is expressed as a power law with two unknown coefficients (pre-factor, exponent) that need to be constrained from complementary information on Hydrometeors, where absolute ice density measurement methods do not apply. Here, at first an extended theoretical study of numerous Hydrometeor shapes simulated in 3-D and arbitrarily projected on a 2-D plane allowed to constrain the temporal evolution of the exponent of the mass-diameter relationship with that of the exponent of the surface-diameter relationship that is measured by the 2-D-array probes. The pre-factor is then constrained from theoretical simulations of the radar reflectivities matching the measured reflectivities along the aircraft trajectory. The study has been performed as part of the Megha-Tropiques satellite project, where two types of mesoscale convective systems (MCS) have been investigated: (i) above the African Continent and (ii) above the Indian Ocean. In general, both mass-diameter coefficients (pre-factor and exponent) decrease with decreasing temperature, the decrease is more pronounced for oceanic MCS. The condensed water contents (CWC) calculated from particle size distributions (PSD) and m(D) also decrease with altitude while the concentrations of the Hydrometeors increase with altitude. The calculated values of CWC are largest for continental MCS.
