The Experts below are selected from a list of 3564 Experts worldwide ranked by ideXlab platform
F S Marzano - One of the best experts on this subject based on the ideXlab platform.
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iterative bayesian retrieval of hydrometeor content from x band polarimetric weather radar
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: F S Marzano, Giovanni Botta, Mario MontopoliAbstract:Dual-polarized weather radars are capable to detect and identify different classes of Hydrometeors, within stratiform and convective storms, exploiting polarimetric diversity. Among the various techniques, a model-supervised Bayesian method for hydrometeor classification, tuned for S- and X-band polarimetric weather radars, can be effectively applied. Once the hydrometeor class is estimated, the retrieval of their water content can also be statistically carried out. However, the critical issue of X-band radar data processing, and in general of any attenuating wavelength active system, is the intervening path attenuation, which is usually not negligible. Any approach aimed at estimating hydrometeor water content should be able to tackle, at the same time, path attenuation correction, hydrometeor classification uncertainty, and retrieval errors. An integrated iterative Bayesian radar algorithm (IBRA) scheme, based on the availability of the differential phase measurement, is presented in this paper and tested during the International H2O Project experiment in Oklahoma in 2002. During the latter campaign, two dual-polarized radars, at S- and X-bands, were deployed and jointly operated with closely matched scanning strategies, giving the opportunity to perform experimental comparisons between coincident measurements at different frequencies. Results of the IBRA technique at X-band are discussed, and the impact of path attenuation correction is quantitatively analyzed by comparing hydrometeor classifications and estimates with those obtained at S-band. The overall results in terms of error budget show a significant improvement with respect to the performance with no path attenuation correction.
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supervised classification and estimation of Hydrometeors from c band dual polarized radars a bayesian approach
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: F S Marzano, Mario Montopoli, D Scaranari, G VulpianiAbstract:In this paper, a Bayesian statistical approach for supervised classification and estimation of Hydrometeors, using a C-band polarimetric radar, is presented and discussed. The Bayesian Radar Algorithm for Hydrometeor Classification at C-band (BRAHCC) is supervised by a backscattering microphysical model, aimed at representing ten different hydrometeor classes in water, ice, and mixed phase. The expected error budget is evaluated by means of contingency tables on the basis of C-band radar noisy and attenuated synthetic data. Its accuracy is better than that obtained from a previously developed fuzzy logic C-band classification algorithm. As a second step of the overall retrieval algorithm, a multivariate regression is adopted to derive water content statistical estimators, exploiting simulated polarimetric radar data for each hydrometeor class. The BRAHCC methodology is then applied to a convective hail event, observed by two C-band dual-polarized radars in a network configuration. The hydrometeor classification along the line of sight, connecting the two C-band radars, is performed using the BRAHCC applied to path-attenuation-corrected data. Qualitative results are consistent with those derived from the fuzzy logic algorithm. Hydrometeor water content temporal evolution is tracked along the radar line of sight. Hail vertical occurrence is derived and compared with an empirical hail detection index applied along the radar connection line during the whole event.
Mario Montopoli - One of the best experts on this subject based on the ideXlab platform.
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iterative bayesian retrieval of hydrometeor content from x band polarimetric weather radar
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: F S Marzano, Giovanni Botta, Mario MontopoliAbstract:Dual-polarized weather radars are capable to detect and identify different classes of Hydrometeors, within stratiform and convective storms, exploiting polarimetric diversity. Among the various techniques, a model-supervised Bayesian method for hydrometeor classification, tuned for S- and X-band polarimetric weather radars, can be effectively applied. Once the hydrometeor class is estimated, the retrieval of their water content can also be statistically carried out. However, the critical issue of X-band radar data processing, and in general of any attenuating wavelength active system, is the intervening path attenuation, which is usually not negligible. Any approach aimed at estimating hydrometeor water content should be able to tackle, at the same time, path attenuation correction, hydrometeor classification uncertainty, and retrieval errors. An integrated iterative Bayesian radar algorithm (IBRA) scheme, based on the availability of the differential phase measurement, is presented in this paper and tested during the International H2O Project experiment in Oklahoma in 2002. During the latter campaign, two dual-polarized radars, at S- and X-bands, were deployed and jointly operated with closely matched scanning strategies, giving the opportunity to perform experimental comparisons between coincident measurements at different frequencies. Results of the IBRA technique at X-band are discussed, and the impact of path attenuation correction is quantitatively analyzed by comparing hydrometeor classifications and estimates with those obtained at S-band. The overall results in terms of error budget show a significant improvement with respect to the performance with no path attenuation correction.
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supervised classification and estimation of Hydrometeors from c band dual polarized radars a bayesian approach
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: F S Marzano, Mario Montopoli, D Scaranari, G VulpianiAbstract:In this paper, a Bayesian statistical approach for supervised classification and estimation of Hydrometeors, using a C-band polarimetric radar, is presented and discussed. The Bayesian Radar Algorithm for Hydrometeor Classification at C-band (BRAHCC) is supervised by a backscattering microphysical model, aimed at representing ten different hydrometeor classes in water, ice, and mixed phase. The expected error budget is evaluated by means of contingency tables on the basis of C-band radar noisy and attenuated synthetic data. Its accuracy is better than that obtained from a previously developed fuzzy logic C-band classification algorithm. As a second step of the overall retrieval algorithm, a multivariate regression is adopted to derive water content statistical estimators, exploiting simulated polarimetric radar data for each hydrometeor class. The BRAHCC methodology is then applied to a convective hail event, observed by two C-band dual-polarized radars in a network configuration. The hydrometeor classification along the line of sight, connecting the two C-band radars, is performed using the BRAHCC applied to path-attenuation-corrected data. Qualitative results are consistent with those derived from the fuzzy logic algorithm. Hydrometeor water content temporal evolution is tracked along the radar line of sight. Hail vertical occurrence is derived and compared with an empirical hail detection index applied along the radar connection line during the whole event.
G Vulpiani - One of the best experts on this subject based on the ideXlab platform.
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supervised classification and estimation of Hydrometeors from c band dual polarized radars a bayesian approach
IEEE Transactions on Geoscience and Remote Sensing, 2008Co-Authors: F S Marzano, Mario Montopoli, D Scaranari, G VulpianiAbstract:In this paper, a Bayesian statistical approach for supervised classification and estimation of Hydrometeors, using a C-band polarimetric radar, is presented and discussed. The Bayesian Radar Algorithm for Hydrometeor Classification at C-band (BRAHCC) is supervised by a backscattering microphysical model, aimed at representing ten different hydrometeor classes in water, ice, and mixed phase. The expected error budget is evaluated by means of contingency tables on the basis of C-band radar noisy and attenuated synthetic data. Its accuracy is better than that obtained from a previously developed fuzzy logic C-band classification algorithm. As a second step of the overall retrieval algorithm, a multivariate regression is adopted to derive water content statistical estimators, exploiting simulated polarimetric radar data for each hydrometeor class. The BRAHCC methodology is then applied to a convective hail event, observed by two C-band dual-polarized radars in a network configuration. The hydrometeor classification along the line of sight, connecting the two C-band radars, is performed using the BRAHCC applied to path-attenuation-corrected data. Qualitative results are consistent with those derived from the fuzzy logic algorithm. Hydrometeor water content temporal evolution is tracked along the radar line of sight. Hail vertical occurrence is derived and compared with an empirical hail detection index applied along the radar connection line during the whole event.
Robert Meneghini - One of the best experts on this subject based on the ideXlab platform.
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A Dual-Wavelength Radar Technique to Detect Hydrometeor Phases
IEEE Transactions on Geoscience and Remote Sensing, 2016Co-Authors: Liang Liao, Robert MeneghiniAbstract:This paper aims to investigate the feasibility of a Ku-band and Ka-band spaceborne/airborne dual-wavelength radar algorithm to discriminate various phase states of precipitating Hydrometeors. A phase-state classification algorithm has been developed from the radar measurements of snow, mixed phase, and rain obtained from stratiform storms. The algorithm, which is presented in the form of a lookup table that links the Ku-band radar reflectivity and dual-frequency ratio to the phase states of Hydrometeors, is checked by applying it to the measurements of the Jet Propulsion Laboratory, California Institute of Technology, using Airborne Precipitation Radar Second Generation (APR-2). In creating the statistically based phase lookup table, the attenuation-corrected (or true) radar reflectivity factors are employed, leading to better accuracy in determining the hydrometeor phase. In practice, however, the true radar reflectivity is not always available before the phase states of the Hydrometeors are determined. Therefore, it is desirable to make use of the measured radar reflectivity in classifying the phase states. To do this, phase identification that uses only measured radar reflectivity is proposed. The procedure is then tested using APR-2 airborne radar data. The analysis of the classification results in stratiform rain indicates that the regions of snow, mixed phase, and rain derived from the phase identification algorithm coincide reasonably well with those determined from the measured radar reflectivity and linear depolarization ratio.
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A Study on the Feasibility of Dual-Wavelength Radar for Identification of Hydrometeor Phases
Journal of Applied Meteorology and Climatology, 2011Co-Authors: Liang Liao, Robert MeneghiniAbstract:Abstract An important objective for the dual-wavelength Ku-/Ka-band precipitation radar (DPR) that will be on board the Global Precipitation Measurement (GPM) core satellite is to identify the phase state of Hydrometeors along the range direction. To assess this, radar signatures are simulated in snow and rain to explore the relation between the differential frequency ratio (DFR), defined as the difference of radar reflectivity factors between Ku and Ka bands, and the radar reflectivity factor at Ku band ZKu for different hydrometeor types. Model simulations indicate that there is clear separation between snow and rain in the ZKu–DFR plane assuming that the snow follows the Gunn–Marshall size distribution and rain follows the Marshall–Palmer size distribution. In an effort to verify the simulated results, the data collected by the Airborne Second-Generation Precipitation Radar (APR-2) in the Wakasa Bay Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) campaign are employed. Using ...
Sergey Y Matrosov - One of the best experts on this subject based on the ideXlab platform.
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observations of ice crystal habits with a scanning polarimetric w band radar at slant linear depolarization ratio mode
Journal of Atmospheric and Oceanic Technology, 2012Co-Authors: Sergey Y Matrosov, Roger Marchand, Gerald G Mace, Matthew D Shupe, A G Hallar, Ian B MccubbinAbstract:AbstractScanning polarimetric W-band radar data were evaluated for the purpose of identifying predominant ice hydrometeor habits. Radar and accompanying cloud microphysical measurements were conducted during the Storm Peak Laboratory Cloud Property Validation Experiment held in Steamboat Springs, Colorado, during the winter season of 2010/11. The observed ice hydrometeor habits ranged from pristine and rimed dendrites/stellars to aggregates, irregulars, graupel, columns, plates, and particle mixtures. The slant 45° linear depolarization ratio (SLDR) trends as a function of the radar elevation angle are indicative of the predominant hydrometeor habit/shape. For planar particles, SLDR values increase from values close to the radar polarization cross coupling of about −21.8 dB at zenith viewing to maximum values at slant viewing. These maximum values depend on predominant aspect ratio and bulk density of Hydrometeors and also show some sensitivity to particle characteristic size. The highest observed SLDRs w...
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dual frequency radar ratio of nonspherical atmospheric Hydrometeors
Geophysical Research Letters, 2005Co-Authors: Sergey Y Matrosov, Andrew J Heymsfield, Zhien WangAbstract:[1] Dual-frequency cloud and precipitation radar systems are being actively developed and installed on different platforms. The use of two radar frequencies (with at least one frequency outside the Rayleigh type of scattering for Hydrometeors of interest) allows independent estimates of hydrometeor effective size. With these estimates, radar-based retrievals of such important parameters as cloud mass content or precipitation rate can be potentially performed with better accuracy compared to single- frequency radar measurements. This study presents quantitative assessments of the effects of nonsphericity of ice cloud particles which influence the dual-frequency ratio used for characteristic size estimates.
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evaluation of a 45 slant quasi linear radar polarization state for distinguishing drizzle droplets pristine ice crystals and less regular ice particles
Journal of Atmospheric and Oceanic Technology, 2002Co-Authors: Roger F. Reinking, Sergey Y Matrosov, Robert A Kropfli, Bruce W BartramAbstract:Abstract A remote sensing capability is needed to detect clouds of supercooled, drizzle-sized droplets, which are a major aircraft icing hazard. Discrimination among clouds of differing ice particle types is also important because both the presence and type of ice influence the survival of liquid in a cloud and the chances for occurrence of these large, most hazardous droplets. This work shows how millimeter-wavelength dual-polarization radar can be used to identify these differing Hydrometeors. It also shows that by measuring the depolarization ratio (DR), the estimation of the hydrometeor type can be accomplished deterministically for drizzle droplets; ice particles of regular shapes; and to a considerable extent, the more irregular ice particles, and that discrimination is strongly influenced by the polarization state of the transmitted microwave radiation. Thus, appropriate selection of the polarization state is emphasized. The selection of an optimal polarization state involves trade-offs in competin...
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Identification of Hydrometeors with Elliptical and Linear Polarization Ka-Band Radar
Journal of Applied Meteorology, 1997Co-Authors: Roger F. Reinking, Sergey Y Matrosov, Roelof T. Bruintjes, Brooks E. MartnerAbstract:Abstract Polarimetric radar can be used to identify various types of Hydrometeors. Ice crystals of the varied growth habits depolarize and backscatter millimeter-wavelength radiation according to crystal aspect ratio, bulk density, and orientation, and the polarization state of the incident radiation. In this paper model calculations of the depolarization caused by various crystal types are extended from previous work, and Ka-band (8.66 mm) radar measurements of linear and elliptical depolarization ratios (LDR and EDR) from various ice Hydrometeors are presented. The measurements for regular crystals are related to the models. Drizzle drops, which are quasi-spherical, serve as a reference. Signature discrimination in cloud systems with more than one type of hydrometeor is addressed. The model calculations illustrate the interplay of the parameters that control depolarization. They predict that in the depolarization signatures, crystals of the various basic planar and columnar habits should generally be mo...
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hydrometer classification with elliptical polarization radar applications for glaciogenic cloud seeding
The Journal of Weather Modification, 1996Co-Authors: Roger F. Reinking, Sergey Y Matrosov, Roelof T. BruintjesAbstract:Polarization capabilities of 8.66-mm-wavelength radar and corresponding hydrometeor depolarization calculations now provide the basis for identifying individual types of ice and liquid Hydrometeors within clouds and precipitation. Model results and radar measurements with correlated snow crystal samples from winter stratiform and convective orographic clouds are examined. The results are interpreted to illustrate how this method for estimating hydrometeor types can be applied to monitor cloud evolution and evaluate the potentials and effects of glaciogenic cloud seeding. This information is derived from hydrometeor depolarizations (and related cloud reflectivities) associated with cloud phase transition, snow crystal growth habit, graupel development, snow crystal aggregation, the presence and nature of the melting level, and the distinction of rain from drizzle below the melting level. The radar’s polarization capability offers the opportunity to monitor the development of Hydrometeors in the volume of cloud affected by seeding or by natural processes. Spatial gradients in hydrometeor types, rates at which a volume is transformed, and the form of precipitation can also be estimated. These features are all important for verifying the changes in Hydrometeors introduced by seeding and interpreting the rates and mechanisms by which the changes occur.
