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[null,"1.","Record Nr.",null,"UNINA9911007170803321"]
[null,null,"Autore",null,"Bringi V. N"]
[null,null,"Titolo",null,"Advances in Weather Radar : Precipitation Science, Scattering and Processing Algorithms, Volume 2"]
[null,null,"Pubbl/distr/stampa",null,"Stevenage : , : Institution of Engineering & Technology, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"1-83724-459-6"]
[{"$nulls":4},"1-5231-6299-6"]
[{"$nulls":4},"1-83953-625-X"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (692 pages)"]
[null,null,"Collana",null,"Radar, Sonar and Navigation Series"]
[null,null,"Soggetti",null,"Radar meteorology"]
[{"$nulls":4},"Precipitation (Meteorology)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Title -- Copyright -- Contents -- About the editors -- Preface -- Acknowledgments -- List of editors -- List of contributors -- List of reviewers -- Introduction to volume 2 -- 1 Phased array weather radar developed in Japan -- 1.1 Overview of ground-based PAWR -- 1.1.1 PAWR -- 1.1.2 MP-PAWR -- 1.2 Calibration of MP-PAWR -- 1.3 Quantitative precipitation estimation by PAWR -- 1.3.1 Observation -- 1.3.2 Comparison with rain gauge measurement -- 1.3.3 Ground clutter issue -- 1.4 Applications of MP-PAWR and PAWR -- 1.4.1 Life cycle of short-lived convective cloud -- 1.4.2 Direct comparison with optical observation -- 1.4.3 Application of VAD method and continuous vertical pointing observation -- 1.4.4 Precipitation system that exists above freezing level (use of VAD method and vertical pointing data) -- 1.4.5 3D structure of misoscale vortex -- 1.5 Summary -- References -- 2 Weather radar data calibration and monitoring -- 2.1 Introduction -- 2.1.1 Calibration and monitoring -- 2.1.2 Calibration levels and scales -- 2.1.3 Typical radar system -- 2.1.4 Calibration families -- 2.2 Measurement of radar moments -- 2.2.1 Weather radar equation -- 2.2.2 Polarimetric moments -- 2.2.3 Doppler moments -- 2.3 Methods -- 2.3.1 Internal calibration -- 2.3.2 External sources -- 2.3.3 External sink -- 2.3.4 External artificial targets -- 2.3.5 Weather targets -- 2.4 Recommendations and outlook to future developments -- References -- 3 Scattering by snow particles -- 3.1 Introduction -- 3.2 Ice particle "]
[{"$nulls":4},"models -- 3.3 Scattering of electromagnetic waves -- 3.4 The volume integral equation -- 3.4.1 Far-field scattering -- 3.5 Scattering methods involving volume discretization -- 3.5.1 Discrete dipole approximation -- 3.5.2 Rayleigh-Gans approximation -- 3.5.3 Self-similar Rayleigh-Gans approximation -- 3.5.4 Independent monomer approximation."]
[{"$nulls":4},"3.5.5 Method of moments -- 3.6 Single-scattering properties databases at microwave and sub-millimeter wavelengths -- 3.6.1 Single-scattering properties of ice hydrometeors -- 3.6.2 Status of current single-scattering properties databases -- References -- 4 Radar and hail: advances in scattering, detection, and sizing -- 4.1 Motivation-why hail? -- 4.2 A primer on hail and hailstorms -- 4.3 Two paradigms for radar-based hail detection and sizing: problems and possibilities -- 4.3.1 Paradigm 1: direct detection and sizing of hailstones -- 4.3.2 Paradigm 2: storm structural proxies for hail -- 4.4 Summary and concluding thoughts -- References -- 5 Understanding the role of rain drop shapes and fall velocities in rainfall estimation from polarimetric weather radars -- 5.1 Introduction -- 5.2 Drop shapes and fall velocities: an overview of previous work -- 5.2.1 Drop shapes -- 5.2.2 Fall speeds -- 5.2.3 Drop shapes and velocities from 2DVD -- 5.3 Scattering calculation for individual drops -- 5.3.1 Review of scattering calculation methods -- 5.3.2 Usability of commercial electromagnetic field solver software -- 5.3.3 Automatization of scattering calculations -- 5.3.4 Accuracy considerations -- 5.3.5 Determination of the RCS of raindrops via artificial neural networks -- 5.4 Example events -- 5.4.1 Outer bands of tropical depression Nate over Alabama -- 5.4.2 Embedded line convection over Alabama -- 5.4.3 Outer bands of category-1 Hurricane Irma over Alabama -- 5.4.4 A widespread event with embedded convective rain cells -- 5.4.5 Outer rain-bands of category-1 Hurricane Dorian -- 5.4.6 Tropical storm Michael over Delmarva peninsula -- 5.5 Summary -- Acknowledgment -- References -- 6 The raindrop size distribution - the unknown that holds everything together -- 6.1 Introduction -- 6.2 The DSD and its statistical moments -- 6.2.1 State variables."]
[{"$nulls":4},"6.2.2 Flux variables -- 6.2.3 Characteristic sizes -- 6.3 Parametric DSD models -- 6.3.1 Inventory of common DSD models -- 6.4 Normalized DSD models -- 6.4.1 Particular cases in DSD normalization -- 6.5 DSDs and weather radar -- 6.5.1 Radar variables -- 6.5.2 Rain rate retrieval from radar -- 6.5.3 DSD retrieval from radar -- 6.6 DSDs in numerical weather prediction models -- 6.7 Conclusions and future directions -- References -- 7 Fusion of radar polarimetry and atmospheric modeling -- 7.1 Introduction -- 7.2 Evaluation methodology, data, and tools -- 7.2.1 A dual strategy for model evaluation -- 7.2.2 Polarimetric C-band and X-band radar observations in Germany -- 7.2.3 The numerical weather prediction models COSMO and ICON-LAM -- 7.2.4 The polarimetric radar operators EMVORADO and B-PRO -- 7.2.5 The Shannon entropy to categorize stratiform and convective events -- 7.2.6 Combined observed and synthetic data at X band and C band -- 7.3 Exploitation of microphysical retrievals for model evaluation and improvement -- 7.3.1 Quantitative precipitation estimation for the July 2021 Ahrtal flooding in western Germany -- 7.3.2 Quasi-vertical profiles of ice microphysical retrievals -- 7.3.3 Hydrometeor classification and quantification schemes -- 7.4 Evaluation in radar observation space -- 7.4.1 Converging modeled and observed quasi-vertical profiles -- 7.4.2 Statistics of observed and modeled polarimetric variables -- 7.4.3 Process signatures and dynamics in convection -- 7.5 (Polarimetric) radar data assimilation -- 7.5.1 The assimilation of 3D reflectivities and radial winds -- 7.5.2 The "]
[{"$nulls":4},"assimilation of 3D polarimetry-derived liquid and ice-water content -- 7.5.3 The assimilation of object information -- 7.6 Summary and conclusions -- Acknowledgments -- References -- 8 End-to-end simulations of dual-polarization tornado debris signatures."]
[{"$nulls":4},"8.1 Background and importance of dual-polarization radar signatures of tornadoes -- 8.1.1 Overview of dual-polarization radar variables and their application to meteorological echoes and debris -- 8.1.2 Significance of TDSs in operational forecasting -- 8.1.3 Determining the structure of tornadoes and their debris fields -- 8.1.4 Challenges to understanding dual-polarization tornado debris signatures -- 8.2 Theory of dual-polarization weather radar simulation -- 8.3 A time-series dual-polarization radar simulator for tornado debris -- 8.3.1 Radar simulator inputs -- 8.3.2 Radar simulator implementation -- 8.4 Radar simulations of tornado debris signatures -- 8.4.1 Electromagnetic representation of debris scatterers -- 8.4.2 TDSs and varied debris characteristics -- 8.4.3 Relationship between TDSs and tornado wind characteristics -- 8.5 Conclusions -- Acknowledgments -- References -- 9 Satellite combined radar-radiometer algorithms -- 9.1 Introduction -- 9.2 Fundamental models and methods -- 9.2.1 Precipitation particles and their electromagnetic properties -- 9.2.2 Radar and radiometer models -- 9.2.3 Elements of optimal estimation theory -- 9.2.4 Additional matters -- 9.3 GPM combined observations and retrievals -- 9.3.1 Observations -- 9.3.2 Machine learning-based evaluation -- 9.3.3 Combined estimates -- 9.4 Summary and conclusions -- References -- 10 Weather radar measurements in Antarctica -- 10.1 About Antarctica -- 10.2 The challenge of measuring clouds and precipitation in Antarctica -- 10.2.1 Ground-based measurements -- 10.2.2 The added value of CloudSat -- 10.3 Ground-based weather radars -- 10.3.1 Added value of ground-based weather radars -- 10.3.2 Deployment challenges -- 10.3.3 Milestone campaigns -- 10.4 Contribution to Antarctic meteorology -- 10.4.1 Quantitative precipitation studies -- 10.4.2 Local-scale precipitation processes."]
[{"$nulls":4},"10.4.3 Large-scale interactions -- 10.4.4 Comparison with satellites -- 10.5 Concluding remarks and perspectives -- Funding and acknowledgment -- References -- 11 Radar advances related to severe weather -- 11.1 Radars are amazing tools to observe severe weather -- 11.2 But, traditional radars and networks cannot answer some of the most critical research questions -- 11.2.1 Radars usually scan too slowly: many hazardous, high-impact, difficult to forecast phenomena evolve very quickly -- 11.2.2 Radar distributions are too coarse: many of the most impactful weather phenomena are small and too far away -- 11.2.3 Radars cannot scan near the ground -- 11.2.4 Radars do not measure vector wind fields -- 11.2.5 Temporary stationary high-density multiple-radar networks: a limited solution for research -- 11.3 How to address these limitations -- 11.3.1 Easily carriable/deployable small radars -- 11.3.2 Radars on airplanes -- 11.3.3 Denser arrays of small radars -- 11.4 Invention of the Doppler On Wheels (DOWs) -- 11.5 Severe and high-impact weather observations with mobile DOWs -- 11.5.1 Tornadoes -- 11.5.2 Hurricanes -- 11.5.3 Other severe and high-impact weather -- 11.6 Mobile multiple-Doppler -- 11.7 Dual-polarization observations of severe storms -- 11.8 Other groups make \"DOWs,\" leading to new paradigm for mesoscale weather studies -- 11.9 Time/space → rapid-scan -- 11.10 A different compromise: the C-band On Wheels (COW) -- 11.11 The modern paradigm: mobile radar combined with mobile in situ observations -- 11.11.1 Fortuitous dual-Doppler tornado data -- 11.12 Where do we go from here? -- 11.12.1 Operational phased array and dense radar networks -- 11.12.2 "]
[{"$nulls":4},"Bistatic radar networks -- 11.12.3 Adaptable/quickly deployable almost-mobile radars may replace stationary research radars -- 11.12.4 Airborne Phased Array Radar (APAR)."]
[{"$nulls":4},"11.12.5 Speculative technologies and \"fishing\"."]
[null,null,null,"Sommario/riassunto","This comprehensive handbook written by a team of international experts discusses the key technical and scientific ideas that have propelled the field of weather radar forward. The book covers the five areas of weather radars: science, engineering, signal processing, electromagnetics, and applications."]
[null,"2.","Record Nr.",null,"UNINA9910154538103321"]
[null,null,"Titolo",null,"Blood pressure"]
[null,null,"Pubbl/distr/stampa",null,"[Oslo, Norway], : Scandinavian University Press"]
[null,null,"ISSN",null,"1651-1999"]
[null,null,"Descrizione fisica",null,"1 online resource"]
[null,null,"Disciplina",null,"612.14"]
[{"$nulls":4},"573.1"]
[null,null,"Soggetti",null,"Blood pressure"]
[{"$nulls":4},"Hypertension"]
[{"$nulls":4},"Blood Pressure"]
[{"$nulls":4},"Pression artérielle"]
[{"$nulls":4},"Hypertension artérielle"]
[{"$nulls":4},"44.85 cardiology"]
[{"$nulls":4},"Hypertonie"]
[{"$nulls":4},"Zeitschrift"]
[{"$nulls":4},"Online-Ressource"]
[{"$nulls":4},"Hypertensie"]
[{"$nulls":4},"Hipertensió"]
[{"$nulls":4},"Pressió sanguínia"]
[{"$nulls":4},"Periodical"]
[{"$nulls":4},"periodicals."]
[{"$nulls":4},"Periodicals."]
[{"$nulls":4},"Périodiques."]
[{"$nulls":4},"Revistes electròniques."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Periodico"]
[null,null,null,"Note generali","Refereed/Peer-reviewed"]
[{"$nulls":4},"Published: [England] : Taylor & Francis, 1999-"]