Advances in Weather Radar. Volume 2: Precipitation science, scattering and processing algorithms

Cover
Contents
About the editors
Preface
Acknowledgements
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 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
		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
		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 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
	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
		10.4.3 Large-scale interactions
		10.4.4 Comparison with satellites
	10.5 Concluding remarks and perspectives
	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 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)
		11.12.5 Speculative technologies and “fishing”
	References
12 Deep-learning-aided rainfall estimation from communications satellite links
	12.1 Deep learning networks for satellite link-based rainfall estimation
	12.2 Communications satellite network
		12.2.1 System description
		12.2.2 Components of C/N
	12.3 Case study I: rainrate estimation with ANNs
		12.3.1 Feature extraction
		12.3.2 Labeling C/N measurements
		12.3.3 Rainfall classification: artificial neural network
		12.3.4 Rainfall estimation: R–A relation
		12.3.5 Performance evaluation
	12.4 Case study II: rainfall map generation with LSTM
	12.5 Case study III: convolutional neural networks
	12.6 Summary
	Abbreviations and acronyms
	References
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