Polarimetric Doppler weather radar: principles and applications

By S. Laroche. 2002

674 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Oceanography

View Paper: https://www.semanticscholar.org/paper/8bafdad9fd39b060fc70ade188cf6103fc2e1d4c

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Citations in the corpus

L-Moment Estimators as Applied to Gamma Drop Size Distributions (2008, methodology): “Figure 4 shows the cumulative histogram for the normalized values of the concentration parameter (normalized with respect to LWC) WW NN̂ , and demonstrates that it tends to be overestimated.”
Polarimetric Radar Characteristics of Melting Hail. Part I: Theoretical Simulations Using Spectral Microphysical Modeling (2013, background): “It is generally assumed that KDP is less affected by hail than is radar reflectivity Z and that the use of the R(KDP) relation is preferable for the rain/hail mixture (Balakrishnan and Zrni c 1990a; Aydin et al. 1995; Bringi and Chandrasekar 2001; Ryzhkov et al. 2005).”
Assimilation of Simulated Polarimetric Radar Data for a Convective Storm Using the Ensemble Kalman Filter. Part I: Observation Operators for Reflectivity and Polarimetric Variables (2008, background): “…and is highly correlated with Z h , r , showing sensitivity only to the oriented oblate raindrops so that it makes a good indicator of the presence of water within the rain–ice mixture, which enables the use of the concept of the deviation analysis from the rain line (Bringi and Chandrasekar 2001).”
Inverted-polarity electrical structures in thunderstorms in the Severe Thunderstorm Electrification and Precipitation Study (STEPS) (2005, methodology): “…the use of a fuzzy logic methodology employing additional radar measurements such as linear depolarization ratio (LDR) and copolar correlation coefficient (ρco), as well as temperature, to infer the bulk hydrometeor type such as rain, graupel, hail, etc. (Bringi and Chandrasekar 2001, Chapter 7).”
Radar observations of the kinematic, microphysical, and precipitation characteristics of two MCSs in TRMM LBA (2002, background): “where ZH (ZV) is the observed reflectivity at horizontal (vertical) incidence in mm(6) m(23) and [Bringi and Chandrasekar, 2001]”
Calibration Issues of Dual-Polarization Radar Measurements (2005, methodology): “Existing methods for ZDR calibration use polarimetric properties of solar radiation or natural weather targets (e.g., Gorgucci et al. 1999; Bringi and Chandrasekar 2001).”
Principles and Applications of Dual-Polarization Weather Radar. Part I: Description of the Polarimetric Radar Variables (2013, background): “More technical expositions are presented in the textbooks by Doviak and Zrnić (1993) and Bringi and Chandrasekar (2001).”
The 29 June 2000 Supercell Observed during STEPS. Part I: Kinematics and Microphysics (2005, background): “…for mapping the three-dimensional structure of precipitation and storm winds; balloonborne in situ electric field mills to measure in-storm parameters including temperature, pres1 All times are coordinated universal time (UTC); local time for this case study is found by subtracting six hours.”
A Theory-Based Hydrometeor Identification Algorithm for X-Band Polarimetric Radars (2009, methodology): “Speciﬁc differential phase is proportional to the inverse of wavelength (Bringi and Chandrasekar 2001): where C is a wavelength-independent constant, W is the liquid water content, and r m is the mass-weighted mean axis ratio.”
A Technique for Autocalibration of Cloud Lidar (2004, methodology): “We assume that the cloud droplet size distribution can be described by a normalized gamma function of the form (e.g., Bringi and Chandrasekar 2001) mD 2(3.67 1 m)D n(D) 5 N f (m) exp , (7)W 1 2 [ ]D D0 0 where NW is the drop number concentration normalized so that the liquid water content is…”
A Methodology for Estimating the Parameters of a Gamma Raindrop Size Distribution Model from Polarimetric Radar Data: Application to a Squall-Line Event from the TRMM/Brazil Campaign (2002, methodology): “…shape oscillations (either due to resonance maintained by vortex shedding or due to collisions) and raindrop canting (due to turbulence) will bias the retrieval of the gamma DSD parameters under the above model assumptions (e.g., see Bringi and Chandrasekar 2001, chapter 7 and references therein).”
Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities (2016, background): “The R –specific differential phase K DP (e.g., Bringi and Chandrasekar 2001; Ryzhkov et al. 2005; Matrosov 2010; Wang and Chandrasekar 2010; Lim et al. 2013) and R –specific attenuation A (e.g., Ryzhkov et al. 2014; Wang et al. 2014) schemes were shown to have advantages over R –”
The Impact of Beam Broadening on the Quality of Radar Polarimetric Data (2007, methodology): “Indeed, the standard deviations of the estimates for all three variables are proportional to (1 2hv) 1/2 (Bringi and Chandrasekar 2001).”
A Variational Scheme for Retrieving Rainfall Rate and Hail Reflectivity Fraction from Polarization Radar (2007, methodology): “The relationship of Bringi and Chandrasekar (2001) is used to calculate rain rate: R(Kdp) = 50.”
Confronting the Challenge of Modeling Cloud and Precipitation Microphysics (2020, background): “Quantities such as differential reflectivity, specific differential phase, copolar correlation coefficient, and linear depolarization ratio each provide somewhat independent information related to these quantities (e.g., Bringi & Chandrasekar, 2001; Kumjian, 2018; Zrnic & Ryzhkov, 1999).”
Comparison of Advanced Radar Polarimetric Techniques for Operational Attenuation Correction at C Band (2008, positive): “…[ dp (dB km 1)] are almost linearly related to specific differential phase when D0 2.5 mm (where D0 is the median volume drop diameter), hh hhKdp, dp dpKdp, 8 where hh,dp depends on drop size distribution, drop shape (equivalently on axis ratio), and temperature (Bringi and Chandrasekar 2001).”