Rigorous Approach to Polarimetric Radar Modeling of Hydrometeor Orientation Distributions

Vivekanandan, Jothiram; Adams, W. M.; Bringi, V. N.

doi:10.1175/1520-0450(1991)030<1053:ratprm>2.0.co;2

Cited by 103 publications

(102 citation statements)

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“…We assume distribution of canting angles of raindrops is Gaussian, with a mean of 0 and a standard deviation of 10 , which have been used commonly in the previous studies (e.g., Keenan et al 2001;Ryzhkov et al 2002). Vivekanandan et al (1991) proposed a quasi-Gaussian distribution with a slightly smaller value of standard deviation.…”

Section: Data and Analysis Methodsmentioning

confidence: 99%

Effect of Natural Variations in Rain Drop Size Distributions on Rain Rate Estimators of 3 cm Wavelength Polarimetric Radar

Maki

Park

Bringi

2005

Journal of the Meteorological Society of Japan

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Statistical errors of rain rate estimators due to natural variations in raindrop size distribution (DSD) are studied for 3-cm wavelength polarimetric radar. Four types of estimators are examined: A classical estimator RðZ H Þ, and three types of polarimetric radar estimators RðK DP Þ, RðZ H ; Z DR Þ, and RðK DP ; Z DR Þ, where R is the rain rate, Z H is the reflectivity factor at horizontal polarization, K DP is the specific differential phase, and Z DR is the differential reflectivity. The T-matrix method is employed for the scattering calculations, and a total of 7,664 one-minute raindrop size spectra, measured with a Joss-Waldvogel type disdrometer are used.According to simulation results, the normalized errors (NEs) of RðZ H Þ, RðK DP Þ, RðK DP ; Z DR Þ, and RðZ H ; Z DR Þ for all DSD samples are 25%, 14%, 9%, and 10%, respectively. The NEs of all estimators, except RðZ H Þ, tend to decrease with increasing rain rate. For rain rates larger than 10 mmh À1 , e.g., the average NEs of RðZ H Þ, RðK DP Þ, RðK DP ; Z DR Þ, and RðZ H ; Z DR Þ are 25%, 9%, 5%, and 7%, respectively. The simulation results show that the classical estimator RðZ H Þ is the most sensitive to variations in DSD and the estimator RðK DP ; Z DR Þ is the least sensitive.The lowest sensitivity of the rain estimator RðK DP ; Z DR Þ to variations in DSD can be explained by the following facts. The difference in the forward-scattering amplitudes at horizontal and vertical polarizations, which contributes K DP , is proportional to the 4.78th power of the drop diameter. On the other hand, the exponent of the backscatter cross section, which contributes to Z H , is proportional to the 6.38th power of the drop diameter. Because the rain rate R is proportional to the 3.67th power of the drop diameter, K DP is less sensitive to DSD variations than Z H . However, DSD spectra with unusually large median volume diameter D 0 can increase the estimation error of RðK DP Þ. The differential reflectivity Z DR reduces the effect of unusual D 0 and is useful for further improvement of the estimator RðK DP Þ. This is due to the fact that Z DR itself is a good measure of D 0 .

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Section: Data and Analysis Methodsmentioning

confidence: 99%

Effect of Natural Variations in Rain Drop Size Distributions on Rain Rate Estimators of 3 cm Wavelength Polarimetric Radar

Maki

Park

Bringi

2005

Journal of the Meteorological Society of Japan

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“…The data were collected from POSS (Precipitation Occurrence Sensor System : Sheppard 1990) from March to September 2001 in Busan. The theoretical radar parameters, Z H , Z DR , K DP , and rain rate, are calculated from the measured drop size distribution using T-matrix scattering (Bringi et al 1990;Vivekanandan et al 1991). The dielectric constant for scattering simulation was calculated by using the method of Ray (1972).…”

Section: Disdrometer Datamentioning

confidence: 99%

“…Moreover, we simultaneously compared Z DR bias using the simulated data from the 2D-video disdrometer (2DVD, Schönhuber et al 2008). The Z DR value was calculated using T-matrix scattering (Bringi et al 1990;Vivekanandan et al 1991) with the assumption in Table 3 and by using the DSDs measured from 2DVD. The Z DR calculated from 2DVD was compared with Z DR measured from radar.…”

Section: Appendixmentioning

confidence: 99%

Rainfall Estimation from an Operational S-Band Dual-Polarization Radar: Effect of Radar Calibration

Kwon

Lee

Kim

2015

Journal of the Meteorological Society of Japan

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The calibration biases of reflectivity (Z H ) and differential reflectivity (Z DR ) from an operational Mt. Bisl S-band dual polarization radar are derived to improve the accuracy of rainfall estimation. The effect of radar calibration in rain estimation is examined by using data from the dense rain gauge network.The calibration biases of Z H are calculated by using the self-consistency constraint between Z H and specific differential phase shift (K DP ). This procedure is performed every 2.5 min. The biases are varied from −3.3 dB to 0.8 dB during the period between July 2010 to October 2011. The Z DR calibration biases are obtained by two methods: 1) vertically pointing measurements, and 2) comparison of observed data with the average Z H -Z DR relationship derived from disdrometric data. The Z DR biases are varied from 0.25 dB to 0.7 dB and both methods show similar results. This Z H -Z DR technique can be applied for a volume scan and does not require a special scan.

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“…These rapidly evolving mesoscale systems will be better understood with the national dual-polarization radar upgrade through use of hydrometeor classification algorithms (HCAs; Liu and Chandrasekar 2000;Zrni c et al 2001;Park et al 2009;Chandrasekar et al 2013). Cold-season microphysical processes observable by polarimetric radars and whose origins are generally agreed upon include dendritic ice crystal growth zones (DGZs; Kennedy and Rutledge 2011;Andri c et al 2013;Bechini et al 2013), plate crystal growth (Pruppacher and Klett 1997;Wolde and Vali 2001;Williams et al 2011Williams et al , 2013, ice particle density and shape modulations caused by riming and crystal aggregation (Vivekanandan et al 1994), hydrometeor melting (Ryzhkov et al 1998), and near-surface refreezing of either rain or freezing rain into sleet 1 (Kumjian et al 2013). Detection of these winter phenomena by dualpolarization HCAs is important.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation

Thompson

Rutledge

Dolan

et al. 2014

Journal of Atmospheric and Oceanic Technology

101

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The purpose of this study is to demonstrate the use of polarimetric observations in a radar-based winter hydrometeor classification algorithm. This is accomplished by deriving bulk electromagnetic scattering properties of stratiform, cold-season rain, freezing rain, sleet, dry aggregated snowflakes, dendritic snow crystals, and platelike snow crystals at X-, C-, and S-band wavelengths based on microphysical theory and previous observational studies. These results are then used to define the expected value ranges, or membership beta functions, of a simple fuzzy-logic hydrometeor classification algorithm. To test the algorithm's validity and robustness, polarimetric radar data and algorithm output from four unique winter storms are investigated alongside surface observations and thermodynamic soundings. This analysis supports that the algorithm is able to realistically discern regions dominated by wet snow, aggregates, plates, dendrites, and other small ice crystals based solely on polarimetric data, with guidance from a melting-level detection algorithm but without external temperature information. Temperature is still used to distinguish rain from freezing rain or sleet below the radar-detected melting level. After appropriate data quality control, little modification of the algorithm was required to produce physically reasonable results on four different radar platforms at X, C, and S bands. However, classification seemed more robust at shorter wavelengths because the specific differential phase is heavily weighted in ice crystal classification decisions. It is suggested that parts, or all, of this algorithm could be applicable in both operational and research settings.

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Rigorous Approach to Polarimetric Radar Modeling of Hydrometeor Orientation Distributions

Cited by 103 publications

References 0 publications

Effect of Natural Variations in Rain Drop Size Distributions on Rain Rate Estimators of 3 cm Wavelength Polarimetric Radar

Effect of Natural Variations in Rain Drop Size Distributions on Rain Rate Estimators of 3 cm Wavelength Polarimetric Radar

Rainfall Estimation from an Operational S-Band Dual-Polarization Radar: Effect of Radar Calibration

A Dual-Polarization Radar Hydrometeor Classification Algorithm for Winter Precipitation

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