Modeling, Error Analysis, and Evaluation of Dual-Polarization Variables Obtained from Simultaneous Horizontal and Vertical Polarization Transmit Radar. Part I: Modeling and Antenna Errors

2010 ◽  
Vol 27 (10) ◽  
pp. 1583-1598 ◽  
Author(s):  
J. C. Hubbert ◽  
S. M. Ellis ◽  
M. Dixon ◽  
G. Meymaris
Keyword(s):  
Phase Difference ◽  
Doppler Radar ◽  
Cross Coupling ◽  
Dual Polarization ◽  
Scattering Model ◽  
Radar Scattering ◽  
Linear Depolarization Ratio ◽  
Antenna Polarization ◽  
Radar Part ◽  
Differential Reflectivity

Abstract In this two-part paper the biases of polarimetric variables from simultaneous horizontally and vertically transmitted (SHV) data are investigated. Here, in Part I, a radar-scattering model is developed and antenna polarization errors are investigated and estimated. In Part II, experimental data from the National Center for Atmospheric Research S-band dual-polarization Doppler radar (S-Pol) and the National Severe Storms Laboratory polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) radar, KOUN, are used to illustrate biases in differential reflectivity (Zdr). The biases in the SHV polarimetric variables are caused by cross coupling of the horizontally (H) and vertically (V) polarized signals. The cross coupling is caused by the following two primary sources: 1) the nonzero mean canting angle of the propagation medium and 2) antenna polarization errors. The biases are strong functions of the differential propagation phase (ϕdp) and the phase difference between the H and V transmitted field components. The radar-scattering model developed here allows for the evaluation of biases caused by cross coupling as a function of ϕdp, with the transmission phase difference as a parameter. Also, antenna polarization errors are estimated using solar scan measurements in combination with estimates of the radar system’s linear depolarization ratio (LDR) measurement limit. Plots are given that show expected biases in SHV Zdr for various values of the LDR system’s limit.

Modeling, Error Analysis, and Evaluation of Dual-Polarization Variables Obtained from Simultaneous Horizontal and Vertical Polarization Transmit Radar. Part II: Experimental Data

2010 ◽  
Vol 27 (10) ◽  
pp. 1599-1607 ◽  
Author(s):  
J. C. Hubbert ◽  
S. M. Ellis ◽  
M. Dixon ◽  
G. Meymaris
Keyword(s):  
Experimental Data ◽  
Electric Fields ◽  
Doppler Radar ◽  
Cross Coupling ◽  
Analysis And Evaluation ◽  
Close Time ◽  
Nonzero Mean ◽  
Antenna Polarization ◽  
Radar Part ◽  
Differential Reflectivity

Abstract In this second article in a two-part work, the biases of weather radar polarimetric variables from simultaneous horizontally and vertically transmit (SHV) data are investigated. The biases are caused by cross coupling of the simultaneously transmitted vertical (V) and horizontal (H) electric fields. There are two primary causes of cross coupling: 1) the nonzero mean canting angle of the propagation medium (e.g., canted ice crystals) and 2) antenna polarization errors. Given herein are experimental data illustrating both bias sources. In Part I, a model is developed and used to quantify cross coupling and its impact on polarization measurements. Here, in Part II, experimental data from the National Center for Atmospheric Research’s (NCAR’s) S-band dual-polarimetric Doppler radar (S-Pol) and the National Severe Storms Laboratory’s polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D), KOUN, are used to illustrate biases in differential reflectivity (Zdr). The S-Pol data are unique: both SHV data and fast alternating H and V transmit (FHV) data are gathered in close time proximity, and thus the FHV data provide “truth” for the SHV data. Specifically, the SHV Zdr bias in rain caused by antenna polarization errors is clearly demonstrated by the data. This has not been shown previously in the literature.

scholarly journals Differential Reflectivity Calibration and Antenna Temperature

2017 ◽  
Vol 34 (9) ◽  
pp. 1885-1906 ◽  
Author(s):  
J. C. Hubbert
Keyword(s):  
Thermal Expansion ◽  
Doppler Radar ◽  
Radar Data ◽  
The Sun ◽  
Dual Polarization ◽  
Calibration Technique ◽  
Atmospheric Research ◽  
Using Data ◽  
Power Measurements ◽  
Differential Reflectivity

AbstractTemporal differential reflectivity bias variations are investigated using the National Center for Atmospheric Research (NCAR) S-band dual-polarization Doppler radar (S-Pol). Using data from the Multi-Angle Snowflake Camera-Ready (MASCRAD) Experiment, S-Pol measurements over extended periods reveal a significant correlation between the ambient temperature at the radar site and the bias. Using radar scans of the sun and the ratio of cross-polar powers, the components of the radar that cause the variation of the bias are identified. It is postulated that the thermal expansion of the antenna is likely the primary cause of the observed bias variation. The cross-polar power (CP) calibration technique, which is based on the solar and cross-polar power measurements, is applied to data from the Plains Elevated Convection at Night (PECAN) field project. The bias from the CP technique is compared to vertical-pointing bias measurements, and the uncertainty of the bias estimates is given. An algorithm is derived to correct the radar data for the time- and temperature-varying bias. Bragg scatter measurements are used to corroborate the CP technique bias measurements.

scholarly journals Impact of the Dual-Polarization Doppler Radar Data on Two Convective Storms with a Warm-Rain Radar Forward Operator

2012 ◽  
Vol 140 (7) ◽  
pp. 2147-2167 ◽  
Author(s):  
Xuanli Li ◽  
John R. Mecikalski
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Three Dimensional ◽  
Radar Data ◽  
Dual Polarization ◽  
Convective Storms ◽  
Warm Rain ◽  
Mesoscale Convective ◽  
Differential Reflectivity ◽  
The Impact

Abstract The dual-polarization (dual pol) Doppler radar can transmit/receive both horizontally and vertically polarized power returns. The dual-pol radar measurements have been shown to provide a more accurate precipitation estimate compared to traditional radars. In this study, the horizontal reflectivity ZH, differential reflectivity ZDR, specific differential phase KDP, and radial velocity VR collected by the C-band Advanced Radar for Meteorological and Operational Research (ARMOR) are assimilated for two convective storms. A warm-rain scheme is constructed to assimilate ZH, ZDR, and KDP data using the three-dimensional variational data assimilation (3DVAR) system with the Advanced Research Weather Research and Forecasting Model (ARW-WRF). The main goals of this study are first to demonstrate and compare the impact of various dual-pol variables in initialization of real case convective storms and second to test how the dual-pol fields may be better used with a 3DVAR system. The results show that the ZH, ZDR, KDP, and VR data substantially improve the initial condition for two mesoscale convective storms. Significant positive impacts on short-term forecast are obtained for both storms. Additionally, KDP and ZDR data assimilation is shown to be superior to ZH and ZDR and ZH-only data assimilation when the warm-rain microphysics is adopted. With the ongoing upgrade of the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network to include dual-pol capabilities (started in early 2011), the findings from this study can be a helpful reference for utilizing the dual-pol radar data in numerical simulations of severe weather and related quantitative precipitation forecasts.

scholarly journals Close-Range Observations of Tornadoes in Supercells Made with a Dual-Polarization, X-Band, Mobile Doppler Radar

2007 ◽  
Vol 135 (4) ◽  
pp. 1522-1543 ◽  
Author(s):  
Howard B. Bluestein ◽  
Michael M. French ◽  
Robin L. Tanamachi ◽  
Stephen Frasier ◽  
Kery Hardwick ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Cross Correlation ◽  
Doppler Radar ◽  
Dual Polarization ◽  
Radar Beam ◽  
X Band ◽  
Close Range ◽  
Debris Cloud ◽  
The Cross ◽  
Differential Reflectivity

Abstract A mobile, dual-polarization, X-band, Doppler radar scanned tornadoes at close range in supercells on 12 and 29 May 2004 in Kansas and Oklahoma, respectively. In the former tornadoes, a visible circular debris ring detected as circular regions of low values of differential reflectivity and the cross-correlation coefficient was distinguished from surrounding spiral bands of precipitation of higher values of differential reflectivity and the cross-correlation coefficient. A curved band of debris was indicated on one side of the tornado in another. In a tornado and/or mesocyclone on 29 May 2004, which was hidden from the view of the storm-intercept team by precipitation, the vortex and its associated “weak-echo hole” were at times relatively wide; however, a debris ring was not evident in either the differential reflectivity field or in the cross-correlation coefficient field, most likely because the radar beam scanned too high above the ground. In this case, differential attenuation made identification of debris using differential reflectivity difficult and it was necessary to use the cross-correlation coefficient to determine that there was no debris cloud. The latter tornado’s parent storm was a high-precipitation (HP) supercell, which also spawned an anticyclonic tornado approximately 10 km away from the cyclonic tornado, along the rear-flank gust front. No debris cloud was detected in this tornado either, also because the radar beam was probably too high.

Microphysical Insights into Ice Pellet Formation Revealed by Fully Polarimetric Ka-Band Doppler Radar

2020 ◽  
Vol 59 (10) ◽  
pp. 1557-1580
Author(s):  
Matthew R. Kumjian ◽  
Dana M. Tobin ◽  
Mariko Oue ◽  
Pavlos Kollias
Keyword(s):  
New York ◽  
Correlation Coefficient ◽  
Doppler Radar ◽  
Radar Data ◽  
Ice Crystals ◽  
Horizontal Polarization ◽  
Ka Band ◽  
Pellet Formation ◽  
Linear Depolarization Ratio ◽  
Differential Reflectivity

AbstractFully polarimetric scanning and vertically pointing Doppler spectral data from the state-of-the-art Stony Brook University Ka-band Scanning Polarimetric Radar (KASPR) are analyzed for a long-duration case of ice pellets over central Long Island in New York from 12 February 2019. Throughout the period of ice pellets, a classic refreezing signature was present, consisting of a secondary enhancement of differential reflectivity ZDR beneath the melting layer within a region of decreasing reflectivity factor at horizontal polarization ZH and reduced copolar correlation coefficient ρhv. The KASPR radar data allow for evaluation of previously proposed hypotheses to explain the refreezing signature. It is found that, upon entering a layer of locally generated columnar ice crystals and undergoing contact nucleation, smaller raindrops preferentially refreeze into ice pellets prior to the complete freezing of larger drops. Refreezing particles exhibit deformations in shape during freezing, leading to reduced ρhv, reduced co-to-cross-polar correlation coefficient ρxh, and enhanced linear depolarization ratio, but these shape changes do not explain the ZDR signature. The presence of columnar ice crystals, though apparently crucial for instigating the refreezing process, does not contribute enough backscattered power to affect the ZDR signature, either.

scholarly journals Wideband Reception and Processing for Dual-Polarization Radars with Dual Transmitters

2007 ◽  
Vol 24 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Sutanay Choudhury ◽  
V. Chandrasekar
Keyword(s):  
Doppler Radar ◽  
Negative Impact ◽  
Spectral Width ◽  
Parameter Estimates ◽  
Dual Polarization ◽  
Mean Velocity ◽  
Colorado State University ◽  
State Water ◽  
Differential Reflectivity ◽  
The Impact

Abstract Oversampling pulsed Doppler radar returns at a rate larger than the pulse bandwidth, whitening the range samples, and subsequent averaging has been pursued as a potential way to decrease the measured standard deviation of signal parameter estimates. It has been shown that the application of oversampling, whitening, and subsequent averaging improves the quality of reflectivity, mean velocity, and spectral width estimates in agreement with theory. Application of this procedure to a dual-polarization radar with dual transmitters is evaluated in this paper. Oversampled data collected from the Colorado State University (CSU)-University of Chicago–Illinois State Water Survey (CHILL) radar using a wideband receiver are analyzed to evaluate the performance of dual-polarization parameter estimators, such as differential reflectivity and differential phase. The negative impact of relative phase characteristics of the transmitted pulses in two polarizations on the copolar correlation, and subsequently on polarimetric parameter estimation, is analyzed. CSU-CHILL radar’s transmitted pulse sampling capability is used to evaluate the impact of the transmitted waveform’s mismatch on whitening and estimation.

X-Band Polarimetric Observations of Cross Coupling in the Ice Phase of Convective Storms in Taiwan

2014 ◽  
Vol 53 (6) ◽  
pp. 1678-1695 ◽  
Author(s):  
J. C. Hubbert ◽  
S. M. Ellis ◽  
W.-Y. Chang ◽  
Y.-C. Liou
Keyword(s):  
Electric Fields ◽  
Cross Coupling ◽  
Radar Data ◽  
Ice Crystals ◽  
Polarimetric Radar ◽  
Differential Phase ◽  
X Band ◽  
Linear Depolarization Ratio ◽  
Differential Reflectivity ◽  
Ice Phase

AbstractIn this paper, experimental X-band polarimetric radar data from simultaneous transmission of horizontal (H) and vertical (V) polarizations (SHV) are shown, modeled, and microphysically interpreted. Both range–height indicator data and vertical-pointing X-band data from the Taiwan Experimental Atmospheric Mobile-Radar (TEAM-R) are presented. Some of the given X-band data are biased, which is very likely caused by cross coupling of the H and V transmitted waves as a result of aligned, canted ice crystals. Modeled SHV data are used to explain the observed polarimetric signatures. Coincident data from the National Center for Atmospheric Research S-band polarimetric radar (S-Pol) are presented to augment and support the X-band polarimetric observations and interpretations. The polarimetric S-Pol data are obtained via fast-alternating transmission of horizontal and vertical polarizations (FHV), and thus the S-band data are not contaminated by the cross coupling (except the linear depolarization ratio LDR) observed in the X-band data. The radar data reveal that there are regions in the ice phase where electric fields are apparently aligning ice crystals near vertically and thus causing negative specific differential phase Kdp. The vertical-pointing data also indicate the presence of preferentially aligned ice crystals that cause differential reflectivity Zdr and differential phase ϕdp to be strong functions of azimuth angle.

scholarly journals ANALYSIS OF INSAR SENSITIVITY TO FOREST STRUCTURE BASED ON RADAR SCATTERING MODEL

2008 ◽  
Vol 84 ◽  
pp. 149-171 ◽  
Author(s):  
Dawei Liu ◽  
Yang Du ◽  
Guoqing Sun ◽  
Wen-Zhe Yan ◽  
Bae-Ian Wu
Keyword(s):  
Forest Structure ◽  
Scattering Model ◽  
Radar Scattering

scholarly journals Quantitative data analysis of ESAR data

2013 ◽  
Vol 11 ◽  
pp. 291-295
Author(s):  
N. Phruksahiran ◽  
M. Chandra
Keyword(s):  
Ground Surface ◽  
Quantitative Information ◽  
Polarization Property ◽  
Data Sets ◽  
Program Code ◽  
Quantitative Data Analysis ◽  
Linear Depolarization Ratio ◽  
German Aerospace ◽  
Differential Reflectivity

Abstract. A synthetic aperture radar (SAR) data processing uses the backscattered electromagnetic wave to map radar reflectivity of the ground surface. The polarization property in radar remote sensing was used successfully in many applications, especially in target decomposition. This paper presents a case study to the experiments which are performed on ESAR L-Band full polarized data sets from German Aerospace Center (DLR) to demonstrate the potential of coherent target decomposition and the possibility of using the weather radar measurement parameter, such as the differential reflectivity and the linear depolarization ratio to obtain the quantitative information of the ground surface. The raw data of ESAR has been processed by the SAR simulator developed using MATLAB program code with Range-Doppler algorithm.

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