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J. C. Hubbert

Abstract

Temporal 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.

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J. Hubbert
and
V. N. Bringi

Abstract

Copolar differential phase is composed of two components, namely, differential propagation phase and differential backscatter phase. To estimate specific differential phase K DP, these two phase components must first be separated when significant differential backscatter phase is present. This paper presents an iterative range filtering technique that can separate these phase components under a wider variety of conditions than is possible with a simple range filter. This technique may also be used when estimating hail signals from range profiles of dual-frequency reflectivity ratios.

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J. C. Hubbert
and
V. N. Bringi

Abstract

A polarimetric radar covariance matrix model is described to study the behavior of the co-to-cross covariances in precipitation. The 2 × 2 propagation matrix with attenuation, differential attenuation, and differential phase is coupled to the backscatter matrix leading to a propagation-modified covariance matrix model. System polarization errors are included in this model as well. This model is used to study the behavior of the magnitude and phase of the co-to-cross covariances and the linear depolarization ratio (LDR) in rainfall. It is shown that the model predictions are consistent with data collected with the Colorado State University (CSU)–University of Chicago–Illinois State Water Survey (CHILL) radar in intense rainfall. A method is also given for estimating the system polarization errors from covariance matrix data collected in intense rainfall.

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J. C. Hubbert
and
V. N. Bringi

Abstract

Effects of three-body scattering on reflectivity signatures at S and C bands can be seen on the back side of large reflectivity storm cores that contain hail. The fingerlike protrusions of elevated reflectivity have been termed flare echoes or “hail spikes.” Three-body scattering occurs when radiation from the radar scattered toward the ground is scattered back to hydrometeors, which then scatter some of the radiation back to the radar. Three-body scatter typically causes differential reflectivity to be very high at high elevations and to be negative at lower elevations at the rear of the storm core. This paper describes a model that can simulate the essential features of the three-body scattering that has been observed in hailstorms. The model also shows that three-body scatter can significantly affect the polarimetric Z DR (differential reflectivity) radar signatures in hailshafts at very low elevation and thus is a possible explanation of the frequently reported negative Z DR signatures in hailshafts near ground.

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J. Hubbert
,
V. N. Bringi
,
L. D. Carey
, and
S. Bolen

Abstract

Polarimetric radar measurements made by the recently upgraded CSU-CHILL radar system in a severe hailstorm are analyzed permitting for the first time the combined use of Z h , Z DR, linear depolarization ratio (LDR), K DP, and ρ to infer hydrometeor types. A chase van equipped for manual collection of hail, and instrumented with a rain gauge, intercepted the storm core for 50 min. The period of golfball-sized hail is easily distinguished by high LDR (greater than or equal to −18 dB), negative Z DR (less than or equal to −0.5 dB), and low ρ (less than or equal to 0.93) values near the surface. Rainfall accumulation over the entire event (about 40 mm) estimated using K DP is in excellent agreement with the rain gauge measurement. Limited dual-Doppler synthesis using the CSU-CHILL and Denver WSR-88D radars permit estimates of the horizontal convergence at altitudes less than 3 km above ground level (AGL) at 1747 and 1812 mountain daylight time (MDT). Locations of peak horizontal convergence at these times are centered on well-defined positive Z DR columns. Vertical sections of multiparameter radar data at 1812 MDT are interpreted in terms of hydrometeor type. In particular, an enhanced LDR “cap” area on top of the the positive Z DR column is interpreted as a region of mixed phase with large drops mixed with partially frozen and frozen hydrometeors. A positive K DP column on the the western fringe of the main updraft is inferred to be the result of drops (1–2 mm) shed by wet hailstones. Swaths of large hail at the surface (inferred from LDR signatures) and positive Z DR at 3.5 km AGL suggest that potential frozen drop embryos are favorably located for growth into large hailstones. Thin section analysis of a sample of the large hailstones shows that 30%–40% have frozen drop embryos.

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J. C. Hubbert
,
G. Meymaris
,
U. Romatschke
, and
M. Dixon

Abstract

Ground clutter filtering is an important and necessary step for quality control of ground-based weather radars. In this paper, ground clutter mitigation is addressed using a time-domain regression filter. Clutter filtering is now widely accomplished with spectral processing where the times series of data corresponding to a radar resolution volume are transformed with a discrete Fourier transform after which the zero and near-zero velocity clutter components are eliminated by setting them to zero. Subsequently for reflectivity, velocity, and spectrum width estimates, interpolation techniques are used to recover some of the power loss due to the clutter filter, which has been shown to reduce bias. The spectral technique requires that the in-phase (I) and quadrature (Q) time series be windowed to reduce clutter power leakage away from zero and near-zero velocities. Unfortunately, window functions such as the Hamming, Hann, and Blackman attenuate the time series signal by 4.01, 4.19, and 5.23 dB for 64-point times series, respectively, and thereby effectively reduce the number of independent samples available for estimating the radar parameters of any underlying weather echo. In this paper, a regression filtering technique is investigated, through simulated data, that does not require the use of such window functions and thus provides for better weather signal statistics. In a follow-on paper that is in preparation the technique will be demonstrated using both S-band polarimetric radar (S-Pol) and NEXRAD data. Here, it is shown that the regression filter rejects clutter as effectively as the spectral technique but has the distinct advantage that estimates of the radar variables are greatly improved. The technique is straightforward and can be executed in real time.

Open access
V. Chandrasekar
,
J. Hubbert
,
V. N. Bringi
, and
P. F. Meischner

Abstract

Equations are derived for transforming radar data obtained with ±45° linear polarization states to conventional radar parameters measured at horizontal and vertical polarization states. The derivation is based on the covariance matrix and assumes a diagonal propagation matrix and a reciprocal scattering matrix with nonzero cross-polar terms. Time series data gathered during the summers of 1990 and 1992 with the German Aerospace Research Establishment (DLR) C-band polarimetric radar, POLDIRAD, located in Oberpfaffenhofen, Germany, are used to validate the polarization transformation method. Data collected in two convective precipitation shafts are analyzed and the resulting signatures are microphysically interpreted. The analysis and the presented data validate the polarization transformation method derived here under the assumption of a diagonal propagation matrix.

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V. Chandrasekar
,
J. Hubbert
,
V. N. Bringi
, and
P. F. Meischner

Abstract

A fully polarimetric radar system consists of an orthogonal dual-polarized transmission mode and a dual-channel receive mode that are typically set to be copolar and cross polar to the transmit state of polarization. The transmit polarization state is switched every pulse repetition time (PRT) between any two orthogonal stales. This paper presents an interpolation technique to construct time series of instantaneous scattering matrices (ISM) from fully polarimetric time series measurements obtained at every PRT from distributed scatterers. It is also shown theoretically that propagation effects need not be removed before transformation. The constructed series of ISMs are then transformed to other polarization bases. The resulting new ISMs are then used to calculate the radar parameters in the new basis. The suggested procedure is studied using data (collected at linear ±45° as well as horizontal and vertical polarization bases) from POLDIRAD, the dual-channel, polarimetric C-band radar operated by the German Aerospace Research Establishment.

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J. Hubbert
,
V. Chandrasekar
,
V. N. Bringi
, and
P. Meischner

Abstract

Dual-polarized coherent radar measurements are used to estimate the differential propagation phase or ϕDP between horizontal and vertical polarization states. The slope of ϕDP is an estimate of the specific differential phase K DP. This process is complicated due to differential phase on backscatter δ between horizontal and vertical polarization states, which can be significant at C band. Filtering techniques are presented for separating δ from propagation phase and then estimating K DP and δ. Also discussed are procedures for the estimation and interpretation of other radar measurables such as conventional radar reflectivity, differential reflectivity Z DP, the magnitude of the copolar correlation coefficient ρ HV (0), and Doppler spectrum width σν. A low noise level is essential for accurate estimation of these parameters. A spectral domain technique that can eliminate some of the noise contained in radar time series data is presented. The techniques are applied to data collected by Poldirad, the German Aerospace Research Establishment's polarimetric C-band radar.

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J. C. Hubbert
,
M. Dixon
,
S. M. Ellis
, and
G. Meymaris

Abstract

Real-time ground-clutter identification and subsequent filtering of clutter-contaminated data is addressed in this two-part paper. Part I focuses on the identification, modeling, and simulation of S-band ground-clutter echo. A new clutter identification parameter, clutter phase alignment (CPA), is presented. CPA is a measure primarily of the phase variability of the in-phase and quadrature-phase time series samples for a given radar resolution volume. CPA is also a function of amplitude variability of the time series. It is shown that CPA is an excellent discriminator of ground clutter versus precipitation echoes. A typically used weather model, time series simulator is shown to inadequately describe experimentally observed CPA. Thus, a new technique for the simulation of ground-clutter echo is developed that better predicts the experimentally observed CPA. Experimental data from the Denver Next Generation Weather Radar (NEXRAD) at the Denver, Colorado, Front Range Airport (KFTG), and NCAR’s S-band dual-polarization Doppler radar (S-Pol) are used to illustrate CPA. In Part II, CPA is used in a fuzzy logic algorithm for improved clutter identification.

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