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

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 , 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 (Z dr). 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 (ϕ dp) 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 ϕ dp, 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 Z dr for various values of the LDR system’s limit.

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

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 , 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 Z dr bias in rain caused by antenna polarization errors is clearly demonstrated by the data. This has not been shown previously in the literature.

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

Abstract

The identification and mitigation of anomalous propagation (AP) and normal propagation (NP) ground clutter is an ongoing problem in radar meteorology. Scatter from ground-clutter targets routinely contaminates radar data and masks weather returns causing poor data quality. The problem is typically mitigated by applying a clutter filter to all radar data, but this also biases weather data at near-zero velocity. Modern radar processors make possible the real-time identification and filtering of AP clutter. A fuzzy logic algorithm is used to distinguish between clutter echoes and precipitation echoes and, subsequently, a clutter filter is applied to those radar resolution volumes where clutter is present. In this way, zero-velocity weather echoes are preserved while clutter echoes are mitigated. Since the radar moments are recalculated from clutter-filtered echoes, the underlying weather echo signatures are revealed, thereby significantly increasing the visibility of weather echo. This paper describes the fuzzy logic algorithm, clutter mitigation decision (CMD), for clutter echo identification. A new feature field, clutter phase alignment (CPA), is introduced and described. A detailed discussion of CPA is given in Part I of this paper. The CMD algorithm is illustrated with 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).

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J. C. Hubbert, S. M. Ellis, W.-Y. Chang, S. Rutledge, and M. Dixon

Abstract

Data collected by the National Center for Atmospheric Research S-band polarimetric radar (S-Pol) during the Terrain-Influenced Monsoon Rainfall Experiment (TiMREX) in Taiwan are analyzed and used to infer storm microphysics in the ice phase of convective storms. Both simultaneous horizontal (H) and vertical (V) (SHV) transmit polarization data and fast-alternating H and V (FHV) transmit polarization data are used in the analysis. The SHV Z dr (differential reflectivity) data show radial stripes of biased data in the ice phase that are likely caused by aligned and canted ice crystals. Similar radial streaks in the linear depolarization ratio (LDR) are presented that are also biased by the same mechanism. Dual-Doppler synthesis and sounding data characterize the storm environment and support the inferences concerning the ice particle types. Small convective cells were observed to have both large positive and large negative K dp (specific differential phase) values. Negative K dp regions suggest that ice crystals are vertically aligned by electric fields. Since high |K dp| values of 0.8° km−1 in both negative and positive K dp regions in the ice phase are accompanied by Z dr values close to 0 dB, it is inferred that there are two types of ice crystals present: 1) smaller aligned ice crystals that cause the K dp signatures and 2) larger aggregates or graupel that cause the Z dr signatures. The inferences are supported with simulated ice particle scattering calculations. A radar scattering model is used to explain the anomalous radial streaks in SHV and LDR.

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J. Vivekanandan, D. S. Zrnic, S. M. Ellis, R. Oye, A. V. Ryzhkov, and J. Straka

Recent studies have shown the utility of polarimetric radar observables and derived fields for discrimination of hydrometeor particle types. Because the values of the radar observables that delineate different particle types overlap and are not sharply defined, the problem is well suited for a fuzzy logic approach. In this preliminary study the authors have developed and implemented a fuzzy logic algorithm for hydrometeor particle identification that is simple and efficient enough to run in real time for operational use. Although there are no in situ measurements available for this particle-type verification, the initial results are encouraging. Plans for further verification and optimization of the algorithm are described.

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J. C. Hubbert, S. M. Ellis, W.-Y. Chang, and Y.-C. Liou

Abstract

In 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 K dp. The vertical-pointing data also indicate the presence of preferentially aligned ice crystals that cause differential reflectivity Z dr and differential phase ϕ dp to be strong functions of azimuth angle.

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M. Christian Schwartz, Virendra P. Ghate, Bruce. A. Albrecht, Paquita Zuidema, Maria P. Cadeddu, Jothiram Vivekanandan, Scott M. Ellis, Pei Tsai, Edwin W. Eloranta, Johannes Mohrmann, Robert Wood, and Christopher S. Bretherton

Abstract

The Cloud System Evolution in the Trades (CSET) aircraft campaign was conducted in the summer of 2015 in the northeast Pacific to observe the transition from stratocumulus to cumulus cloud regime. Fourteen transects were made between Sacramento, California, and Kona, Hawaii, using the NCAR’s High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Gulfstream V (GV) aircraft. The HIAPER W-band Doppler cloud radar (HCR) and the high-spectral-resolution lidar (HSRL), in their first deployment together on board the GV, provided crucial cloud and precipitation observations. The HCR recorded the raw in-phase (I) and quadrature (Q) components of the digitized signal, from which the Doppler spectra and its first three moments were calculated. HCR/HSRL data were merged to develop a hydrometeor mask on a uniform georeferenced grid of 2-Hz temporal and 20-m vertical resolutions. The hydrometeors are classified as cloud or precipitation using a simple fuzzy logic technique based on the HCR mean Doppler velocity, HSRL backscatter, and the ratio of HCR reflectivity to HSRL backscatter. This is primarily applied during zenith-pointing conditions under which the lidar can detect the cloud base and the radar is more sensitive to clouds. The microphysical properties of below-cloud drizzle and optically thin clouds were retrieved using the HCR reflectivity, HSRL backscatter, and the HCR Doppler spectrum width after it is corrected for the aircraft speed. These indicate that as the boundary layers deepen and cloud-top heights increase toward the equator, both the cloud and rain fractions decrease.

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