Combined Doppler and Polarimetric Radar Measurements: Correction for Spectrum Aliasing and Nonsimultaneous Polarimetric Measurements

Unal, C.M.H.; Moisseev, Dmitri

doi:10.1175/1520-0426(2004)021<0443:cdaprm>2.0.co;2

Cited by 32 publications

(31 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This property is also applied in the Doppler domain by using the spectral cross-correlation coefficient. Earlier studies (Moisseev et al 2000;Unal and Moisseev 2004) have shown the ability of the spectral cross-correlation coefficient to remove clutter from the atmospheric spectrographs. In Moisseev et al (2000), the bins of the Doppler spectra related to a modulus of the spectral cross-correlation coefficient inferior to 0.8 are discarded.…”

Section: The Concept Of Spectral Polarimetrymentioning

confidence: 99%

“…The limitation of this method is that it is only applicable to the sidelobe clutter (Moisseev and Chandrasekar 2009). Subsequently, the phase of sr co (y) (i.e., the spectral differential phase) is used to develop a technique combining dealiasing and clutter suppression (Unal and Moisseev 2004). The latter technique is outlined in appendix A.…”

Section: The Concept Of Spectral Polarimetrymentioning

confidence: 99%

“…At the S band, the phase of sr co (y) or spectral differential phase can be clipped when it exceeds the interval [2sc max , sc max ] to partially remove side-and main-lobe clutter. To choose sc max the ranges of propagation path and rain rate should be considered (Unal and Moisseev 2004). For the STAR mode, this technique, based on the complex spectral cross-correlation coefficient thresholding, which can be easily implemented in real time, can be compared with a very recently proposed technique (Moisseev and Chandrasekar 2009) based on a fuzzy logic algorithm that is applied to the modulus of the spectral cross-correlation coefficient and to the textures of spectral differential phase and spectral differential reflectivity.…”

Section: Suggested Use Of Algorithms Based On Spectral Polarimetrymentioning

confidence: 99%

“…Separation and characterization of different radar targets can be achieved as soon as their polarimetric responses are related to different Doppler velocity bins. A spectral covariance matrix (Unal and Moisseev 2004), or covariance matrix elements for every Doppler velocity bin, can be calculated. In that case, the part of the Doppler spectrum that clearly exhibits polarimetric properties very different from the expected atmospheric targets ones is discarded.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Spectral Polarimetric Radar Clutter Suppression to Enhance Atmospheric Echoes

Unal

2009

Journal of Atmospheric and Oceanic Technology

View full text Add to dashboard Cite

The clutter present in the Doppler spectra of atmospheric targets can be removed by using polarimetry. The purpose is to suppress the Doppler velocity bins where spectral polarimetric parameters have atypical values. This procedure largely improves profiles of moments and polarimetric parameters of atmospheric targets. Several spectral polarimetric clutter-reduction techniques, which are based on thresholding and intended for real-time processing, are discussed in this paper. A new method, the double spectral linear depolarization ratio clutter-suppression technique, is proposed. Very satisfactory performances are obtained with this method, which can be used in the full range of elevations (08-908). Spectral polarimetric clutter-suppression techniques for real-time processing were studied for the S-band high-resolution Transportable Atmospheric Radar (TARA) profiler. For this study, precipitation, cloud, and clear-air scattering are considered examples of atmospheric echoes. After successful testing in 2008, the double spectral linear depolarization ratio filter was implemented in the real-time processing of the X-band scanning drizzle radar (IDRA).

show abstract

Section: The Concept Of Spectral Polarimetrymentioning

confidence: 99%

Section: The Concept Of Spectral Polarimetrymentioning

confidence: 99%

Section: Suggested Use Of Algorithms Based On Spectral Polarimetrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spectral Polarimetric Radar Clutter Suppression to Enhance Atmospheric Echoes

Unal

2009

Journal of Atmospheric and Oceanic Technology

View full text Add to dashboard Cite

show abstract

“…Processing and scanning techniques have also been improved to overcome certain shortages like Doppler spectrum aliasing, ground clutter contamination, and second-trip echo return. Unal and Moisseev (2004) introduced a simple processing technique for the Doppler analysis combining simultaneous measures required for polarimetry analysis and maximum unambiguous Doppler velocity. Many operational Doppler weather radars operate with a dual-pulse-repetition frequency (dual-PRF) technique in order to extend the unambiguous Doppler velocity interval (Dazhang et al 1982;Holleman and Beekhuis 2003).…”

Section: Introductionmentioning

confidence: 99%

A Quality Control Concept for Radar Reflectivity, Polarimetric Parameters, and Doppler Velocity

Friedrich

Hagen

Einfalt³

2006

Journal of Atmospheric and Oceanic Technology

View full text Add to dashboard Cite

Over the last few years the use of weather radar data has become a fundamental part of various applications like rain-rate estimation, nowcasting of severe weather events, and assimilation into numerical weather prediction models. The increasing demand for radar data necessitates an automated, flexible, and modular quality control. In this paper a quality control procedure is developed for radar reflectivity factors, polarimetric parameters, and Doppler velocity. It consists of several modules that can be extended, modified, and omitted depending on the user requirement, weather situation, and radar characteristics. Data quality is quantified on a pixel-by-pixel basis and encoded into a quality-index field that can be easily interpreted by a nontrained end user or an automated scheme that generates radar products. The qualityindex algorithms detect and quantify the influence of beam broadening, the height of the first radar echo, ground clutter contamination, return from non-weather-related objects, and attenuation of electromagnetic energy by hydrometeors on the quality of the radar measurement. The quality-index field is transferred together with the radar data to the end user who chooses the amount of data and the level of quality used for further processing. The calculation of quality-index fields is based on data measured by the polarimetric C-band Doppler radar (POLDIRAD) located in the Alpine foreland in southern Germany.

show abstract

Inferring microstructure and turbulence properties in rain through observations and simulations of signal spectra measured with Doppler–polarimetric radars

Yanovsky

2011

Polarimetric Detection, Characterization and Remote Sensing

View full text Add to dashboard Cite

Doppler radars are able to measure important parameters of the target velocity. In contrast, polarimetric radars are very sensitive to features of the target shape and orientation relative to the radar beam direction. This chapter describes a novel Doppler-polarimetric approach to radar remote sensing. The combination of the Doppler ability and polarization diversity in the radar technology enables more comprehensive investigations of objects and phenomena in radar coverage. The discussion is adapted to the case of atmospheric remote sensing. A special case of cloud and precipitation observations is considered in greater detail. Mathematical models of signals and spectra of Doppler-polarimetric returns are discussed. It is demonstrated (theoretically, by simulation, and by real data processing) that important parameters of dynamic characteristics and microstructure of meteorological objects can be retrieved from Doppler-polarimetric observations. These results lead to new interesting and important applications like turbulence intensity measurement, drop size distribution estimation, recognition of type of scatterers, detection of hail zones, etc.

show abstract

Combined Doppler and Polarimetric Radar Measurements: Correction for Spectrum Aliasing and Nonsimultaneous Polarimetric Measurements

Cited by 32 publications

References 16 publications

Spectral Polarimetric Radar Clutter Suppression to Enhance Atmospheric Echoes

Spectral Polarimetric Radar Clutter Suppression to Enhance Atmospheric Echoes

A Quality Control Concept for Radar Reflectivity, Polarimetric Parameters, and Doppler Velocity

Inferring microstructure and turbulence properties in rain through observations and simulations of signal spectra measured with Doppler–polarimetric radars

Contact Info

Product

Resources

About