scholarly journals Whitening in Range to Improve Weather Radar Spectral Moment Estimates. Part II: Experimental Evaluation

2003 ◽  
Vol 20 (11) ◽  
pp. 1449-1459 ◽  
Author(s):  
Igor R. Ivić ◽  
Dušan S. Zrnić ◽  
Sebastián M. Torres
Keyword(s):  
Experimental Evaluation ◽  
Weather Radar ◽  
Spectral Moment ◽  
Moment Estimates

scholarly journals Considerations for Oversampling in Azimuth on the Phased Array Weather Radar

2015 ◽  
Vol 32 (9) ◽  
pp. 1614-1629 ◽  
Author(s):  
Lesya Borowska ◽  
Guifu Zhang ◽  
Dusan S. Zrnić
Keyword(s):  
Phased Array ◽  
Weighted Average ◽  
Weather Radar ◽  
Severe Weather ◽  
Spectral Moment ◽  
Spectral Moments ◽  
Severe Storms ◽  
Moment Estimate ◽  
National Network ◽  
Weather Radars

AbstractWhen spectral moments in the azimuth are spaced by less than a beamwidth, it is called oversampling. Superresolution is a type of oversampling that refers to sampling at half a beamwidth on the national network of Doppler weather radars [Weather Surveillance Radar-1988 Doppler (WSR-88D)]. Such close spacing is desirable because it extends the range at which small severe weather features, such as tornadoes or microbursts, can be resolved. This study examines oversampling for phased array radars. The goal of the study is to preserve the same effective beamwidth as on the WSR-88D while obtaining smaller spectral moment estimate errors at the same or faster volume update times. To that effect, a weighted average of autocorrelations of radar signals from three consecutive radials is proposed. Errors in three spectral moments obtained from these autocorrelations are evaluated theoretically. Methodologies on how to choose weights that preserve the desirable effective beamwidth are presented. The results are demonstrated on the fields of spectral moments obtained with the National Weather Radar Testbed (NWRT), a phased array weather radar at NOAA’s National Severe Storms Laboratory (NSSL).

scholarly journals Effects of Radial-Based Noise Power Estimation on Spectral Moment Estimates

2014 ◽  
Vol 31 (12) ◽  
pp. 2671-2691 ◽  
Author(s):  
Igor R. Ivić ◽  
Jane C. Krause ◽  
Olen E. Boydstun ◽  
Amy E. Daniel ◽  
Alan D. Free ◽  
...  
Keyword(s):  
Internal Noise ◽  
Power Estimation ◽  
Radar Data ◽  
Meteorological Variables ◽  
Weather Data ◽  
Noise Power ◽  
Spectral Moment ◽  
System Noise ◽  
Moment Estimates ◽  
Antenna Position

Abstract A radar antenna intercepts thermal radiation from various sources, including the ground, the sun, the sky, precipitation, and man-made radiators. In the radar receiver, this external radiation produces noise that constructively adds to the receiver internal noise and results in the overall system noise. Consequently, the system noise power is dependent on the antenna position and needs to be estimated accurately. Inaccurate noise power measurements may lead to a reduction of coverage if the noise power is overestimated or to radar data images cluttered by noise speckles if the noise power is underestimated. Moreover, when an erroneous noise power is used at low to moderate signal-to-noise ratios, estimators can produce biased meteorological variables. Therefore, to obtain the best quality of radar products, it is desirable to compute meteorological variables using the noise power measured at each antenna position. An effective technique that achieves this by estimating the noise power in real time from measured powers at each scan direction and in parallel with weather data collection has been proposed. Herein, the effects of such radial-based noise power estimation on spectral moment estimates are investigated.

“I Can See What You’re Saying”: Clinical Utility of Spectral Moment Analysis

2011 ◽  
Vol 21 (2) ◽  
pp. 44-54
Author(s):  
Kerry Callahan Mandulak
Keyword(s):  
Speech Production ◽  
Speech Signal ◽  
Clinical Utility ◽  
Acoustic Analysis ◽  
Moment Analysis ◽  
Analysis Tool ◽  
Spectral Moment ◽  
Clinical Measure ◽  
Perceptual Analysis ◽  
Disordered Speech

Spectral moment analysis (SMA) is an acoustic analysis tool that shows promise for enhancing our understanding of normal and disordered speech production. It can augment auditory-perceptual analysis used to investigate differences across speakers and groups and can provide unique information regarding specific aspects of the speech signal. The purpose of this paper is to illustrate the utility of SMA as a clinical measure for both clinical speech production assessment and research applications documenting speech outcome measurements. Although acoustic analysis has become more readily available and accessible, clinicians need training with, and exposure to, acoustic analysis methods in order to integrate them into traditional methods used to assess speech production.

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