A method for estimation of spectral moments on pulsed weather radars is presented. This scheme operates on oversampled echoes in range; that is, samples of in-phase and quadrature-phase components are collected at a rate several times larger than the reciprocal of the transmitted pulse length. The spectral moments are estimated by suitably combining weighted averages of these oversampled signals in range with usual processing of samples (spaced at the pulse repetition time) at a fixed range location. The weights in range are derived from a whitening transformation; hence, the oversampled signals become uncorrelated and, consequently, the variance of the estimates decreases significantly. Because the estimate errors are inversely proportional to the volume scanning times, it follows that storms can be surveyed much faster than is possible with current processing methods, or equivalently, for the current volume scanning time, accuracy of the estimates can be greatly improved. This significant improvement is achievable at large signal-to-noise ratios.

Since 2007 the advancement of the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) hardware and software capabilities has been supporting the implementation of high-temporal-resolution (;1 min) sampling. To achieve the increase in computational power and data archiving needs required for high-temporal-resolution sampling, the signal processor was upgraded to a scalable, Linux-based cluster with a distributed computing architecture. The development of electronic adaptive scanning, which can reduce update times by focusing data collection on significant weather, became possible through functionality added to the radar control interface and real-time controller. Signal processing techniques were implemented to address data quality issues, such as artifact removal and range-and-velocity ambiguity mitigation, absent from the NWRT PAR at its installation. The hardware and software advancements described above have made possible the development of conventional and electronic scanning capabilities that achieve high-temporalresolution sampling. Those scanning capabilities are sector-and elevation-prioritized scanning, beam multiplexing, and electronic adaptive scanning. Each of these capabilities and related sampling trade-offs are explained and demonstrated through short case studies.

Wind turbines cause contamination of weather radar signals that is often detrimental and difficult to distinguish from cloud returns. Because the turbines are always at the same location, it would seem simple to identify where wind turbine clutter (WTC) contaminates the weather radar data. However, under certain atmospheric conditions, anomalous propagation of the radar beam can occur such that WTC corrupts weather data on constantly evolving locations, or WTC can be relatively weak such that contamination on predetermined locations does not occur. Because of the deficiency of using turbine locations as a proxy for WTC, an effective detection algorithm is proposed to perform automatic flagging of contaminated weather radar data, which can then be censored or filtered. Thus, harmful effects can be reduced that may propagate to automatic algorithms or may hamper the forecaster's ability to issue timely warnings. In this work, temporal and spectral features related to WTC signatures are combined in a fuzzy logic algorithm to classify the radar return as being contaminated by WTC or not. The performance of the algorithm is quantified using simulations and the algorithm is applied to a real data case from the radar facility in Dodge City, Kansas (KDDC). The results illustrate that WTC contamination can be detected automatically, thereby improving the quality control of weather radar data.

Botulinum neurotoxin injections are a valuable treatment modality for many therapeutic indications as well as in the aesthetic field for facial rejuvenation. As successful treatment requires repeated injections over a long period of time, secondary resistance to botulinum toxin preparations after repeated injections is an ongoing concern. We report five case studies in which neutralizing antibodies to botulinum toxin type A developed after injection for aesthetic use and resulted in secondary treatment failure. These results add to the growing number of reports in the literature for secondary treatment failure associated with high titers of neutralizing antibodies in the aesthetic field. Clinicians should be aware of this risk and implement injection protocols that minimize resistance development.

No 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 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. In this paper, an effective method is proposed to estimate the noise power in real time from measured powers at each radial. The technique uses a set of criteria to detect radar range resolution volumes that do not contain weather signals and uses those to estimate the noise power. The algorithm is evaluated using both simulated and real time series data; results show that the proposed technique accurately produces estimates of the system noise power. An operational implementation of this technique is expected to significantly improve the quality of weather radar products with a relatively small computational burden.

This paper describes a real-time implementation of adaptive range oversampling processing on the National Weather Radar Testbed phased-array radar. It is demonstrated that, compared to conventional matched-filter processing, range oversampling can be used to reduce scan update times by a factor of 2 while producing meteorological data with similar quality. Adaptive range oversampling uses moment-specific transformations to minimize the variance of meteorological variable estimates. An efficient algorithm is introduced that allows for seamless integration with other signal processing functions and reduces the computational burden. Through signal processing, a new dimension is added to the traditional trade-off triangle that includes the variance of estimates, spatial coverage, and update time. That is, by trading an increase in computational complexity, data with higher temporal resolution can be collected and the variance of estimates can be improved without affecting the spatial coverage.

We develop a canonical, adaptive cascade-structure IIR notch filter to detect and track multiple time-varying frequencies in additive white Gaussian noise. The algorithm uses allpass frequency transformation filters and a truncated gradient. Simulations indicate that our algorithm is computationally simple, converges rapidly, and has good frequency resolution.