Christopher D. Curtis

Beam Multiplexing Using the Phased-Array Weather Radar

Abstract The recently installed S-band phased-array radar (PAR) at the National Weather Radar Testbed (NWRT) offers fast and flexible beam steering through electronic beam forming. This capability allows the implementation of a novel scanning strategy termed beam multiplexing (BMX), with the goal of providing fast updates of weather information with high statistical accuracy. For conventional weather radar the data acquisition time for a sector scan or a volume coverage pattern (VCP) can be reduced by increasing the antenna’s rotation rate to the extent that the pedestal allows. However, statistical errors of the spectral moment estimates will increase due to the fewer samples that are available for the estimation. BMX is developed to exploit the idea of collecting independent samples and maximizing the usage of radar resources. An improvement factor is introduced to quantify the BMX performance, which is defined by the reduction in data acquisition time using BMX when the same data accuracy obtained by a...

Refractivity Retrieval Using the Phased-Array Radar: First Results and Potential for Multimission Operation

In this paper, an investigation of the potential of rapid refractivity retrieval is presented. The retrieval technique utilizes radar phase measurements of ground clutter to derive near-surface refractivity, which has been commonly used as a proxy for humidity, given its close relation to vapor pressure. Surface humidity is an important meteorological parameter and has been known to play an important role in convective initiation. In this paper, the refractivity retrieval technique is exploited by using smaller numbers of samples for phase calculation, which is a fundamental process in refractivity retrieval. The impetus for this paper is to explore the possibility of rapid refractivity retrieval by exploiting the rapid beam-steering capability of a phased-array radar. Using the National Weather Radar Testbed in Norman, OK, a 64-pulse per radial raw-data set was collected for conventional refractivity processing. Then, subsets of the 64 samples were extracted to emulate shorter dwell periods and the corresponding more rapid experiments. The test cases that were considered are 2, 4, 8, 16, and 32 samples. Refractivity fields retrieved using smaller numbers of samples are compared against the reference field, which was obtained using the entire 64-sample data set. It will be shown that, statistically, significant refractivity fields can be obtained from as short as a two-sample dwell.

Adaptive Range Oversampling to Achieve Faster Scanning on the National Weather Radar Testbed Phased-Array Radar

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.

Pseudowhitening of weather Radar signals to improve spectral moment and polarimetric variable estimates at low signal-to-noise ratios

Pseudowhitening of oversampled signals in range is proposed as a method to improve the performance of spectral moment and polarimetric variable estimators on weather surveillance radars. In an attempt to overcome the noise sensitivity of the whitening transformation, a solution based on the minimum mean-square-error criterion is considered first; however, this transformation is less practical than whitening because it requires knowledge of the signal-to-noise ratio at every range location. Pseudowhitening techniques are introduced as practical solutions that achieve a suboptimal compromise between variance reduction and noise sensitivity. Based on regularization methods for the solution of ill-conditioned problems, two pseudowhitening schemes are proposed: the clipped singular value decomposition transformation and the sharpening filter. By comparing their statistical performance with theoretical minimum bounds, it is shown that pseudowhitening-based estimators are almost optimal under practical conditions. Estimators based on pseudowhitening techniques avoid the pitfalls of their whitening-transformation-based counterparts and lead to more accurate radar products and/or rapid data acquisition for a much wider range of signal-to-noise ratios.

Radial-Based Noise Power Estimation for Weather Radars

AbstractA 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...

Multichannel Receiver Design, Instrumentation, and First Results at the National Weather Radar Testbed

When the National Weather Radar Testbed (NWRT) was installed in 2004, a single-channel digital receiver was implemented so that the radar could mimic typical Weather Surveillance Radar (WSR) version 1988 Doppler (WSR-88D) capability. This, however, left unused eight other channels, built into the antenna. This paper describes the hardware instrumentation of a recently completed project that digitizes the radar signals produced by these channels. The NWRT is the nations first phased array devoted to weather observations, and this testbed serves as an evaluation platform to test new hardware and signal processing concepts. The multichannel digital data will foster a new generation of adaptive/fast scanning techniques and space-antenna/interferometry measurements, which will then be used for improved weather forecasting via data assimilation. The multichannel receiver collects signals from the sum, azimuth-difference, elevation-difference, and five broad-beamed auxiliary channels. One of the major advantages of the NWRT is the capability to adaptively scan weather phenomena at a higher temporal resolution than is possible with the WSR-88D. Access to the auxiliary channels will enable clutter mitigation and advanced array processing for higher data quality with shorter dwell times. Potential benefits of higher quality and higher resolution data include: better understanding of storm dynamics and convective initiation; better detection of small-scale phenomena, including tornadoes and microbursts; and crossbeam wind, shear, and turbulence estimates. These items have the distinct possibility to ultimately render increased lead time for warnings and improved weather prediction. Finally, samples of recently collected data are presented in the results section of this paper.

The Impact of Signal Processing on the Range-Weighting Function for Weather Radars

AbstractThe range-weighting function (RWF) determines how individual scatterer contributions are weighted as a function of range to produce the meteorological data associated with a single resolution volume. The RWF is commonly defined in terms of the transmitter pulse envelope and the receiver filter impulse response, and it determines the radar range resolution. However, the effective RWF also depends on the range-time processing involved in producing estimates of meteorological variables. This is a third contributor to the RWF that has become more significant in recent years as advanced range-time processing techniques have become feasible for real-time implementation on modern radar systems. In this work, a new formulation of the RWF for weather radars that incorporates the impact of signal processing is proposed. Following the derivation based on a general signal processing model, typical scenarios are used to illustrate the variety of RWFs that can result from different range-time signal processing ...

Multipatterns of the National Weather Radar Testbed Mitigate Clutter Received via Sidelobes

Abstract The phased-array radar (PAR) of the National Weather Radar Testbed (NWRT) has a unique hybrid (mechanical and electrical) azimuth scan capability, allowing weather observations with different antenna patterns. Observations show the standard deviation of the sample mean power of weather echoes received through the main lobe of a set of squinted beams is less than the clutter received via sidelobes. This then allows use of a multipattern technique to cancel sidelobe echoes from moving scatterers, echoes that cannot be filtered with a ground-clutter canceler. Although the multipattern technique was developed to cancel clutter received through sidelobes, results show clutter from objects moving within the beam can also be canceled.

Real-Time Measurement of the Range Correlation for Range Oversampling Processing

As range-oversampling processing has become more practical for weather radars, implementation issues have become important to ensure the best possible performance. For example, all of the linear transformations that have been utilized for range-oversampling processing directly depend on the normalized range correlation matrix. Hence, accurately measuring the correlation in range time is essential to avoid reflectivity biases and to ensure the expected variance reduction. Although the range correlation should be relatively stable over time, hardware changes and drift due to changing environmental conditions can have measurable effects on the modified pulse. To reliably track changes in the range correlation, an automated real-time method is needed that does not interfere with normal data collection. A method is proposed that uses range-oversampled data from operational radar scans and that works with radar returns from both weather and ground clutter. In this paper, the method is described, tested using simulations, and validated with time series data.

Adaptive Nullforming to Mitigate Ground Clutter on the National Weather Radar Testbed Phased Array Radar

With the decreasing cost of phased array antennas, their use for weather surveillance is becoming more practical. A significant advantage of phased arrays that can be applied to weather surveillance is spatial filtering. Using adaptive nullforming to spatially filter clutter is a novel approach to clutter mitigation, which is not possible with conventional parabolic reflector antennas. Moreover, spatial filtering is also applicable to phased-array-specific techniques such as beam multiplexing and adaptive scanning when only a few pulses are available for processing; this situation is particularly challenging for conventional ground clutter filters. The National Weather Radar Testbed Phased Array Radar (NWRT PAR) provides an opportunity to test some of these new capabilities. In this paper, a linearly constrained minimum power algorithm with an additional quadratic constraint is applied to weather data collected using the NWRT PAR and its multichannel receiver. Both the original algorithm and a recursive least squares version are utilized to show reflectivity and velocity data where both weather and ground clutter are present. Doppler spectra from selected range gates are examined to illustrate the performance of adaptive nullforming. Issues such as the number of samples needed to estimate the covariance matrix are explored. As far as we know, this is the first time that these types of techniques have been used to mitigate ground clutter contamination on a weather surveillance radar.