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.

No abstract

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 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.

One of the main challenges of using phased array radar for weather observations is the implementation of dual polarization with acceptable errors of polarimetric variable estimates. This is because the differences between the copolar antenna patterns at the horizontal and vertical polarizations, as well as cross-polar fields, can introduce unacceptable measurement biases, as the main beam is electronically steered away from the principal planes. Because the sufficient cross-polar isolation is difficult to achieve by the phased array antenna hardware and because the copolar as well as cross-polar patterns inevitably vary with each beam position, it is crucial to properly evaluate errors of estimates due to radiation patterns. Herein, a method that combines the measured or simulated radiation patterns and simulated time series is introduced. The method is suited for phased array and parabolic antennas, and it allows for evaluation of radiation-pattern-induced polarimetric variable biases and standard deviations specific to the antenna used to produce the patterns. The method can be used either as an alternative to a well-established approach using analytical derivations or as a tool for cross validation of the bias computations. For standard deviation evaluation in the presence of antenna cross-polar fields, the analytical approach becomes overly complex, which inexorably leads to the introduction of numerous approximations to obtain the results. These approximations inevitably compromise the accuracy of such computations. The method proposed herein avoids such approximations and therefore provides a valuable tool for accurate assessment of polarimetric measurement precision.

Demonstration of a method for improved Doppler spectral moment estimation is made on NOAA's research and development Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma. Time series data have been recorded using a commercial processor and digital receiver whereby the sampling frequency is several times larger than the reciprocal of the transmitted pulse width. The in-phase and quadrature-phase components of oversampled weather signals are used to estimate the first three spectral moments by suitably combining weighted averages in range with usual processing at fixed range locations. The weights are chosen in such a manner that the resulting signals become uncorrelated. Consequently, the variance of estimates decreases significantly as is verified by this experiment.

Currently, signal detection and censoring in operational weather radars is performed by using thresholds of the estimated signal-to-noise ratio (SNR) and/or the magnitude of the autocorrelation coefficient at the first temporal lag. The growing popularity of polarimetric radars prompts the quest for improved detection schemes that take advantage of the signals from the two orthogonally polarized electric fields. A hybrid approach is developed based on the sum of the cross-correlation estimates as well as the powers and autocorrelations from each of the dual-polarization returns. The hypothesis that ''signal is present'' is accepted if the sum exceeds a predetermined threshold; otherwise, the data are considered to represent noise and are censored. The threshold is determined by the acceptable rate of false detections that is less than or equal to a preset value. The scheme is evaluated both in simulations and through implementation on time series data collected by the research weather surveillance radar (KOUN) in Norman, Oklahoma.

No abstract