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Providing timely warnings for severe and potentially tornadic convection is a critical component of the NWS mission, and owing to the associated large reflectivity gradients, sidelobe contamination is possible. This paper focuses on elevation sidelobe contamination appearing in the low-level inflow region of supercells. A qualitative conceptual model of the Weather Surveillance Radar-1988 Doppler (WSR-88D) antenna pattern interacting with supercells is introduced, along with Doppler power spectrum representations of the potential mix of returned power from the main lobe and the sidelobes. These tools inform the multiple ways elevation sidelobe contamination appears in the low levels, primarily below 3 km (10 kft) of radar data. The most common manifestation is somewhat noisy data similar to particulates or biota in clear air. Trained NWS forecasters are accustomed to mentally filtering out noisy clear-air returns as less important. Elevation sidelobe contamination can be mixed with the three-body scatter spike (TBSS) artifact, though the TBSS remains the more salient feature. The most consequential form is the apparent circulation, and when it is incorrectly interpreted as valid, contributes to the false alarm ratio (FAR) for NWS tornado warnings. Quantitative results on the effect of elevation sidelobe contamination on FAR are presented. Diagnostic techniques are emphasized, and with familiarization, can be used in real-time warning operations to identify the apparent circulation as either valid or an imposter. Identification of these contaminated velocity signatures offers a unique opportunity to reduce the NWS tornado warning FAR without also reducing the probability of detection (POD). Significance Statement: The WSR-88D weather radars provide overall high-quality data for users. However, with some severe thunderstorms, an artifact called elevation sidelobe contamination can produce what looks like a rotation signature, but it may not be real. These ambiguous velocity signatures can contribute to tornado warnings based on rotation signatures that are false circulations. This paper specifically focuses on elevation sidelobe contamination due to its impact on tornado warning decisions. Diagnostic techniques, including several examples, are presented here to aid the reader in correctly identifying elevation sidelobe contamination and why it may occur. Correct identification of an apparent circulation as an imposter due to contamination is a unique opportunity to improve NWS tornado warning performance by reducing warning false alarms. [ABSTRACT FROM AUTHOR]

Ground clutter filtering is an important and necessary step for quality control of ground-based weather radars. In this paper, ground clutter mitigation is addressed using a time-domain regression filter. Clutter filtering is now widely accomplished with spectral processing where the times series of data corresponding to a radar resolution volume are transformed with a discrete Fourier transform after which the zero and near-zero velocity clutter components are eliminated by setting them to zero. Subsequently for reflectivity, velocity, and spectrum width estimates, interpolation techniques are used to recover some of the power loss due to the clutter filter, which has been shown to reduce bias. The spectral technique requires that the in-phase (I) and quadrature (Q) time series be windowed to reduce clutter power leakage away from zero and near-zero velocities. Unfortunately, window functions such as the Hamming, Hann, and Blackman attenuate the time series signal by 4.01, 4.19, and 5.23 dB for 64-point times series, respectively, and thereby effectively reduce the number of independent samples available for estimating the radar parameters of any underlying weather echo. In this paper, a regression filtering technique is investigated, through simulated data, that does not require the use of such window functions and thus provides for better weather signal statistics. In a follow-on paper that is in preparation the technique will be demonstrated using both S-band polarimetric radar (S-Pol) and NEXRAD data. Here, it is shown that the regression filter rejects clutter as effectively as the spectral technique but has the distinct advantage that estimates of the radar variables are greatly improved. The technique is straightforward and can be executed in real time. [ABSTRACT FROM AUTHOR]

The highly successful fleet of Weather Surveillance Radar-1988 Doppler (WSR-88D) radars is approaching its end of service, and research efforts are under way to inform a decision toward a possible WSR-88D replacement. A methodology to link radar design characteristics to impacts on how radar data are used to diagnose hazardous weather was developed through a unique partnership between radar-engineering innovations in radar simulations and National Weather Service (NWS) radar data interpretation expertise. Deep commitment to two-way learning across disciplines resulted in a methodology that is both efficient and highly relevant to the NWS hazardous weather warning program. The methodology presented in this paper is a model for revealing complex trade-offs between weather radar characteristics and their resultant impact on NWS data interpretation for threat identification. This qualitative methodology is presented in the context of a broader proof-of-concept study from which it was developed. Adapted for further research, it can support the crucial role of deriving quantitative radar design criteria that balance the trade-offs among radar capabilities, cost, and impact to users. That is, the proposed methodology supports the evaluation of candidates for a potential WSR-88D replacement and any necessary major system upgrades in the interim. Significance Statement: Defining the requirements for an operational weather radar system is ideally achieved with clearly identified trade-offs among cost, radar design characteristics, and impacts on user data interpretation. This work is an advancement of the historic evolution of weather radar development to support the U.S. National Weather Service (NWS) mission, based on collaboration among researchers, radar engineers, and NWS forecasters. The methodology presented here is an adaptable tool for revealing these essential, but complex trade-offs, providing a roadmap for further studies toward the next-generation NWS weather radar fleet. [ABSTRACT FROM AUTHOR]

In Doppler weather radars, the presence of unfolding errors or outliers is a well-known quality issue for radial velocity fields estimated using the dual-pulse repetition frequency (PRF) technique. Postprocessing methods have been developed to correct dual-PRF outliers, but these need prior application of a dealiasing algorithm for an adequate correction. This paper presents an alternative procedure based on circular statistics that corrects dual-PRF errors in the presence of extended Nyquist aliasing. The correction potential of the proposed method is quantitatively tested by means of velocity field simulations and is exemplified in the application to real cases, including severe storm events. The comparison with two other existing correction methods indicates an improved performance in the correction of clustered outliers. The technique proposed is well suited for real-time applications requiring high-quality Doppler radar velocity fields, such as wind shear and mesocyclone detection algorithms, or assimilation in numerical weather prediction models. [ABSTRACT FROM AUTHOR]

By fitting a parametric vortex model directly to aliased radar radial velocities scanned from a hurricane, the maximum tangential velocity and its radial distance from the hurricane vortex center can be estimated by the recently developed alias-robust vortex analysis. This vortex analysis can be refined to produce a suitable reference radial velocity field on each tilt of a radar scan for the reference check in the first main step of dealiasing. This paper presents the techniques developed for the refinements and shows how and to what extent the refined vortex analysis can improve the reference check and thus enhance the dealiased data coverage, especially over severely aliased data areas around the vortex core of a hurricane or typhoon. In addition, stringent threshold conditions are used in the reference check and the subsequent continuity check to ensure the accepted data are free of alias or almost so. The robustness and improved performance of the method are exemplified by the results tested with severely aliased radial velocities scanned by operational WSR-88D radars from hurricanes in the United States and by operational China New Generation Weather Radar (CINRAD) base data format SA radars from typhoons in China. [ABSTRACT FROM AUTHOR]

A method for the operational monitoring of the weather radar antenna mechanics and signal processing is presented. The method is based on the analysis of sun signals in the polar volume data produced during the operational scanning of weather radars. Depending on the hardware of the radar, the volume coverage pattern, the season, and the latitude of the radar, several tens of sun hits are found per day. The method is an extension of that for determining the weather radar antenna pointing and for monitoring the receiver stability and the differential reflectivity offset. In the method the width of the sun image in elevation and in azimuth is analyzed from the data, together with the center point position and the total power, analyzed in the earlier methods. This paper describes how the width values are obtained in the majority of cases without affecting the quality of the position and power values. Results from the daily analysis reveal signal processing features and failures that are difficult to find out otherwise in weather data. Moreover, they provide a tool for monitoring the stability of the antenna system, and hence the method has great potential for routine monitoring of radar signal processing and the antenna mechanics. Hence, it is recommended that the operational solar analysis be extended into the analysis of the width. [ABSTRACT FROM AUTHOR]