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A method named BTREC, which is in fact an extension of tracking a radar echo by cross-correlation (TREC) method, is proposed based on the Barnes filter in this paper. BTREC is an efficient objective analysis method for smoothing the motion vectors of radar echo patterns. A comparative analysis of the BTREC vectors and the COTREC (another extension of TREC, which is restricted by the 2D continuity equation) vectors is conducted. The results show that the BTREC method corrects the noise and inconsistencies in TREC vectors (often induced by shielding, ground clutter, and rapid morphological changes of the radar patterns) more thoroughly than COTREC. Then the BTREC, COTREC, and TREC methods are applied to extrapolate the real radar echo pattern over Jiangxi Province in China for two cases (1900–2100 UTC 4 June and 0900–1100 UTC 30 June 2018). Results show that the BTREC method performs the best out of the three methods to forecast the radar echo patterns in the following 1 and 2 h, and has the least distortion. To further confirm that, 892 radar composite reflectivity mosaic images with precipitation in summer (June–August) over Jiangxi are collected to test the three methods. The results show that in the 1- and 2-h extrapolations, the mean differences of the threat score (TS), correlation (CORR), and probability of detection (POD) between BTREC and TREC are obviously more than 0, while that of the false alarm ratio (FAR) is remarkably less than 0 (the threshold to identify whether a grid is correctly predicted is set to 10 dBZ). Although the mean difference of TS, CORR, POD, and FAR between COTREC and TREC have similar variation, their magnitudes are obviously decreased (especially for the 2-h extrapolation). This further indicates that the BTREC method obviously improves the forecast of the radar echo pattern within the next 2 h compared to the COTREC and TREC methods. [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]

Since the dual-polarization upgrade of the Weather Surveillance Radar-1988 Doppler (WSR-88D), the polarimetric variables have become a fundamental tool for better interpretation and forecasting of hazardous weather events. Thus, improving their quality has been an important long-standing effort. In this paper, we introduce the hybrid-scan estimators (HSE), which use the available data in split cuts of operational volume coverage patterns (VCP) to provide better estimates of differential reflectivity, differential phase, and correlation coefficient. The HSE are designed to choose between the data provided by either one of the two scans in split cuts based on their expected statistical performance, resulting in the same or better data quality compared to the conventional estimators. The performance improvement realized with the HSE is characterized with simulations and illustrated with data from WSR-88D. While relatively simple, an operational implementation of the HSE could bring improvements to forecasters' data interpretation and algorithm performance, both of which rely on dual-polarization radar data. [ABSTRACT FROM AUTHOR]

To detect wake vortices in all weather conditions, lidar and radar sensors are complementary. It is important to determine accurately the limitations of the radar detection in clear air and to determine the parameters influencing the detection. This paper is the first study to present the simulation and analysis of the radar signatures of wake vortices on the basis of state-of-the-art 3D large-eddy simulations. The setup of the large-eddy simulations is described and the evolution of the wake vortices is illustrated for the three simulated cases. A modified version of the model developed by Muschinski et al. is used here for the calculation of the radar-backscattered signal and the second moment of the Doppler spectrum that is used to estimate the wake vortex circulation. The simulator uses two different radar configurations for the retrieval of the circulation, which is compared with the circulation obtained directly from the large-eddy simulations. The retrieval of the circulation is fairly accurate if the level of backscattered signal is high enough above the detection threshold of the radar. [ABSTRACT FROM AUTHOR]

The CSU–CHILL radar is a dual-wavelength, dual-polarization weather radar system operating at S and X band with coaxial beams. This radar system offers a unique environment to develop and/or validate algorithms that cut across its wavelengths and polarizations. This paper presents a method to retrieve resonance scattering regions from the difference in intrinsic reflectivities after attenuation correction, which is performed using measured reflectivity fields only. The algorithm to retrieve these regions dominated by non-Rayleigh scattering is applied to different storm events, and the obtained data field capturing the difference in S- and X-band reflectivities due to resonance effects (which we will call Mie signal for convenience) is compared to the collocated dual-polarization fields. The obtained Mie signal is also compared to hail reports. In both cases, the retrieved Mie signal is found to be consistent with the rest of the dual-polarization data fields, and in some situations, it is shown to bring information not directly discernible from the usual dual-polarization radar variables. [ABSTRACT FROM AUTHOR]

A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase KDP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Special attention is given to the optimization of the factor α used for computation of a path-integrated attenuation from a total span of differential phase along the propagation path in rain. It is suggested to estimate α from a slope of the ZDR dependence on Z in rain. The use of real-time adjusted α allows us to capture the variations of the drop size distributions, and therefore improve the QPE accuracy. It is demonstrated that the factor α is generally higher for tropical rain type compared to continental rain. Since the R(A) approach is only valid for pure rainfall, the R(KDP) relation is suggested as a complement in areas of hail contamination. The paper contains a description of the basic version of the R(A) and R(KDP) algorithm and recommendations for its further optimization. [ABSTRACT FROM AUTHOR]