Velocity Dealiasing Research Articles

Doppler Velocity De-Aliasing Based on Lag-1 Cross-Correlation for Dual-Polarization Weather Radar

Doppler Velocity De-Aliasing Based on Lag-1 Cross-Correlation for Dual-Polarization Weather Radar

Velocity Dealiasing for 94 GHz Vertically Pointing MMCR with Dual-PRF Technique

Velocity aliasing is unavoidable for millimeter-wave cloud radar (MMCR) due to its short wavelength. In the vertically pointing MMCR, a special aliasing state called half-folding will cause the traditional postprocessing dealiasing methods used for weather radar, including the dual-PRF method, to fail. In this paper, we propose a method that applies the dual-PRF technique to spectral dealiasing. By utilizing the property that the true velocity difference between peaks should be the same in both PRFs, our method is able to solve a special case of half-folding caused by multiple peaks, which is ignored by other spectral dealiasing methods. The special case, which we call implicit half-folding, occurs in the presence of multiple peaks in a Doppler power spectrum, where none of the peaks are folded, and they appear to be in the same Nyquist interval, whereas the peaks are actually not in the same Nyquist interval. Observations from a 94 GHz vertically pointing MMCR called TJ-II were used to demonstrate various aliasing cases, including the implicit half-folding case. As a result, our method successfully solved all aliasing cases while the other method failed when the implicit half-folding case occurred.

An Algorithm Using DBSCAN to Solve the Velocity Dealiasing Problem

Velocity dealiasing is an essential task for correcting the radial velocity data collected by Doppler radar. To improve the accuracy of velocity dealiasing, traditional dealiasing algorithms usually set a series of empirical thresholds, combine three- or four-dimensional data, or introduce other observation data as a reference. In this study, we transform the velocity dealiasing problem into a clustering problem and solve this problem using the density-based spatial clustering of applications with noise (DBSCAN) method. This algorithm is verified with a case study involving radar data on the tropical cyclone Mangkhut in 2018. The results show that the accuracy of the proposed algorithm is close to that of the four-dimensional dealiasing (4DD) method proposed by James and Houze; yet, it only requires two-dimensional velocity data and eliminates the need for other reference data. The results of the case study also show that the 4DD algorithm filters out many observation gates close to the missing data or radar center, whereas the proposed algorithm tends to retain and correct these gates.

A Velocity Dealiasing Scheme Based on Minimization of Velocity Differences between Regions

The velocity dealiasing is an essential work of automatic weather phenomenon identification, nowcasting, and disaster monitoring based on radial velocity data. The noise data, strong wind shear, and isolated echo region in the Doppler radar radial velocity data severely interfere with the velocity dealiasing algorithm. This paper proposes a two-step velocity dealiasing algorithm based on the minimization of velocity differences between regions to solve this problem. The first step is to correct aliased velocities by minimizing the sum of gradients in every region to eliminate abnormal velocity gradients between points. The interference of noise data and strong wind shear can be reduced by minimizing the whole gradients in a region. The second step is to dealiase velocities by the velocity differences between different isolated regions. The velocity of an unknown isolated region is determined by the velocities of all known regions. This step improves the dealiasing results of isolated regions. In this paper, 604 volume scan samples, including typhoons, squall lines, and heavy precipitation, were used to test the algorithm. The statistical results and analysis show that the proposed algorithm can dealiase the velocity field with a high probability of detection and a low false alarm rate.

A Vortex-Based Doppler Velocity Dealiasing Algorithm for Tropical Cyclones

AbstractIn this study, a vortex-based Doppler velocity dealiasing (VDVD) algorithm for tropical cyclones (TCs) is proposed. The algorithm uses a Rankine combined vortex model as a reference field for dealiasing based on an inner–outer iterative procedure. The structure of the reference vortex is adjusted in an inner iterative procedure of VDVD that applies the ground-based velocity track display (GBVTD) technique. The outer loop of the VDVD based on the GBVTD-simplex algorithm is used for center correction. The VDVD is able to recover not only the aliased Doppler velocities from a simulated symmetric vortex but also those superimposed with wavenumber-1 asymmetry, radial wind, or mean flow. For real cases, the VDVD provides dealiased Doppler velocity with 99.4% accuracy for all pixels, based on 472 elevation sweeps from a typhoon without landfall. It is suggested that the VDVD algorithm can improve the quality of downstream applications such as Doppler wind retrievals and radar data assimilations of TCs and other storms, such as tornadoes and mesocyclones, with vortex signatures.

A novel velocity dealiasing algorithm for S-Band weather radars

Noise, especially continuous noise, is an important factor that affects the velocity dealiasing of Doppler weather radars. A challenge in the design of dealiasing algorithm is how to effectively suppress noise. Therefore, a novel anti-noise dealiasing algorithm (AND) for Doppler velocities was proposed in this paper. In comparison with traditional methods, the AND utilizes a new separation-restoration noise suppression scheme, which can significantly reduce the interference of noise on dealiasing without losing data. This algorithm utilizes three fitting curves as references to correct ambiguous echoes, and it can further suppress residual noise and dynamically match wind fields at varying scales. The AND has been applied to the CINRAD-SA network of operational radars in China, achieving a better performance on noisy echoes, isolated echoes, typhoon echoes, and tornado echoes.

Mesocyclone-Targeted Doppler Velocity Dealiasing

AbstractThe alias-robust variational (AR-Var) analysis developed originally for dealiasing raw velocities scanned from winter ice storms by operational WSR-88D radars was recently extended for dealiasing raw velocities scanned from all storms to increase the dealiased data coverage. The extended AR-Var (eAR-Var)-based dealiasing can detect tornadic mesocyclones and estimate their vortex center locations as by-products, but its dealiased data often leave rejected data holes in the critical vortex core and vicinity areas of detected mesocyclones. To solve this problem, a mesocyclone-targeted dealiasing routine is developed in this paper to perform two additional steps after the eAR-Var dealiasing. In particular, a reference check is performed in the first step, with the required reference velocities produced by a newly designed alias-robust vortex wind analysis to recover the rejected data in the vortex core, and then a continuity check is performed in the second step to recover the remaining rejected data around and beyond the vortex core. The mesocyclone-targeted dealiasing is tested extensively with severely aliased velocity data scanned from tornadic storms and is found to be effective and efficient for recovering the rejected data in and around the vortex core of the detected mesocyclone, provided the required data coverage conditions and analysis acceptance conditions are satisfied.

An Automated Method for Depicting Mesocyclone Paths and Intensities

Abstract The location and intensity of mesocyclone circulations can be tracked in real time by accumulating azimuthal shear values over time at every location of a uniform spatial grid. Azimuthal shear at low (0–3 km AGL) and midlevels (3–6 km AGL) of the atmosphere is computed in a noise-tolerant manner by fitting the Doppler velocity observations in the neighborhood of a pulse volume to a plane and finding the slope of that plane. Rotation tracks created in this manner are contaminated by nonmeteorological signatures caused by poor velocity dealiasing, ground clutter, radar test patterns, and spurious shear values. To improve the quality of these fields for real-time use and for an accumulated multiyear climatology, new dealiasing strategies, data thresholding, and multiple hypothesis tracking (MHT) techniques have been implemented. These techniques remove nearly all nonmeteorological contaminants, resulting in much clearer rotation tracks that appear to match mesocyclone paths and intensities closely.

Improved Doppler Velocity Dealiasing for Radar Data Assimilation and Storm-Scale Vortex Detection

The Doppler velocity dealiasing technique based on alias-robust VAD and variational (AR-Var) analyses developed at the National Severe Storms Laboratory for radar data quality control and assimilation is further improved in its two-step procedures: the reference check in the first step and the continuity check in the second step. In the first step, the alias-robust variational analysis is modified adaptively and used in place of the alias-robust velocity-azimuth display (VAD) analysis for all scan modes (rather than solely the WSR-88D volume coverage pattern 31 with the Nyquist velocityvNreduced below 12 m s−1and the TDWR Mod80 withvNreduced below 15 m s−1), so more raw data can pass the stringent threshold conditions used by the reference check in the first step. This improves the dealiased data coverage without false dealiasing to better satisfy the high data quality standard required by radar data assimilation. In the second step, new procedures are designed and added to the continuity check to increase the dealiased data coverage over storm-scale areas threatened by intense mesocyclones and their generated tornados. The performances of the improved dealiasing technique versus the existing techniques are exemplified by the results obtained for tornadic storms scanned by the operational KTLX radar in Oklahoma.

A Velocity Dealiasing Technique Using Rapidly Updated Analysis from a Four-Dimensional Variational Doppler Radar Data Assimilation System

Abstract A Doppler velocity dealiasing algorithm is developed within the storm-scale four-dimensional radar data assimilation system known as the Variational Doppler Radar Analysis System (VDRAS). The innovative aspect of the algorithm is that it dealiases Doppler velocity at each grid point independently by using three-dimensional wind fields obtained either from an objective analysis using conventional observations and mesoscale model output or from a rapidly updated analysis of VDRAS that assimilates radar data. This algorithm consists of three steps: preserving horizontal shear, global dealiasing using reference wind from the objective analysis or the VDRAS analysis, and local dealiasing. It is automated and intended to be used operationally for radar data assimilation using numerical weather prediction models. The algorithm was tested with 384 volumes of radar data observed from the Next Generation Weather Radar (NEXRAD) for a severe thunderstorm that occurred during 15 June 2002. It showed that the algorithm was effective in dealiasing large areas of aliased velocities when the wind from the objective analysis was used as the reference and that more accurate dealiasing was achieved by using the continuously cycled VDRAS analysis.

Multi-PRI Signal Processing for the Terminal Doppler Weather Radar. Part II: Range–Velocity Ambiguity Mitigation

Abstract Multiple pulse-repetition interval (multi-PRI) transmission is part of an adaptive signal transmission and processing algorithm being developed to combat range–velocity (RV) ambiguity for the Terminal Doppler Weather Radar (TDWR). In Part I of this two-part paper, an adaptive clutter filtering procedure that yields low biases in the moments estimates was presented. In this part, algorithms for simultaneously providing range-overlay protection and velocity dealiasing using multi-PRI signal transmission and processing are presented. The effectiveness of the multi-PRI RV ambiguity mitigation scheme is demonstrated using simulated and real weather radar data, with excellent results. Combined with the adaptive clutter filter, this technique will be used within the larger context of an adaptive signal transmission and processing scheme in which phase-code processing will be a complementary alternative.

Multi-PRI Signal Processing for the Terminal Doppler Weather Radar. Part I: Clutter Filtering

Abstract Multiple pulse repetition interval (multi-PRI) transmission is part of an adaptive signal transmission and processing algorithm being developed to aggressively combat range–velocity ambiguity in weather radars. In the past, operational use of multi-PRI pulse trains has been hampered due to the difficulty in clutter filtering. This paper presents finite impulse response clutter filter designs for multi-PRI signals with excellent magnitude and phase responses. These filters provide strong suppression for use on low-elevation scans and yield low biases of velocity estimates so that accurate velocity dealiasing is possible. Specifically, the filters are designed for use in the Terminal Doppler Weather Radar (TDWR) and are shown to meet base data bias requirements equivalent to the Federal Aviation Administration’s specifications for the current TDWR clutter filters. Also an adaptive filter selection algorithm is proposed that bases its decision on clutter power estimated during an initial long-PRI surveillance scan. Simulations show that this adaptive algorithm yields satisfactory biases for reflectivity, velocity, and spectral width. Implementation of such a scheme would enable automatic elimination of anomalous propagation signals and constant adjustment to evolving ground clutter conditions, an improvement over the current TDWR clutter filtering system.

A Three-Step Dealiasing Method for Doppler Velocity Data Quality Control

Abstract A three-step method is developed for Doppler radar velocity dealiasing. First, the modified velocity azimuth display (VAD) method of Tabary et al. is adopted and applied to raw (aliased) velocity data to estimate horizontal vector velocities averaged on selected circles of radar scans. These velocities are used as preliminary references to detect and pre-dealiase or flag (if not correctable) possible alias errors. Then, the traditional VAD method is applied to the pre-dealiased velocity data to produce refined reference velocities to further detect and dealiase or flag alias errors in the second step. After these two steps, flagged data points are confined in small areas (often associated with strong shear and/or convergence flows), so the dealiased velocities along the boundary of each small area provide reliable starting points for the continuity check in the third step. The two-dimensional continuity check can start from any and every boundary point and proceed inward until all the flagged dat...

Exploiting 3D volume data from the Czech weather radar network

Volume reflectivity and Doppler wind products, elaborated by independent in-house software for processing volume data from the Czech weather radar network, are tested. Statistics of average vertical reflectivity profiles (VPR) from radar Skalky, calculated for 34-month period, is presented. VPRs are used also for the testing of bright-band detection algorithm. Bright-band heights are compared with aerological measurements of 0°C isotherm heights.To overcome the limitations of the vendor software, VAD method including software based bad sample rejection and velocity de-aliasing was implemented. Vertical profiles of wind (VPW) are processed at two radars, the statistics of data availability for 5-month period is given.

Two- and Three-Dimensional De-aliasing of Doppler Radar Velocities

Two versions of a two-dimensional approach to Doppler radar velocity de-aliasing were developed and tested on various datasets. One version used fully recursive segmentation of the data and the technique of spatial computer vision to de-alias isolated areas of velocity, whereas the other version utilized spatially constrained segmentation, objective analysis and a nonmeteorological knowledge base for de-aliasing homogenous velocity regions. The utility of the NEXRAD VAD algorithm-derived windfield information was evaluated. A recursive three-dimensional de-aliasing approach was also developed in an attempt to reduce the need for auxiliary windfield information. These approaches were tested on two- (and three) dimensional data in both R, Θ, (Φ) and X, Y, (Z) coordinates and found to produce nearly identical results. Spatial filtering of the data proved to be a nearly preprocessing step and an elective filter was developed which preserves valid velocity information while reducing computational requirements. The importance of removing ground clutter was found to increase as the unambiguous velocity of the radar is reduced and a new scheme was developed and included in the de-aliasing process.