scholarly journals An Automated 2D Multipass Doppler Radar Velocity Dealiasing Scheme

2006 ◽  
Vol 23 (9) ◽  
pp. 1239-1248 ◽  
Author(s):  
Jian Zhang ◽  
Shunxin Wang
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Velocity Fields ◽  
Velocity Data ◽  
Computationally Efficient ◽  
High Confidence ◽  
Reference Velocity ◽  
External Reference ◽  
Wind Velocities ◽  
Radar Velocity

Abstract An automated 2D multipass velocity dealiasing scheme has been developed to correct velocity fields when wind velocities are very large compared to the Nyquist velocity of the weather Doppler radars. The new velocity dealiasing algorithm is based on the horizontal continuity of velocity fields. The algorithm first determines a set of reference radials and gates by finding the weakest wind region. Then from these reference radials and gates, the scheme checks continuities among adjacent gates and corrects for the velocity values with large differences that are close to 2 × (Nyquist velocity). Multiple passes of unfolding are performed and velocities identified as “folded” with low confidence in an earlier pass are not unfolded until a discontinuity is detected with high confidence at a subsequent pass. The new velocity dealiasing scheme does not need external reference velocity data as do many existing algorithms, thus making it more easily applicable. Over 1000 radar volume scans that include tornadoes, hurricanes, and typhoons are selected to test and to evaluate the new algorithm. The results show that the new algorithm is very robust and very computationally efficient. In cases with many data voids, the new algorithm shows improvements over the current WSR-88D operational velocity dealiasing scheme. The new dealiasing algorithm is a simple and stand-alone program that can be a very useful tool to various Doppler radar data users.

scholarly journals UNRAVEL: A Robust Modular Velocity Dealiasing Technique for Doppler Radar

2020 ◽  
Vol 37 (5) ◽  
pp. 741-758
Author(s):  
Valentin Louf ◽  
Alain Protat ◽  
Robert C. Jackson ◽  
Scott M. Collis ◽  
Jonathan Helmus
Keyword(s):  
Modular Design ◽  
Doppler Radar ◽  
Three Dimensional ◽  
Reference Points ◽  
Weather Data ◽  
Doppler Velocity ◽  
Weather Radars ◽  
Reference Velocity ◽  
External Reference ◽  
Radar Velocity

AbstractUnfold Radar Velocity (UNRAVEL) is an open-source modular Doppler velocity dealiasing algorithm for weather radars. UNRAVEL is an algorithm that does not need external reference velocity data, making it easily applicable. The proposed algorithm includes 11 core modules and 2 dealiasing strategies. UNRAVEL is an iterative algorithm. The goal is to build the dealiasing results starting with the strictest possible continuity tests in azimuth and range and, after each step, relaxing the parameters to include more results from a progressively growing number of reference points. UNRAVEL also has modules that perform 3D continuity checks. Thanks to this modular design, the number of dealiasing strategies can be expanded in order to optimize the dealiasing results. While the first driver dealiases Doppler velocity from each tilt independently from one another, the second driver also performs a three-dimensional continuity check of the velocity using successive elevations. The proposed dealiasing algorithm is tested using severe weather data from an S-band Doppler radar that have been aliased to mimic aliased radial velocity patterns that would be observed by a C-band Doppler radar. Artificially aliasing S-band data permits creation of a reference to which the performance of various dealiasing techniques can be compared. Comparisons show that UNRAVEL consistently outperforms other established dealiasing algorithms for the test period selected in this work.

A Variational Method for Filling in Missing Data in Doppler Velocity Fields

2021 ◽  
Author(s):  
VINCENT T. WOOD ◽  
ROBERT P. DAVIES-JONES ◽  
ALAN SHAPIRO
Keyword(s):  
Missing Data ◽  
Doppler Radar ◽  
Signal To Noise Ratio ◽  
Original Data ◽  
Radar Data ◽  
Velocity Fields ◽  
Doppler Velocity ◽  
Doppler Radar Data ◽  
Ground Clutter ◽  
Tornado Warnings

AbstractSingle-Doppler radar data are often missing in important regions of a severe storm due to low return power, low signal-to-noise ratio, ground clutter associated with normal and anomalous propagation, and missing radials associated with partial or total beam blockage. Missing data impact the ability of WSR-88D algorithms to detect severe weather. To aid the algorithms, we develop a variational technique that fills in Doppler velocity data voids smoothly by minimizing Doppler velocity gradients while not modifying good data. This method provides estimates of the analysed variable in data voids without creating extrema.Actual single-Doppler radar data of four tornadoes are used to demonstrate the variational algorithm. In two cases, data are missing in the original data, and in the other two, data are voided artificially. The filled-in data match the voided data well in smoothly varying Doppler velocity fields. Near singularities such as tornadic vortex signatures, the match is poor as anticipated. The algorithm does not create any velocity peaks in the former data voids, thus preventing false triggering of tornado warnings. Doppler circulation is used herein as a far-field tornado detection and advance-warning parameter. In almost all cases, the measured circulation is quite insensitive to the data that have been voided and then filled. The tornado threat is still apparent.

scholarly journals A 3.5-Dimensional Variational Method for Doppler Radar Data Assimilation and Its Application to Phased-Array Radar Observations

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Qin Xu ◽  
Li Wei ◽  
Wei Gu ◽  
Jiandong Gong ◽  
Qingyun Zhao
Keyword(s):  
Data Assimilation ◽  
Variational Method ◽  
Radial Velocity ◽  
Phased Array ◽  
Doppler Radar ◽  
Radar Data ◽  
Velocity Fields ◽  
Squall Line ◽  
Phased Array Radar ◽  
Radar Data Assimilation

A 3.5-dimensional variational method is developed for Doppler radar data assimilation. In this method, incremental analyses are performed in three steps to update the model state upon the background state provided by the model prediction. First, radar radial-velocity observations from three consecutive volume scans are analyzed on the model grid. The analyzed radial-velocity fields are then used in step 2 to produce incremental analyses for the vector velocity fields at two time levels between the three volume scans. The analyzed vector velocity fields are used in step 3 to produce incremental analyses for the thermodynamic fields at the central time level accompanied by the adjustments in water vapor and hydrometeor mixing ratios based on radar reflectivity observations. The finite element B-spline representations and recursive filter are used to reduce the dimension of the analysis space and enhance the computational efficiency. The method is applied to a squall line case observed by the phased-array radar with rapid volume scans at the National Weather Radar Testbed and is shown to be effective in assimilating the phased-array radar observations and improve the prediction of the subsequent evolution of the squall line.

Range-Correcting Azimuthal Shear in Doppler Radar Data

2013 ◽  
Vol 28 (1) ◽  
pp. 194-211 ◽  
Author(s):  
Jennifer F. Newman ◽  
Valliappa Lakshmanan ◽  
Pamela L. Heinselman ◽  
Michael B. Richman ◽  
Travis M. Smith
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Detection Algorithm ◽  
Primary Component ◽  
Velocity Data ◽  
Ongoing Research ◽  
Radar Systems ◽  
Azimuthal Shear ◽  
Order Of Magnitude ◽  
Range Correction

Abstract The current tornado detection algorithm (TDA) used by the National Weather Service produces a large number of false detections, primarily because it calculates azimuthal shear in a manner that is adversely impacted by noisy velocity data and range-degraded velocity signatures. Coincident with the advent of new radar-derived products and ongoing research involving new weather radar systems, the National Severe Storms Laboratory is developing an improved TDA. A primary component of this algorithm is the local, linear least squares derivatives (LLSD) azimuthal shear field. The LLSD method incorporates rotational derivatives of the velocity field and is affected less strongly by noisy velocity data in comparison with traditional “peak to peak” azimuthal shear calculations. LLSD shear is generally less range dependent than peak-to-peak shear, although some range dependency is unavoidable. The relationship between range and the LLSD shear values of simulated circulations was examined to develop a range correction for LLSD shear. A linear regression and artificial neural networks (ANNs) were investigated as range-correction models. Both methods were used to produce fits for the simulated shear data, although the ANN excelled as it could capture the nonlinear nature of the data. The range-correction methods were applied to real radar data from tornadic and nontornadic events to measure the capacity of the corrected shear to discriminate between tornadic and nontornadic circulations. The findings presented herein suggest that both methods increased shear values during tornadic periods by nearly an order of magnitude, facilitating differentiation between tornadic and nontornadic scans in tornadic events.

scholarly journals An Algorithm Using DBSCAN to Solve the Velocity Dealiasing Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wei Zhao ◽  
Qinglan Li ◽  
Kuifeng Jin
Keyword(s):  
Doppler Radar ◽  
Reference Data ◽  
Spatial Clustering ◽  
Radar Data ◽  
Observation Data ◽  
Two Dimensional ◽  
Velocity Data ◽  
Clustering Problem ◽  
Radial Velocity Data

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.

The Multiple-Vortex Structure of the El Reno, Oklahoma, Tornado on 31 May 2013

2018 ◽  
Vol 146 (8) ◽  
pp. 2483-2502 ◽  
Author(s):  
Howard B. Bluestein ◽  
Kyle J. Thiem ◽  
Jeffrey C. Snyder ◽  
Jana B. Houser
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Rapid Scan ◽  
X Band ◽  
Close Range ◽  
Formation And Evolution ◽  
Using Data ◽  
Secondary Vortices ◽  
The Right ◽  
Gust Front

Abstract This study documents the formation and evolution of secondary vortices associated within a large, violent tornado in Oklahoma based on data from a close-range, mobile, polarimetric, rapid-scan, X-band Doppler radar. Secondary vortices were tracked relative to the parent circulation using data collected every 2 s. It was found that most long-lived vortices (those that could be tracked for ≥15 s) formed within the radius of maximum wind (RMW), mainly in the left-rear quadrant (with respect to parent tornado motion), passing around the center of the parent tornado and dissipating closer to the center in the right-forward and left-forward quadrants. Some secondary vortices persisted for at least 1 min. When a Burgers–Rott vortex is fit to the Doppler radar data, and the vortex is assumed to be axisymmetric, the secondary vortices propagated slowly against the mean azimuthal flow; if the vortex is not assumed to be axisymmetric as a result of a strong rear-flank gust front on one side of it, then the secondary vortices moved along approximately with the wind.

scholarly journals Technical a Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1989
Author(s):  
Raphaël Nussbaumer ◽  
Baptiste Schmid ◽  
Silke Bauer ◽  
Felix Liechti
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Doppler Radar ◽  
Animal Movement ◽  
Weather Radar ◽  
Gaussian Mixture ◽  
Radar Data ◽  
Volume Data ◽  
Simple Method ◽  
Single Polarization

Recent and archived data from weather radar networks are extensively used for the quantification of continent-wide bird migration patterns. While the process of discriminating birds from weather signals is well established, insect contamination is still a problem. We present a simple method combining two Doppler radar products within a Gaussian mixture model to estimate the proportions of birds and insects within a single measurement volume, as well as the density and speed of birds and insects. This method can be applied to any existing archives of vertical bird profiles, such as the European Network for the Radar surveillance of Animal Movement repository, with no need to recalculate the huge amount of original polar volume data, which often are not available.

scholarly journals Evaluation of Fengyun-4A Lightning Mapping Imager (LMI) Performance during Multiple Convective Episodes over Beijing

2021 ◽  
Vol 13 (9) ◽  
pp. 1746
Author(s):  
Zhixiong Chen ◽  
Xiushu Qie ◽  
Juanzhen Sun ◽  
Xian Xiao ◽  
Yuxin Zhang ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Detection Efficiency ◽  
Ground Truth ◽  
Radar Data ◽  
Detection Capability ◽  
Cloud Properties ◽  
Order Of Magnitude ◽  
Total Lightning ◽  
Matching Criteria ◽  
Event Based

This study investigates the characteristics of space-borne Lightning Mapping Imager (LMI) lightning products and their relationships with cloud properties using ground-based total lightning observations from the Beijing Broadband Lightning Network (BLNET) and cloud information from S-band Doppler radar data. LMI showed generally consistent lightning spatial distributions with those of BLNET, and yielded a considerable lightning detection capability over regions with complex terrain. The ratios between the LMI events, groups and flashes were approximately 9:3:1, and the number of LMI-detected flashes was roughly one order of magnitude smaller than the number of BLNET-detected flashes. However, in different convective episodes, the LMI detection capability was likely to be affected by cloud properties, especially in strongly electrified convective episodes associated with frequent lightning discharging and thick cloud depth. As a result, LMI tended to detect lightning flashes located in weaker and shallower cloud portions associated with fewer cloud shielding effects. With reference to the BLNET total lightning data as the ground truth of observation (both intra-cloud lightning and cloud-to-ground lightning flashes), the LMI event-based detection efficiency (DE) was estimated to reach 28% under rational spatiotemporal matching criteria (1.5 s and 65 km) over Beijing. In terms of LMI flash-based DE, it was much reduced compared with event-based DE. The LMI flash-based ranged between 1.5% and 3.5% with 1.5 s and 35–65 km matching scales. For 330 ms and 35 km, the spatiotemporal matching criteria used to evaluate Geostationary Lightning Mapper (GLM), the LMI flash-based DE was smaller (<1%).

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