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Ming Xue, Shun Liu, and Tian-You Yu

Abstract

For the detection of severe weather phenomena such as tornados, mesocyclones, and strong wind shear, the azimuthal resolution of radial velocity measurements is more important. The typical azimuthal resolutions of 1° for the Weather Surveillance Radar-1988 Doppler (WSR-88D) radars and of 2° for the planned Center for Collaborative Adaptive Sensing of Atmosphere (CASA) radars are not sufficient for this purpose, especially at far ranges. Oversampling is one strategy that can potentially provide more details about the azimuthal structures of flows, and can be achieved by processing raw data at azimuthal increments smaller than the radar beamwidth. In the presence of dual-Doppler observations, the variational method can be used to effectively recover subbeamwidth structures from these oversampled data, which, combined with the typically higher range resolutions, can provide high-resolution wind analyses that are valuable for, for example, tornado detection. This idea is tested in this paper using simulated data as well as reprocessed level-I data from a research WSR-88D radar, for model-simulated and actually observed tornadoes, respectively. The results confirm that much more detailed, often subbeamwidth, flow structures can indeed be recovered through azimuthal oversampling and a properly configured variational analysis, and the detailed flow analysis is expected to significantly improve one’s ability in identifying small-scale features such as tornadoes from radial velocity observations.

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Shun Liu, Qin Xu, and Pengfei Zhang

Abstract

Based on the Bayesian statistical decision theory, a probabilistic quality control (QC) technique is developed to identify and flag migrating-bird-contaminated sweeps of level II velocity scans at the lowest elevation angle using the QC parameters presented in Part I. The QC technique can use either each single QC parameter or all three in combination. The single-parameter QC technique is shown to be useful for evaluating the effectiveness of each QC parameter based on the smallness of the tested percentages of wrong decision by using the ground truth information (if available) or based on the smallness of the estimated probabilities of wrong decision (if there is no ground truth information). The multiparameter QC technique is demonstrated to be much better than any of the three single-parameter QC techniques, as indicated by the very small value of the tested percentages of wrong decision for no-flag decisions (not contaminated by migrating birds). Since the averages of the estimated probabilities of wrong decision are quite close to the tested percentages of wrong decision, they can provide useful information about the probability of wrong decision when the multiparameter QC technique is used for real applications (with no ground truth information).

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Pengfei Zhang, Shun Liu, and Qin Xu

Abstract

Radar echoes from migrating birds can severely contaminate Doppler velocity measurements. For meteorological applications, especially quantitative applications in radar data assimilation, it is necessary to remove bird-contaminated velocity scans by using an automated identification technique. Such a technique should be also useful for ornithologists in selecting bird echoes automatically from radar scans. This technique can be developed in two steps: (i) extract the main features of migrating-bird echoes from reflectivity and Doppler velocity images and find proper parameters to quantify these features; (ii) utilize these parameters to develop an automated quality control procedure to identify and flag migrating-bird-contaminated Doppler velocity scans (sweeps). The first step is accomplished in this study (Part I) by identifying possible migrating-bird echoes in the level II data collected from the Oklahoma KTLX radar during the 2003 spring migrating season. The identifications are further verified by polarimetric radar measurements from the National Severe Storms Laboratory (NSSL) KOUN radar, Geostationary Operational Environmental Satellite (GOES) IR images, and rawinsonde measurements. Three proper parameters are found, and their histograms are prepared for the second step of development (reported in Part II).

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Shun Liu, Ming Xue, and Qin Xu

Abstract

A wavelet-based algorithm is developed to detect tornadoes from Doppler weather radar radial-velocity observations. Within this algorithm, a relative region-to-region velocity difference (RRVD) is defined based on the scale- and location-dependent wavelet coefficients and this difference represents the relative magnitude of the radial velocity shear between two adjacent regions of different scales. The RRVD fields of an idealized tornado and a realistic tornado from a high-resolution numerical simulation are analyzed first. It is found that the value of RRVD in the tornado region is significantly larger than those at other locations and large values of RRVD exist at more than one scale. This characteristic forms the basis of the new algorithm presented in this work for identifying tornadoes. Different from traditional tornadic vortex signature detection algorithms that typically rely on the velocity difference between adjacent velocity gate pairs at a single spatial scale, the new algorithm examines region-to-region radial wind shears at a number of different spatial scales. Multiscale regional wind shear examination not only can be used to discard a nontornadic vortex signature to reduce the false alert rate of tornado detection but also has the ability of capturing tornadic signatures at various scales for improving the detection and warning. The potential advantage of the current algorithm is demonstrated by applying it to the radar data collected by Oklahoma City, Oklahoma (KTLX), Weather Surveillance Radar-1988 Doppler (WSR-88D) on 8 May 2003 for a central Oklahoma tornado case.

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Shun Liu, Chongjian Qiu, Qin Xu, and Pengfei Zhang

Abstract

A temporal interpolation is required for three-dimensional Doppler wind analysis when the precise measurement time is counted for each radar beam position. The time interpolation is traditionally done by a linear scheme either in the measurement space or in the analysis space. Because a volume scan often takes 5–10 min, the linear time interpolation is not accurate enough to capture the rapidly changing winds associated with a fast-moving and fast-growing storm. Performing the linear interpolation in a frame moving with the storm can reduce the error, but the analyzed wind field is traditionally assumed to be stationary in the moving frame. The stationary assumption simplifies the computation but ignores the time variation of the true wind field in the moving frame. By incorporating a linear time interpolation into the moving frame wind analysis, an improved scheme is developed. The merits of the new scheme are demonstrated by idealized examples and numerical experiments with simulated radar observations. The new scheme is also applied to real radar data for a supercell storm.

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Qin Xu, Kang Nai, Li Wei, Pengfei Zhang, Shun Liu, and David Parrish

Abstract

This paper describes a new velocity–azimuth display (VAD)-based dealiasing method developed for automated radar radial velocity data quality control to satisfy the high-quality standard and efficiency required by operational radar data assimilation. The method is built on an alias-robust velocity–azimuth display (AR-VAD) analysis. It upgrades and simplifies the previous three-step dealiasing method in three major aspects. First, the AR-VAD is used with sufficiently stringent threshold conditions in place of the original modified VAD for the preliminary reference check to produce alias-free seed data in the first step. Second, the AR-VAD is more accurate than the traditional VAD for the refined reference check in the original second step, so the original second step becomes unnecessary and is removed. Third, a block-to-point continuity check procedure is developed, in place of the point-to-point continuity check in the original third step, which serves to enhance the use of the available seed data in a properly enlarged block area around each flagged data point that is being checked with multiple threshold conditions to avoid false dealiasing. The new method has been tested extensively with aliased radial velocity data collected under various weather conditions, including hurricane high-wind conditions. The robustness of the new method is exemplified by the results tested with three cases. The limitations of the new method and possible improvements are discussed.

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Shun Liu, Geoff DiMego, Shucai Guan, V. Krishna Kumar, Dennis Keyser, Qin Xu, Kang Nai, Pengfei Zhang, Liping Liu, Jian Zhang, Kenneth Howard, and Jeff Ator

Abstract

Real-time access to level II radar data became available in May 2005 at the National Centers for Environmental Prediction (NCEP) Central Operations (NCO). Using these real-time data in operational data assimilation requires the data be processed reliably and efficiently through rigorous data quality controls. To this end, advanced radar data quality control techniques developed at the National Severe Storms Laboratory (NSSL) are combined into a comprehensive radar data processing system at NCEP. Techniques designed to create a high-resolution reflectivity mosaic developed at the NSSL are also adopted and installed within the NCEP radar data processing system to generate hourly 3D reflectivity mosaics and 2D-derived products. The processed radar radial velocity and 3D reflectivity mosaics are ingested into NCEP’s data assimilation systems to improve operational numerical weather predictions. The 3D reflectivity mosaics and 2D-derived products are also used for verification of high-resolution numerical weather prediction. The NCEP radar data processing system is described.

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