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).

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... 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...

By using the non‐isotropic form of error covariance function derived for radial‐velocity fields on conical surfaces of low‐elevation radar scans, the conventional innovation method is reformulated to estimate Doppler radar radial‐velocity observation error covariance and background vector wind error covariance from time series of radar radial‐velocity innovation (observation minus independent background) fields. The method is applied to radar radial‐velocity innovation data obtained at the lowest elevation of radar scans for a real case. The results show that radar radial‐velocity observation errors are correlated between neighbouring range gates and between neighbouring beams. The radial‐velocity observation error correlation functions are estimated, along with the observation error variance and background vector wind error covariance functions, by the reformulated method. Published in 2007 by John Wiley & Sons, Ltd.

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).

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.