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

Single-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.

Retrieved Thermodynamic Structure of Hurricane Rita (2005) from Airborne Multi-Doppler Radar Data

The newly developed SAMURAI-TR is used to estimate three-dimensional temperature and pressure perturbations in Hurricane Rita on 23 September 2005 from multi-Doppler radar data during the RAINEX field campaign. These are believed to be the first fully three-dimensional gridded thermodynamic observations from a TC. Rita was a major hurricane at this time and was affected by 13 m s−1 deep-layer vertical wind shear. Analysis of the contributions of the kinematic and retrieved thermodynamic fields to different azimuthal wavenumbers suggests the interpretation of eyewall convective forcing within a three-level framework of balanced, quasi-balanced, and unbalanced motions. The axisymmetric, wavenumber-0 structure was approximately in thermal-wind balance, resulting in a large pressure drop and temperature increase toward the center. The wavenumber-1 structure was determined by the interaction of the storm with environmental vertical wind shear resulting in a quasi-balance between shear and shear-induced kinematic and thermo-dynamic perturbations. The observed wavenumber-1 thermodynamic asymmetries corroborate results of previous studies on the response of a vortex tilted by shear, and add new evidence that the vertical motion is nearly hydrostatic on the wavenumber-1 scale. Higher-order wavenumbers were associated with unbalanced motions and convective cells within the eyewall. The unbalanced vertical acceleration was positively correlated with buoyant forcing from thermal perturbations and negatively correlated with perturbation pressure gradients relative to the balanced vortex.

An Automatic Recognition Method for Airflow Field Structures of Convective Systems Based on Single Doppler Radar Data

Airflow structures within convective systems are important predictors of damaging convective disasters. To automatically recognize different kinds of airflow structures (the convergence, divergence, cyclonic rotation, and anticyclonic rotation) within convective systems, an airflow structure recognition method is proposed, in this paper, based on a regular hexagonal template. On the basis of single Doppler radar data, the template is designed according to the appearance model of airflows in radial velocity maps. The proposed method is able to output types and intensities of airflow structures within convective systems. In addition, the outputs of the proposed method are integrated into a projection map of the airflow field structure types and intensities (PMAFSTI), which is developed in this work to visualize three-dimensional airflow structures within convective cells. The proposed airflow structure automatic recognition method and the PMAFSTI were tested using three typical cases. Results of the tests suggest the following: (1) At different evolution stages of the convective systems, e.g., growth, split, and dissipation, the three-dimensional distribution of the airflow fields within convective systems could be clearly observed through the PMAFSTI and (2) on the basis of recognizing the structures of the airflow field, the complex airflow field, such as a squall line, could be further divided into several small parts making the analysis of convective systems more scientific and elaborate.