Distance Velocity-Azimuth Display (DVAD)—New interpretation and analysis of Doppler velocity

AMS Citation:
Lee, W. -C., X. Tang, and B. J. -D. Jou, 2014: Distance Velocity-Azimuth Display (DVAD)—New interpretation and analysis of Doppler velocity. Monthly Weather Review, 142, 573-589, doi:10.1175/MWR-D-13-00196.1.
Resource Type:article
Title:Distance Velocity-Azimuth Display (DVAD)—New interpretation and analysis of Doppler velocity
Abstract: The concept and mathematical framework of the distance velocity–azimuth display (DVAD) methodology is presented. DVAD uses rVd (Doppler velocity scaled by the distance from the radar to a gate, r) as the basis to display, interpret, and extract information from single Doppler radar observations. Both linear and nonlinear wind fields can be represented by the same Cartesian polynomial with different orders. DVAD is mathematically concise and superior to the velocity-azimuth display (VAD) in interpreting and deducing flow characteristics. The rVd pattern of a two-dimensional linear wind field is exclusively in the form of a bivariate quadratic equation representing conic sections (e.g., ellipse, parabola, and hyperbola) centered at the radar depending only on divergence and deformation. The presence of a constant background flow translates the conic sections to a different origin away from the radar. It is possible to graphically estimate the characteristics of a linear wind field from the conical sections without performing a VAD analysis. DVAD analysis can deduce quantitative flow characteristics by a least squares fitting and/or a derivative method, and is a natural way to account for nonlinearity. The rVd pattern behaves similar to a type of velocity potential in fluid mechanics where ∇ (rVd) is a proxy of the true wind vector and is used to estimate the general flow pattern in the vicinity of the radar.
Subject(s):Wind, Data processing, Kinematics, Radars/Radar observations
Peer Review:Refereed
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OpenSky citable URL: ark:/85065/d7kw5gzm
Publisher's Version: 10.1175/MWR-D-13-00196.1
  • Wen-Chau Lee - NCAR/UCAR
  • Xiaowen Tang
  • Ben Jou
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