Wind field evaluation by using radar data and vector spline interpolation

2011 ◽  
Author(s):  
Daniel Delahaye ◽  
Christophe Rabut ◽  
Stephane Puechmorel
Keyword(s):  
Wind Field ◽  
Spline Interpolation ◽  
Field Evaluation ◽  
Radar Data ◽  
Vector Spline

Characteristics of the wind field in clouds based on incoherent radar data

1990 ◽  
Vol 33 (2) ◽  
pp. 125-129 ◽  
Author(s):  
V. M. Mel'nikov
Keyword(s):  
Wind Field ◽  
Radar Data

Wind Field Retrieval and Display for Doppler Radar Data

2008 ◽  
pp. 171-182
Author(s):  
Shyh-Kuang Ueng ◽  
Yu-Chong Chiang
Keyword(s):  
Wind Field ◽  
Doppler Radar ◽  
Radar Data ◽  
Doppler Radar Data

scholarly journals An Improved Time Interpolation for Three-Dimensional Doppler Wind Analysis

2004 ◽  
Vol 43 (10) ◽  
pp. 1379-1391 ◽  
Author(s):  
Shun Liu ◽  
Chongjian Qiu ◽  
Qin Xu ◽  
Pengfei Zhang
Keyword(s):  
Wind Field ◽  
Linear Time ◽  
Three Dimensional ◽  
Linear Interpolation ◽  
Radar Data ◽  
Moving Frame ◽  
Beam Position ◽  
Wind Analysis ◽  
Linear Scheme ◽  
Temporal Interpolation

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.

scholarly journals Single-Doppler Velocity Retrieval of the Wind Field in a Tornadic Supercell Using Mobile, Phased-Array, Doppler Radar Data

2018 ◽  
Vol 35 (8) ◽  
pp. 1649-1663 ◽  
Author(s):  
Yu-Chieng Liou ◽  
Howard B. Bluestein ◽  
Michael M. French ◽  
Zachary B. Wienhoff
Keyword(s):  
Temporal Resolution ◽  
Wind Field ◽  
Phased Array ◽  
Doppler Radar ◽  
Radar Data ◽  
Moving Frame ◽  
Doppler Velocity ◽  
High Temporal Resolution ◽  
Wind Component ◽  
Using Data

AbstractA three-dimensional data assimilation (3DVar) least squares–type single-Doppler velocity retrieval (SDVR) algorithm is utilized to retrieve the wind field of a tornadic supercell using data collected by a mobile, phased-array, Doppler radar [Mobile Weather Radar (MWR) 05XP] with very high temporal resolution (6 s). It is found that the cyclonic circulation in the hook-echo region can be successfully recovered by the SDVR algorithm. The quality of the SDVR analyses is evaluated by dual-Doppler syntheses using data collected by two mobile Doppler radars [Doppler on Wheels 6 and 7 (DOW6 and DOW7, respectively)]. A comparison between the SDVR analyses and dual-Doppler syntheses confirms the conclusion reached by an earlier theoretical analysis that because of the temporally discrete nature of the radar data, the wind speed retrieved by single-Doppler radar is always underestimated, and this underestimate occurs more significantly for the azimuthal (crossbeam) wind component than for the radial (along beam) component. However, the underestimate can be mitigated by increasing the radar data temporal resolution. When the radar data are collected at a sufficiently high rate, the azimuthal wind component may be overestimated. Even with data from a rapid scan, phased-array, Doppler radar, our study indicates that it is still necessary to calculate the SDVR in an optimal moving frame of reference. Finally, the SDVR algorithm’s robustness is demonstrated. Even with a temporal resolution (2 min) much lower than that of the phased-array radar, the cyclonic flow structure in the hook-echo region can still be retrieved through SDVR using data observed by DOW6 or DOW7, although a difference in the retrieved fields does exist. A further analysis indicates that this difference is caused by the location of the radars.

Using a Low-Order Model to Detect and Characterize Tornadoes in Multiple-Doppler Radar Data

2009 ◽  
Vol 137 (4) ◽  
pp. 1230-1249 ◽  
Author(s):  
Corey K. Potvin ◽  
Alan Shapiro ◽  
Tian-You Yu ◽  
Jidong Gao ◽  
Ming Xue
Keyword(s):  
Cost Function ◽  
Wind Field ◽  
Doppler Radar ◽  
Radar Data ◽  
Horizontal Wind ◽  
Order Model ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Objective Detection ◽  
Low Order

Abstract A new multiple-Doppler radar analysis technique is presented for the objective detection and characterization of tornado-like vortices. The technique consists of fitting radial wind data from two or more radars to a simple analytical model of a vortex and its near-environment. The model combines a uniform flow, linear shear flow, linear divergence flow (all of which compose a broadscale flow), and a modified combined Rankine vortex (representing the tornado). The vortex and its environment are allowed to translate. The parameters in the low-order model are determined by minimizing a cost function that accounts for the discrepancy between the model and observed radial winds. Since vortex translation is taken into account, the cost function can be evaluated over time as well as space, and thus the observations can be used at the actual times and locations where they were acquired. The technique is first tested using analytically simulated observations whose wind field and error characteristics are systematically varied. An Advanced Regional Prediction System (ARPS) high-resolution numerical simulation of a supercell and associated tornado is then used to emulate an observation dataset. The method is tested with two virtual radars for several radar-sampling strategies. Finally, the technique is applied to a dataset of real dual-Doppler observations of a tornado that struck central Oklahoma on 8 May 2003. The method shows skill in retrieving the tornado path and radar-grid-scale features of the horizontal wind field in the vicinity of the tornado. The best results are obtained using a two-step procedure in which the broadscale flow is retrieved first.

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