scholarly journals Inferring Photospheric Velocity Fields Using a Combination of Minimum Energy Fit, Local Correlation Tracking, and Doppler Velocity

2008 ◽  
Vol 677 (1) ◽  
pp. 751-768 ◽  
Author(s):  
B. Ravindra ◽  
D. W. Longcope ◽  
W. P. Abbett
Keyword(s):  
Minimum Energy ◽  
Velocity Fields ◽  
Doppler Velocity ◽  
Local Correlation ◽  
Correlation Tracking

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

2021 ◽  
Author(s):  
VINCENT T. WOOD ◽  
ROBERT P. DAVIES-JONES ◽  
ALAN SHAPIRO
Keyword(s):  
Missing Data ◽  
Doppler Radar ◽  
Signal To Noise Ratio ◽  
Original Data ◽  
Radar Data ◽  
Velocity Fields ◽  
Doppler Velocity ◽  
Doppler Radar Data ◽  
Ground Clutter ◽  
Tornado Warnings

AbstractSingle-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.

scholarly journals Analysis of Convective Thunderstorm Split Cells in South-Eastern Romania

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Daniel Carbunaru ◽  
Sabina Stefan ◽  
Monica Sasu ◽  
Victor Stefanescu
Keyword(s):  
Velocity Field ◽  
Velocity Fields ◽  
Severe Weather ◽  
Doppler Velocity ◽  
Doppler Weather Radar ◽  
Dynamic Features ◽  
South Eastern ◽  
Wind Profiles ◽  
Convective Cells ◽  
Splitting Process

The mesoscale configurations are analysed associated withthesplitting process of convective cells responsible for severe weather phenomena in the south-eastern part of Romania. The analysis was performed using products from the S-band Doppler weather radar located in Medgidia. The cases studied were chosen to cover various synoptic configurations when the cell splitting process occurs. To detect the presence and intensity of the tropospheric jet, the Doppler velocity field and vertical wind profiles derived from radar algorithms were used. The relative Doppler velocity field was used to study relative flow associated with convective cells. Trajectories and rotational characteristics associated with convective cells were obtained from reflectivity and relative Doppler velocity fields at various elevations. This analysis highlights the main dynamic features associated with the splitting process of convective cells: the tropospheric jet and vertical moisture flow associated with the configuration of the flow relative to the convective cells for the lower and upper tropospheric layers. These dynamic characteristics seen in the Doppler based velocity field and in the relative Doppler velocity field to the storm can indicate further evolution of convective developments, with direct implications to very short range forecast (nowcasting).

scholarly journals Evolving Features of Hβ Doppler Velocity Fields at Sites of Flares

1993 ◽  
Vol 141 ◽  
pp. 267-270
Author(s):  
Wei Li ◽  
Guoxiang Ai ◽  
Hongqi Zhang
Keyword(s):  
Velocity Field ◽  
Active Regions ◽  
Velocity Fields ◽  
Time Sequence ◽  
Line Of Sight ◽  
Doppler Velocity

AbstractWe analyzed eight active regions with more than 600 flare kernels and ribbons, and relevant time sequence Hβ chromospheric Dopplergrams. These data showed that during several hours prior to the flares, the velocity field evolves so that the sites of the flare kernels and ribbons become close to the inversion line of the velocity field. This result holds regardless of whether or not the flare sites are wholly located in blue-shifted areas, or are far from the the inversion line of the line-of-sight velocity field, or are partly within red-shifted areas.

scholarly journals ILCT: Recovering Photospheric Velocities from Magnetograms by Combining the Induction Equation with Local Correlation Tracking

2004 ◽  
Vol 610 (2) ◽  
pp. 1148-1156 ◽  
Author(s):  
B. T. Welsch ◽  
G. H. Fisher ◽  
W. P. Abbett ◽  
S. Regnier
Keyword(s):  
Local Correlation ◽  
Correlation Tracking ◽  
Induction Equation

A Python-based interface to examine motions in time series of solar images

2016 ◽  
Vol 12 (S327) ◽  
pp. 25-27
Author(s):  
J. I. Campos-Rozo ◽  
S. Vargas Domínguez
Keyword(s):  
Time Series ◽  
Graphical User Interface ◽  
Solar Physics ◽  
Velocity Fields ◽  
Proper Motions ◽  
Local Correlation ◽  
Scientific Analysis ◽  
User Friendly ◽  
Selection Of ◽  
Analysis Of Time Series

AbstractPython is considered to be a mature programming language, besides of being widely accepted as an engaging option for scientific analysis in multiple areas, as will be presented in this work for the particular case of solar physics research. SunPy is an open-source library based on Python that has been recently developed to furnish software tools to solar data analysis and visualization. In this work we present a graphical user interface (GUI) based on Python and Qt to effectively compute proper motions for the analysis of time series of solar data. This user-friendly computing interface, that is intended to be incorporated to the Sunpy library, uses a local correlation tracking technique and some extra tools that allows the selection of different parameters to calculate, vizualize and analyze vector velocity fields of solar data, i.e. time series of solar filtergrams and magnetograms.

scholarly journals Measuring solar active region inflows with local correlation tracking of granulation

2017 ◽  
Vol 606 ◽  
pp. A28 ◽  
Author(s):  
B. Löptien ◽  
A. C. Birch ◽  
T. L. Duvall ◽  
L. Gizon ◽  
B. Proxauf ◽  
...  
Keyword(s):  
Active Region ◽  
Solar Active Region ◽  
Local Correlation ◽  
Correlation Tracking

H? Doppler-velocity fields within sites of flares in a solar active region

Solar Physics ◽  
1996 ◽  
Vol 163 (1) ◽  
pp. 121-131 ◽  
Author(s):  
Wei Li ◽  
Guoxiang Ai ◽  
Hongqi Zhang
Keyword(s):  
Active Region ◽  
Velocity Fields ◽  
Solar Active Region ◽  
Doppler Velocity

scholarly journals Local Correlation Tracking in Time Series

2006 ◽  
Author(s):  
Spiros Papadimitriou ◽  
Jimeng Sun ◽  
Philip Yu
Keyword(s):  
Time Series ◽  
Local Correlation ◽  
Correlation Tracking

scholarly journals The helicity, induced electric field and Poynting flux of AR 11158 and their relationship with the X-class flare

2012 ◽  
Vol 8 (S294) ◽  
pp. 535-536
Author(s):  
Jihong Liu ◽  
Jiangtao Su ◽  
Hongqi Zhang
Keyword(s):  
Magnetic Fields ◽  
Electric Field ◽  
Gradual Change ◽  
Local Correlation ◽  
Poynting Flux ◽  
Tracking Technique ◽  
Correlation Tracking ◽  
Class Flare

AbstractWith the photospheric vector magnetic fields provided by SDO/HMI team, the helicity accumulation, induced electric field and Poynting flux is calculated for AR 11158 by using the local correlation tracking technique (LCT). It is found that the helicity accumulation reaches 6000×1040Mx2, the average densities of the induced electric field about 0.15-0.35 V cm−1, and that of the Poynting flux about 50-240 W m−2, within 50 hours. One main flare of X2.2 occurs in the increasing phase of the helicity accumulation, which also corresponds to the decreasing phase of the induced electric field and the gradual change phase of the Poynting flux. Before the flare, all these quantities increase rapidly for about 20 hours firstly, then increase gradually or even decrease for 8-9 hours.

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