scholarly journals The Radial Velocity Curve of V652 Her (BD+13°3224)

1985 ◽  
Vol 87 ◽  
pp. 109-115
Author(s):  
P.W. Hill ◽  
C.S. Jeffery
Keyword(s):  
Radial Velocity ◽  
Time Resolution ◽  
High Frequency ◽  
Velocity Curve ◽  
Velocity Data ◽  
Effective Surface ◽  
Radial Velocity Curve ◽  
Minimum Radius ◽  
Helium Star ◽  
Radial Velocity Data

AbstractNew radial velocity data for the pulsating extreme helium star V652 Her (BD+13°3224) have been obtained with a time resolution of 100 s. High frequency structure in the radial velocity curve is detected, and a comparison with previous data suggests that the detailed shape of the velocity curve is variable. The data imply that the effective surface gravity must increase by a factor of 4 at minimum radius.

Can Osawa'S Star HD 221568 be an Oblique Rotator?

1976 ◽  
Vol 32 ◽  
pp. 603-610
Author(s):  
Keiichi Kodaira
Keyword(s):  
Radial Velocity ◽  
Line Intensity ◽  
Orbital Motion ◽  
Intensity Variation ◽  
Velocity Curve ◽  
Velocity Variation ◽  
Velocity Data ◽  
Rotator Model ◽  
Oblique Rotator ◽  
Radial Velocity Data

SummaryRadial velocity data of HD 221568 are critically reexamined. The revised data lead to a velocity curve essentially the same as obtained by Kodaira (1967). It is shown that the velocity variation of HD 221568 could be attributed to orbital motion, if an oblique rotator model is to be assumed for the cause of line-intensity variation.

Quality Assessment Techniques Applied to Surface Radial Velocity Maps Obtained from High-Frequency Radars

2015 ◽  
Vol 32 (10) ◽  
pp. 1915-1927 ◽  
Author(s):  
Sung Yong Kim
Keyword(s):  
Quality Assessment ◽  
Radial Velocity ◽  
High Frequency ◽  
Transfer Functions ◽  
Driving Forces ◽  
Partial Information ◽  
Spatial Coherence ◽  
Low Frequency ◽  
Velocity Data ◽  
Radial Velocity Data

AbstractThis paper presents examples of the data quality assessment of surface radial velocity maps obtained from shore-based single and multiple high-frequency radars (HFRs) using statistical and dynamical approaches in a hindcast mode. Since a single radial velocity map contains partial information regarding a true current field, archived radial velocity data embed geophysical signals, such as tides, wind stress, and near-inertial and low-frequency variance. The spatial consistency of the geophysical signals and their dynamic relationships with driving forces are used to conduct the quality assurance and quality control of radial velocity data. For instance, spatial coherence, tidal amplitudes and phases, and wind-radial transfer functions are used to identify a spurious range and azimuthal bin. The uncertainty and signal-to-noise ratio of radial data are estimated with the standard deviation and cross correlation of paired radials sampled at nearby grid points that belong to two different radars. This review paper can benefit HFR users and operators and those who are interested in analyzing HFR-derived surface radial velocity data.

Application of a probabilistic neural network in radial velocity curve analysis of the spectroscopic binary stars PV Pup, BV Dra, AI Phe, V1130 Tau, NSV 223, and V502 Oph

2011 ◽  
Vol 89 (10) ◽  
pp. 1035-1040 ◽  
Author(s):  
K. Karami ◽  
K. Ghaderi ◽  
R. Mohebi ◽  
M.M. Soltanzadeh
Keyword(s):  
Radial Velocity ◽  
Binary Stars ◽  
Binary Systems ◽  
Probabilistic Neural Network ◽  
Curve Analysis ◽  
Velocity Data ◽  
Radial Velocity Curve ◽  
Spectroscopic Binary ◽  
Radial Velocity Data ◽  
Good Agreement

Using measured radial velocity data from six double-lined spectroscopic binary systems PV Pup, BV Dra, AI Phe, V1130 Tau, NSV 223 (or DZ Psc), and V502 Oph, we find corresponding orbital and spectroscopic elements via the method introduced by Karami et al. (New Astron. 14, 478 (2009)). Our numerical results are in good agreement with those obtained by others using more traditional methods. Using a statistical analysis, we also conclude that for BV Dra, V1130 Tau, NSV 223 (or DZ Psc), and V502 Oph, a circular orbit is quite consistent.

scholarly journals Kinematics of Disk Stars in the Solar Neighbourhood

1998 ◽  
Vol 11 (1) ◽  
pp. 574-574
Author(s):  
A.E. Gómez ◽  
S. Grenier ◽  
S. Udry ◽  
M. Haywood ◽  
V. Sabas ◽  
...  
Keyword(s):  
Dispersion Relation ◽  
Radial Velocity ◽  
Velocity Dispersion ◽  
Tangential Velocity ◽  
Thin Disk ◽  
The Other ◽  
Proper Motions ◽  
Velocity Data ◽  
Kinematic Data ◽  
Radial Velocity Data

Using Hipparcos parallaxes and proper motions together with radial velocity data and individual ages estimated from isochones, the velocity ellipsoid has been determined as a function of age. On the basis of the available kinematic data two different samples were considered: a first one (7789 stars) for which only tangential velocities were calculated and a second one containing 3104 stars with available U, V and W velocity components and total velocities ≤ 65 km.s-1. The main conclusions are: -Mixing is not complete at about 0.8-1 Gyr. -The shape of the velocity ellipsoid changes with time getting rounder from σu/σv/σ-w = 1/0.63/0.42 ± 0.04 at about 1 Gyr to1/0.7/0.62 ±0.04 at 4-5 Gyr. -The age-velocity-dispersion relation (from the sample with kinematical selection) rises to a maximum, thereafter remaining roughly constant; there is no dynamically significant evolution of the disk after about 4-5 Gyr. -Among the stars with solar metallicities and log(age) > 9.8 two groups are identified: one has typical thin disk characteristics, the other is older than 10 Gyr and lags the LSR at about 40 km.s-1 . -The variation of the tangential velocity with age(without selection on the tangential velocity) shows a discontinuity at about 10 Gyr, which may be attributed to stars typically of the thick disk populations for ages > 10 Gyr.

An Efficient Dual-Resolution Approach for Ensemble Data Assimilation and Tests with Simulated Doppler Radar Data

2008 ◽  
Vol 136 (3) ◽  
pp. 945-963 ◽  
Author(s):  
Jidong Gao ◽  
Ming Xue
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radial Velocity ◽  
Computational Cost ◽  
Radar Data ◽  
Horizontal Resolution ◽  
Velocity Data ◽  
Background Error ◽  
Cross Covariance ◽  
Radial Velocity Data

Abstract A new efficient dual-resolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both high-resolution and lower-resolution grids using the EnKF algorithm with flow-dependent background error covariances estimated from the lower-resolution ensemble. It is shown that the flow-dependent and dynamically evolved background error covariances thus estimated are effective in producing quality analyses on the high-resolution grid. The DR method has the advantage of being able to significantly reduce the computational cost of the EnKF analysis. In the system, the lower-resolution ensemble provides the flow-dependent background error covariance, while the single-high-resolution forecast and analysis provides the benefit of higher resolution, which is important for resolving the internal structures of thunderstorms. The relative smoothness of the covariance obtained from the lower 4-km-resolution ensemble does not appear to significantly degrade the quality of analysis. This is because the cross covariance among different variables is of first-order importance for “retrieving” unobserved variables from the radar radial velocity data. For the DR analysis, an ensemble size of 40 appears to be a reasonable choice with the use of a 4-km horizontal resolution in the ensemble and a 1-km resolution in the high-resolution analysis. Several sensitivity tests show that the DR EnKF system is quite robust to different observation errors. A 4-km thinned data resolution is a compromise that is acceptable under the constraint of real-time applications. A data density of 8 km leads to a significant degradation in the analysis.

scholarly journals Precipitation cloud identification based on faster-RCNN for Doppler weather radar

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yuanbo Ran ◽  
Haijiang Wang ◽  
Li Tian ◽  
Jiang Wu ◽  
Xiaohong Li
Keyword(s):  
Radial Velocity ◽  
Vertical Direction ◽  
Weather Radar ◽  
Radar Reflectivity ◽  
Convective Precipitation ◽  
Horizontal Wind ◽  
Doppler Weather Radar ◽  
Velocity Data ◽  
Precipitation Processes ◽  
Radial Velocity Data

AbstractPrecipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmosphere after condensation, which can be divided into stratiform cloud and convective cloud. Different precipitation clouds often accompany different precipitation processes. Accurate identification of precipitation clouds is significant for the prediction of severe precipitation processes. Traditional identification methods mostly depend on the differences of radar reflectivity distribution morphology between stratiform and convective precipitation clouds in three-dimensional space. However, all of them have a common shortcoming that the radial velocity data detected by Doppler Weather Radar has not been applied to the identification of precipitation clouds because it is insensitive to the convective movement in the vertical direction. This paper proposes a new method for precipitation clouds identification based on deep learning algorithm, which is according the distribution morphology of multiple radar data. It mainly includes three parts, which are Constant Altitude Plan Position Indicator data (CAPPI) interpolation for radar reflectivity, Radial projection of the ground horizontal wind field by using radial velocity data, and the precipitation clouds identification based on Faster-RCNN. The testing result shows that the method proposed in this paper performs better than the traditional methods in terms of precision. Moreover, this method boasts great advantages in running time and adaptive ability.

The CORAVEL Radial Velocity Data Base

2017 ◽  
Vol 13 (S334) ◽  
pp. 271-272
Author(s):  
Stéphane Udry ◽  
Maxime Marmier ◽  
Michel Mayor ◽  
Johannes Andersen ◽  
Birgitta Nordström
Keyword(s):  
Radial Velocity ◽  
Data Base ◽  
Radial Velocities ◽  
Velocity Data ◽  
Radial Velocity Data

AbstractFrom 1977 to 1999, thousands of accurate radial velocities in both hemispheres were made on a large variety of programmes with the two CORAVEL scanners. The data base of ~350000 individual observations will now be made available to complement the Gaia data.

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