scholarly journals A Velocity Dealiasing Scheme Based on Minimization of Velocity Differences between Regions

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Yue Yuan ◽  
Ping Wang ◽  
Di Wang ◽  
Junzhi Shi
Keyword(s):  
Radial Velocity ◽  
Wind Shear ◽  
Doppler Radar ◽  
Heavy Precipitation ◽  
Probability Of Detection ◽  
Strong Wind ◽  
Velocity Data ◽  
Step Velocity ◽  
Noise Data ◽  
Radial Velocity Data

The velocity dealiasing is an essential work of automatic weather phenomenon identification, nowcasting, and disaster monitoring based on radial velocity data. The noise data, strong wind shear, and isolated echo region in the Doppler radar radial velocity data severely interfere with the velocity dealiasing algorithm. This paper proposes a two-step velocity dealiasing algorithm based on the minimization of velocity differences between regions to solve this problem. The first step is to correct aliased velocities by minimizing the sum of gradients in every region to eliminate abnormal velocity gradients between points. The interference of noise data and strong wind shear can be reduced by minimizing the whole gradients in a region. The second step is to dealiase velocities by the velocity differences between different isolated regions. The velocity of an unknown isolated region is determined by the velocities of all known regions. This step improves the dealiasing results of isolated regions. In this paper, 604 volume scan samples, including typhoons, squall lines, and heavy precipitation, were used to test the algorithm. The statistical results and analysis show that the proposed algorithm can dealiase the velocity field with a high probability of detection and a low false alarm rate.

scholarly journals Application of the Multigrid Method to the Two-Dimensional Doppler Radar Radial Velocity Data Assimilation

2010 ◽  
Vol 27 (2) ◽  
pp. 319-332 ◽  
Author(s):  
Wei Li ◽  
Yuanfu Xie ◽  
Shiow-Ming Deng ◽  
Qi Wang
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Multigrid Method ◽  
Doppler Radar ◽  
Wind Data ◽  
In Situ Observation ◽  
Velocity Data ◽  
Significant Information ◽  
Tangential Wind ◽  
Radial Velocity Data

Abstract In recent years, the Earth System Research Laboratory (ESRL) of the National Oceanic and Atmospheric Administration (NOAA) has developed a space and time mesoscale analysis system (STMAS), which is currently a sequential three-dimensional variational data assimilation (3DVAR) system and is developing into a sequential 4DVAR in the near future. It is implemented by using a multigrid method based on a variational approach to generate grid analyses. This study is to test how STMAS deals with 2D Doppler radar radial velocity and to what degree the 2D Doppler radar radial velocity can improve the conventional (in situ) observation analysis. Two idealized experiments and one experiment with real Doppler radar radial velocity data, handled by STMAS, demonstrated significant improvement of the conventional observation analysis. Because the radar radial wind data can provide additional wind information (even it is incomplete: e.g., missing tangential wind vector), the analyses by assimilating both radial wind data and conventional data showed better results than those by assimilating only conventional data. Especially in the case of sparse conventional data, radar radial wind data can provide significant information and improve the analyses considerably.

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.

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.

scholarly journals Radial velocity data analysis with compressed sensing techniques

2016 ◽  
Vol 464 (1) ◽  
pp. 1220-1246 ◽  
Author(s):  
Nathan C. Hara ◽  
G. Boué ◽  
J. Laskar ◽  
A. C. M. Correia
Keyword(s):  
Data Analysis ◽  
Compressed Sensing ◽  
Radial Velocity ◽  
Velocity Data ◽  
Radial Velocity Data
Sign in / Sign up

Export Citation Format

Share Document