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

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.

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Precipitation Cloud Identification Based on Faster-RCNN for Doppler Weather Radar

10.21203/rs.3.rs-101255/v1 ◽

2020 ◽

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

Abstract Precipitation clouds are visible aggregates of hydrometeor in the air that floating in the atmos-phere 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) inversion 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 typical existing algorithms in terms of accuracy rate. Moreover, this method boasts great advantages in running time and adaptive ability.

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Impact of Doppler radar reflectivity and velocity data assimilation on the quality of precipitation forecasting in Belarus in different seasons

10.5194/egusphere-egu2020-779 ◽

2020 ◽

Author(s):

Palina Zaiko ◽

Siarhei Barodka ◽

Aliaksandr Krasouski

Keyword(s):

Data Assimilation ◽

Weather Radar ◽

Radar Data ◽

Radar Reflectivity ◽

Precipitation Forecast ◽

Doppler Weather Radar ◽

Velocity Data ◽

Numerical Weather ◽

Convective Processes ◽

Different Seasons

Heavy precipitation forecast remains one of the biggest problems in numerical weather prediction. Modern remote sensing systems allow tracking of rapidly developing convective processes and provide additional data for numerical weather models practically in real time. Assimilation of Doppler weather radar data also allows to specify the position and intensity of convective processes in atmospheric numerical models.The primary objective of this study is to evaluate the impact of Doppler&#160; radar reflectivity and velocity assimilation in the WRF-ARW mesoscale model for the territory of Belarus in different seasons of the year. Specifically, we focus on the short-range numerical forecasting of mesoscale convective systems passage over the territory of Belarus in 2017-2019 with assimilated radar data.Proceeding with weather radar observations available for our cases, we first perform the necessary processing of the raw radar data to eliminate noise, reflections and other kinds of clutter. For identification of non-meteorological noise fuzzy echo classification was used. Then we use the WRF-DA (3D-Var) system to assimilate the processed radar observations from 3 Belarusian Doppler weather radar in the WRF model. Assimilating both radar reflectivity and radial velocity data in the model we aim to better represent not only the distribution of clouds and their moisture content, but also the detailed dynamical aspects of convective circulation. Finally, we analyze WRF modelling output obtained with assimilated radar data and compare it with available meteorological observations and with other model runs (including control runs with no data assimilation or with assimilation of conventional weather stations data only), paying special attention to the accuracy of precipitation forecast 12 hours in advance.

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Kinematics of Disk Stars in the Solar Neighbourhood

Highlights of Astronomy ◽

10.1017/s1539299600022310 ◽

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.

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THEPOSTERIORDISTRIBUTION OF sin(i) VALUES FOR EXOPLANETS WITHMTsin(i) DETERMINED FROM RADIAL VELOCITY DATA

The Astrophysical Journal ◽

10.1088/0004-637x/739/1/26 ◽

2011 ◽

Vol 739 (1) ◽

pp. 26 ◽

Cited By ~ 21

Author(s):

Shirley Ho ◽

Edwin L. Turner

Keyword(s):

Radial Velocity ◽

Velocity Data ◽

Radial Velocity Data

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

Monthly Weather Review ◽

10.1175/2007mwr2120.1 ◽

2008 ◽

Vol 136 (3) ◽

pp. 945-963 ◽

Cited By ~ 31

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.

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The CORAVEL Radial Velocity Data Base

Proceedings of the International Astronomical Union ◽

10.1017/s1743921317006834 ◽

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

International Astronomical Union Colloquium ◽

10.1017/s0252921100090679 ◽

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.

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