Terminal Doppler weather radar clutter control

2002 ◽  
Author(s):  
J.E. Evans ◽  
W.H. Drury ◽  
D.P. Hynek ◽  
T.S. Lee ◽  
B.H. Stevens
Keyword(s):  
Weather Radar ◽  
Doppler Weather Radar ◽  
Radar Clutter

Advanced Polarimetric Doppler Weather Radar Simulator

2014 ◽  
Author(s):  
S. Lischi ◽  
A. Lupidi ◽  
M. Martorella ◽  
F. Cuccoli ◽  
L. Facheris ◽  
...  
Keyword(s):  
Weather Radar ◽  
Doppler Weather Radar ◽  
Radar Simulator

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.

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