Short-Range Doppler-Radar Signatures from Industrial Wind Turbines: Theory, Simulations, and Measurements

2016 ◽  
Vol 65 (9) ◽  
pp. 2108-2119 ◽  
Author(s):  
Jose-Maria Munoz-Ferreras ◽  
Zhengyu Peng ◽  
Yao Tang ◽  
Roberto Gomez-Garcia ◽  
Daan Liang ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Short Range ◽  
Doppler Radar ◽  
Radar Signatures

Doppler radar signatures analysis by using joint bispectrum-based time-frequency distributions

2009 ◽  
Author(s):  
Jaakko T. Astola ◽  
Karen O. Egiazarian ◽  
Pavel A. Molchanov ◽  
Alexander V. Totsky
Keyword(s):  
Doppler Radar ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Radar Signatures

scholarly journals Practical investigation of multiband mono‐ and bistatic radar signatures of wind turbines

2017 ◽  
Vol 11 (6) ◽  
pp. 909-921 ◽  
Author(s):  
Francesco Fioranelli ◽  
Matthew Ritchie ◽  
Alessio Balleri ◽  
Hugh Griffiths
Keyword(s):  
Wind Turbines ◽  
Bistatic Radar ◽  
Radar Signatures ◽  
Practical Investigation

Micro-Doppler radar signatures of human activity

2010 ◽  
Author(s):  
Michael C. Moulton ◽  
Matthew L. Bischoff ◽  
Carla Benton ◽  
Douglas T. Petkie
Keyword(s):  
Human Activity ◽  
Doppler Radar ◽  
Radar Signatures

Intelligent target recognition using micro-Doppler radar signatures

2009 ◽  
Author(s):  
Thayananthan Thayaparan ◽  
Ljubisa Stankovic ◽  
Igor Djurovic ◽  
Suresh Penamati ◽  
Kamisetti Venkataramaniah
Keyword(s):  
Doppler Radar ◽  
Target Recognition ◽  
Radar Signatures

Assimilation of Doppler Radar Observations with a Regional 3DVAR System: Impact of Doppler Velocities on Forecasts of a Heavy Rainfall Case

2005 ◽  
Vol 44 (6) ◽  
pp. 768-788 ◽  
Author(s):  
Qingnong Xiao ◽  
Ying-Hwa Kuo ◽  
Juanzhen Sun ◽  
Wen-Chau Lee ◽  
Eunha Lim ◽  
...  
Keyword(s):  
Radial Velocity ◽  
Vertical Velocity ◽  
Short Range ◽  
Heavy Rainfall ◽  
Doppler Radar ◽  
Radar Data ◽  
Radial Velocities ◽  
Doppler Velocity ◽  
Rainfall Forecast ◽  
Background Fields

Abstract In this paper, the impact of Doppler radar radial velocity on the prediction of a heavy rainfall event is examined. The three-dimensional variational data assimilation (3DVAR) system for use with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) is further developed to enable the assimilation of radial velocity observations. Doppler velocities from the Korean Jindo radar are assimilated into MM5 using the 3DVAR system for a heavy rainfall case that occurred on 10 June 2002. The results show that the assimilation of Doppler velocities has a positive impact on the short-range prediction of heavy rainfall. The dynamic balance between atmospheric wind and thermodynamic fields, based on the Richardson equation, is introduced to the 3DVAR system. Vertical velocity (w) increments are included in the 3DVAR system to enable the assimilation of the vertical velocity component of the Doppler radial velocity observation. The forecast of the hydrometeor variables of cloud water (qc) and rainwater (qr) is used in the 3DVAR background fields. The observation operator for Doppler radial velocity is developed and implemented within the 3DVAR system. A series of experiments, assimilating the Korean Jindo radar data for the 10 June 2002 heavy rainfall case, indicates that the scheme for Doppler velocity assimilation is stable and robust in a cycling mode making use of high-frequency radar data. The 3DVAR with assimilation of Doppler radial velocities is shown to improve the prediction of the rainband movement and intensity change. As a result, an improved skill for the short-range heavy rainfall forecast is obtained. The forecasts of other quantities, for example, winds, are also improved. Continuous assimilation with 3-h update cycles is important in producing an improved heavy rainfall forecast. Assimilation of Doppler radar radial velocities using the 3DVAR background fields from a cycling procedure produces skillful rainfall forecasts when verified against observations.

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