Asymmetry of Doppler spectrum in stenosis differentiation

1989 ◽  
Vol 27 (5) ◽  
pp. 456-462 ◽  
Author(s):  
K. Kaluzynski ◽  
A. Tedgui
Keyword(s):  
Doppler Spectrum

Haemodynamic changes in human kidney allografts following administration of nifedipine: assessment with doppler spectrum analysis

1992 ◽  
Vol 5 ◽  
pp. S17-S20 ◽  
Author(s):  
J. W. S. Merkus ◽  
L. B. Hilbrands ◽  
A. J. Hoitsma ◽  
W. N. J. C. van Asten ◽  
R. A. P. Koene ◽  
...  
Keyword(s):  
Spectrum Analysis ◽  
Human Kidney ◽  
Doppler Spectrum ◽  
Haemodynamic Changes

scholarly journals Suppression of Wind Ripples and Microwave Backscattering Due to Turbulence Generated by Breaking Surface Waves

2020 ◽  
Vol 12 (21) ◽  
pp. 3618
Author(s):  
Stanislav Ermakov ◽  
Vladimir Dobrokhotov ◽  
Irina Sergievskaya ◽  
Ivan Kapustin
Keyword(s):  
Remote Sensing ◽  
Surface Waves ◽  
Wave Breaking ◽  
Wave Train ◽  
Wind Waves ◽  
Breaking Waves ◽  
Doppler Spectrum ◽  
Strong Wave ◽  
Wave Maker ◽  
Radar Backscattering

The role of wave breaking in microwave backscattering from the sea surface is a problem of great importance for the development of theories and methods on ocean remote sensing, in particular for oil spill remote sensing. Recently it has been shown that microwave radar return is determined by both Bragg and non-Bragg (non-polarized) scattering mechanisms and some evidence has been given that the latter is associated with wave breaking, in particular, with strong breaking such as spilling or plunging. However, our understanding of mechanisms of the action of strong wave breaking on small-scale wind waves (ripples) and thus on the radar return is still insufficient. In this paper an effect of suppression of radar backscattering after strong wave breaking has been revealed experimentally and has been attributed to the wind ripple suppression due to turbulence generated by strong wave breaking. The experiments were carried out in a wind wave tank where a frequency modulated wave train of intense meter-decimeter-scale surface waves was generated by a mechanical wave maker. The wave train was compressed according to the gravity wave dispersion relation (“dispersive focusing”) into a short-wave packet at a given distance from the wave maker. Strong wave breaking with wave crest overturning (spilling) occurred for one or two highest waves in the packet. Short decimeter-centimeter-scale wind waves were generated at gentle winds, simultaneously with the long breaking waves. A Ka-band scatterometer was used to study microwave backscattering from the surface waves in the tank. The scatterometer looking at the area of wave breaking was mounted over the tank at a height of about 1 m above the mean water level, the incidence angle of the microwave radiation was about 50 degrees. It has been obtained that the radar return in the presence of short wind waves is characterized by the radar Doppler spectrum with a peak roughly centered in the vicinity of Bragg wave frequencies. The radar return was strongly enhanced in a wide frequency range of the radar Doppler spectrum when a packet of long breaking waves arrived at the area irradiated by the radar. After the passage of breaking waves, the radar return strongly dropped and then slowly recovered to the initial level. Measurements of velocities in the upper water layer have confirmed that the attenuation of radar backscattering after wave breaking is due to suppression of short wind waves by turbulence generated in the breaking zone. A physical analysis of the effect has been presented.

Microwave Front End for True Ground Speed Measurements

1995 ◽  
Vol 48 (1) ◽  
pp. 88-96 ◽  
Author(s):  
Jürgen Kehrbeck ◽  
Eberhardt Heidrich ◽  
Werner Wiesbeck
Keyword(s):  
Doppler Spectrum ◽  
Automotive Applications ◽  
Distance Measurements ◽  
Theoretical Simulation ◽  
Ground Speed ◽  
The Road ◽  
Dual Channel ◽  
Front End ◽  
Sensor Module ◽  
Microwave Components

A dual channel microwave Doppler-Sensor-Module for 24 GHz is presented. This front end is well suited for true ground speed and distance measurements in all kinds of automotive applications. The microwave components such as oscillator, mixer, antenna and their characteristics in the MIC are discussed. The influence of the antenna pattern and the road surface on the Doppler spectrum is treated in a 3D-field theoretical simulation. This simulation takes the antenna nearfield and the distributed scattering of the road into account.

Advanced open-source tools for detecting and analyzing peaks in cloud radar Doppler spectra

2021 ◽  
Author(s):  
Teresa Vogl ◽  
Martin Radenz ◽  
Heike Kalesse-Los
Keyword(s):  
Supercooled Liquid ◽  
Detection Algorithm ◽  
Supervised Machine Learning ◽  
Doppler Spectrum ◽  
The Past ◽  
Cloud Radar ◽  
Depolarisation Ratio ◽  
Instrument Type ◽  
Peak Detection Algorithm ◽  
Binary Tree Structure

<p>Cloud radar Doppler spectra contain vertically highly resolved valuable information about the hydrometeors present in the cloud. A mixture of different hydrometeor types can lead to several peaks in the Doppler spectrum due to their different fall speeds, giving a hint about the size/ density/ number of the respective particles. Tools to separate and interpret peaks in cloud radar Doppler spectra have been developed in the past, but their application is often limited to certain radar settings, or the code not freely available to other users.</p> <p>We here present the effort of joining two methods, which have been developed and published (Radenz et al., 2019; Kalesse et al., 2019) with the aim to make them insensitive to instrument type and settings, and available on GitHub, and applicable to all cloud radars which are part of the ACTRIS CloudNet network.</p> <p>A supervised machine learning peak detection algorithm (PEAKO, Kalesse et al., 2019) is used to derive the optimal parameters to detect peaks in cloud radar Doppler spectra for each set of instrument settings. In the next step, these parameters are used by peakTree (Radenz et al., 2019), which is a tool for converting multi-peaked (cloud) radar Doppler spectra into a binary tree structure. PeakTree yields the (polarimetric) radar moments of each detected peak and can thus be used to classify the hydrometeor types. This allows us to analyze Doppler spectra of different cloud radars with respect to, e.g. the occurrence of supercooled liquid water or ice needles/columns with high linear depolarisation ratio (LDR).</p>

scholarly journals Doppler spectrum of microwave SAR signals from two-dimensional time-varying sea surface

2016 ◽  
Vol 30 (10) ◽  
pp. 1265-1276 ◽  
Author(s):  
Yunhua Wang ◽  
Yanmin Zhang ◽  
Huimin Li ◽  
Ge Chen
Keyword(s):  
Sea Surface ◽  
Doppler Spectrum ◽  
Time Varying ◽  
Two Dimensional

Separating Cloud and Drizzle Radar Moments during Precipitation Onset Using Doppler Spectra

2013 ◽  
Vol 30 (8) ◽  
pp. 1656-1671 ◽  
Author(s):  
Edward P. Luke ◽  
Pavlos Kollias
Keyword(s):  
Cloud Base ◽  
Doppler Spectrum ◽  
Retrieval Algorithm ◽  
Cloud Droplets ◽  
Gaussian Shape ◽  
Model Parameterization ◽  
Atmospheric Radiation Measurement ◽  
Shape Property ◽  
A New Technique ◽  
Turbulence Parameters

Abstract The retrieval of cloud, drizzle, and turbulence parameters using radar Doppler spectra is challenged by the convolution of microphysical and dynamical influences and the overall uncertainty introduced by turbulence. A new technique that utilizes recorded radar Doppler spectra from profiling cloud radars is presented here. The technique applies to areas in clouds where drizzle is initially produced by the autoconversion process and is detected by a positive skewness in the radar Doppler spectrum. Using the Gaussian-shape property of cloud Doppler spectra, the cloud-only radar Doppler spectrum is estimated and used to separate the cloud and drizzle contributions. Once separated, the cloud spectral peak can be used to retrieve vertical air motion and eddy dissipation rates, while the drizzle peak can be used to estimate the three radar moments of the drizzle particle size distribution. The technique works for nearly 50% of spectra found near cloud top, with efficacy diminishing to roughly 15% of spectra near cloud base. The approach has been tested on a large dataset collected in the Azores during the Atmospheric Radiation Measurement Program (ARM) Mobile Facility deployment on Graciosa Island from May 2009 through December 2010. Validation of the proposed technique is achieved using the cloud base as a natural boundary between radar Doppler spectra with and without cloud droplets. The retrieval algorithm has the potential to characterize the dynamical and microphysical conditions at cloud scale during the transition from cloud to precipitation. This has significant implications for improving the understanding of drizzle onset in liquid clouds and for improving model parameterization schemes of autoconversion of cloud water into drizzle.

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