A concept for evaluating the performance of wet radomes for phased-array weather radars

2012 ◽  
Author(s):  
Jorge L. Salazar ◽  
Paul Siquiera ◽  
Jorge Trabal ◽  
Eric J. Knapp ◽  
David J. McLaughlin
Keyword(s):  
Phased Array ◽  
Weather Radars

scholarly journals Investigating EM Dipole Radiating Element for Dual Polarized Phased Array Weather Radars

10.5772/66502 ◽  
2017 ◽  
Author(s):  
Ridhwan Khalid Mirza ◽  
Yan (Rockee) Zhang ◽  
Dusan Zrnic ◽  
Richard Doviak
Keyword(s):  
Phased Array ◽  
Weather Radars ◽  
Radiating Element ◽  
Dual Polarized

Adaptive Beamspace Processing for Phased-Array Weather Radars

2016 ◽  
Vol 54 (10) ◽  
pp. 5688-5698 ◽  
Author(s):  
Feng Nai ◽  
Sebastian M. Torres ◽  
Robert D. Palmer
Keyword(s):  
Phased Array ◽  
Weather Radars

scholarly journals On the Use of Auxiliary Receive Channels for Clutter Mitigation With Phased Array Weather Radars

2009 ◽  
Vol 47 (1) ◽  
pp. 272-284 ◽  
Author(s):  
K.D. Le ◽  
R.D. Palmer ◽  
Boon Leng Cheong ◽  
Tian-You Yu ◽  
Guifu Zhang ◽  
...  
Keyword(s):  
Phased Array ◽  
Weather Radars ◽  
Clutter Mitigation

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

2021 ◽  
Author(s):  
Daichi Kitahara ◽  
Hiroki Kuroda ◽  
Akira Hirabayashi ◽  
Eiichi Yoshikawa ◽  
Hiroshi Kikuchi ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Spatial Resolution ◽  
Array Signal Processing ◽  
Phased Array ◽  
Optimization Problems ◽  
Doppler Frequency ◽  
Coefficient Matrix ◽  
Weather Radars ◽  
Power Spectral

<div>We propose nonlinear beamforming for phased array weather radars (PAWRs). Conventional beamforming is linear in the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals. For distributed targets such as raindrops, however, the number of scatterers is significantly large, differently from the case of point targets that are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of a periodogram of each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of the power spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, (i) major components of the periodogram are concentrated in the vicinity of the mean Doppler frequency, and (ii) frequency indices of the major components are similar between adjacent elevations. These are expressed as group-sparsities of the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group- parsities. We consider two optimization problems which evaluate the group-sparsities in slightly different ways, and both problems are solved with the alternating direction method of multipliers including nonlinear mappings. Simulations using real PAWR data show that the proposed method dramatically improves the spatial resolution.</div>

Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

2021 ◽  
Author(s):  
Daichi Kitahara ◽  
Hiroki Kuroda ◽  
Akira Hirabayashi ◽  
Eiichi Yoshikawa ◽  
Hiroshi Kikuchi ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Spatial Resolution ◽  
Array Signal Processing ◽  
Phased Array ◽  
Optimization Problems ◽  
Doppler Frequency ◽  
Coefficient Matrix ◽  
Weather Radars ◽  
Power Spectral

<div>We propose nonlinear beamforming for phased array weather radars (PAWRs). Conventional beamforming is linear in the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals. For distributed targets such as raindrops, however, the number of scatterers is significantly large, differently from the case of point targets that are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of a periodogram of each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of the power spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, (i) major components of the periodogram are concentrated in the vicinity of the mean Doppler frequency, and (ii) frequency indices of the major components are similar between adjacent elevations. These are expressed as group-sparsities of the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group-sparsities. We consider two optimization problems. One problem roughly evaluates the group-sparsities and is relatively easy to solve. The other evaluates the group-sparsities more accurately, but requires more time to solve. Both problems are solved with the alternating direction method of multipliers including nonlinear mappings. Simulations using synthetic and real-world PAWR data show that the proposed method dramatically improves the spatial resolution.</div>

scholarly journals Considerations for Oversampling in Azimuth on the Phased Array Weather Radar

2015 ◽  
Vol 32 (9) ◽  
pp. 1614-1629 ◽  
Author(s):  
Lesya Borowska ◽  
Guifu Zhang ◽  
Dusan S. Zrnić
Keyword(s):  
Phased Array ◽  
Weighted Average ◽  
Weather Radar ◽  
Severe Weather ◽  
Spectral Moment ◽  
Spectral Moments ◽  
Severe Storms ◽  
Moment Estimate ◽  
National Network ◽  
Weather Radars

AbstractWhen spectral moments in the azimuth are spaced by less than a beamwidth, it is called oversampling. Superresolution is a type of oversampling that refers to sampling at half a beamwidth on the national network of Doppler weather radars [Weather Surveillance Radar-1988 Doppler (WSR-88D)]. Such close spacing is desirable because it extends the range at which small severe weather features, such as tornadoes or microbursts, can be resolved. This study examines oversampling for phased array radars. The goal of the study is to preserve the same effective beamwidth as on the WSR-88D while obtaining smaller spectral moment estimate errors at the same or faster volume update times. To that effect, a weighted average of autocorrelations of radar signals from three consecutive radials is proposed. Errors in three spectral moments obtained from these autocorrelations are evaluated theoretically. Methodologies on how to choose weights that preserve the desirable effective beamwidth are presented. The results are demonstrated on the fields of spectral moments obtained with the National Weather Radar Testbed (NWRT), a phased array weather radar at NOAA’s National Severe Storms Laboratory (NSSL).

scholarly journals Nonlinear Beamforming Based on Group-Sparsities of Periodograms for Phased Array Weather Radar

2022 ◽  
Author(s):  
Daichi Kitahara ◽  
Hiroki Kuroda ◽  
Akira Hirabayashi ◽  
Eiichi Yoshikawa ◽  
Hiroshi Kikuchi ◽  
...  
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Spatial Resolution ◽  
Array Signal Processing ◽  
Phased Array ◽  
Optimization Problems ◽  
Doppler Frequency ◽  
Coefficient Matrix ◽  
Weather Radars ◽  
Power Spectral

<div>We propose nonlinear beamforming for phased array weather radars (PAWRs). Conventional beamforming is linear in the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals. For distributed targets such as raindrops, however, the number of scatterers is significantly large, differently from the case of point targets that are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of a periodogram of each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of the power spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, (i) major components of the periodogram are concentrated in the vicinity of the mean Doppler frequency, and (ii) frequency indices of the major components are similar between adjacent elevations. These are expressed as group-sparsities of the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group-sparsities. We consider two optimization problems. One problem roughly evaluates the group-sparsities and is relatively easy to solve. The other evaluates the group-sparsities more accurately, but requires more time to solve. Both problems are solved with the alternating direction method of multipliers including nonlinear mappings. Simulations using synthetic and real-world PAWR data show that the proposed method dramatically improves the spatial resolution.</div>

A Simulation Framework to Support the Design and Evaluation of Adaptive Scanning for Phased-Array Weather Radars

2020 ◽  
Vol 37 (12) ◽  
pp. 2321-2339
Author(s):  
Sebastián M. Torres ◽  
David Schvartzman
Keyword(s):  
Phased Array ◽  
Radar Data ◽  
Simulation Framework ◽  
Weather Event ◽  
Qualitative Comparison ◽  
Weather Radars ◽  
Weather Events ◽  
Quantitative Metrics ◽  
Nexrad Radar ◽  
Scanning Algorithm

AbstractWe propose a simulation framework that can be used to design and evaluate the performance of adaptive scanning algorithms on different phased-array weather radar designs. The simulator is proposed as tool to 1) compare the performance of different adaptive scanning algorithms on the same weather event, 2) evaluate the performance of a given adaptive scanning algorithm on several weather events, and 3) evaluate the performance of a given adaptive scanning algorithm on a given weather event using different radar designs. We illustrate the capabilities of the proposed framework to design and evaluate the performance of adaptive algorithms aimed at reducing the update time using adaptive scanning. The example concept of operations is based on a fast low-fidelity surveillance scan and a high-fidelity adaptive scan. The flexibility of the proposed simulation framework is tested using two phased-array-radar designs and three complementary adaptive scanning algorithms: focused observations, beam clustering, and dwell tailoring. Based on a significant weather event observed by an operational NEXRAD radar, our experimental results consist of radar data that were simulated as if the same event had been observed by arbitrary combinations of radar systems and adaptive scanning configurations. Results show that simulated fields of radar data capture the main data-quality impacts from the use of adaptive scanning and can be used to obtain quantitative metrics and for qualitative comparison and evaluation by forecasters. That is, the proposed simulator could provide an effective interface with meteorologists and could support the development of concepts of operations that are based on adaptive scanning to meet the evolutionary observational needs of the U.S. National Weather Service.

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