An Approach to Simulate the Effects of Antenna Patterns on Polarimetric Variable Estimates

An Approach to Simulate the Effects of Antenna Patterns on Polarimetric Variable Estimates AbstractOne of the main challenges of using phased array radar for weather observations is the implementation of dual polarization with acceptable errors of polarimetric variable estimates. This is because the differences between the copolar antenna patterns at the horizontal and vertical polarizations, as well as cross-polar fields, can introduce unacceptable measurement biases, as the main beam is electronically steered away from the principal planes. Because the sufficient cross-polar isolation is difficult to achieve by the phased array antenna hardware and because the copolar as well as cross-polar patterns inevitably vary with each beam position, it is crucial to properly evaluate errors of estimates due to radiation patterns. Herein, a method that combines the measured or simulated radiation patterns and simulated time series is introduced. The method is suited for phased array and parabolic antennas, and it allows for evaluation of radiation-pattern-induced polarimetric variable biases and standard deviations specific to the antenna used to produce the patterns. The method can be used either as an alternative to a well-established approach using analytical derivations or as a tool for cross validation of the bias computations. For standard deviation evaluation in the presence of antenna cross-polar fields, the analytical approach becomes overly complex, which inexorably leads to the introduction of numerous approximations to obtain the results. These approximations inevitably compromise the accuracy of such computations. The method proposed herein avoids such approximations and therefore provides a valuable tool for accurate assessment of polarimetric measurement precision. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Atmospheric and Oceanic Technology American Meteorological Society

An Approach to Simulate the Effects of Antenna Patterns on Polarimetric Variable Estimates

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0426
eISSN
1520-0426
D.O.I.
10.1175/JTECH-D-17-0015.1
Publisher site
See Article on Publisher Site

Abstract

AbstractOne of the main challenges of using phased array radar for weather observations is the implementation of dual polarization with acceptable errors of polarimetric variable estimates. This is because the differences between the copolar antenna patterns at the horizontal and vertical polarizations, as well as cross-polar fields, can introduce unacceptable measurement biases, as the main beam is electronically steered away from the principal planes. Because the sufficient cross-polar isolation is difficult to achieve by the phased array antenna hardware and because the copolar as well as cross-polar patterns inevitably vary with each beam position, it is crucial to properly evaluate errors of estimates due to radiation patterns. Herein, a method that combines the measured or simulated radiation patterns and simulated time series is introduced. The method is suited for phased array and parabolic antennas, and it allows for evaluation of radiation-pattern-induced polarimetric variable biases and standard deviations specific to the antenna used to produce the patterns. The method can be used either as an alternative to a well-established approach using analytical derivations or as a tool for cross validation of the bias computations. For standard deviation evaluation in the presence of antenna cross-polar fields, the analytical approach becomes overly complex, which inexorably leads to the introduction of numerous approximations to obtain the results. These approximations inevitably compromise the accuracy of such computations. The method proposed herein avoids such approximations and therefore provides a valuable tool for accurate assessment of polarimetric measurement precision.

Journal

Journal of Atmospheric and Oceanic TechnologyAmerican Meteorological Society

Published: Sep 29, 2017

References

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