Comparison of Three Methodologies for Removal of Random‐Noise‐Induced Biases From Second‐Order Statistical Parameters of Lidar and Radar Measurements
Random‐noise‐induced biases are inherent issues to the accurate derivation of second‐order statistical parameters (e.g., variances, fluxes, energy densities, and power spectra) from lidar and radar measurements. We demonstrate here for the first time an altitude‐interleaved method for eliminating su...
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ftpubmed:oai:pubmedcentral.nih.gov:9286857 2023-05-15T13:50:20+02:00 Comparison of Three Methodologies for Removal of Random‐Noise‐Induced Biases From Second‐Order Statistical Parameters of Lidar and Radar Measurements Jandreau, Jackson Chu, Xinzhao 2021-12-30 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286857/ http://www.ncbi.nlm.nih.gov/pubmed/35865261 https://doi.org/10.1029/2021EA002073 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286857/ http://www.ncbi.nlm.nih.gov/pubmed/35865261 http://dx.doi.org/10.1029/2021EA002073 © 2021 The Authors. Earth and Space Science published by Wiley Periodicals LLC on behalf of American Geophysical Union. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY Earth Space Sci Research Article Text 2021 ftpubmed https://doi.org/10.1029/2021EA002073 2022-07-31T01:45:19Z Random‐noise‐induced biases are inherent issues to the accurate derivation of second‐order statistical parameters (e.g., variances, fluxes, energy densities, and power spectra) from lidar and radar measurements. We demonstrate here for the first time an altitude‐interleaved method for eliminating such biases, following the original proposals by Gardner and Chu (2020, https://doi.org/10.1364/ao.400375) who demonstrated a time‐interleaved method. Interleaving in altitude bins provides two statistically independent samples over the same time period and nearly the same altitude range, thus enabling the replacement of variances that include the noise‐induced biases with covariances that are intrinsically free of such biases. Comparing the interleaved method with previous variance subtraction (VS) and spectral proportion (SP) methods using gravity wave potential energy density calculated from Antarctic lidar data and from a forward model, this study finds the accuracy and precision of each method differing in various conditions, each with its own strengths and weakness. VS performs well in high‐SNR, yet its accuracy fails at lower‐SNR as it often yields negative values. SP is accurate and precise under high‐SNR, remaining accurate in worse conditions than VS would, yet develops a positive bias under low‐SNR. The interleaved method is accurate in all SNRs but requires a large number of samples to drive random‐noise terms in covariances toward zero and to compensate for the reduced precision due to the splitting of return signals. Therefore, selecting the proper bias removal/elimination method for actual signal and sample conditions is crucial in utilizing lidar/radar data, as neglecting this can conceal trends or overstate atmospheric variability. Text Antarc* Antarctic PubMed Central (PMC) Antarctic Gardner ENVELOPE(65.903,65.903,-70.411,-70.411) Earth and Space Science 9 1 |
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Research Article Jandreau, Jackson Chu, Xinzhao Comparison of Three Methodologies for Removal of Random‐Noise‐Induced Biases From Second‐Order Statistical Parameters of Lidar and Radar Measurements |
topic_facet |
Research Article |
description |
Random‐noise‐induced biases are inherent issues to the accurate derivation of second‐order statistical parameters (e.g., variances, fluxes, energy densities, and power spectra) from lidar and radar measurements. We demonstrate here for the first time an altitude‐interleaved method for eliminating such biases, following the original proposals by Gardner and Chu (2020, https://doi.org/10.1364/ao.400375) who demonstrated a time‐interleaved method. Interleaving in altitude bins provides two statistically independent samples over the same time period and nearly the same altitude range, thus enabling the replacement of variances that include the noise‐induced biases with covariances that are intrinsically free of such biases. Comparing the interleaved method with previous variance subtraction (VS) and spectral proportion (SP) methods using gravity wave potential energy density calculated from Antarctic lidar data and from a forward model, this study finds the accuracy and precision of each method differing in various conditions, each with its own strengths and weakness. VS performs well in high‐SNR, yet its accuracy fails at lower‐SNR as it often yields negative values. SP is accurate and precise under high‐SNR, remaining accurate in worse conditions than VS would, yet develops a positive bias under low‐SNR. The interleaved method is accurate in all SNRs but requires a large number of samples to drive random‐noise terms in covariances toward zero and to compensate for the reduced precision due to the splitting of return signals. Therefore, selecting the proper bias removal/elimination method for actual signal and sample conditions is crucial in utilizing lidar/radar data, as neglecting this can conceal trends or overstate atmospheric variability. |
format |
Text |
author |
Jandreau, Jackson Chu, Xinzhao |
author_facet |
Jandreau, Jackson Chu, Xinzhao |
author_sort |
Jandreau, Jackson |
title |
Comparison of Three Methodologies for Removal of Random‐Noise‐Induced Biases From Second‐Order Statistical Parameters of Lidar and Radar Measurements |
title_short |
Comparison of Three Methodologies for Removal of Random‐Noise‐Induced Biases From Second‐Order Statistical Parameters of Lidar and Radar Measurements |
title_full |
Comparison of Three Methodologies for Removal of Random‐Noise‐Induced Biases From Second‐Order Statistical Parameters of Lidar and Radar Measurements |
title_fullStr |
Comparison of Three Methodologies for Removal of Random‐Noise‐Induced Biases From Second‐Order Statistical Parameters of Lidar and Radar Measurements |
title_full_unstemmed |
Comparison of Three Methodologies for Removal of Random‐Noise‐Induced Biases From Second‐Order Statistical Parameters of Lidar and Radar Measurements |
title_sort |
comparison of three methodologies for removal of random‐noise‐induced biases from second‐order statistical parameters of lidar and radar measurements |
publisher |
John Wiley and Sons Inc. |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286857/ http://www.ncbi.nlm.nih.gov/pubmed/35865261 https://doi.org/10.1029/2021EA002073 |
long_lat |
ENVELOPE(65.903,65.903,-70.411,-70.411) |
geographic |
Antarctic Gardner |
geographic_facet |
Antarctic Gardner |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
op_source |
Earth Space Sci |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9286857/ http://www.ncbi.nlm.nih.gov/pubmed/35865261 http://dx.doi.org/10.1029/2021EA002073 |
op_rights |
© 2021 The Authors. Earth and Space Science published by Wiley Periodicals LLC on behalf of American Geophysical Union. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1029/2021EA002073 |
container_title |
Earth and Space Science |
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9 |
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1 |
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1766253365209071616 |