A machine learning approach for classifying bird and insect radar echoes with S-band Polarimetric Weather Radar

Author:

Jatau Precious1,Melnikov Valery2,Yu Tian-You3

Affiliation:

1. Advanced Radar Research Center, Cooperative Institute for Mesoscale Meteorological Studies, and, School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma

2. Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma and National Severe Storms Laboratory, Norman, Oklahoma

3. Advanced Radar Research Center, School of Electrical and Computer Engineering, and School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA

Abstract

AbstractThe S-bandWSR-88D weather radar is sensitive enough to observe biological scatterers like birds and insects. However, their non-spherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (or insect) features by coherently averaging dual polarization measurements from multiple radar scans, containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence, and the variation of these echoes over local regions. Next, ridge classifier and decision tree machine learning algorithms are trained, first only with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of all models for both methods, are analyzed using metrics computed from the test data. Further studies on different patterns of birds/insects, including roosting birds, bird migration and insect migration cases, are used to further investigate the generality of our models. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well across all these tests. Our recommendation is that this classifier can be used operationally on the US Next-Generation Radars (NEXRAD), as a first step in classifying biological echoes. It would be used in conjunction with the existing Hydrometeor Classification Algorithm (HCA), where the HCA would first separate biological from non-biological echoes, then our algorithm would be applied to further separate biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that is capable of detecting diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.

Publisher

American Meteorological Society

Subject

Atmospheric Science,Ocean Engineering

Reference82 articles.

1. study of cross - validation and bootstrap for accuracy estimation and model selection th Joint on San CA;Kohavi;Proc Int Conf Artificial Intelligence,1995

2. The hydrometeor classification algorithm for the polarimetric WSR-88D: Description and application to an MCS;Park;Wea. Forecasting,2009

3. Identifying doppler velocity contamination caused by migrating birds. Part II: Bayes identification and probability tests;Liu;J. Atmos. Oceanic Technol.,2005

4. Dual-polarization radar products for biological applications;Stepanian;Ecosphere,2016

5. Probing the clear atmosphere with high power, high resolution radars;Hardy;Proc. IEEE,1969

Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Insects as radar targets: size, form, density and permittivity;International Journal of Remote Sensing;2024-04-18

2. Extracting Bird and Insect Migration Echoes From Single-Polarization Weather Radar Data Using Semi-Supervised Learning;IEEE Transactions on Geoscience and Remote Sensing;2024

3. Classification of Biological Scatters Using Polarimetric Weather Radar;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

4. An Animal Migration Forecast Model With Weather Radar and Meteorological Data;IEEE Transactions on Geoscience and Remote Sensing;2024

5. Segmentation of polarimetric radar imagery using statistical texture;Atmospheric Measurement Techniques;2023-10-12

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3