Comparison of VTEC due to GPS and assimilation of the IRI-Plas model during a geomagnetic storm condition over Indian region

Kavitha Devireddy, K. Sreeteja, Yaseen, Santhosh Kumar Veerlapati, C. Keerthi Chandra, Naveen Kumar Perumalla
{"title":"Comparison of VTEC due to GPS and assimilation of the IRI-Plas model during a geomagnetic storm condition over Indian region","authors":"Kavitha Devireddy, K. Sreeteja, Yaseen, Santhosh Kumar Veerlapati, C. Keerthi Chandra, Naveen Kumar Perumalla","doi":"10.1109/InGARSS48198.2020.9358931","DOIUrl":null,"url":null,"abstract":"Ionosphere is one of the largest sources of error for single frequency GNSS (Global Navigation Satellite Systems) users. The IRI-Plas is the widely used ionospheric and plasmaspheric climatic model for estimating VTEC (Vertical Total Electron Content) globally. This paper focuses on the performance of the IRI-Plas-2017 model with ingestion of GIM-TEC (Global Ionospheric Maps) input option at two low latitude stations, Hyderabad (Lat:17.2°N; Lon:78.5°E) and Bangalore (Lat: 12.9°N; Lon: 77.6°E) over the Indian region. The predicted TEC due to the model is compared with GPS TEC (Global Positioning System).The analysis is carried out for 7th, 8th and 9th September 2017 (Pre storm, Storm and post storm days). In this work, Symmetric Kullbacke Leibler Distance (SKLD), Cross Correlation (CC) coefficient and the metric norm (L2N) methods are used to evaluate the performance of IRI-Plas-TEC (with and without TEC input) with GPS TEC. From the results it is observed that TEC predicted by the assimilation option produced smaller estimation errors and substantial improvement of the model performance for ionospheric disturbances.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"42 1","pages":"166-169"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/InGARSS48198.2020.9358931","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Ionosphere is one of the largest sources of error for single frequency GNSS (Global Navigation Satellite Systems) users. The IRI-Plas is the widely used ionospheric and plasmaspheric climatic model for estimating VTEC (Vertical Total Electron Content) globally. This paper focuses on the performance of the IRI-Plas-2017 model with ingestion of GIM-TEC (Global Ionospheric Maps) input option at two low latitude stations, Hyderabad (Lat:17.2°N; Lon:78.5°E) and Bangalore (Lat: 12.9°N; Lon: 77.6°E) over the Indian region. The predicted TEC due to the model is compared with GPS TEC (Global Positioning System).The analysis is carried out for 7th, 8th and 9th September 2017 (Pre storm, Storm and post storm days). In this work, Symmetric Kullbacke Leibler Distance (SKLD), Cross Correlation (CC) coefficient and the metric norm (L2N) methods are used to evaluate the performance of IRI-Plas-TEC (with and without TEC input) with GPS TEC. From the results it is observed that TEC predicted by the assimilation option produced smaller estimation errors and substantial improvement of the model performance for ionospheric disturbances.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
印度地区一次地磁风暴条件下GPS VTEC与iris - plas模式同化的比较
电离层是单频GNSS(全球导航卫星系统)用户最大的误差来源之一。IRI-Plas是全球广泛使用的电离层和等离子层气候模式,用于估算垂直总电子含量(VTEC)。本文重点研究了采用全球电离层地图(Global Ionospheric Maps)输入选项的iri - plas2017模式在海德拉巴(Lat:17.2°N;东经78.5°)和班加罗尔(北纬12.9°;东经77.6度)在印度地区上空。并与GPS(全球定位系统)进行了比较。分析是在2017年9月7日、8日和9日(风暴前、风暴和风暴后的日子)进行的。本文采用对称kullbackleibler距离(SKLD)、互相关(CC)系数和度量范数(L2N)方法,对GPS TEC与iri - plasp -TEC(有和没有TEC输入)的性能进行了评价。结果表明,同化选项预报的TEC误差较小,电离层扰动模型的性能有较大改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
InGARSS 2020 Copyright Page Automatic Road Delineation Using Deep Neural Network Sparse Representation of Injected Details for MRA-Based Pansharpening InGARSS 2020 Reviewers Experimental Analysis of the Hongqi-1 H9 Satellite Imagery for Geometric Positioning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1