Article(id=1322506598094844869, tenantId=1146029695717560320, journalId=1281212831689347082, issueId=1322506567065383670, articleNumber=null, orderNo=null, doi=10.14075/j.jgg.2024.11.546, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1732809600000, receivedDateStr=2024-11-29, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793239743970, onlineDateStr=2026-10-29, pubDate=1752508800000, pubDateStr=2025-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793239743970, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793239743970, creator=13701087609, updateTime=1793239743970, updator=13701087609, issue=Issue{id=1322506567065383670, tenantId=1146029695717560320, journalId=1281212831689347082, year='2025', volume='45', issue='7', pageStart='661', pageEnd='770', issueExtLink='null', onlineDate='null', pubDate='1752508800000', pubDateStr='2025-07-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793239736572, creator='13701087609', updateTime=1793239736572, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=725, endPage=731, ext={EN=ArticleExt(id=1322506598275199942, articleId=1322506598094844869, tenantId=1146029695717560320, journalId=1281212831689347082, language=EN, title=Spaceborne GNSS-R Typhoon Position Inversion Based on CYGNSS Satellite, columnId=null, journalTitle=Journal of Geodesy and Geodynamics, columnName=null, runingTitle=null, highlight=null, articleAbstract=

We take typhoon Irma as an example, conduct a study on spaceborne GNSS-R typhoon position inversion based on CYGNSS satellite. The experimental results show that CYGNSS-related observations can effectively detect the occurrence of typhoon events. By utilizing CYGNSS measurements and DBSCAN clustering algorithm, the typhoon center position can be effectively retrieved, with a minimum deviation of about 30 km and an average deviation of about 45 km. This study helps to further expand the application scope of spaceborne GNSS-R in typhoon monitoring.

, authors=Dongliang GUAN1, Lei ZHOU1, Liangliang WANG2, Haiwen SUN1, Zhiling ZHOU1, Yimin XIA1, authorsList=Dongliang GUAN, Lei ZHOU, Liangliang WANG, Haiwen SUN, Zhiling ZHOU, Yimin XIA, authorCompany=null, correspAuthors=null, authorNote=

GUAN Dongliang, PhD, lecturer, majors in GNSS-R, E-mail:

, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322506601538368470, articleId=1322506598094844869, tenantId=1146029695717560320, journalId=1281212831689347082, language=CN, title=基于CYGNSS卫星的星载GNSS-R台风位置反演, columnId=1322506169105633839, journalTitle=大地测量与地球动力学, columnName=现代大地测量, runingTitle=null, highlight=null, articleAbstract=

以台风Irma为例,利用CYGNSS卫星开展基于星载GNSS-R的台风位置反演研究。实验结果表明,CYGNSS相关观测量能有效探测到台风事件的发生;利用CYGNSS观测量,基于DBSCAN聚类算法,可有效反演出台风中心位置,其最小偏差约为30 km,平均偏差约为45 km。本研究有助于进一步拓宽星载GNSS-R在台风监测方面的应用范围。

, authors=管栋良1, 周磊1, 王亮亮2, 孙海文1, 周志凌1, 夏逸旻1, authorsList=管栋良, 周磊, 王亮亮, 孙海文, 周志凌, 夏逸旻, authorCompany=null, correspAuthors=null, authorNote=

管栋良,博士,讲师,主要从事GNSS-R研究,E-mail:

, correspAuthorsNote=null, copyrightStatement=版权所有,未经授权,不得转载。, copyrightOwner=大地测量与地球动力学编辑部, extLink=null, articleAbsUrl=null, sourceXml=cg61CqkPoCjRNYJITvy/kg==, magXml=Oher0H4WHrsic4NOKp7+bQ==, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=NVPnVmg5Vua/ICzGV4T5og==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=3fDPNO9bbrjdxwlzoMX88Q==, mapNumber=null, fund=null)}, authors=[Author(id=1322506602108793822, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506598094844869, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=guan.dongliang@njtech.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322506602272371681, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506598094844869, authorId=1322506602108793822, language=EN, stringName=Dongliang GUAN, firstName=Dongliang, middleName=null, lastName=GUAN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1 School of Geomatics Science and Technology, Nanjing Tech University, 30 South-Puzhu Road, Nanjing 211816, China, bio={"content":"

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GUAN Dongliang, PhD, lecturer, majors in GNSS-R, E-mail:

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journalId=1281212831689347082, articleId=1322506598094844869, language=CN, label=图6, caption=基于CYGNSS观测量NBRCS和LES的DBSCAN聚类分析, figureFileSmall=N5rIBJAyPYA2RoJMzt/U5A==, figureFileBig=IvSyg3e1OTheq4wZSZQctw==, tableContent=null), ArticleFig(id=1322506607754326034, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506598094844869, language=EN, label=Tab. 1, caption=

Comparison of time and location between CYGNSS tracks and typhoon Irma

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台风Irma CYGNSS轨迹
时间 地理位置 时间 距离偏差/km
2017-09-01 09:00:00 18.2°N、36.5°W 08:46:54—08:52:59 ~11
2017-09-02 09:00:00 19.0°N、41.8°W 07:52:28—08:00:38 ~45
2017-09-03 09:00:00 18.0°N、47.5°W 08:15:43—08:26:43 ~18
2017-09-04 09:00:00 16.9°N、52.3°W 08:24:32—08:35:29 ~10
2017-09-05 06:00:00 16.6°N、56.4°W 07:02:39—07:12:47 ~2
), ArticleFig(id=1322506607842406419, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506598094844869, language=CN, label=表1, caption=

CYGNSS轨迹与台风Irma时间和地理位置比较

, figureFileSmall=null, figureFileBig=null, tableContent=
台风Irma CYGNSS轨迹
时间 地理位置 时间 距离偏差/km
2017-09-01 09:00:00 18.2°N、36.5°W 08:46:54—08:52:59 ~11
2017-09-02 09:00:00 19.0°N、41.8°W 07:52:28—08:00:38 ~45
2017-09-03 09:00:00 18.0°N、47.5°W 08:15:43—08:26:43 ~18
2017-09-04 09:00:00 16.9°N、52.3°W 08:24:32—08:35:29 ~10
2017-09-05 06:00:00 16.6°N、56.4°W 07:02:39—07:12:47 ~2
), ArticleFig(id=1322506607938875412, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506598094844869, language=EN, label=Tab. 2, caption=

Comparison of typhoon positions derived from CYGNSS with those from NHC

, figureFileSmall=null, figureFileBig=null, tableContent=
日期 CYGNSS NHC 时间差/min 反演位置偏差/km
纬度 经度 时间 纬度 经度 时间
2017-09-01 18.14°N 36.22°W 08:49 18.2°N 36.5°W 09:00 11 30.2
2017-09-02 19.41°N 41.56°W 07:56 19.0°N 41.8°W 09:00 64 49.7
2017-09-03 18.03°N 47.22°W 08:20 18.0°N 47.5°W 09:00 40 30.1
2017-09-04 16.63°N 52.08°W 08:30 16.9°N 52.3°W 09:00 30 34.6
2017-09-05 16.96°N 57.11°W 07:07 16.6°N 56.4°W 06:00 67 84.2
), ArticleFig(id=1322506609641762837, tenantId=1146029695717560320, journalId=1281212831689347082, articleId=1322506598094844869, language=CN, label=表2, caption=

基于CYGNSS反演的台风位置与NHC台风位置比较

, figureFileSmall=null, figureFileBig=null, tableContent=
日期 CYGNSS NHC 时间差/min 反演位置偏差/km
纬度 经度 时间 纬度 经度 时间
2017-09-01 18.14°N 36.22°W 08:49 18.2°N 36.5°W 09:00 11 30.2
2017-09-02 19.41°N 41.56°W 07:56 19.0°N 41.8°W 09:00 64 49.7
2017-09-03 18.03°N 47.22°W 08:20 18.0°N 47.5°W 09:00 40 30.1
2017-09-04 16.63°N 52.08°W 08:30 16.9°N 52.3°W 09:00 30 34.6
2017-09-05 16.96°N 57.11°W 07:07 16.6°N 56.4°W 06:00 67 84.2
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基于CYGNSS卫星的星载GNSS-R台风位置反演
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管栋良 1 , 周磊 1 , 王亮亮 2 , 孙海文 1 , 周志凌 1 , 夏逸旻 1
大地测量与地球动力学 | 现代大地测量 2025,45(7): 725-731
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大地测量与地球动力学 |现代大地测量 2025 , 45 (7) : 725 -731
基于CYGNSS卫星的星载GNSS-R台风位置反演
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管栋良1 , 周磊1, 王亮亮2, 孙海文1, 周志凌1, 夏逸旻1
作者信息
  • 1 南京工业大学测绘科学与技术学院, 南京市浦珠南路30号, 211816
  • 2 齐鲁空天信息研究院, 济南市工业北路 17218号, 250132
作者简介:

管栋良,博士,讲师,主要从事GNSS-R研究,E-mail:

Spaceborne GNSS-R Typhoon Position Inversion Based on CYGNSS Satellite
Dongliang GUAN1 , Lei ZHOU1, Liangliang WANG2, Haiwen SUN1, Zhiling ZHOU1, Yimin XIA1
Affiliations
  • 1 School of Geomatics Science and Technology, Nanjing Tech University, 30 South-Puzhu Road, Nanjing 211816, China
  • 2 Qilu Aerospace Information Research Institute, 17218 North-Gongye Road, Jinan 250132, China
  • GUAN Dongliang, PhD, lecturer, majors in GNSS-R, E-mail:

出版时间: 2025-07-15 doi: 10.14075/j.jgg.2024.11.546
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以台风Irma为例,利用CYGNSS卫星开展基于星载GNSS-R的台风位置反演研究。实验结果表明,CYGNSS相关观测量能有效探测到台风事件的发生;利用CYGNSS观测量,基于DBSCAN聚类算法,可有效反演出台风中心位置,其最小偏差约为30 km,平均偏差约为45 km。本研究有助于进一步拓宽星载GNSS-R在台风监测方面的应用范围。

星载GNSS-R  /  CYGNSS  /  台风位置  /  风速  /  DBSCAN聚类算法

We take typhoon Irma as an example, conduct a study on spaceborne GNSS-R typhoon position inversion based on CYGNSS satellite. The experimental results show that CYGNSS-related observations can effectively detect the occurrence of typhoon events. By utilizing CYGNSS measurements and DBSCAN clustering algorithm, the typhoon center position can be effectively retrieved, with a minimum deviation of about 30 km and an average deviation of about 45 km. This study helps to further expand the application scope of spaceborne GNSS-R in typhoon monitoring.

spaceborne GNSS-R  /  CYGNSS  /  typhoon position  /  wind speed  /  DBSCAN clustering algorithm
管栋良, 周磊, 王亮亮, 孙海文, 周志凌, 夏逸旻. 基于CYGNSS卫星的星载GNSS-R台风位置反演. 大地测量与地球动力学, 2025 , 45 (7) : 725 -731 . DOI: 10.14075/j.jgg.2024.11.546
Dongliang GUAN, Lei ZHOU, Liangliang WANG, Haiwen SUN, Zhiling ZHOU, Yimin XIA. Spaceborne GNSS-R Typhoon Position Inversion Based on CYGNSS Satellite[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (7) : 725 -731 . DOI: 10.14075/j.jgg.2024.11.546
台风是一种强烈的热带气旋,通常发生在热带和亚热带海洋上。其以低气压中心为核心,周围伴有强烈的旋转风暴和强降雨,会对海岸线及往内几百公里范围造成灾难性的破坏。因此,预测台风的位置与强度至关重要。数字气象预测模型是预测和跟踪台风的主要工具,其准确性依赖于输入模型的同化数据,包括星载观测数据和实地观测数据。
静止气象卫星可以大范围地持续监测台风的演化,跟踪台风的运动轨迹。然而,由于地球同步轨道卫星距离地球表面太远,其空间分辨较差。此外,光学传感器无法透过云层观测海面。装有微波传感器设备的低轨道卫星可以透过云层监测海面风速,如微波散射计ASCAT和微波极化辐射计WindSat等。但上述卫星的重复周期较长,无法持续观测快速演化中的台风。
GNSS反射(GNSS-reflectometry, GNSS-R) 作为一种全新的遥感技术,可有效进行对地观测,是现有卫星遥感技术的重要补充。该技术以现有的全球导航卫星系统为信号源,通过分析反射信号的相位、频率以及振幅等信息,反演反射界面的特征[1-3]。GNSS-R技术已广泛应用于对地观测,如海面风场反演[4-5]、测高[6-7]、海冰探测[8-9]、土壤湿度监测[10-12]、植被生长监测[13-15]、水体识别[16-17]和积雪监测[18-20]等。与传统遥感卫星相比,GNSS-R卫星只需搭载GNSS-R接收机、天线和配套设备,因此可用微卫星作为搭载平台,造价低,易于组建由多颗GNSS-R卫星构成的对地观测星座,从而增加同一时刻下的观测区域,缩短全球海面观测周期,使全球海面准实时监测成为可能[21]。此外,GNSS-R以L波段的GNSS信号为信号源,不易受降雨影响,具备全天候观测能力。
基于此,各国纷纷开展星载GNSS-R技术研究,并相继发射各自的GNSS-R卫星或在已有的卫星任务基础之上搭载GNSS-R接收机。其中,美国的CYGNSS卫星是第一个开展工程化应用的GNSS-R卫星,其主要任务是通过GNSS-R技术测量海面风速来研究热带台(飓)风的形成和发展。
部分学者[22-24]使用模拟的CYGNSS数据进行数据同化,将其融入GSI系统或HWRF模型开展台风预测研究。相关实验表明,融入模拟的CYGNSS数据有助于提高台风预测精度。Mueller等[25]利用全球数据同化和预报系统同化CYGNSS实测风场信息,并发现在最初的60 h内,台风轨迹预报精度显著提高20~40 km。Pu等[26]研究表明,与ASCAT散射计风速相比,同化了CYGNSS实测风速的台风预测模型能够在模型开始预测的最初48 h内更好地对台风路径和强度进行预测。
上述研究主要是探讨模拟的CYGNSS风速和实测CYGNSS风速在预测台风路径和强度方面的应用。但现有的研究并未探讨CYGNSS实测数据在台风观测中的直接应用,比如星载GNSS-R能否直接观测到台风事件的发生?能否根据CYGNSS观测数据反演台风位置信息?因此,本文以2017年台风Irma为例,探讨星载GNSS-R卫星CYGNSS在台风监测方面的能力,并利用CYGNSS观测数据和DBSCAN(density-based spatial clustering of applications with noise)聚类分析算法反演台风的中心位置信息。
图 1为基于CYGNSS卫星的台风中心位置反演流程图,具体计算流程如下:
1) 基于时空匹配方法,以美国国家飓风研究中心(NHC)发布的台风位置数据为参考,匹配出在时间和空间上均与台风中心位置最为靠近的CYGNSS卫星数据。
2) 基于时空匹配后的CYGNSS数据,逐一分析正则化双基雷达截面系数(normalized bistatic radar cross section, NBRCS)、前沿斜率(leading edge slope, LES)、信噪比(signal to noise ratio,SNR)和CYGNSS风速在台风中心位置的变化规律。
3) 为进一步验证CYGNSS风速产品的准确度,将其分别与ASCAT风速产品、WindSat风速产品和SMAP风速产品进行对比分析。
4) 根据NBRCS、LES、SNR和CYGNSS风速在台风中心位置的变化情况,确定用于反演台风中心位置的特征量。
5) 采用DBSCAN算法对台风位置特征量进行聚类分析,反演得到台风中心位置。
DBSCAN是一种比较有代表性的基于密度的空间聚类算法,能够将具有足够密度的区域划分为簇,同时可在含有噪声的空间数据库中发现任意形状的聚类。该算法通过距离来衡量点之间的邻近关系,进而决定数据点是否属于某个簇。距离计算方法可采用欧氏距离、曼哈顿距离或者余弦距离。本文采用欧氏距离,表达式为:
$d(p, q)=\sqrt{\sum\limits_{i=1}^n\left(p_i-q_i\right)^2}$
式中,p、q为数据点;pi、qi分别为p和q点在第i维的坐标。本文中数据点为CYGNSS观测数据的镜面反射点,用经纬度表示其坐标。
DBSCAN聚类算法有2个关键参数,分别为邻域半径ε和一个簇的最小点数n。该算法的实现步骤为:
1) 设置邻域半径ε和一个簇的最小点数n。
2) 标记每个数据点的邻域,即计算每个数据点邻域半径ε范围内的点数,使用欧氏距离计算点与点之间的距离。
3) 核心点检测:如果某个数据点在其邻域内至少有n个点,则该点是核心点,核心点为聚类的中心。
4) 扩展簇范围:从一个核心点出发,将所有邻域内的点添加到该簇。对于邻域内的点,如果为核心点,进一步扩展其邻域,直到没有新的点可以加入当前簇。
5) 循环迭代,直到每个数据点都被访问过,最终未被包含在任何簇中的点被标记为噪声点。
台风Irma开始于2017-08-30,结束于2017-09-12。本文使用09-01—05期间CYGNSS Level 1数据、Level 2数据、ASCAT风速产品、WindSat风速产品、SMAP风速产品以及NHC提供的台风位置数据。此外,根据台风Irma的移动轨迹,研究区域选取为5°~35°N、30°~80°W。
基于时空匹配方法,得到09-01—05期间与台风中心位置在时间和空间上最为靠近的CYGNSS轨迹数据,结果见表 1。表中CYGNSS轨迹时间是指一条完整的CYGNSS星下点轨迹从出现到结束的时间,距离偏差是指台风中心位置与CYGNSS轨迹的垂直距离。从表 1可以看出,选取的5条CYGNSS轨迹中,最短的时间偏差小于15 min,最长的不到2 h。有4条CYGNSS轨迹距离台风中心位置小于20 km,其中最近的仅为2 km。
图 2为表 1中所列出的5条CYGNSS轨迹与台风中心的位置信息,台风Irma的移动方向为从东到西,图中黑色线表示台风Irma的移动轨迹,黑色圆点表示台风中心所在位置。图 2(a)~2(d)分别为CYGNSS风速和CYGNSS特征量SNR、NBRCS和LES在台风中心处的变化。从图 2(a)可以看出,当CYGNSS反射点逐渐靠近台风中心时,CYGNSS风速逐渐增大。在09-05,CYGNSS在台风中心处测得的风速达到34 m/s。5条CYGNSS轨迹都未显示出台风风眼处的低风速特征。一方面,可能是因为CYGNSS轨迹并未经过台风风眼,而只是经过其旁边区域;另一方面,可能是因为CYGNSS空间分辨率(~25 km)较低。如果CYGNSS卫星经过台风Irma时,Irma台风风眼直径较小(< 25 km),则CYGNSS很难观测到。
除CYGNSS风速外,CYGNSS观测量NBRCS和LES同样可以用来监测海面台风,如图 2(c)和2(d)所示。当CYGNSS反射点靠近台风中心处时,NBRCS和LES同时减小,这是因为台风中心处的海面平均坡度较大,使得雷达截面散射系数变小。此外,CYGNSS特征置SNR也探测到台风引起的变化,越接近台风中心,SNR越小。为清楚地显示SNR在台风处的变化情况,图 2(b)只显示SNR小于5 dB的数据。
CYGNSS星座包含8颗小卫星,因此其重返时间较短,CYGNSS可以跟踪台风Irma的移动路径(图 2)。为了显示的方便,图 2只显示每天最接近台风中心的CYGNSS路径,实际上有更多的CYGNSS路径可以用于观测和跟踪台风。
为进一步验证CYGNSS风速在台风附近的准确度,分别选取ASCAT、WindSat和SMAP风速产品与其进行比较。
分别选取09-02 23:17:30—23:19:30和09-05 00:57:30—00:59:30 CYGNSS卫星反射路径所对应的风速与ASCAT风速产品进行比较,结果如图 3(a)、3(b)所示。此时对应的台风风眼出现时间分别约为23:48:00和00:48:00。从图 3(c)、3(d)可以看出,CYGNSS风速变化与ASCAT风速变化保持一致,越接近台风中心处,其对应的风速越大。但在09-02 CYGNSS卫星反射路径上,CYGNSS风速小于ASCAT风速,且越接近台风中心处,二者相差越大。而09-05 CYGNSS风速大于ASCAT风速,同时出现较大的振荡起伏。
分别选取09-03 08:21:00— 08:25:00和09-05 08:11:00—08:16:00 CYGNSS卫星反射路径所对应的风速与WindSat卫星风速产品进行比较,结果如图 4(a)、4(b)所示。此时对应的台风风眼出现时间分别约为09:12:00和10:18:00。
在09-03,CYGNSS观测时间比WindSat观测时间提前约50 min。尽管二者之间的时间偏差接近1 h,但是二者的风速变化保持一致。在08:21:00—08:23:00,二者的风速都在逐渐减小;在08:23:00—08:24:00,二者的风速先增大后减小;08:24:00之后,二者的风速又逐渐变大。同时可以发现,CYGNSS风速低于WindSat风速,尤其是在台风中心处。
在09-05,尽管CYGNSS和WindSat的时间偏差接近2 h,但是二者的风速变化仍保持一致,且在08:14:00左右达到各自观测的风速最大值。与09-03 CYGNSS卫星的观测情况相似,其风速也小于同一位置的WindSat风速。
图 5进一步给出08-31—09-05在台风Irma中心处收集到的所有CYGNSS风速和与之相对应的ASCAT、WindSat和SMAP风速产品。CYGNSS风速和ASCAT风速、WindSat风速、SMAP风速之间的相关系数分别为0.63、0.76和0.67。由图 5可知,CYGNSS风速几乎都小于15 m/s,且小于与之对应的ASCAT、WindSat和SMAP风速,尤其是在风速大于10 m/s时。因此,当风速较大时,CYGNSS Level 2数据存在风速被低估的问题。
CYGNSS团队通过建立NBRCS、LES和风速之间的经验回归模型来反演海面风速[21]。因此,CYGNSS风速产品精度取决于模型建立时所采用的参考风速范围及精度。CYGNSS风速产品采用ECMWF的ERA5数据集和HWRF模型作为参考风速,用以匹配相对应的CYGNSS观测量NBRCS和LES。虽然最高参考风速达到70 m/s左右,但是高风速(>15 m/s)的样本数量仍相对较少,这可能是高风速场景下CYGNSS风速产品被低估的原因。
由§2.2和§2.3可知,CYGNSS风速和CYGNSS观测量NBRCS、LES均可探测到台风位置信息。但CYGNSS风速无法直接从CYGNSS观测值中获取,需要借助风速反演模型获得。此外,在高风速条件下,CYGNSS风速存在被低估的问题。因此,本文选取CYGNSS观测量NBRCS和LES作为台风中心探测特征量。从图 2(c)、2(d)可以看出,随着CYGNSS反射点越接近台风中心位置,其NBRCS和LES值越小。首先将每条CYGNSS轨迹上的NBRCS和LES数据按从小到大排列,然后选取每条轨迹上前10个NBRCS和LES用于台风中心位置反演。利用DBSCAN聚类算法对选取的NBRCS和LES进行聚类分析。
图 6为对09-04 CYGNSS观测量NBRCS和LES进行DBSCAN聚类分析的结果。从图中可以看出,通过聚类分析可将选取的NBRCS和LES分成2个簇,分别以0和1来表示。本文选取聚类分析后CYGNSS反射点个数最大的簇作为反演台风中心位置的数据集,取该数据集中所有CYGNSS反射点经纬度的均值作为台风中心位置坐标。表 2为基于表 1中5条CYGNSS路径反演得到的台风中心位置,将其与NHC提供的台风位置进行比较。
从表 2可以看出,在09-01和09-03,CYGNSS反演得到的台风位置与NHC提供的台风位置最为接近,只相差约30 km。在09-05,CYGNSS反演得到的台风位置与NHC提供的台风位置差异最大,相差约84 km。这是因为在09-01和09-03,CYGNSS与台风中心出现在同一位置的时间只相差约11 min和40 min,而在09-05,两者的时间差约为67 min。总体而言,在5条CYGNSS轨迹中,有3条轨迹预测得到的台风中心位置偏差小于35 km,只有1条轨迹预测得到的台风中心位置偏差大于50 km,5条轨迹预测得到的台风中心位置平均偏差约为45 km。实验结果表明,星载GNSS-R卫星CYGNSS能够较好地反演出台风中心位置信息。
本文主要探讨星载GNSS-R技术在台风探测中的应用,重点研究基于CYGNSS卫星的台风中心位置反演。实验结果表明,CYGNSS相关观测量(NBRCS、LES和SNR)和CYGNSS风速产品均能有效探测到台风的发生。随着CYGNSS反射点越接近台风中心位置,其NBRCS和LES值越小;反之,离台风中心位置越远,其NBRCS和LES值则越大。值的注意的是,与WindSat、ASCAT和SMAP卫星风速产品相比,CYGNSS风速存在被低估的问题。
利用CYGNSS观测量NBRCS和LES,基于DBSCAN聚类算法,反演得到台风中心位置。结果表明,CYGNSS卫星数据能够较好地反演出台风中心位置,且在时间和空间上距离台风中心位置越近,反演的台风中心位置越准确。与NHC提供的台风中心位置相比,最小偏差约为30 km,最大偏差约为84 km,平均偏差约为45 km。本研究进一步拓宽了星载GNSS-R在台风监测方面的应用范围。
  • 国家重点研发计划(2021YFB3901301)
  • 江苏省自然科学基金(BK20220366)
  • 江苏省自然资源厅科技创新项目(JSZRKJ202406)
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2025年第45卷第7期
42
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doi: 10.14075/j.jgg.2024.11.546
  • 接收时间:2024-11-29
  • 首发时间:2026-10-29
  • 出版时间:2025-07-15
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  • 收稿日期:2024-11-29
基金
National Key Research and Development Program of China(2021YFB3901301)
国家重点研发计划(2021YFB3901301)
Natural Science Foundation of Jiangsu Province(BK20220366)
江苏省自然科学基金(BK20220366)
Science and Technology Innovation Project of Department of Natural Resources of Jiangsu Province(JSZRKJ202406)
江苏省自然资源厅科技创新项目(JSZRKJ202406)
作者信息
    1 南京工业大学测绘科学与技术学院, 南京市浦珠南路30号, 211816
    2 齐鲁空天信息研究院, 济南市工业北路 17218号, 250132
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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