Article(id=1233908315927925275, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1233908308151685504, articleNumber=null, orderNo=null, doi=10.12284/hyxb2021176, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1613318400000, receivedDateStr=2021-02-15, revisedDate=1618761600000, revisedDateStr=2021-04-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1772116267425, onlineDateStr=2026-02-26, pubDate=1637769600000, pubDateStr=2021-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772116267425, onlineIssueDateStr=2026-02-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772116267425, creator=13701087609, updateTime=1772116267425, updator=13701087609, issue=Issue{id=1233908308151685504, tenantId=1146029695717560320, journalId=1149651085930835976, year='2021', volume='43', issue='11', pageStart='1', pageEnd='180', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772116265571, creator=13701087609, updateTime=1772116265571, updator=13701087609, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=146, endPage=156, ext={EN=ArticleExt(id=1233908316955529778, articleId=1233908315927925275, tenantId=1146029695717560320, journalId=1149651085930835976, language=EN, title=Determination of tropical cyclone location and intensity using HY-2B scatterometer data, columnId=1194652705852465724, journalTitle=Haiyang Xuebao, columnName=Article, runingTitle=null, highlight=null, articleAbstract=

Tropical cyclone disaster is one of the most serious natural disasters, and its impact mainly depends on the center location and the intensity. Monitoring the location and the intensity of tropical cyclones is of great significance for improving the accuracy of tropical cyclone forecast and for reducing the impact of tropical cyclone disasters. In this paper, the characteristics of the HY-2B scatterometer wind field, as well as its divergence and curl, are analyzed. It is found that the divergence or curl of the wind field near the cyclone center shows remarkable signatures, such that a new method is proposed to identify the cyclone center, and then the results are compared with the conventional method. Moreover, a method for estimating the size of the tropical cyclone is introduced to evaluate the cyclone intensity. Finally, the remote sensing data of Typhoon Francisco and Typhoon Bualoi are used to verify the proposed methods. The results show that the difference between the cyclone center position determined by the scatterometer and the one of the optimal path is generally less than 20 km. Meanwhile, the determined wind radii correspond well with the development of the tropical cyclone.

, correspAuthors=Wenming Lin, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2021 Pratacultural Science. All rights reserved., 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, authorCompany=null, fund=null, authors=null, authorsList=Siqi Liu, Wenming Lin, Zhixiong Wang, Shuyan Lang), CN=ArticleExt(id=1233908319497278163, articleId=1233908315927925275, tenantId=1146029695717560320, journalId=1149651085930835976, language=CN, title=基于HY-2B散射计的热带气旋定位定强研究, columnId=1149698756456657529, journalTitle=海洋学报, columnName=论文, runingTitle=null, highlight=null, articleAbstract=

热带气旋灾害是最严重的自然灾害之一,其影响程度主要取决于气旋的中心位置和强度。提高热带气旋中心位置及强度监测水平对于改进热带气旋分析预报精度、减少热带气旋的灾害影响具有重要意义。本文以HY-2B散射计为例,分析了散射计风场散度和旋度的分布特征,发现气旋中心附近风场的散度或旋度具有显著的分布规律,由此提出了一种新的气旋中心定位方法,并与传统的直接定位法进行比较研究。在此基础上,进一步提出了热带气旋风圈大小估计的方法,用于评估气旋的强度。最后,利用台风“范斯高”和“博罗依”的遥感数据对文中的方法进行验证,结果表明,使用新方法定位的气旋中心位置与最佳路径之间的差异小于20 km,散射计17 m/s风圈大小的变化一定程度上反映了气旋的发展规律。

, correspAuthors=林文明, authorNote=null, correspAuthorsNote=
林文明(1984-),男,福建省仙游县人,教授,研究方向为海洋微波遥感、先进数据处理方法、雷达定标技术以及海面风场遥感及应用。E-mail:
, copyrightStatement=版权所有©《海洋学报》编辑部 2021, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=VZBdZYOQATgwt84InRt52Q==, magXml=pQ+dcYYnPrtBlAg/BT1LXA==, pdfUrl=null, pdf=3SnRcsTsJTGvFafkCWeG3Q==, pdfFileSize=2840906, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=uQ7FmD0FMBJ2iSigK+Pn1A==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=8yiNxPdlTIrR9qD8w5LMdA==, mapNumber=null, authorCompany=null, fund=null, authors=

刘思琦(2000-),女,江苏省徐州市人,研究方向为海洋微波遥感。E-mail:

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刘思琦(2000-),女,江苏省徐州市人,研究方向为海洋微波遥感。E-mail:

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Monthly Weather Review, 1984, 112(7): 1408−1418., articleTitle=null, refAbstract=null)], funds=[Fund(id=1233931837656068289, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, awardId=null, language=CN, fundingSource=国家自然科学基金青年基金(41906153);江苏省高等学校大学生创新创业训练计划(202010300072Y);南方海洋科学与工程广东省实验室(广州)人才团队引进重大专项(GML2019ZD0302), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1233931830903239559, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, xref=1, ext=[AuthorCompanyExt(id=1233931830911628167, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, companyId=1233931830903239559, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China), AuthorCompanyExt(id=1233931830920016776, 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tableContent=null), ArticleFig(id=1233931834778775639, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=图4, caption=热带气旋中心定位的技术路线, figureFileSmall=QI+kxVnSHVQ80EPMyTxGjg==, figureFileBig=04lLsB0cVsbC4L6vBgVW7g==, tableContent=null), ArticleFig(id=1233931834879438940, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Fig. 5, caption=Typhoon center’s location by locating directly

a. Wind stress component; b. divergence field; c. curl field; d. DC field

, figureFileSmall=TGnMED5ls6vkDJ6NHdCD3Q==, figureFileBig=2vBaU53bRjoCvX1v2m9EAg==, tableContent=null), ArticleFig(id=1233931835038822499, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=图5, caption=直接定位法定位台风中心位置

a. 风矢量场;b. 散度场;c. 旋度场;d. 复合场

, figureFileSmall=TGnMED5ls6vkDJ6NHdCD3Q==, figureFileBig=2vBaU53bRjoCvX1v2m9EAg==, tableContent=null), ArticleFig(id=1233931835139485800, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Fig. 6, caption=Contour of wind stress component, figureFileSmall=EulKTGk59pAwDRsMDkLTqw==, figureFileBig=nnjpu4LowIzE7a3M7KbMaQ==, tableContent=null), ArticleFig(id=1233931835290480747, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=图6, caption=风应力分量等值线分布, figureFileSmall=EulKTGk59pAwDRsMDkLTqw==, figureFileBig=nnjpu4LowIzE7a3M7KbMaQ==, tableContent=null), ArticleFig(id=1233931835395338350, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Fig. 7, caption=Contour of divergence (a) and curl (b) of wind stress, figureFileSmall=qLw40jtgsXeGMTXAY30IRA==, figureFileBig=sh5f5DJ+zY3RpjTAvEfS5Q==, tableContent=null), ArticleFig(id=1233931835487613042, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=图7, caption=风应力散度(a)和旋度(b)等值线分布, figureFileSmall=qLw40jtgsXeGMTXAY30IRA==, figureFileBig=sh5f5DJ+zY3RpjTAvEfS5Q==, tableContent=null), ArticleFig(id=1233931835600859257, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Fig. 8, caption=Tropical cyclone center locating by wind field characteristic parameters’ spatial geometric signatures

a. Wind stress component; b. divergence field; c. curl field; d. the location results show, wind stress component (green triangle), divergence (red rectangle), curl (blue circle), data interpolated through the best track dataset (yellow cross)

, figureFileSmall=xMxFDoJfc/elASATkB7hBQ==, figureFileBig=XGR9PDex1JBLb/D3nJS2+Q==, tableContent=null), ArticleFig(id=1233931835705716860, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=图8, caption=利用风场特征参数的空间几何特征定位热带气旋中心

a. 风应力分量;b. 散度场;c. 旋度场;d. 定位结果显示,风应力分量(绿色三角)、散度(红色矩形)、旋度(蓝色圆形)、最佳路径插值数据(黄色十字)

, figureFileSmall=xMxFDoJfc/elASATkB7hBQ==, figureFileBig=XGR9PDex1JBLb/D3nJS2+Q==, tableContent=null), ArticleFig(id=1233931835802185858, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Fig. 9, caption=The method of calculating the 17 m/s wind radii

a. 24 effective azimuths; b. example diagram of sea surface wind speed profile

, figureFileSmall=AN/Q+Ct6Jb91iLEsYfGsGg==, figureFileBig=AIulDnqEHbfawU+jgT9/wA==, tableContent=null), ArticleFig(id=1233931835923820680, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=图9, caption=计算17 m/s风圈半径方法

a. 24个有效方位;b. 海面风速剖面示例

, figureFileSmall=AN/Q+Ct6Jb91iLEsYfGsGg==, figureFileBig=AIulDnqEHbfawU+jgT9/wA==, tableContent=null), ArticleFig(id=1233931836032872589, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Fig. 10, caption=17 m/s wind circle of Typhoon Francisco, figureFileSmall=nXL6vbvRTuIfNb+rHxjyKA==, figureFileBig=pPpwCHgvTdhDSzwi8Bu1zw==, tableContent=null), ArticleFig(id=1233931836116758674, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=图10, caption=台风“范斯高”17 m/s风圈示意图, figureFileSmall=nXL6vbvRTuIfNb+rHxjyKA==, figureFileBig=pPpwCHgvTdhDSzwi8Bu1zw==, tableContent=null), ArticleFig(id=1233931836250976406, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Fig. 11, caption=17 m/s wind circle of Typhoon Bualoi, figureFileSmall=ZYaLJkowCy22OpFi7hu1mQ==, figureFileBig=C0dIPKjmY3d04LtFd4NvRQ==, tableContent=null), ArticleFig(id=1233931836376805532, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=图11, caption=台风“博罗依”17 m/s风圈示意图, figureFileSmall=ZYaLJkowCy22OpFi7hu1mQ==, figureFileBig=C0dIPKjmY3d04LtFd4NvRQ==, tableContent=null), ArticleFig(id=1233931836515217565, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Table 1, caption=

Intensity’s contrast of Typhoon Franciscoat four moments

, figureFileSmall=null, figureFileBig=null, tableContent=
日期
参数
8月3日
15时
8月4日
16时
8月5日
4时
8月5日
16时
HSCAT R17/km45.3666.8785.6788.49
HSCAT最大风速/(m·s−1)21.6520.9121.2426.18
最佳路径最大风速/(m·s−1)23.0028.0032.3832.64
最佳路径最低气压/hPa990.00980.00974.19973.24
), ArticleFig(id=1233931836624269473, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=表1, caption=

4个时刻的台风“范斯高”强度对比

, figureFileSmall=null, figureFileBig=null, tableContent=
日期
参数
8月3日
15时
8月4日
16时
8月5日
4时
8月5日
16时
HSCAT R17/km45.3666.8785.6788.49
HSCAT最大风速/(m·s−1)21.6520.9121.2426.18
最佳路径最大风速/(m·s−1)23.0028.0032.3832.64
最佳路径最低气压/hPa990.00980.00974.19973.24
), ArticleFig(id=1233931836737515686, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Table 2, caption=

Intensity’s contrast of Typhoon Bualoiat four moments

, figureFileSmall=null, figureFileBig=null, tableContent=
日期
参数
10月21日4时10月22日4时10月22日15时10月24日5时
HSCAT R17/km75.93123.50131.10126.83
HSCAT最大风速/
(m·s−1)
26.6431.7326.9230.42
最佳路径最大风速/
(m·s−1)
36.0450.0058.0046.44
最佳路径最低气压/hPa968.27940.00925.00947.61
), ArticleFig(id=1233931836850761898, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=表2, caption=

4个时刻的台风“博罗依”强度对比

, figureFileSmall=null, figureFileBig=null, tableContent=
日期
参数
10月21日4时10月22日4时10月22日15时10月24日5时
HSCAT R17/km75.93123.50131.10126.83
HSCAT最大风速/
(m·s−1)
26.6431.7326.9230.42
最佳路径最大风速/
(m·s−1)
36.0450.0058.0046.44
最佳路径最低气压/hPa968.27940.00925.00947.61
), ArticleFig(id=1233931836955619500, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Table 3, caption=

The center locations of Typhoon Francisco and Typhoon Bualoi based on HSCAT (unit: km)

, figureFileSmall=null, figureFileBig=null, tableContent=
台风“范斯高”8月3日15时8月4日16时8月5日4时8月5日16时
$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$
直接定位法散度场41.0364.8936.1636.1637.9859.7318.4748.68
旋度场21.7088.6339.2853.3064.8332.7466.2724.67
复合场31.3647.0843.9253.3072.1650.4055.2456.00
几何定位法风应力分量46.0532.6410.7413.9735.7519.14 6.7220.03
散度场12.3428.76 7.3620.5526.98 4.39 6.1614.96
旋度场 0.6030.0412.58 8.4016.5116.84 5.2317.74
台风“博罗依”10月21日4时10月22日4时10月22日15时10月24日5时
$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$
直接定位法散度场44.6454.9551.3847.9912.5729.8629.1529.15
旋度场39.3669.5160.4025.8429.2646.4084.8246.45
复合场26.1026.1022.3827.1312.4847.2625.8552.99
几何定位法风应力分量45.5022.9623.1420.0713.06 3.4433.3622.53
散度场37.8545.6017.1315.1317.9711.7015.1916.27
旋度场52.8144.5820.36 6.6619.3219.32 6.6730.13
), ArticleFig(id=1233931837081448625, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=表3, caption=

基于HSCAT数据的台风“范斯高”和台风“博罗依”中心位置对比(单位:km)

, figureFileSmall=null, figureFileBig=null, tableContent=
台风“范斯高”8月3日15时8月4日16时8月5日4时8月5日16时
$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$
直接定位法散度场41.0364.8936.1636.1637.9859.7318.4748.68
旋度场21.7088.6339.2853.3064.8332.7466.2724.67
复合场31.3647.0843.9253.3072.1650.4055.2456.00
几何定位法风应力分量46.0532.6410.7413.9735.7519.14 6.7220.03
散度场12.3428.76 7.3620.5526.98 4.39 6.1614.96
旋度场 0.6030.0412.58 8.4016.5116.84 5.2317.74
台风“博罗依”10月21日4时10月22日4时10月22日15时10月24日5时
$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$
直接定位法散度场44.6454.9551.3847.9912.5729.8629.1529.15
旋度场39.3669.5160.4025.8429.2646.4084.8246.45
复合场26.1026.1022.3827.1312.4847.2625.8552.99
几何定位法风应力分量45.5022.9623.1420.0713.06 3.4433.3622.53
散度场37.8545.6017.1315.1317.9711.7015.1916.27
旋度场52.8144.5820.36 6.6619.3219.32 6.6730.13
), ArticleFig(id=1233931837219860663, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=EN, label=Table 4, caption=

The center locations of Typhoon Francisco and Typhoon Bualoi based on ASCAT (unit: km)

, figureFileSmall=null, figureFileBig=null, tableContent=
台风“范斯高”8月3日8时8月4日7时8月4日20时8月5日9时
$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$
直接定位法散度场22.4018.0630.0464.4632.6829.6222.2463.14
旋度场22.4092.9418.5832.2036.6231.4339.7357.40
复合场22.4061.21 7.3882.0040.1214.7546.7354.62
几何定位法风应力分量27.6972.7222.5344.0316.2618.0816.5726.35
散度场32.2772.8022.8330.23 8.2110.26 7.2729.53
旋度场28.1263.5220.5932.28 2.6512.0610.5017.12
台风“博罗依”10月21日19时10月22日7时10月23日8时10月23日19时
$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$
直接定位法散度场16.7813.65 6.8669.66 7.3458.6221.1822.38
旋度场36.1736.1723.8426.4819.3417.7329.7770.83
复合场14.4120.6523.8470.4220.3817.7321.1829.53
几何定位法风应力分量19.7413.6214.9333.31 5.4938.13 9.7121.24
散度场14.9718.2316.2721.2912.9137.6841.6954.86
旋度场20.2627.6819.8628.6021.4042.7929.7730.67
), ArticleFig(id=1233931837312135354, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1233908315927925275, language=CN, label=表4, caption=

基于ASCAT数据的台风“范斯高”和台风“博罗依”中心位置对比(单位:km)

, figureFileSmall=null, figureFileBig=null, tableContent=
台风“范斯高”8月3日8时8月4日7时8月4日20时8月5日9时
$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$
直接定位法散度场22.4018.0630.0464.4632.6829.6222.2463.14
旋度场22.4092.9418.5832.2036.6231.4339.7357.40
复合场22.4061.21 7.3882.0040.1214.7546.7354.62
几何定位法风应力分量27.6972.7222.5344.0316.2618.0816.5726.35
散度场32.2772.8022.8330.23 8.2110.26 7.2729.53
旋度场28.1263.5220.5932.28 2.6512.0610.5017.12
台风“博罗依”10月21日19时10月22日7时10月23日8时10月23日19时
$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$$ D_1$$ D_2$
直接定位法散度场16.7813.65 6.8669.66 7.3458.6221.1822.38
旋度场36.1736.1723.8426.4819.3417.7329.7770.83
复合场14.4120.6523.8470.4220.3817.7321.1829.53
几何定位法风应力分量19.7413.6214.9333.31 5.4938.13 9.7121.24
散度场14.9718.2316.2721.2912.9137.6841.6954.86
旋度场20.2627.6819.8628.6021.4042.7929.7730.67
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基于HY-2B散射计的热带气旋定位定强研究
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刘思琦 1 , 林文明 1, * , 王志雄 1 , 郎姝燕 2, 3
海洋学报 | 论文 2021,43(11): 146-156
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海洋学报 | 论文 2021, 43(11): 146-156
基于HY-2B散射计的热带气旋定位定强研究
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刘思琦1 , 林文明1, * , 王志雄1, 郎姝燕2, 3
作者信息
  • 1南京信息工程大学 海洋科学学院,江苏 南京 210044
  • 2国家卫星海洋应用中心,北京 100081
  • 3南方海洋科学与工程广东省实验室(广州),广东 广州 511458
  • 刘思琦(2000-),女,江苏省徐州市人,研究方向为海洋微波遥感。E-mail:

通讯作者:

林文明(1984-),男,福建省仙游县人,教授,研究方向为海洋微波遥感、先进数据处理方法、雷达定标技术以及海面风场遥感及应用。E-mail:
Determination of tropical cyclone location and intensity using HY-2B scatterometer data
Siqi Liu1 , Wenming Lin1, * , Zhixiong Wang1, Shuyan Lang2, 3
Affiliations
  • 1School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
  • 2National Satellite Ocean Application Service, Beijing 100081, China
  • 3Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 511458, China
出版时间: 2021-11-25 doi: 10.12284/hyxb2021176
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热带气旋灾害是最严重的自然灾害之一,其影响程度主要取决于气旋的中心位置和强度。提高热带气旋中心位置及强度监测水平对于改进热带气旋分析预报精度、减少热带气旋的灾害影响具有重要意义。本文以HY-2B散射计为例,分析了散射计风场散度和旋度的分布特征,发现气旋中心附近风场的散度或旋度具有显著的分布规律,由此提出了一种新的气旋中心定位方法,并与传统的直接定位法进行比较研究。在此基础上,进一步提出了热带气旋风圈大小估计的方法,用于评估气旋的强度。最后,利用台风“范斯高”和“博罗依”的遥感数据对文中的方法进行验证,结果表明,使用新方法定位的气旋中心位置与最佳路径之间的差异小于20 km,散射计17 m/s风圈大小的变化一定程度上反映了气旋的发展规律。

散射计  /  热带气旋  /  中心定位  /  风圈半径  /  海洋二号B星

Tropical cyclone disaster is one of the most serious natural disasters, and its impact mainly depends on the center location and the intensity. Monitoring the location and the intensity of tropical cyclones is of great significance for improving the accuracy of tropical cyclone forecast and for reducing the impact of tropical cyclone disasters. In this paper, the characteristics of the HY-2B scatterometer wind field, as well as its divergence and curl, are analyzed. It is found that the divergence or curl of the wind field near the cyclone center shows remarkable signatures, such that a new method is proposed to identify the cyclone center, and then the results are compared with the conventional method. Moreover, a method for estimating the size of the tropical cyclone is introduced to evaluate the cyclone intensity. Finally, the remote sensing data of Typhoon Francisco and Typhoon Bualoi are used to verify the proposed methods. The results show that the difference between the cyclone center position determined by the scatterometer and the one of the optimal path is generally less than 20 km. Meanwhile, the determined wind radii correspond well with the development of the tropical cyclone.

scatterometer  /  tropical cyclone  /  center locating  /  wind radii  /  HY-2B satellite
刘思琦, 林文明, 王志雄, 郎姝燕. 基于HY-2B散射计的热带气旋定位定强研究. 海洋学报, 2021 , 43 (11) : 146 -156 . DOI: 10.12284/hyxb2021176
Siqi Liu, Wenming Lin, Zhixiong Wang, Shuyan Lang. Determination of tropical cyclone location and intensity using HY-2B scatterometer data[J]. Haiyang Xuebao, 2021 , 43 (11) : 146 -156 . DOI: 10.12284/hyxb2021176
热带气旋是一种形成于热带或副热带洋面上的低气压天气系统,通常伴有强风、暴雨和巨浪等恶劣天气或海洋现象,一旦侵入沿海区域会造成严重的财产损失和人员伤亡。热带气旋灾害的影响程度主要取决于气旋强度、移动路径以及承灾体的脆弱性,因此,实时准确的热带气旋中心位置和强度信息对于防灾、减灾具有重要的意义。当前,国内外已经构建了地面雷达、无人机或有人机以及遥感卫星相结合的热带气旋监测体系。其中,卫星遥感作为一种大范围动态监测手段,是同时获取热带气旋中心位置和强度信息最有效的手段。
过去数10年,研究人员利用气象卫星云图数据对强热带气旋、特别是台风的中心定位开展了大量的研究。例如,乔文峰[1]提出一种基于灰色预测和活动轮廓模型的台风中心定位方案,可以有效确定台风的中心位置。蒋众民[2]对台风以及风云二号气象卫星数据的特点进行了分析,并利用深度学习模型对有眼台风进行中心定位,可以有效地识别不同形态台风的中心位置。庞君如等[3]通过提取卫星云图纹理的特征参数、构建并求解台风中心定位最优目标函数的方法,实现了不同类型台风中心的准确定位。刘佳和王旭东[4]首先基于气象卫星时间序列遥感图像计算了云导风的风场,进而利用密度矩阵计算台风的中心位置。这些方法虽然能够有效识别台风的中心位置,但对云纹理特征不规则、强度较弱的热带气旋的定位精度较低。此外,由于气象卫星的云导风数据反映的是云顶高度附近的风场,与距海面10 m处的风场具有较大的差异,因此,研究人员较少使用气象卫星云图开展热带气旋定强研究。
星载微波散射计具有全天时、全天候的监测能力,并且通常具有1000 km以上的观测刈幅,是目前海面风场监测最主要的技术手段。特别地,国内外在轨运行卫星散射计的数量迅速增加,初步形成了虚拟卫星散射计星座,可实现对同一海面区域的高频次覆盖。因此,卫星散射计数据在热带气旋定位、定强方面具有很大的应用潜力。Jaiswal等[5]使用印度Oceansat-2散射计高分辨数据开展了台风研究。Hu等[6]直接利用散射计的风速和风向信息,开发了一种热带气旋中心自动定位算法,能有效识别强热带气旋的中心位置。赵勇等[7]利用HY-2A散射计数据进行台风“苏力”的海表面风场结构的研究,基于台风风场分解原理实现台风中心定位。但这些方法未能充分挖掘台风散度场和旋度场的几何分布规律,从而未能进一步实现台风中心的精准定位。Dickey等[8]对夏威夷岛西侧气旋进行了研究,描述了QuikSCAT涡旋速度场的典型几何特征。王晓霞等[9]对比了高风速阈值涡度场定位法和高风速阈值复合场定位法两种不同的台风中心定位算法,指出高风速阈值复合场定位法具有更好的定位精度。在热带气旋强度研究方面,人们常用最大风速半径描述热带气旋的强度范围,尽管它不足以描述完整的风场结构。Lee等[10]基于QuikSCAT海面风场数据,发现了位于西北太平洋和北大西洋海域内的台风在其发展期间往往保持着相同的尺度大小。杨典等[11]提出了一种基于散射计风场数据的台风定强方法,通过计算台风所在海区的海表面风速平均值来消除高风速所造成的误差。
本文在国内外研究的基础上,首先分析了卫星散射计海面风场的散度和旋度特征,提出一种新的基于卫星散射计数据的热带气旋定位方法,进而估计热带气旋中心附近17 m/s风圈的大小,以实现气旋中心位置及强度范围监测。
当前业务化运行的卫星散射计主要有两种,分别是Ku波段笔形波束扫描散射计(如中国HY-2系列卫星散射计)和C波段固定扇形波束散射计(欧洲Metop系列卫星散射计)。本文主要利用HY-2B散射计(HSCAT)海面风场数据开展热带气旋定位、定强研究,并使用欧洲Metop卫星搭载的先进散射计(ASCAT)对下面章节提出的算法进行普适性验证。HSCAT和ASCAT风场数据都是经过业务化处理的二级(L2B)地球物理数据产品,网格分辨率分别是25 km×25 km和12.5 km×12.5 km;数据产品均包括监测时间、各观测点的经纬度、风速以及风向等参数。此外,为了评估卫星散射计在热带气旋定位、定强中是否具有独特的优势,选取欧洲中期天气预报中心(ECMWF)的预报风场数据作为对比数据。HSCAT和ASCAT的L2B风场产品均有时空匹配好的ECMWF预报风场数据。最后,使用中国气象局热带气旋资料中心提供的最佳路径数据对热带气旋中心定位的结果进行验证。最佳路径数据集记录了发生在西北太平洋海域上热带气旋发生的时间(间隔6 h)、中心位置、最大风速和最低气压等关键信息。
案例研究的主要目的是验证热带气旋定位、定强方法的有效性,因此案例选取遵循如下原则:首先,散射计观测刈幅需完全覆盖热带气旋的中心区域,因为气旋的中心位置如果不在散射计的观测范围之内,无法直接利用散射计风场进行中心定位;其次,同一个热带气旋的生命周期内,散射计要尽可能多地对其进行重复观测。最终选取1908号台风“范斯高”和1921号台风“博罗依”作为研究案例。图1是最佳路径数据集给出的台风“范斯高”和“博罗依”中心位置轨迹。
HSCAT分别于2019年8月3日15时、8月4日16时、8月5日4时以及8月5日16时观测到了台风“范斯高”完整的海面风场,如图2所示。
同时,本文选取了4景能够较完整呈现“博罗依”台风结构的HSCAT海面风场数据,具体时间为2019年10月21日4时、10月22日4时、10月22日15时、10月24日5时,如图3所示。
卫星散射计L2B数据是按照刈幅网格组织的,风场网格单元在顺轨方向和交轨方向等间隔分布,间距为25 km或12.5 km。为了方便计算,需要把风矢量分解成交轨方向$ \hat{c}$和顺轨方向$ \hat{a}$两个分量$ \left( u_c,v_a \right) $。散射计各观测点上风速和风向分别记作$ W$$ \varphi $,那么把(W, φ)转换为$ \left( u_c,v_a \right) $的具体算法如下:
首先,把风矢量分解成沿纬向$ \hat{x}$和沿经向$ \hat{y}$的两个分量$ \left( u,v \right) $
$\left\{ {\begin{array}{*{20}{l}}{u=W\sin \left( \varphi \right)}\\{v=W\cos \left( \varphi \right)}\end{array}} \right.,$
其次,利用坐标系旋转,由方向向量$ \left( u,v \right) $得到$ \left( u_c,v_a \right) $
$\left\{ {\begin{array}{*{20}{c}}{{u_c}=u\cos \theta + v\sin \theta }\\{ {v_a}= - u\sin \theta + v\cos \theta }\end{array}} \right.,$
式中,$ \theta $为坐标系旋转的角度,即向量$ \hat c$$ \hat{x}$的夹角,可由下式得到:
$\theta =\left\{ {\begin{array}{*{20}{l}}{\alpha \;\;\;\;\;\;,\;\;\; {{上升轨}}},\\{\text{π} - \alpha ,\;\;\;{{下降轨}}},\end{array}} \right.$
式中,$ \alpha $为刈幅倾斜角[12]。以下所有计算都是基于风场的顺轨方向和交轨方向两个分量$ \left( u_c,v_a \right) $,故忽略下标ca
本文热带气旋中心定位使用的基本特征参数是风场的散度和旋度。根据亥姆霍兹定理[13],散度(div)和旋度(curl)可用来表征矢量场$ \overrightarrow{V} $的全部特征,分别由下列公式计算。
${\rm{div}}\overrightarrow{V}=\nabla \cdot \overrightarrow{V}=\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y},$
${\rm{curl}}\overrightarrow{V}=\nabla \times \overrightarrow{V}=\left( \frac{\partial v}{\partial x}-\frac{\partial u}{\partial y} \right) \overrightarrow{e_{\textit{z}}},$
式中,$\nabla $为直角坐标系下的哈密顿算符。实际计算过程需要选择一个合适的差分方法以代替微分。本文基于一阶差分方法[14],对其改进并应用于散度和旋度的计算中。
${{\left( {\dfrac{{{{\partial}} f}}{{\partial x}}} \right)}_{{x_0},{y_0}}} \approx \dfrac{{{f_{01}} - {f_{00}}}}{{d\left( {\left[ {{x_0},{y_0}} \right],\left[ {{x_1},{y_0}} \right]} \right)}},$
${{\left( {\dfrac{{\partial f}}{{\partial y}}} \right)}_{{x_0},{y_0}}} \approx \dfrac{{{f_{10}} - {f_{00}}}}{{d\left( {\left[ {{x_0},{y_0}} \right],\left[ {{x_0},{y_1}} \right]} \right)}},$
式中,$ {x}_{i} $$ {y}_{i} $ (i=0, 1)分别为横轴和纵轴方向的格点序号;$ {f}_{ij} $(i, j=0, 1)为相应格点的值;$ d\left( \cdots \right) $表示相邻风矢量单元之间的距离,约等于25 km(HSCAT)或12.5 km(ASCAT)。
需要注意的是,卫星散射计直接测量的物理量是海面毛细波粗糙度调制的雷达后向散射信号的强度,而海面粗糙度是由风应力驱动的。因此,散射计遥感的海面风场本质上是风应力的一种等效表示形式[15]。直接利用风场分量$ \left( u_c,v_a \right) $计算的散度和旋度不能完全表征海面与大气间的相互作用,下面先把风速转换成风应力,再利用风应力场的散度和旋度开展热带气旋定位定强研究。风应力的计算公式为
$\tau=\rho _{\rm{a}}C_D| \overrightarrow{W} |\overrightarrow{W},$
式中,$\, \rho _{\rm{a}}=1.223\; {\rm{kg}}/{\rm{m}}^{3}$为空气密度;$ C_D$为拖曳系数;$\overrightarrow{W}$为风矢量。这里使用Large和Pond[16]的模型计算拖曳系数。风应力场的旋转分量在海面形成Ekman输送,辐散分量对海洋表层海水有直接的驱动作用,因此能够有效表征海气相互过程,是一种具有广泛应用潜力的特征参数。
基于预处理的HSCAT海面风场数据,首先计算得到风应力分量、风应力散度、风应力旋度等参数。然后,利用传统的直接定位法和本文提出的几何法两种气旋中心定位方法分别计算台风中心位置;最后,将ASCAT散射计海面风场数据、ECMWF模式数据、最佳路径数据集作为对比数据,进行结果分析。总技术路线如图4所示。
直接定位法为一种直接利用风场特征参量的极值进行定位的方法。发生在西北太平洋的热带气旋,其风场通常呈逆时针辐合。因此,理论上散度场在气旋中心存在负的极小值、旋度场在气旋中心存在正的极大值。赵勇等[7]计算了风矢量的散度、旋度,并通过二者乘积得到复合场(DC,式(9)),最后利用复合场来实现台风中心定位。
${\rm{DC}}={\rm{div}}\overrightarrow{V}\times {\rm{curl}}\overrightarrow{V}.$
本文则首先根据$ \left( u_c,v_a \right) $得到顺轨方向和交轨方向的两个风应力分量:
$\left\{ {\begin{array}{*{20}{c}}{{\tau _c}={\rho _{\rm{a}}}{C_D}\left| {{u_c}} \right|{u_c}}\\{{\tau _a}={\rho _{\rm{a}}}{C_D}\left| {{v_a}} \right|{v_a}}\end{array}} \right.,$
然后再直接搜索风应力散度场和复合场中的最小值、旋度场中的最大值来实现台风中心定位(即直接定位法),结果如图5所示。
几何定位法是一种利用风场特征参量的空间几何分布特点进行定位的方法。图6表示沿着气旋中心的经向,风应力τc分量在中心点两侧分别存在一个极大值和一个极小值;沿着气旋中心的纬向,风应力τa分量在中心点两侧分别存在一个极大值和一个极小值;并且二者的极值关于气旋中心呈现中心对称。图7说明气旋中心附近的风应力散度和旋度展示出区别于其他海气作用现象的几何特征:存在一对正散度/旋度区和一对负散度/旋度区,二者皆关于气旋中心呈中心对称分布。对于散度场,其负值区的流体将做辐合运动,表示为气旋风场区域的“汇”;正值区的流体将做辐散运动,表示为气旋风场区域的“源”。而正负区域的成对出现,一定程度上反映了气旋风场结构的通量守恒。对于旋度场,本文所考虑的是绝对涡度的垂直分量,其与气柱高度$ h$的比值为常数,即满足位涡守恒定理,公式为
${\rm{curl}}\overrightarrow V/h={{常数}}{\rm{.}}$
由连续方程可知,水平散度与垂直运动密切相关,公式为
$\dfrac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=-\dfrac{\partial w}{\partial \textit{z}}.$
若海面的垂直速度为0,则当大气底层有水平辐合时,便有垂直上升运动产生,即旋度大于0;而当有水平辐散时,则有垂直下沉运动,即旋度小于0[17]。因此,旋度场的几何特征与散度场的类似,都存在着一对正值区和一对负值区。
因此,可以利用上述风场特征参量的几何分布特性进行气旋中心定位。若使用的是风应力场的分量,通过搜索一定区域内(阈值设为150 km)成对出现的风应力分量的最大值和最小值,将二者连线交点作为气旋中心。若使用的是风应力场的散度和旋度,则搜索一定区域内(阈值设为150 km)两个局部最大值和两个局部最小值,将局部最大值连线和局部最小值连线的交点作为气旋中心。定位结果如图8所示。
热带气旋的强度大小通常被定义为最外层闭合等压线的平均半径(ROCI)和17 m/s风圈半径(R17),而Merrill[18]发现,ROCI会随季节和地区变化且与热带气旋强度的相关性较弱。由此,本节选用R17来反映气旋的结构特征和强度大小,并提出一种计算热带气旋17 m/s风圈半径尺度的方法。具体而言,首先以3.2节定位的气旋中心为准,将空间划分为24个方位,如图9a所示;其次,通过空间双线性插值获取每个方位线上一系列等间隔点上的风速,绘制各方位角风速随距离变化的风剖面曲线(图9b),进而提取各方位上的17 m/s风圈半径$ d_i\left( i=1,2,\cdots ,24 \right) $;最后,得出计算公式为
$R17=\sum\limits_{i=1}^{24}{d_i}/24\quad \left( i=1,2,\cdots ,24 \right), $
并用$ R17$表征该时刻热带气旋的尺度大小。实际的风剖面曲线中,在风暴边缘向内到最大风速位置之间可能存在多个17 m/s风速,此时,使用距离中心位置最近的17 m/s风速对应的距离作为该方位上的$ d_i$
针对HSCAT在2019年8月3−5日和10月21−24日两时间段内观测的台风“范斯高” “博罗依”的图像,计算得到台风中心到17 m/s等值风圈的平均距离(图10图11),并分别将二者与4个时刻散射计的最大风速和最佳路径的最大风速对比,结果见表1表2
观察表1表2中HSCAT最大风速、最佳路径最大风速和最低气压的变化趋势,发现HSCAT最大风速与后两者的相关性较差,主要原因有:首先,强热带气旋往往伴随着强降雨,这对Ku波段散射计海面风场的质量影响较大,即降雨率越大、散射计风场误差就越大;其次,散射计海面风场反演须使用若干个不同方位角测量的雷达后向散射系数,不同方位观测的时间间隔最长可达4 min,而最佳路径最大风速表征的是2 min持续的风速,因此散射计观测与最佳路径的时间尺度不一样。相反地,HSCAT $ R17$变化的趋势则与最佳路径最大风速的变化趋势基本一致、与最低气压的变化趋势基本相反,说明散射计的R17值能够更好地表征气旋的影响强度和范围。
利用第3节的方法对HSCAT在2019年8月3−5日和10月21−24日期间的观测数据进行处理,获取台风“范斯高”和“博罗依”的中心位置。图2图3中每一幅图像均与中国气象局热带气旋资料中心发布的最佳路径数据集相匹配,即利用线性插值方法,使用相邻两时刻所记录的气旋中心的参数,来确定该时刻气旋的中心位置。最后,计算散射计气旋中心位置与最佳路径数据之间的距离$ D_1$。为了验证结果的可靠性,采用同样的方法对同期的ECMWF模式数据进行处理,得到结果$ D_2$,如表3所示。
根据表3的计算结果,可以得出以下结论:
(1)对于直接定位法,利用复合场求解的台风中心与最佳路径数据之间的差异通常小于利用散度场和旋度场的求解结果。
(2)相比于直接定位法,无论是HSCAT海面风场数据还是ECMWF模式数据,使用几何定位法求解出的台风中心位置多数情况下更加精准。
(3)8月3日15时的台风“范斯高”以及10月21日4时的台风“博罗依”仍处于生成阶段,风场结构尚未发展成熟,导致定位精度较差。而在其他3个时刻,应用几何定位法的求解结果与最佳路径数据之间的差异在大多数情况下在25 km之内,即小于HSCAT海面风场数据的空间分辨率。这说明几何定位法突破了网格的空间分辨率的限制,从而使定位更加精准。
(4)对比$ D_1$$ D_2$,可以发现利用ECMWF模式数据得到的台风位置有时更加“精确”,当应用几何定位法求解时,此种差异尤为明显。产生差异的原因主要有:最佳路径数据是由数值天气预报模式再分析处理得到的,它与ECMWF模式数据之间可能存在较大的相关性。在台风发展后期,ECMWF模式通过同化全球卫星数据和各种观测数据,其预报结果已经与真实的结果非常接近。ECMWF模式数据源于数值模型,具有数据平滑的特点,所表现的风应力分量、散度场和旋度场在台风中心处关于台风中心相当对称,因而与几何中心定位法更加契合。最后,HSCAT的网格分辨率为25 km,而ECMWF数据是由网格分辨率为0.125º的模式风场插值到HSCAT的网格上,本质上具有更高的网格分辨率。
为了进一步阐明基于ECMWF模式数据与HSCAT海面风场数据所得到的定位结果之间存在偏差的原因,利用空间网格分辨率为12.5 km的ASCAT散射计海面风场数据及其匹配好的ECMWF数据重复表3的实验,结果如表4。需要注意的是,ASCAT二级产品中的ECMWF数据是由网格分辨率为0.562 5º的模式风场插值到ASCAT的网格上得到的。
可以发现,此时的ECMWF模式数据未表现出明显的优越性,而基于ASCAT散射计数据的定位结果大多更加准确。由此说明数据空间分辨率是引起定位结果存在偏差的部分原因。
本文在过去研究人员利用散射计风矢量、散度和旋度等物理参数对台风进行中心定位的基础上,提出了一种基于风应力场及其散度、旋度的几何特征进行热带气旋中心定位的几何定位方法。以2019年的台风“范斯高”和台风“博罗依”为例,对HY-2B散射计观测数据进行分析处理,定位结果与中国气象局热带气旋资料中心的最佳路径数据相比,二者间的差异较小,表明该方法可以精准定位台风中心。在确定台风中心位置之后,将气旋中心到17 m/s等值风圈半径的平均距离($ R17$)作为热带气旋强度的指示因子。通过对比前后时刻气旋的中心位置和平均半径大小,反映出气旋的移动和强度变化,一定程度上可以用来分析和预测气旋的路径与强度变化,这对于提升热带气旋遥感监测技术和防灾能力具有重要作用。
下一步我们将在目前研究的基础上,对强度较小的热带气旋进行定位、定强处理,验证本文方法在热带低压、热带风暴等条件下的精度。同时,将该方法推广至目前国内外在轨运行的所有卫星散射计,充分利用现有的虚拟卫星散射计星座,提高热带气旋定位、定强的监测能力。
致谢:本文使用的HY-2B散射计数据由国家卫星海洋应用中心提供;ASCAT近海岸风场数据由欧洲气象卫星组织(EUMETSAT)海洋与海冰卫星应用设施(OSI SAF)提供;最佳路径数据从中国气象局热带气旋资料中心下载(http://tcdata.typhoon.org.cn/),在此表示感谢。
  • 国家自然科学基金青年基金(41906153);江苏省高等学校大学生创新创业训练计划(202010300072Y);南方海洋科学与工程广东省实验室(广州)人才团队引进重大专项(GML2019ZD0302)
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2021年第43卷第11期
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doi: 10.12284/hyxb2021176
  • 接收时间:2021-02-15
  • 首发时间:2026-02-26
  • 出版时间:2021-11-25
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  • 收稿日期:2021-02-15
  • 修回日期:2021-04-19
基金
国家自然科学基金青年基金(41906153);江苏省高等学校大学生创新创业训练计划(202010300072Y);南方海洋科学与工程广东省实验室(广州)人才团队引进重大专项(GML2019ZD0302)
作者信息
    1南京信息工程大学 海洋科学学院,江苏 南京 210044
    2国家卫星海洋应用中心,北京 100081
    3南方海洋科学与工程广东省实验室(广州),广东 广州 511458

通讯作者:

林文明(1984-),男,福建省仙游县人,教授,研究方向为海洋微波遥感、先进数据处理方法、雷达定标技术以及海面风场遥感及应用。E-mail:
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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
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