Detection of tropical cyclone genesis via quantitative satellite ocean surface wind pattern and intensity analyses using decision trees

Microwave remote sensing can be used to measure ocean surface winds, which can be used to detect tropical cyclone (TC) formation in an objective and quantitative way. This study develops a new model using WindSat data and a machine learning approach. Dynamic and hydrologic indices are quantified fro...

Full description

Saved in:

Bibliographic Details
Published in:	Remote sensing of environment Vol. 183; pp. 205 - 214
Main Authors:	Park, Myung-Sook, Kim, Minsang, Lee, Myong-In, Im, Jungho, Park, Seonyoung
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 15.09.2016
Subjects:	algorithms artificial intelligence Circulation convection Cyclones decision support systems Decision trees Disturbances Dynamic pattern and intensity recognition Dynamics Formations hurricanes hydrology Machine learning methodology Microwave sea surface wind Ocean surface rain Rainfall remote sensing satellites Tropical cyclone wind speed Dynamic pattern and intensity recognition Microwave sea surface wind Tropical cyclone Machine learning
ISSN:	0034-4257, 1879-0704
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Microwave remote sensing can be used to measure ocean surface winds, which can be used to detect tropical cyclone (TC) formation in an objective and quantitative way. This study develops a new model using WindSat data and a machine learning approach. Dynamic and hydrologic indices are quantified from WindSat wind and rainfall snapshot images over 352 developing and 973 non-developing tropical disturbances from 2005 to 2009. The degree of cyclonic circulation symmetry near the system center is quantified using circular variances, and the degree of strong wind aggregation (heavy rainfall) is defined using a spatial pattern analysis program tool called FRAGSTATS. In addition, the circulation strength and convection are defined based on the areal averages of wind speed and rainfall. An objective TC formation detection model is then developed by applying those indices to a machine-learning decision tree algorithm using calibration data from 2005 to 2007. Results suggest that the circulation symmetry and intensity are the most important parameters that characterize developing tropical disturbances. Despite inherent sampling issues associated with the polar orbiting satellite, a validation from 2008 to 2009 shows that the model produced a positive detection rate of approximately 95.3% and false alarm rate of 28.5%, which is comparable with the pre-existing objective methods based on cloud-pattern recognition. This study suggests that the quantitative microwave-sensed dynamic ocean surface wind pattern and intensity recognition model provides a new method of detecting TC formation. •A tropical cyclone genesis detection model is developed using remote sensing.•Dynamical and hydrologic factors are quantified from WindSat.•An objective model is constructed using decision trees algorithm.•The model shows the circulation symmetry and intensity are the most important.•Validation shows that the model produces high hit rate and low false alarm rate.
AbstractList	Microwave remote sensing can be used to measure ocean surface winds, which can be used to detect tropical cyclone (TC) formation in an objective and quantitative way. This study develops a new model using WindSat data and a machine learning approach. Dynamic and hydrologic indices are quantified from WindSat wind and rainfall snapshot images over 352 developing and 973 non-developing tropical disturbances from 2005 to 2009. The degree of cyclonic circulation symmetry near the system center is quantified using circular variances, and the degree of strong wind aggregation (heavy rainfall) is defined using a spatial pattern analysis program tool called FRAGSTATS. In addition, the circulation strength and convection are defined based on the areal averages of wind speed and rainfall. An objective TC formation detection model is then developed by applying those indices to a machine-learning decision tree algorithm using calibration data from 2005 to 2007. Results suggest that the circulation symmetry and intensity are the most important parameters that characterize developing tropical disturbances. Despite inherent sampling issues associated with the polar orbiting satellite, a validation from 2008 to 2009 shows that the model produced a positive detection rate of approximately 95.3% and false alarm rate of 28.5%, which is comparable with the pre-existing objective methods based on cloud-pattern recognition. This study suggests that the quantitative microwave-sensed dynamic ocean surface wind pattern and intensity recognition model provides a new method of detecting TC formation. •A tropical cyclone genesis detection model is developed using remote sensing.•Dynamical and hydrologic factors are quantified from WindSat.•An objective model is constructed using decision trees algorithm.•The model shows the circulation symmetry and intensity are the most important.•Validation shows that the model produces high hit rate and low false alarm rate. Microwave remote sensing can be used to measure ocean surface winds, which can be used to detect tropical cyclone (TC) formation in an objective and quantitative way. This study develops a new model using WindSat data and a machine learning approach. Dynamic and hydrologic indices are quantified from WindSat wind and rainfall snapshot images over 352 developing and 973 non-developing tropical disturbances from 2005 to 2009. The degree of cyclonic circulation symmetry near the system center is quantified using circular variances, and the degree of strong wind aggregation (heavy rainfall) is defined using a spatial pattern analysis program tool called FRAGSTATS. In addition, the circulation strength and convection are defined based on the areal averages of wind speed and rainfall. An objective TC formation detection model is then developed by applying those indices to a machine-learning decision tree algorithm using calibration data from 2005 to 2007. Results suggest that the circulation symmetry and intensity are the most important parameters that characterize developing tropical disturbances. Despite inherent sampling issues associated with the polar orbiting satellite, a validation from 2008 to 2009 shows that the model produced a positive detection rate of approximately 95.3% and false alarm rate of 28.5%, which is comparable with the pre-existing objective methods based on cloud-pattern recognition. This study suggests that the quantitative microwave-sensed dynamic ocean surface wind pattern and intensity recognition model provides a new method of detecting TC formation.
Author	Kim, Minsang Im, Jungho Park, Myung-Sook Park, Seonyoung Lee, Myong-In
Author_xml	– sequence: 1 givenname: Myung-Sook surname: Park fullname: Park, Myung-Sook – sequence: 2 givenname: Minsang surname: Kim fullname: Kim, Minsang – sequence: 3 givenname: Myong-In orcidid: 0000-0001-8983-8624 surname: Lee fullname: Lee, Myong-In email: milee@unist.ac.kr – sequence: 4 givenname: Jungho surname: Im fullname: Im, Jungho email: ersgis@unist.ac.kr – sequence: 5 givenname: Seonyoung surname: Park fullname: Park, Seonyoung
BookMark	eNqNkU1r3DAQhkVJoZu0P6A3HXvxVl-WbXoq6ScEemnPYiyPgxZH2mjkLfsL-rejzfbUQxoYmBG8r0Z6n0t2EVNExt5KsZVC2ve7bSbcqjpuRS1hX7CN7LuhEZ0wF2wjhDaNUW33il0S7YSQbd_JDfvzCQv6ElLkaeYlp33wsHB_9EtdwG8xIgXihwD8foVYQoESDsgJCi5LKMiTR4ic1jyDR_47xInvoRTMkUOdQywYKZRjPcFyJCS-Uoi3fEIf6LS3ZER6zV7OsBC--duv2K8vn39ef2tufnz9fv3xpvFGD6Vp1ahmpVBbnOSIagJtDahRm07WDsPkRw-jMEZjb8GMUzv1AxoLdpxBWH3F3p3v3ed0vyIVdxfI169AxLSSU6ImJXXfqf9KZa9bW2Ps-2dIZWuHVtiuSuVZ6nMiyji7fQ53kI9OCndC6XauonQnlE7Uenx094_HP3JIsWQIy5POD2cn1kwPAbMjHzB6nEKu2N2UwhPuBykSvnk
CitedBy_id	crossref_primary_10_1007_s11600_022_00849_w crossref_primary_10_3389_fmars_2022_1046964 crossref_primary_10_1080_19476337_2018_1545800 crossref_primary_10_1016_j_swevo_2017_11_003 crossref_primary_10_1007_s11430_023_1383_6 crossref_primary_10_3390_rs11101195 crossref_primary_10_3390_atmos13030381 crossref_primary_10_1109_JSTARS_2025_3544865 crossref_primary_10_3389_feart_2022_902596 crossref_primary_10_1029_2019EF001226 crossref_primary_10_1007_s00382_022_06316_y crossref_primary_10_1002_2016JD025535 crossref_primary_10_3390_rs14020254 crossref_primary_10_3390_w9050332 crossref_primary_10_3390_rs10040631 crossref_primary_10_1002_joc_7457 crossref_primary_10_1007_s42488_024_00128_x crossref_primary_10_3390_rs12010149 crossref_primary_10_1016_j_atmosenv_2024_120891 crossref_primary_10_3390_jmse10111686 crossref_primary_10_1016_j_future_2018_12_042 crossref_primary_10_1016_j_wace_2021_100405 crossref_primary_10_1080_17538947_2024_2356118 crossref_primary_10_1007_s11707_020_0849_6 crossref_primary_10_1109_TGRS_2021_3069217 crossref_primary_10_3390_rs9070685 crossref_primary_10_3390_atmos12070802 crossref_primary_10_1109_TGRS_2023_3266814 crossref_primary_10_1007_s12145_021_00711_5 crossref_primary_10_1016_j_jweia_2023_105591 crossref_primary_10_3390_rs14010073 crossref_primary_10_3390_jmse11102010 crossref_primary_10_3390_app12010181 crossref_primary_10_3390_atmos11070676 crossref_primary_10_3389_fbuil_2022_811460
Cites_doi	10.1109/TGRS.2005.862506 10.1029/2007JC004163 10.1016/j.rse.2008.03.001 10.1175/BAMS-87-9-1195 10.1080/01431160601075582 10.1109/TGRS.2011.2179662 10.1175/2009MWR2989.1 10.1175/MWR-D-11-00239.1 10.1016/j.rse.2005.09.008 10.1080/15481603.2013.819161 10.1175/JAS-D-11-0225.1 10.1175/WAF-D-14-00023.1 10.3390/rs70709184 10.1080/15481603.2014.900983 10.1080/15481603.2015.1026050 10.1175/1520-0434(1998)013<0172:DOAOST>2.0.CO;2 10.1080/15481603.2015.1076561 10.1175/WAF-D-14-00113.1 10.1175/1520-0469(2004)061<1209:TROVHT>2.0.CO;2 10.1007/s13143-013-0038-6 10.1109/TGRS.2004.836867 10.3390/rs4020327 10.1175/2010JTECHA1522.1 10.1175/JAS-D-12-083.1 10.1175/MWR-D-14-00119.1 10.1007/s00382-010-0922-z 10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2 10.5589/m02-035 10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2 10.1109/LGRS.2010.2048694 10.1016/S0034-4257(97)00049-7 10.1016/j.landurbplan.2014.07.005 10.1175/JAS-D-11-0311.1 10.1175/JAS3604.1 10.1175/2008WAF2007109.1
ContentType	Journal Article
Copyright	2016 Elsevier Inc.
Copyright_xml	– notice: 2016 Elsevier Inc.
DBID	AAYXX CITATION 7SN C1K F1W H96 L.G 8FD FR3 H8D KR7 L7M 7S9 L.6
DOI	10.1016/j.rse.2016.06.006
DatabaseName	CrossRef Ecology Abstracts Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Aquatic Science & Fisheries Abstracts (ASFA) Professional Technology Research Database Engineering Research Database Aerospace Database Civil Engineering Abstracts Advanced Technologies Database with Aerospace AGRICOLA AGRICOLA - Academic
DatabaseTitle	CrossRef Aquatic Science & Fisheries Abstracts (ASFA) Professional Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Ecology Abstracts ASFA: Aquatic Sciences and Fisheries Abstracts Environmental Sciences and Pollution Management Aerospace Database Civil Engineering Abstracts Engineering Research Database Technology Research Database Advanced Technologies Database with Aerospace AGRICOLA AGRICOLA - Academic
DatabaseTitleList	Aerospace Database Aquatic Science & Fisheries Abstracts (ASFA) Professional AGRICOLA
DeliveryMethod	fulltext_linktorsrc
Discipline	Geography Geology Environmental Sciences
EISSN	1879-0704
EndPage	214
ExternalDocumentID	10_1016_j_rse_2016_06_006 S0034425716302449
GroupedDBID	--K --M -~X .DC .~1 0R~ 123 1B1 1RT 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXUO ABFNM ABFYP ABJNI ABLST ABMAC ABPPZ ABQEM ABQYD ABYKQ ACDAQ ACGFS ACIWK ACLVX ACPRK ACRLP ACSBN ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFRAH AFTJW AFXIZ AGHFR AGUBO AGYEJ AHEUO AHHHB AIEXJ AIKHN AITUG AJBFU AJOXV AKIFW ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ATOGT AXJTR BKOJK BLECG BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE IMUCA J1W KCYFY KOM LY3 LY9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG RNS ROL RPZ SDF SDG SDP SES SPC SPCBC SSE SSJ SSZ T5K TN5 TWZ WH7 ZCA ZMT ~02 ~G- ~KM 29P 41~ 6TJ 9DU AAHBH AAQXK AATTM AAXKI AAYWO AAYXX ABDPE ABEFU ABUFD ABWVN ABXDB ACLOT ACRPL ACVFH ADCNI ADMUD ADNMO ADVLN ADXHL AEGFY AEIPS AEUPX AFFNX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS FA8 FEDTE FGOYB G-2 HMA HMC HVGLF HZ~ H~9 OHT R2- SEN SEP SEW VOH WUQ XOL ~HD 7SN C1K F1W H96 L.G 8FD FR3 H8D KR7 L7M 7S9 L.6
ID	FETCH-LOGICAL-c439t-52b2f22e36ed1be2da364a2b34714a2a9dcbcab0443e86a4bd5d89e46a6bfa063
ISICitedReferencesCount	42
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000382345400017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0034-4257
IngestDate	Sun Nov 09 11:55:08 EST 2025 Thu Oct 02 08:11:34 EDT 2025 Tue Oct 07 09:22:25 EDT 2025 Tue Nov 18 22:09:28 EST 2025 Sat Nov 29 02:51:19 EST 2025 Fri Feb 23 02:30:03 EST 2024
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Dynamic pattern and intensity recognition Microwave sea surface wind Tropical cyclone Machine learning
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c439t-52b2f22e36ed1be2da364a2b34714a2a9dcbcab0443e86a4bd5d89e46a6bfa063
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0001-8983-8624
OpenAccessLink	http://www.sciencedirect.com/science/article/pii/S0034425716302449
PQID	1815695067
PQPubID	23462
PageCount	10
ParticipantIDs	proquest_miscellaneous_2000313872 proquest_miscellaneous_1835615888 proquest_miscellaneous_1815695067 crossref_primary_10_1016_j_rse_2016_06_006 crossref_citationtrail_10_1016_j_rse_2016_06_006 elsevier_sciencedirect_doi_10_1016_j_rse_2016_06_006
PublicationCentury	2000
PublicationDate	2016-09-15
PublicationDateYYYYMMDD	2016-09-15
PublicationDate_xml	– month: 09 year: 2016 text: 2016-09-15 day: 15
PublicationDecade	2010
PublicationTitle	Remote sensing of environment
PublicationYear	2016
Publisher	Elsevier Inc
Publisher_xml	– name: Elsevier Inc
References	Velden, Harper, Wells, Beven, Zehr, Olander (bb0210) 2006; 87 Li, Im, Beier (bb0120) 2013; 50 Gaiser, St Germain, Twarog, Poe, Purdy, Richardson (bb0040) 2004; 42 Im, Jensen, Tullis (bb0080) 2008; 29 Dvorak (bb0015) 1972; 36 Dvorak (bb0025) 1984; 11 Houze (bb0065) 2010; 138 Park, Ho, Kim, Elsberry (bb0155) 2011; 37 Hendricks, Montgomery, Davis (bb0055) 2004; 61 Lu, Im, Rhee, Hodgson (bb0125) 2014; 130 Friedl, Brodley (bb0035) 1997; 61 Kim, Im, Ha, Choi, Ha (bb0105) 2014; 51 Park, Penny, Elsberry, Billings, Doyle (bb0165) 2013; 70 Adams, Hennon, Jones, Ahmad (bb0005) 2006; 44 Jensen, Im, Hardin, Jensen (bb0095) 2008 Im, Jensen (bb0070) 2005; 99 Hennon, Helms, Knapp, Bowen (bb0060) 2011; 28 Kerns, Chen (bb0100) 2013; 141 Kim, Im, Han, Kim, Lee, Shin, Kim (bb0110) 2015; 52 Davis, Ahijevych (bb0010) 2012; 69 Tsai, Elsberry, Jordan (bb0205) 2013; 49 Meissner, Wentz (bb0135) 2013; 50 Dvorak (bb0020) 1975; 103 Velden, Olander, Zehr (bb0215) 1998; 13 Quinlan (bb0180) 2003 Park, Elsberry (bb0150) 2013; 70 Pineros, Ritchie, Tyo (bb0170) 2010; 7 Zhang, Fu, Peng, Li (bb0225) 2015; 30 Gray (bb0045) 1968; 96 Jensen (bb0085) 2015 Jensen, Im (bb0090) 2007 Im, Jensen, Jensen, Gladden, Waugh, Serrato (bb0075) 2012; 4 Wood, Rodriguez-Herrera, Ritchie, Piñeros, Hernández, Tyo (bb0220) 2015; 30 McGarigal, Cushman, Ene (bb0130) 2012 Montgomery, Nicholls, Cram, Saunders (bb0140) 2006; 63 Figa-Saldaña, Wilson, Attema, Gelsthorpe, Drinkwater, Stoffelen (bb0030) 2002; 28 Kucas, Darlow (bb0115) 2012; 1 Park, Kim, Ho, Elsberry, Lee (bb0160) 2015; 143 Nolan (bb0145) 2007; 56 Rhee, Im, Carbone, Jensen (bb0190) 2008; 112 Schumacher, DeMaria, Knaff (bb0195) 2009; 24 Quilfen, Prigent, Chapron, Mouche, Houti (bb0175) 2007; 112 Han, Lee, Im, Kim, Lee, Ahn, Chung (bb0050) 2015; 7 Torbick, Corbiere (bb0200) 2015; 52 Tsai (10.1016/j.rse.2016.06.006_bb0205) 2013; 49 Wood (10.1016/j.rse.2016.06.006_bb0220) 2015; 30 Davis (10.1016/j.rse.2016.06.006_bb0010) 2012; 69 Im (10.1016/j.rse.2016.06.006_bb0080) 2008; 29 McGarigal (10.1016/j.rse.2016.06.006_bb0130) Park (10.1016/j.rse.2016.06.006_bb0155) 2011; 37 Quinlan (10.1016/j.rse.2016.06.006_bb0180) Zhang (10.1016/j.rse.2016.06.006_bb0225) 2015; 30 Friedl (10.1016/j.rse.2016.06.006_bb0035) 1997; 61 Pineros (10.1016/j.rse.2016.06.006_bb0170) 2010; 7 Schumacher (10.1016/j.rse.2016.06.006_bb0195) 2009; 24 Park (10.1016/j.rse.2016.06.006_bb0165) 2013; 70 Quilfen (10.1016/j.rse.2016.06.006_bb0175) 2007; 112 Gray (10.1016/j.rse.2016.06.006_bb0045) 1968; 96 Im (10.1016/j.rse.2016.06.006_bb0075) 2012; 4 Li (10.1016/j.rse.2016.06.006_bb0120) 2013; 50 Figa-Saldaña (10.1016/j.rse.2016.06.006_bb0030) 2002; 28 Adams (10.1016/j.rse.2016.06.006_bb0005) 2006; 44 Gaiser (10.1016/j.rse.2016.06.006_bb0040) 2004; 42 Torbick (10.1016/j.rse.2016.06.006_bb0200) 2015; 52 Lu (10.1016/j.rse.2016.06.006_bb0125) 2014; 130 Park (10.1016/j.rse.2016.06.006_bb0150) 2013; 70 Hennon (10.1016/j.rse.2016.06.006_bb0060) 2011; 28 Kucas (10.1016/j.rse.2016.06.006_bb0115) 2012; 1 Park (10.1016/j.rse.2016.06.006_bb0160) 2015; 143 Dvorak (10.1016/j.rse.2016.06.006_bb0025) 1984; 11 Kim (10.1016/j.rse.2016.06.006_bb0110) 2015; 52 Velden (10.1016/j.rse.2016.06.006_bb0215) 1998; 13 Han (10.1016/j.rse.2016.06.006_bb0050) 2015; 7 Jensen (10.1016/j.rse.2016.06.006_bb0085) 2015 Dvorak (10.1016/j.rse.2016.06.006_bb0015) 1972; 36 Im (10.1016/j.rse.2016.06.006_bb0070) 2005; 99 Houze (10.1016/j.rse.2016.06.006_bb0065) 2010; 138 Meissner (10.1016/j.rse.2016.06.006_bb0135) 2013; 50 Jensen (10.1016/j.rse.2016.06.006_bb0090) 2007 Nolan (10.1016/j.rse.2016.06.006_bb0145) 2007; 56 Hendricks (10.1016/j.rse.2016.06.006_bb0055) 2004; 61 Velden (10.1016/j.rse.2016.06.006_bb0210) 2006; 87 Jensen (10.1016/j.rse.2016.06.006_bb0095) 2008 Montgomery (10.1016/j.rse.2016.06.006_bb0140) 2006; 63 Dvorak (10.1016/j.rse.2016.06.006_bb0020) 1975; 103 Kerns (10.1016/j.rse.2016.06.006_bb0100) 2013; 141 Kim (10.1016/j.rse.2016.06.006_bb0105) 2014; 51 Rhee (10.1016/j.rse.2016.06.006_bb0190) 2008; 112
References_xml	– volume: 44 start-page: 656 year: 2006 end-page: 667 ident: bb0005 article-title: Evaluation of hurricane ocean vector winds from WindSat publication-title: IEEE Transactions on Geoscience and Remote Sensing – volume: 61 start-page: 399 year: 1997 end-page: 409 ident: bb0035 article-title: Decision tree classification of land cover from remotely sensed data publication-title: Remote Sensing of Environment – volume: 51 start-page: 158 year: 2014 end-page: 174 ident: bb0105 article-title: Machine learning approaches to coastal water quality monitoring using GOCI satellite data publication-title: GIScience and Remote Sensing – volume: 49 start-page: 409 year: 2013 end-page: 420 ident: bb0205 article-title: Objective verifications and false alarm analyses of western North Pacific tropical cyclone event forecasts by the ECMWF 32-day ensemble publication-title: Asia-Pacific Journal of Atmospheric Sciences – volume: 87 start-page: 1195 year: 2006 end-page: 1210 ident: bb0210 article-title: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 publication-title: Bulletin of the American Meteorological Society – volume: 28 start-page: 404 year: 2002 end-page: 412 ident: bb0030 article-title: The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: A follow on for European wind scatterometers publication-title: Canadian Journal of Remote Sensing – volume: 24 start-page: 456 year: 2009 end-page: 471 ident: bb0195 article-title: Objective estimation of the 24-h probability of tropical cyclone formation publication-title: Weather Forecasting – volume: 70 start-page: 37 year: 2013 end-page: 55 ident: bb0165 article-title: Latent heating and cooling rates in developing and nondeveloping tropical disturbances during TCS-08: Radar-equivalent retrievals from mesoscale numerical models and ELDORA publication-title: Journal of the Atmospheric Sciences – volume: 96 start-page: 669 year: 1968 end-page: 700 ident: bb0045 article-title: Global view of the origin of tropical disturbances and storms publication-title: Monthly Weather Review – volume: 61 start-page: 1209 year: 2004 end-page: 1232 ident: bb0055 article-title: The role of “vortical” hot towers in the formation of tropical cyclone Diana (1984) publication-title: Journal of the Atmospheric Sciences – volume: 143 start-page: 88 year: 2015 end-page: 110 ident: bb0160 article-title: Tropical cyclone Mekkhala (2008)'s formation over South China Sea: Mesoscale, synoptic-scale, and largescale contributions publication-title: Monthly Weather Review – year: 2003 ident: bb0180 article-title: C5.0 online tutorial – volume: 30 start-page: 446 year: 2015 end-page: 454 ident: bb0225 article-title: Discriminating developing versus nondeveloping tropical disturbances in the Western North Pacific through decision tree analysis publication-title: Weather and Forecasting – volume: 112 year: 2007 ident: bb0175 article-title: The potential of QuikSCAT and WindSat observations for the estimation of sea surface wind vector under severe weather conditions publication-title: Journal of Geophysical Research – volume: 99 start-page: 326 year: 2005 end-page: 340 ident: bb0070 article-title: A change detection model based on neighborhood correlation image analysis and decision tree classification publication-title: Remote Sensing of Environment – year: 2015 ident: bb0085 article-title: Introductory digital image processing: A remote sensing perspective – volume: 70 start-page: 15 year: 2013 end-page: 35 ident: bb0150 article-title: Latent heating and cooling rates in developing and non-developing tropical disturbances during TCS-08: TRMM and ELDORA retrievals publication-title: Journal of the Atmospheric Sciences – volume: 28 start-page: 1007 year: 2011 end-page: 1018 ident: bb0060 article-title: An objective algorithm for detecting and tracking tropical cloud clusters: Implications for tropical cyclogenesis prediction publication-title: Journal of Atmospheric and Oceanic Technology – volume: 7 start-page: 826 year: 2010 end-page: 830 ident: bb0170 article-title: Detecting tropical cyclone genesis from remotely sensed infrared image data publication-title: IEEE Geoscience and Remote Sensing Letters – volume: 103 start-page: 420 year: 1975 end-page: 430 ident: bb0020 article-title: Tropical cyclone intensity analysis and forecasting from satellite imagery publication-title: Monthly Weather Review – volume: 4 start-page: 327 year: 2012 end-page: 353 ident: bb0075 article-title: Vegetation cover analysis of hazardous waste sites in Utah and Arizona using hyperspectral remote sensing publication-title: Remote Sensing – volume: 37 start-page: 1483 year: 2011 end-page: 1499 ident: bb0155 article-title: Diurnal circulations and their multi-scale interaction leading to rainfall over the South China Sea upstream of the Philippines during intraseasonal monsoon westerly wind bursts publication-title: Climate Dynamics – volume: 141 start-page: 192 year: 2013 end-page: 210 ident: bb0100 article-title: Cloud clusters and tropical cyclogenesis: Developing and nondeveloping systems and their large-scale environment publication-title: Monthly Weather Review – year: 2012 ident: bb0130 article-title: FRAGSTATS v4: Spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst – volume: 56 start-page: 241 year: 2007 end-page: 266 ident: bb0145 article-title: What is the trigger for tropical cyclogenesis? publication-title: Australian Meteorological Magazine – volume: 52 start-page: 746 year: 2015 end-page: 764 ident: bb0200 article-title: Mapping urban sprawl and impervious surfaces in the northeast United States for the past four decades publication-title: GIScience and Remote Sensing – volume: 112 start-page: 3099 year: 2008 end-page: 3111 ident: bb0190 article-title: Delineation of climate regions using in-situ and remotely-sensed data for the Carolinas publication-title: Remote Sensing of Environment – volume: 1 start-page: 325 year: 2012 end-page: 330 ident: bb0115 article-title: A subjective method for assessing tropical cyclogenesis at the joint typhoon warning center publication-title: Tropical Cyclone research and Review – volume: 50 start-page: 3004 year: 2013 end-page: 3026 ident: bb0135 article-title: The emissivity of the ocean surface between 6 and 90 publication-title: IEEE Transactions on Geoscience and Remote Sensing – volume: 13 start-page: 172 year: 1998 end-page: 186 ident: bb0215 article-title: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite imagery publication-title: Weather and Forecasting – volume: 69 start-page: 1284 year: 2012 end-page: 1305 ident: bb0010 article-title: Mesoscale structural evolution of three tropical weather systems observed during PREDICT publication-title: Journal of the Atmospheric Sciences – volume: 29 start-page: 399 year: 2008 end-page: 423 ident: bb0080 article-title: Object-based change detection using correlation image analysis and image segmentation techniques publication-title: International Journal of Remote Sensing – volume: 11 year: 1984 ident: bb0025 article-title: Tropical cyclone intensity analysis using satellite data publication-title: NOAA Technical Report NESDIS – volume: 50 start-page: 361 year: 2013 end-page: 384 ident: bb0120 article-title: Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest publication-title: GIScience and Remote Sensing – volume: 130 start-page: 134 year: 2014 end-page: 148 ident: bb0125 article-title: Building type classification using spatial attributes derived from LiDAR remote sensing data publication-title: Landscape and Urban Planning – volume: 63 start-page: 355 year: 2006 end-page: 386 ident: bb0140 article-title: A vortical hot tower route to tropical cyclogenesis publication-title: Journal of the Atmospheric Sciences – volume: 138 start-page: 293 year: 2010 end-page: 344 ident: bb0065 article-title: Clouds in tropical cyclones publication-title: Monthly Weather Review – volume: 42 start-page: 2347 year: 2004 end-page: 2361 ident: bb0040 article-title: The WindSat space borne polarimetric microwave radiometer: Sensor description and early orbit performance publication-title: IEEE Transactions on Geoscience and Remote Sensing – start-page: 7 year: 2007 end-page: 31 ident: bb0090 article-title: Remote sensing change detection in urban environments publication-title: Geo-spatial technologies in urban environments: Policy, practice, and pixels – volume: 36 year: 1972 ident: bb0015 article-title: A technique for the analysis and forecasting of tropical cyclone intensities from satellite pictures publication-title: NOAA Technical Memorandum NESS – volume: 30 start-page: 1663 year: 2015 end-page: 1672 ident: bb0220 article-title: Tropical cyclogenesis detection in the North Pacific using the deviation angle variance technique publication-title: Weather Forecasting – volume: 7 start-page: 9184 year: 2015 end-page: 9204 ident: bb0050 article-title: Detection of convective initiation using meteorological imager on board communication, ocean, and meteorological satellite based on machine learning approaches publication-title: Remote Sensing – start-page: 269 year: 2008 end-page: 281 ident: bb0095 article-title: Image classification publication-title: The SAGE handbook of remote sensing – volume: 52 start-page: 239 year: 2015 end-page: 256 ident: bb0110 article-title: Landfast sea ice monitoring using multisensory fusion in the Antarctic publication-title: GIScience and Remote Sensing – volume: 44 start-page: 656 issue: 3 year: 2006 ident: 10.1016/j.rse.2016.06.006_bb0005 article-title: Evaluation of hurricane ocean vector winds from WindSat publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2005.862506 – volume: 112 year: 2007 ident: 10.1016/j.rse.2016.06.006_bb0175 article-title: The potential of QuikSCAT and WindSat observations for the estimation of sea surface wind vector under severe weather conditions publication-title: Journal of Geophysical Research doi: 10.1029/2007JC004163 – volume: 112 start-page: 3099 year: 2008 ident: 10.1016/j.rse.2016.06.006_bb0190 article-title: Delineation of climate regions using in-situ and remotely-sensed data for the Carolinas publication-title: Remote Sensing of Environment doi: 10.1016/j.rse.2008.03.001 – volume: 87 start-page: 1195 year: 2006 ident: 10.1016/j.rse.2016.06.006_bb0210 article-title: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30years publication-title: Bulletin of the American Meteorological Society doi: 10.1175/BAMS-87-9-1195 – volume: 29 start-page: 399 issue: 2 year: 2008 ident: 10.1016/j.rse.2016.06.006_bb0080 article-title: Object-based change detection using correlation image analysis and image segmentation techniques publication-title: International Journal of Remote Sensing doi: 10.1080/01431160601075582 – volume: 50 start-page: 3004 issue: 8 year: 2013 ident: 10.1016/j.rse.2016.06.006_bb0135 article-title: The emissivity of the ocean surface between 6 and 90GHz over a large range of wind speeds and earth incidence angles publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2011.2179662 – volume: 138 start-page: 293 year: 2010 ident: 10.1016/j.rse.2016.06.006_bb0065 article-title: Clouds in tropical cyclones publication-title: Monthly Weather Review doi: 10.1175/2009MWR2989.1 – volume: 141 start-page: 192 year: 2013 ident: 10.1016/j.rse.2016.06.006_bb0100 article-title: Cloud clusters and tropical cyclogenesis: Developing and nondeveloping systems and their large-scale environment publication-title: Monthly Weather Review doi: 10.1175/MWR-D-11-00239.1 – volume: 99 start-page: 326 year: 2005 ident: 10.1016/j.rse.2016.06.006_bb0070 article-title: A change detection model based on neighborhood correlation image analysis and decision tree classification publication-title: Remote Sensing of Environment doi: 10.1016/j.rse.2005.09.008 – volume: 50 start-page: 361 year: 2013 ident: 10.1016/j.rse.2016.06.006_bb0120 article-title: Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest publication-title: GIScience and Remote Sensing doi: 10.1080/15481603.2013.819161 – ident: 10.1016/j.rse.2016.06.006_bb0130 – volume: 69 start-page: 1284 year: 2012 ident: 10.1016/j.rse.2016.06.006_bb0010 article-title: Mesoscale structural evolution of three tropical weather systems observed during PREDICT publication-title: Journal of the Atmospheric Sciences doi: 10.1175/JAS-D-11-0225.1 – volume: 30 start-page: 446 year: 2015 ident: 10.1016/j.rse.2016.06.006_bb0225 article-title: Discriminating developing versus nondeveloping tropical disturbances in the Western North Pacific through decision tree analysis publication-title: Weather and Forecasting doi: 10.1175/WAF-D-14-00023.1 – volume: 7 start-page: 9184 year: 2015 ident: 10.1016/j.rse.2016.06.006_bb0050 article-title: Detection of convective initiation using meteorological imager on board communication, ocean, and meteorological satellite based on machine learning approaches publication-title: Remote Sensing doi: 10.3390/rs70709184 – volume: 51 start-page: 158 year: 2014 ident: 10.1016/j.rse.2016.06.006_bb0105 article-title: Machine learning approaches to coastal water quality monitoring using GOCI satellite data publication-title: GIScience and Remote Sensing doi: 10.1080/15481603.2014.900983 – volume: 52 start-page: 239 year: 2015 ident: 10.1016/j.rse.2016.06.006_bb0110 article-title: Landfast sea ice monitoring using multisensory fusion in the Antarctic publication-title: GIScience and Remote Sensing doi: 10.1080/15481603.2015.1026050 – volume: 13 start-page: 172 year: 1998 ident: 10.1016/j.rse.2016.06.006_bb0215 article-title: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite imagery publication-title: Weather and Forecasting doi: 10.1175/1520-0434(1998)013<0172:DOAOST>2.0.CO;2 – volume: 52 start-page: 746 year: 2015 ident: 10.1016/j.rse.2016.06.006_bb0200 article-title: Mapping urban sprawl and impervious surfaces in the northeast United States for the past four decades publication-title: GIScience and Remote Sensing doi: 10.1080/15481603.2015.1076561 – volume: 30 start-page: 1663 year: 2015 ident: 10.1016/j.rse.2016.06.006_bb0220 article-title: Tropical cyclogenesis detection in the North Pacific using the deviation angle variance technique publication-title: Weather Forecasting doi: 10.1175/WAF-D-14-00113.1 – volume: 61 start-page: 1209 year: 2004 ident: 10.1016/j.rse.2016.06.006_bb0055 article-title: The role of “vortical” hot towers in the formation of tropical cyclone Diana (1984) publication-title: Journal of the Atmospheric Sciences doi: 10.1175/1520-0469(2004)061<1209:TROVHT>2.0.CO;2 – volume: 49 start-page: 409 issue: 4 year: 2013 ident: 10.1016/j.rse.2016.06.006_bb0205 article-title: Objective verifications and false alarm analyses of western North Pacific tropical cyclone event forecasts by the ECMWF 32-day ensemble publication-title: Asia-Pacific Journal of Atmospheric Sciences doi: 10.1007/s13143-013-0038-6 – volume: 42 start-page: 2347 issue: 11 year: 2004 ident: 10.1016/j.rse.2016.06.006_bb0040 article-title: The WindSat space borne polarimetric microwave radiometer: Sensor description and early orbit performance publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2004.836867 – volume: 4 start-page: 327 year: 2012 ident: 10.1016/j.rse.2016.06.006_bb0075 article-title: Vegetation cover analysis of hazardous waste sites in Utah and Arizona using hyperspectral remote sensing publication-title: Remote Sensing doi: 10.3390/rs4020327 – volume: 28 start-page: 1007 year: 2011 ident: 10.1016/j.rse.2016.06.006_bb0060 article-title: An objective algorithm for detecting and tracking tropical cloud clusters: Implications for tropical cyclogenesis prediction publication-title: Journal of Atmospheric and Oceanic Technology doi: 10.1175/2010JTECHA1522.1 – volume: 70 start-page: 15 issue: 1 year: 2013 ident: 10.1016/j.rse.2016.06.006_bb0150 article-title: Latent heating and cooling rates in developing and non-developing tropical disturbances during TCS-08: TRMM and ELDORA retrievals publication-title: Journal of the Atmospheric Sciences doi: 10.1175/JAS-D-12-083.1 – volume: 1 start-page: 325 year: 2012 ident: 10.1016/j.rse.2016.06.006_bb0115 article-title: A subjective method for assessing tropical cyclogenesis at the joint typhoon warning center publication-title: Tropical Cyclone research and Review – volume: 56 start-page: 241 year: 2007 ident: 10.1016/j.rse.2016.06.006_bb0145 article-title: What is the trigger for tropical cyclogenesis? publication-title: Australian Meteorological Magazine – volume: 143 start-page: 88 issue: 1 year: 2015 ident: 10.1016/j.rse.2016.06.006_bb0160 article-title: Tropical cyclone Mekkhala (2008)'s formation over South China Sea: Mesoscale, synoptic-scale, and largescale contributions publication-title: Monthly Weather Review doi: 10.1175/MWR-D-14-00119.1 – volume: 36 year: 1972 ident: 10.1016/j.rse.2016.06.006_bb0015 article-title: A technique for the analysis and forecasting of tropical cyclone intensities from satellite pictures publication-title: NOAA Technical Memorandum NESS – volume: 11 year: 1984 ident: 10.1016/j.rse.2016.06.006_bb0025 article-title: Tropical cyclone intensity analysis using satellite data publication-title: NOAA Technical Report NESDIS – volume: 37 start-page: 1483 year: 2011 ident: 10.1016/j.rse.2016.06.006_bb0155 article-title: Diurnal circulations and their multi-scale interaction leading to rainfall over the South China Sea upstream of the Philippines during intraseasonal monsoon westerly wind bursts publication-title: Climate Dynamics doi: 10.1007/s00382-010-0922-z – volume: 103 start-page: 420 year: 1975 ident: 10.1016/j.rse.2016.06.006_bb0020 article-title: Tropical cyclone intensity analysis and forecasting from satellite imagery publication-title: Monthly Weather Review doi: 10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2 – start-page: 269 year: 2008 ident: 10.1016/j.rse.2016.06.006_bb0095 article-title: Image classification – ident: 10.1016/j.rse.2016.06.006_bb0180 – volume: 28 start-page: 404 issue: 3 year: 2002 ident: 10.1016/j.rse.2016.06.006_bb0030 article-title: The advanced scatterometer (ASCAT) on the meteorological operational (MetOp) platform: A follow on for European wind scatterometers publication-title: Canadian Journal of Remote Sensing doi: 10.5589/m02-035 – volume: 96 start-page: 669 year: 1968 ident: 10.1016/j.rse.2016.06.006_bb0045 article-title: Global view of the origin of tropical disturbances and storms publication-title: Monthly Weather Review doi: 10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2 – volume: 7 start-page: 826 issue: 4 year: 2010 ident: 10.1016/j.rse.2016.06.006_bb0170 article-title: Detecting tropical cyclone genesis from remotely sensed infrared image data publication-title: IEEE Geoscience and Remote Sensing Letters doi: 10.1109/LGRS.2010.2048694 – volume: 61 start-page: 399 issue: 3 year: 1997 ident: 10.1016/j.rse.2016.06.006_bb0035 article-title: Decision tree classification of land cover from remotely sensed data publication-title: Remote Sensing of Environment doi: 10.1016/S0034-4257(97)00049-7 – volume: 130 start-page: 134 year: 2014 ident: 10.1016/j.rse.2016.06.006_bb0125 article-title: Building type classification using spatial attributes derived from LiDAR remote sensing data publication-title: Landscape and Urban Planning doi: 10.1016/j.landurbplan.2014.07.005 – start-page: 7 year: 2007 ident: 10.1016/j.rse.2016.06.006_bb0090 article-title: Remote sensing change detection in urban environments – volume: 70 start-page: 37 year: 2013 ident: 10.1016/j.rse.2016.06.006_bb0165 article-title: Latent heating and cooling rates in developing and nondeveloping tropical disturbances during TCS-08: Radar-equivalent retrievals from mesoscale numerical models and ELDORA publication-title: Journal of the Atmospheric Sciences doi: 10.1175/JAS-D-11-0311.1 – year: 2015 ident: 10.1016/j.rse.2016.06.006_bb0085 – volume: 63 start-page: 355 year: 2006 ident: 10.1016/j.rse.2016.06.006_bb0140 article-title: A vortical hot tower route to tropical cyclogenesis publication-title: Journal of the Atmospheric Sciences doi: 10.1175/JAS3604.1 – volume: 24 start-page: 456 year: 2009 ident: 10.1016/j.rse.2016.06.006_bb0195 article-title: Objective estimation of the 24-h probability of tropical cyclone formation publication-title: Weather Forecasting doi: 10.1175/2008WAF2007109.1
SSID	ssj0015871
Score	2.4171839
Snippet	Microwave remote sensing can be used to measure ocean surface winds, which can be used to detect tropical cyclone (TC) formation in an objective and...
SourceID	proquest crossref elsevier
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	205
SubjectTerms	algorithms artificial intelligence Circulation convection Cyclones decision support systems Decision trees Disturbances Dynamic pattern and intensity recognition Dynamics Formations hurricanes hydrology Machine learning methodology Microwave sea surface wind Ocean surface rain Rainfall remote sensing satellites Tropical cyclone wind speed
Title	Detection of tropical cyclone genesis via quantitative satellite ocean surface wind pattern and intensity analyses using decision trees
URI	https://dx.doi.org/10.1016/j.rse.2016.06.006 https://www.proquest.com/docview/1815695067 https://www.proquest.com/docview/1835615888 https://www.proquest.com/docview/2000313872
Volume	183
WOSCitedRecordID	wos000382345400017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1879-0704 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0015871 issn: 0034-4257 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLZKB4IXBIOJcZOREA9MQbk5l8cJujG0FiQ6qW-R4zijU5WUJi3rL-Bf8Fs5x7GTarBqPPCSVqmdtD2fj7_Y53yHkNeeb6d-lOVWFka-5buuxDGH5V4CIcIgj5kQqthEOBpFk0n8pdf7ZXJhVrOwKKLLy3j-X00N58DYmDr7D-ZuLwon4D0YHY5gdjjeyPAfZC2F4YH1opw3GiBrMSuBUJ6jb5tWB6spx4TKQuWYYfRQxZU2J0YNCFydr5aLnMOg_zFFJQGlwlloqSYV9F6jcBMKmsjqYKkWHDJdrgeD13VkolH-lgAIuAX2a4KsN_Lruk2sJmp7uAb_Y3015J-3FZ-H06LieqLtQoiG6xKan3QY12nfxfm3cnNJwwkw_qJJ6jRu2kPcNMrVnZv2Nh2tzTbmbLdJRP1jOmhWJi7eLSpURHUCJdVq_0V6e_Q5OTo7PU3Gg8n4zfy7hVXJcPdel2i5RXbckMVRn-wcngwmn9p9KhaFTU1G_YXNvrmKILxy1-uYzxUOoIjN-AG5r59I6GGDpIekJ4tdsjfoDAQf6hmg2iV3j6UWOd8ld45VWej1I_KzBR0tc2pARzXoqAYdBdDRTdDRFnRUgY5q0FEEHdWgowA62oKOGtBRBTpqQEcV6B6Ts6PB-P1HS5f4sAQw4dpiburm4CC8QGZOKt2Me4HP3dQDzgSvPM5EKnhq-74no4D7acayKJZ-wIM050Cv90i_gB_yhFA_dwSwzzAX-FQArD9nqS1FyjyWM7DePrHN358IrX-PZVhmiQl0vEjAYglaLFHBnsE-edt2mTfiL9sa-8amiWavDStNAI3bur0y9k_As-N2HS9kuawSB4WcYgZ0clsbDx6AWBRF17fBZDzP8aLQfXqDez0j97oh-Zz068VSviC3xaqeVouXGv-_AR2a5og
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Detection+of+tropical+cyclone+genesis+via+quantitative+satellite+ocean+surface+wind+pattern+and+intensity+analyses+using+decision+trees&rft.jtitle=Remote+sensing+of+environment&rft.au=Park%2C+Myung-Sook&rft.au=Kim%2C+Minsang&rft.au=Lee%2C+Myong-In&rft.au=Im%2C+Jungho&rft.date=2016-09-15&rft.issn=0034-4257&rft.volume=183&rft.spage=205&rft.epage=214&rft_id=info:doi/10.1016%2Fj.rse.2016.06.006&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0034-4257&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0034-4257&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0034-4257&client=summon