Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases
ThThe development of numerical wave prediction models for hindcast applications allows a detailed description of wave climate in locations where long-term instrumental records are not available. Wave hindcast databases (WHDBs) have become a powerful tool for the design of offshore and coastal struct...
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ftunivcarlosmadr:oai:e-archivo.uc3m.es:10016/34922 2024-01-21T10:08:37+01:00 Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases Mínguez Solana, Roberto Reguero, B. C. Luceño, A. Méndez, F.J. 2012-02-01 http://hdl.handle.net/10016/34922 https://doi.org/10.1175/JTECH-D-11-00059.1 eng eng American Meteorological Society Mínguez, R., Reguero, B. G., Luceño, A., & Méndez, F. J. (2012). Regression Models for Outlier Identification (Hurricanes and Typhoons) in Wave Hindcast Databases. Journal of Atmospheric and Oceanic Technology, 29 (2), pp. 267-285. 0739-0572 http://hdl.handle.net/10016/34922 https://doi.org/10.1175/JTECH-D-11-00059.1 267 2 285 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY 29 AR/0000029964 © 2012 American Meteorological Society open access Error analysis Ocean models Regression analysis Statistical techniques Estadística research article VoR 2012 ftunivcarlosmadr https://doi.org/10.1175/JTECH-D-11-00059.1 2023-12-27T00:20:14Z ThThe development of numerical wave prediction models for hindcast applications allows a detailed description of wave climate in locations where long-term instrumental records are not available. Wave hindcast databases (WHDBs) have become a powerful tool for the design of offshore and coastal structures, offering important advantages for the statistical characterization of wave climate all over the globe (continuous time series, wide spatial coverage, constant time span, homogeneous forcing, and more than 60-yr-long time series). However, WHDBs present several deficiencies reported in the literature. One of these deficiencies is related to typhoons and hurricanes, which are inappropriately reproduced by numerical models. The main reasons are (i) the difficulty of specifying accurate wind fields during these events and (ii) the insufficient spatiotemporal resolution used. These difficulties make the data related to these events appear as ‘‘outliers’’ when compared with instrumental records. These bad data distort results from calibration and/or correction techniques. In this paper, several methods for detecting the presence of typhoons and/or hurricane data are presented, and their automatic outlier identification capabilities are analyzed and compared. All the methods are applied to a global wave hindcast database and results are compared with existing hurricane and buoy databases in the Gulf of Mexico, Caribbean Sea, and North Atlantic Ocean. Article in Journal/Newspaper North Atlantic Universidad Carlos III de Madrid: e-Archivo Journal of Atmospheric and Oceanic Technology 29 2 267 285 |
institution |
Open Polar |
collection |
Universidad Carlos III de Madrid: e-Archivo |
op_collection_id |
ftunivcarlosmadr |
language |
English |
topic |
Error analysis Ocean models Regression analysis Statistical techniques Estadística |
spellingShingle |
Error analysis Ocean models Regression analysis Statistical techniques Estadística Mínguez Solana, Roberto Reguero, B. C. Luceño, A. Méndez, F.J. Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases |
topic_facet |
Error analysis Ocean models Regression analysis Statistical techniques Estadística |
description |
ThThe development of numerical wave prediction models for hindcast applications allows a detailed description of wave climate in locations where long-term instrumental records are not available. Wave hindcast databases (WHDBs) have become a powerful tool for the design of offshore and coastal structures, offering important advantages for the statistical characterization of wave climate all over the globe (continuous time series, wide spatial coverage, constant time span, homogeneous forcing, and more than 60-yr-long time series). However, WHDBs present several deficiencies reported in the literature. One of these deficiencies is related to typhoons and hurricanes, which are inappropriately reproduced by numerical models. The main reasons are (i) the difficulty of specifying accurate wind fields during these events and (ii) the insufficient spatiotemporal resolution used. These difficulties make the data related to these events appear as ‘‘outliers’’ when compared with instrumental records. These bad data distort results from calibration and/or correction techniques. In this paper, several methods for detecting the presence of typhoons and/or hurricane data are presented, and their automatic outlier identification capabilities are analyzed and compared. All the methods are applied to a global wave hindcast database and results are compared with existing hurricane and buoy databases in the Gulf of Mexico, Caribbean Sea, and North Atlantic Ocean. |
format |
Article in Journal/Newspaper |
author |
Mínguez Solana, Roberto Reguero, B. C. Luceño, A. Méndez, F.J. |
author_facet |
Mínguez Solana, Roberto Reguero, B. C. Luceño, A. Méndez, F.J. |
author_sort |
Mínguez Solana, Roberto |
title |
Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases |
title_short |
Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases |
title_full |
Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases |
title_fullStr |
Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases |
title_full_unstemmed |
Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases |
title_sort |
regression models for outlier identification (hurricanes and typhoons) in wave hindcast databases |
publisher |
American Meteorological Society |
publishDate |
2012 |
url |
http://hdl.handle.net/10016/34922 https://doi.org/10.1175/JTECH-D-11-00059.1 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
Mínguez, R., Reguero, B. G., Luceño, A., & Méndez, F. J. (2012). Regression Models for Outlier Identification (Hurricanes and Typhoons) in Wave Hindcast Databases. Journal of Atmospheric and Oceanic Technology, 29 (2), pp. 267-285. 0739-0572 http://hdl.handle.net/10016/34922 https://doi.org/10.1175/JTECH-D-11-00059.1 267 2 285 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY 29 AR/0000029964 |
op_rights |
© 2012 American Meteorological Society open access |
op_doi |
https://doi.org/10.1175/JTECH-D-11-00059.1 |
container_title |
Journal of Atmospheric and Oceanic Technology |
container_volume |
29 |
container_issue |
2 |
container_start_page |
267 |
op_container_end_page |
285 |
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1788699395727818752 |