{"title":"VOS和era -中期波资料的比较","authors":"R. Vettor, C. Soares","doi":"10.1115/omae2019-95287","DOIUrl":null,"url":null,"abstract":"\n Accuracy of Voluntary Observing Ship’s reports is evaluated by a one-to-one comparison with ERA-interim database, specifically considering significant wave height. A first screening allows to detect the most common and undeniable mistakes, as for instance clear errors in reporting the position of the vessel, and delete these observations. Moreover, previous literature is considered to remove systematic biases. Then each report is matched with the appropriate numerical data in terms of location and time, in order to evaluate the scattering of the data, to identify the outliers, and to further prune the database. The procedure allows not only to maintain a database clean from clearly wrong information, which can compromise the statistics, but also to recognize areas and conditions in which the mismatch between numerical data and observations is critical, eventually speculating on the motivations.","PeriodicalId":314553,"journal":{"name":"Volume 3: Structures, Safety, and Reliability","volume":" 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Comparison of VOS and ERA-Interim Wave Data\",\"authors\":\"R. Vettor, C. Soares\",\"doi\":\"10.1115/omae2019-95287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Accuracy of Voluntary Observing Ship’s reports is evaluated by a one-to-one comparison with ERA-interim database, specifically considering significant wave height. A first screening allows to detect the most common and undeniable mistakes, as for instance clear errors in reporting the position of the vessel, and delete these observations. Moreover, previous literature is considered to remove systematic biases. Then each report is matched with the appropriate numerical data in terms of location and time, in order to evaluate the scattering of the data, to identify the outliers, and to further prune the database. The procedure allows not only to maintain a database clean from clearly wrong information, which can compromise the statistics, but also to recognize areas and conditions in which the mismatch between numerical data and observations is critical, eventually speculating on the motivations.\",\"PeriodicalId\":314553,\"journal\":{\"name\":\"Volume 3: Structures, Safety, and Reliability\",\"volume\":\" 2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 3: Structures, Safety, and Reliability\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/omae2019-95287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 3: Structures, Safety, and Reliability","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/omae2019-95287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Accuracy of Voluntary Observing Ship’s reports is evaluated by a one-to-one comparison with ERA-interim database, specifically considering significant wave height. A first screening allows to detect the most common and undeniable mistakes, as for instance clear errors in reporting the position of the vessel, and delete these observations. Moreover, previous literature is considered to remove systematic biases. Then each report is matched with the appropriate numerical data in terms of location and time, in order to evaluate the scattering of the data, to identify the outliers, and to further prune the database. The procedure allows not only to maintain a database clean from clearly wrong information, which can compromise the statistics, but also to recognize areas and conditions in which the mismatch between numerical data and observations is critical, eventually speculating on the motivations.