Filling in missing sea-surface temperature satellite data over the Eastern Mediterranean Sea using the DINEOF algorithm

A. Nikolaidis, G. Georgiou, D. Hadjimitsis, E. Akylas
{"title":"Filling in missing sea-surface temperature satellite data over the Eastern Mediterranean Sea using the DINEOF algorithm","authors":"A. Nikolaidis, G. Georgiou, D. Hadjimitsis, E. Akylas","doi":"10.2478/s13533-012-0148-1","DOIUrl":null,"url":null,"abstract":"The Data Interpolating Empirical Orthogonal Functions method is a special technique based on Empirical Orthogonal Functions and developed to reconstruct missing data from satellite images, which is especially useful for filling in missing data from geophysical fields. Successful experiments in the Western Mediterranean encouraged extension of the application eastwards using a similar experimental implementation. The present study summarizes the experimental work done, the implementation of the method and its ability to reconstruct the sea-surface temperature fields over the Eastern Mediterranean basin, and specifically in the Levantine Sea. L3 type Satellite Sea-surface Temperature data has been used and reprocessed in order to recover missing information from cloudy images. Data reconstruction with this method proved to be extremely effective, even when using a relatively small number of time steps, and markedly accelerated the procedure. A detailed comparison with the two oceanographic models proves the accuracy of the method and the validity of the reconstructed fields.","PeriodicalId":49092,"journal":{"name":"Central European Journal of Geosciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Central European Journal of Geosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/s13533-012-0148-1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

The Data Interpolating Empirical Orthogonal Functions method is a special technique based on Empirical Orthogonal Functions and developed to reconstruct missing data from satellite images, which is especially useful for filling in missing data from geophysical fields. Successful experiments in the Western Mediterranean encouraged extension of the application eastwards using a similar experimental implementation. The present study summarizes the experimental work done, the implementation of the method and its ability to reconstruct the sea-surface temperature fields over the Eastern Mediterranean basin, and specifically in the Levantine Sea. L3 type Satellite Sea-surface Temperature data has been used and reprocessed in order to recover missing information from cloudy images. Data reconstruction with this method proved to be extremely effective, even when using a relatively small number of time steps, and markedly accelerated the procedure. A detailed comparison with the two oceanographic models proves the accuracy of the method and the validity of the reconstructed fields.
利用DINEOF算法填补地中海东部海面温度卫星数据缺失
数据插值经验正交函数法是一种基于经验正交函数的卫星图像缺失数据重建技术,特别适用于地球物理场缺失数据的填充。在西地中海的成功试验鼓励采用类似的试验实施将应用向东扩展。本文总结了该方法的实验工作、实现方法及其重建东地中海盆地特别是黎凡特海海表温度场的能力。利用L3型卫星海面温度数据进行再处理,以恢复云图中缺失的信息。事实证明,即使使用相对较少的时间步长,用这种方法进行数据重建也是非常有效的,并且显著加快了过程。通过与两种海洋模式的详细比较,证明了该方法的准确性和重建场的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Central European Journal of Geosciences
Central European Journal of Geosciences GEOSCIENCES, MULTIDISCIPLINARY-
自引率
0.00%
发文量
0
审稿时长
>12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信