Long-term in situ Eulerian Sea surface temperature records along the Portuguese Coast

被引:1
|
作者
Santos, Nuno Pessanha [1 ,2 ,3 ]
Moura, Ricardo [4 ]
da Silva, Catarina Santos [3 ]
Lamas, Luisa [5 ]
Lobo, Victor [3 ,6 ]
Neto, Miguel de Castro
机构
[1] Portuguese Mil Acad, Acad Mil, Portuguese Mil Res Ctr CINAMIL, Rua Gomes Freire, P-1169203 Lisbon, Portugal
[2] Inst Super Tecn ISR IST, Inst Syst & Robot, P-1049001 Lisbon, Portugal
[3] Portuguese Naval Acad, Portuguese Navy Res Ctr CINAV, Escola Naval, P-2810001 Almada, Portugal
[4] Univ Nova Lisboa, Nova Math, Ctr Matemat & Aplicacoes, Caparica, Portugal
[5] Portuguese Hydrog Inst, Inst Hidrog, Rua Trinas 49, P-1249093 Lisbon, Portugal
[6] Univ Nova Lisboa, NOVA Informat Management Sch Nova IMS, P-1070312 Lisbon, Portugal
来源
DATA IN BRIEF | 2024年 / 54卷
关键词
D O I
10.1016/j.dib.2024.110287
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Monitoring ocean surface temperature is critical to infer the variability of the upper layers of the ocean, from short temporal scales to climatic change scales. Analysis of the climatological trends and anomalies is fundamental to comprehend the long-term effects of climate change on marine ecosystems and coastal regions. The original data for the dataset presented was collected by the Portuguese Hydrographic Institute (Instituto Hidrogr & aacute;fico) using seven Ondograph and Meteo-oceanography buoys anchored offshore along the Portuguese coast to acquire ocean surface temperatures. The original raw data was pre-processed to provide averages over 3 -hour periods and daily averages, and this cleaned data constitutes the provided dataset. The 3 -hour temperature averages were obtained mainly between 2011 and 2015, and the daily temperature averages were obtained in intervals that vary with the considered buoy, having an average interval of 14 years per buoy. The data gathered provides a considerable temporal window, enabling the creation of data series and the implementation of data mining algorithms to develop decision support systems. Collecting data in situ makes it possible to validate simulated results obtained using approximation models. This allows for more accurate temperature readings and facilitates testing and correcting created models.
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页数:8
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