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Long-term in situ Eulerian Sea surface temperature records along the Portuguese Coast.
Pessanha Santos, Nuno; Moura, Ricardo; Santos da Silva, Catarina; Lamas, Luisa; Lobo, Victor; de Castro Neto, Miguel.
Affiliation
  • Pessanha Santos N; Portuguese Military Research Center (CINAMIL), Portuguese Military Academy (Academia Militar), Lisbon 1169-203, Portugal.
  • Moura R; Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST), Lisbon 1049-001, Portugal.
  • Santos da Silva C; Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada 2810-001, Portugal.
  • Lamas L; Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada 2810-001, Portugal.
  • Lobo V; Centro de Matemática e Aplicações (Nova Math), Universidade Nova de Lisboa, Caparica 2829-516, Portugal.
  • de Castro Neto M; Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada 2810-001, Portugal.
Data Brief ; 54: 110287, 2024 Jun.
Article in En | MEDLINE | ID: mdl-38962202
ABSTRACT
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 Hidrográfico) 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.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Data Brief Year: 2024 Document type: Article Affiliation country: Portugal Country of publication: Netherlands

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Data Brief Year: 2024 Document type: Article Affiliation country: Portugal Country of publication: Netherlands