Core–shell clustering approach for detection and analysis of coastal upwelling

Susana Nascimento, Alexandre Martins, Paulo Relvas, Joaquim F. Luís, Boris Mirkin

Research output: Contribution to journalArticlepeer-review

Abstract

A comprehensive approach is presented to analyze season's coastal upwelling represented by weekly sea surface temperature (SST) image grids. The proposed model, core–shell clustering, assumes that the season's upwelling can be divided into shorter periods of stability, time ranges, consisting of constant core and variable shell parts. A one-by-one core–shell clustering algorithm is provided. The algorithm parameters are automatically derived from the least-squares clustering criterion. The approach applies to SST gridded data for sixteen successive years (2004–2019) of coastal upwelling in the western Iberian coast, the northernmost branch of the Canary Current Upwelling System. Our results show that at each season, there are 3 to 5 time intervals, the ranges, at which the upwelling presents stable core patterns of relatively cold water surrounded by somewhat larger shell areas of warmer waters. Based on other experimental computations performed by our team, we conclude that this pattern is not just a purely local phenomenon but has a more global meaning. Inter-annual time series analysis are consistent among themselves and with existing expert domain knowledge.
Original languageEnglish
Article number105421
Number of pages12
JournalComputers and Geosciences
Volume179
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Coastal upwelling
  • Core–shell cluster
  • Spatio-temporal clustering
  • SST images
  • Time series segmentation

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