Analysis of temperature time-series: Embedding dynamics into the MDS method

• Tipo: Artigo em Revista Científica Internacional
• Data: 2014

Autores

lopes, am (Autor) FEUP; tenreiro machado, ja (Autor) Outra

Revista

Título: Communications in Nonlinear Science and Numerical Simulation

Vol. 19

Páginas: 851-871

ISSN: 1007-5704

Editora: Elsevier

Indexação

ISI Web of Knowledge - 48 Citações

Scopus - 51 Citações

Outras Informações

ID Authenticus: P-008-FHF

DOI: 10.1016/j.cnsns.2013.08.031

Abstract (EN): Global warming and the associated climate changes are being the subject of intensive research due to their major impact on social, economic and health aspects of the human life. Surface temperature time-series characterise Earth as a slow dynamics spatiotemporal system, evidencing long memory behaviour, typical of fractional order systems. Such phenomena are difficult to model and analyse, demanding for alternative approaches. This paper studies the complex correlations between global temperature time-series using the Multidimensional scaling (MDS) approach. MDS provides a graphical representation of the pattern of climatic similarities between regions around the globe. The similarities are quantified through two mathematical indices that correlate the monthly average temperatures observed in meteorological stations, over a given period of time. Furthermore, time dynamics is analysed by performing the MDS analysis over slices sampling the time series. MDS generates maps describing the stations' locus in the perspective that, if they are perceived to be similar to each other, then they are placed on the map forming clusters. We show that MDS provides an intuitive and useful visual representation of the complex relationships that are present among temperature time-series, which are not perceived on traditional geographic maps. Moreover, MDS avoids sensitivity to the irregular distribution density of the meteorological stations.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 21