Analysis of the relationships between the El Niño phenomenon and climatological variables using complex networks

• Grant number: 18/04029-7
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: July 01, 2018
• End date: June 30, 2020
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Lilian Berton
• Grantee: Matheus Augusto de Castro Santos
• Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Federal de São Paulo (UNIFESP). Campus São José dos Campos. São José dos Campos , SP, Brazil

Abstract

The analysis of climatological data is important to understand the evolution and to develop more precise methods for the prediction of phenomena like El Niño. Different climatological data have been collected over the years in different places around the planet, such as temperature, precipitation, pressure, among others. The analysis of such data sets has traditionally been carried out through classical statistical approaches. However, the application of traditional linear signal processing methods and the interpretation of their results can be affected by the dynamic complexity of the processes involved. Recently, applications using complex network theory have been developed. Several phenomena were analyzed such as El Niño, teleconnections, monsoons and interesting results were obtained. In this work, we will analyze the relationships between climatological variables and the El Niño phenomenon through complex networks, aiming to obtain a better understanding of the dynamics of El Niño or its prediction. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DE CASTRO SANTOS, MATHEUS A.; VEGA-OLIVEROS, DIDIER A.; ZHAO, LIANG; BERTON, LILIAN. Classifying El Ni & x00F1;o-Southern Oscillation Combining Network Science and Machine Learning. IEEE ACCESS, v. 8, p. 55711-55723, 2020. (18/01722-3, 18/24260-5, 16/23698-1, 15/50122-0, 18/04029-7)