Análise de correlação cruzada da variável climática temperatura do ar entre estações meteorológicas no estado da Bahia, BR

• Ano de defesa: 2021
• Autor(a) principal: Costa, Moisés Domingos Namila da
• Orientador(a): Zebende, Gilney Figueira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Estadual de Feira de Santana
• Programa de Pós-Graduação: Mestrado em Modelagem em Ciência da Terra e do Ambiente
• Departamento: DEPARTAMENTO DE CIÊNCIAS EXATAS
• País: Brasil
• Palavras-chave em Português: Aquecimento global; Coeficiente de correlação cruzada DCCA; Estado da Bahia; Ilha de calor; Temperatura do ar; Variabilidade climática
• Palavras-chave em Inglês: Air Temperature; Climate Variability; DCCA Cross Correlation Coefficient; Global Warming; Heat Island; State of Bahia
• Área do conhecimento CNPq: METEOROLOGIA::CLIMATOLOGIA; GEOGRAFIA FISICA::CLIMATOLOGIA GEOGRAFICA; SANEAMENTO AMBIENTAL::QUALIDADE DO AR, DAS AGUAS E DO SOLO
• Link de acesso: http://tede2.uefs.br:8080/handle/tede/1423

Scientific studies suggest that climate variability on planet Earth is a natural phenomenon caused by the variation of energy reaching the planet caused by solar cycles, orbital variation of the planet itself, meteorite impacts, among other natural events since thousands of years. However, the radioactive forces caused by factors internal to the planet Earth itself, such as the generation of polluting gases, has immediate effects on the Earth’s ecosystem causing the Heat Islands and Global Warming. Being that the knowledge about the natural and anthropic phenomena around planet Earth is of great interest to the scientific community, mainly in the development of effective mechanisms for the mitigation of climate problems, this research, in particular, studied the level of interaction of the climatic environmental variable air temperature between twelve (12) meteorological stations in the State of Bahia (northeast of Brazil) by means of the DCCA cross correlation coefficient (ρDCCA), in which, based on maps of cross correlations between weather stations obtained as results, it was demonstrated that the Air Temperature variable throughout the studied region has positive interaction where the cross correlation coefficient is strong for different time scales. However, realising the need to develop practical proposals for short, medium and long term so that the impact of climate change on the region is minimised.