Validação dos dados de temperatura do ar, umidade relativa do ar e déficit de pressão de vapor estimados por reanálise no estado de Mato Grosso

• Ano de defesa: 2021
• Autor(a) principal: Lotufo Neto, Névio
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Mato Grosso; Brasil; Instituto de Física (IF); UFMT CUC - Cuiabá; Programa de Pós-Graduação em Física Ambiental
• Palavras-chave em Português: Modelo de performance; América do Sul; Análises estatísticas; CNPQ::CIENCIAS EXATAS E DA TERRA::FISICA; Model performance; South America; Statistical analysis
• Link de acesso: http://ri.ufmt.br/handle/1/4506

The variables influence climate change in different sectors of society, however, there are limitations regarding access to data given in Mato Grosso. Thus, the reanalysis data appear as an alternative, however, it is necessary to assess what are the differences with the tools. The objective of the study was to evaluate and validate the air temperature, relative humidity and vapor pressure deficit data estimated by the products ERA-5, GLDAS, JRA-55, MERRA-2 and NCEP / NCAR in relation to the measured data on automatic surface weather stations in the state of Mato Grosso. The nomenclature of air temperature, relative humidity and vapor pressure deficit were evaluated on daily, monthly and annual scales. The indicators were evaluated using the Spearman correlation index, Lin coefficient, Willmott index, Root mean square error, Mean absolute error and Bland Altman. The ERA-5 product has the best performance on a daily monthly and annual scale when compared to the other reanalysis products. On the other hand, the NCEP/NCAR assigned, monthly and yearly sources dissipate the greatest differences with the data measured in the surface automatic meteorological stations. Finally, the five resonance reanalysis products for air temperature, relative humidity and vapor pressure deficit with high correlation values, Lin and concordance, as well as low error values on a daily, monthly and annual scale.