Brazilian daily rainfall gridded data from a quality controlled dataset

Detalhes bibliográficos
Ano de defesa: 2024
Autor(a) principal: Vidal-Barbosa, José Lindemberg
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal da Paraíba
Brasil
Engenharia Civil e Ambiental
Programa de Pós-Graduação em Engenharia Civil e Ambiental
UFPB
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://repositorio.ufpb.br/jspui/handle/123456789/33310
Resumo: Accurate meteorological data are crucial for assessing the impacts of spatiotemporal variability in climate change on hydrology, agroecosystems, etc. This work addresses the significance of high-quality precipitation records in the current climate scenario, emphasizing their importance not only in the scientific and technical domains, but also for public institutions managing pluviometric networks. The primary objective of this study was to develop high-resolution grids (0.25° × 0.25°) of daily precipitation, utilizing data from more than 11,000 stations spanning 1961 to 2020. The dataset was sourced from the Brazilian National Hydrometeorological Network (RHN) was subjected to an automatic quality control procedure. The automatic quality control procedure involves two consecutive steps: Basic Quality Control and Absolute Quality Control. Monthly quality assessments categorized station quality as Very Low, Low, Acceptable, Good, or Excellent, and later as High Quality (HQ) and Low Quality (LQ). The methodology was evaluated using a visually inspected dataset from the Brazilian National Center for Monitoring and Early Warnings of Natural Disasters (CEMADEN). The results showed an accuracy of 98.4% in correctly identifying high-quality stations. Out of over 103 million daily records, approximately 1.6% were flagged as very low or low quality and subsequently discarded. The inverse distance weighting (IDW) interpolation method was employed for the remaining 101 million daily records to produce high-resolution gridded data of daily precipitation from 1961 to 2020. The cross-validation statistics of the interpolated data performed better than those of previous studies on the same dataset, and the gridded data estimations represented both reference climate normals well.