Short-term forecast improvement of maximum temperature by state-space model approach: the study case of the TO CHAIR project

• Autor(a) principal: Pereira, F. Catarina
• Data de Publicação: 2023
• Outros Autores: Gonçalves, A. Manuela, Costa, Marco
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/34405
• topic: State-space models; Temperature; Kalman filter; Time series; Data assimilation

In the context of “TO CHAIR” project, this work aims to improve the accuracy of short-term forecasts of maximum air temperature obtained from the https://weatherstack.com/website. The proposed methodology is based on a state-space representation that incorporates the latent process, the state, which is estimated recursively using the Kalman filter. The proposed model linearly and stochastically relates the forecasts from the website (as a covariate) to the observations of the maximum temperature recorded at the study site. The specification of the state-space model is performed using the maximum likelihood method under the assumption of normality of errors, where empirical confidence intervals are presented. In addition, this work also presents a treatment of outliers based on the ratios between the observed maximum temperature and the website forecasts.