Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/87991
Título: | Short-term forecast improvement of maximum temperature by state-space model approach: the study case of the TO CHAIR project |
Autor(es): | Pereira, F. Catarina Gonçalves, A. Manuela Costa, Marco |
Palavras-chave: | State-space models Temperature Kalman filter Time series Data assimilation |
Data: | 2023 |
Editora: | Springer |
Revista: | Stochastic Environmental Research and Risk Assessment (SERRA) |
Resumo(s): | 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. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/87991 |
DOI: | 10.1007/s00477-022-02290-3 |
ISSN: | 1436-3240 |
Versão da editora: | https://link.springer.com/article/10.1007/s00477-022-02290-3 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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s00477-022-02290-3.pdf | 957 kB | Adobe PDF | Ver/Abrir |