Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/34405
Title: | Short-term forecast improvement of maximum temperature by state-space model approach: the study case of the TO CHAIR project |
Author: | Pereira, F. Catarina Gonçalves, A. Manuela Costa, Marco |
Keywords: | State-space models Temperature Kalman filter Time series Data assimilation |
Issue Date: | Jan-2023 |
Publisher: | Springer |
Abstract: | 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. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/34405 |
DOI: | 10.1007/s00477-022-02290-3 |
Publisher Version: | https://link.springer.com/article/10.1007/s00477-022-02290-3 |
Appears in Collections: | CIDMA - Artigos ESTGA - Artigos PSG - Artigos |
Files in This Item:
File | Description | Size | Format | |
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Pereira_et_al_2023 SERRA.pdf | 1.74 MB | Adobe PDF | View/Open |
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