Abstract
The aim of this study is the reconstruction of meteorological data that are missing in a given station by means of the data from neighbouring stations. To achieve this, the Analogue Ensemble (AnEn) method was applied to the Principal Components (PCs) of the time series dataset, computed via Principal Component Analysis. This combination allows exploring the possibility of reducing the number of meteorological variables used in the reconstruction. The proposed technique is greatly influenced by the choice of the number of PCs used in the data reconstruction. The number of favorable PC varies according to the predicted variable and weather station. This choice is directly linked to the variables correlation. The application of AnEn using PCs leads to improvements of 8% to 21% in the RMSE of wind speed.
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This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.
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Balsa, C., Breve, M.M., Rodrigues, C.V., Costa, L.S., Rufino, J. (2022). An Exploratory Study on Hindcasting with Analogue Ensembles of Principal Components. In: Guarda, T., Portela, F., Augusto, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2022. Communications in Computer and Information Science, vol 1675. Springer, Cham. https://doi.org/10.1007/978-3-031-20319-0_36
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