Abstract
The focus of this study is the reconstruction of missing meteorological data at a station based on data from neighboring stations. To that end, the Principal Components Analysis (PCA) method was applied to the Analogue Ensemble (AnEn) method to reduce the data dimensionality. The proposed technique is greatly influenced by the choice of stations according to proximity and correlation to the predicted one. PCA associated with AnEn decreased the errors in the prediction of some meteorological variables by 30% and, at the same time, decreased the prediction time by 48%. It was also verified that our implementation of this methodology in MATLAB is around two times faster than in R.
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Acknowledgements
The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).
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Balsa, C., Breve, M.M., André, B., Rodrigues, C.V., Rufino, J. (2023). PCAnEn - Hindcasting with Analogue Ensembles of Principal Components. In: Garcia, M.V., Gordón-Gallegos, C. (eds) CSEI: International Conference on Computer Science, Electronics and Industrial Engineering (CSEI). CSEI 2022. Lecture Notes in Networks and Systems, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-031-30592-4_13
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