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- W4367591792 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." @default.
- W4367591792 created "2023-05-02" @default.
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- W4367591792 date "2023-01-01" @default.
- W4367591792 modified "2023-10-01" @default.
- W4367591792 title "PCAnEn - Hindcasting with Analogue Ensembles of Principal Components" @default.
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- W4367591792 doi "https://doi.org/10.1007/978-3-031-30592-4_13" @default.
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