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Using analog ensembles with alternative metrics for hindcasting with multistations

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Detalhes bibliográficos
Resumo:This study concerns making weather predictions for a location where no data is available, using meteorological datasets from nearby stations. The hindcast with multiple stations is performed with different variants of the Analog Ensemble (AnEn) method. In addition to the traditional Monache metric used to identify analogs in datasets from one or two stations, several new metrics are explored, namely cosine similarity, normalization, and k-means clustering. These were analyzed and benchmarked to find the ones that bring improvements. The best results were obtained with the k-means metric, yielding between 3% and 30% of lower quadratic error when compared against the Monache metric. Also, by making the predictors to include two stations, the performance of the hindcast improved, decreasing the error up to 16%, depending on the correlation between the predictor stations.
Autores principais:Balsa, Carlos
Outros Autores:Rodrigues, Carlos Veiga; Lopes, Isabel Maria; Rufino, José
Assunto:Analog ensembles Metrics Hindcasting Time series Meteorological data
Ano:2020
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB

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