A carregar...
Publicação
Hindcasting with multistations using analog ensembles
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 610.32 KB | Adobe PDF |
Orientador(es)
Resumo(s)
A hindcast with multiple stations was performed with vari- ous Analog Ensembles (AnEn) algorithms. The different strategies were analyzed and benchmarked in order to improve the prediction. The un-
derlying problem consists in making weather predictions for a location where no data is available, using meteorological time series from nearby stations. Various methods are explored, from the basic one, originally de-scribed by Monache and co-workers, to methods using cosine similarity, normalization, and K-means clustering. Best results were obtained with the K-means metric, wielding between 3% and 30% of lower quadratic error when compared against the Monache metric. Increasing the predictors to two stations improved the performance of the hindcast, leading up to 16% of lower error, depending on the correlation between the predictor stations.
Descrição
Palavras-chave
Analog ensembles Hindcasting Time series Meteoro- logical data
Contexto Educativo
Citação
Chesneau, Alexandre; Balsa, Carlos; Rodrigues, C. Veiga; Lopes, Isabel Maria (2019) . Hindcasting with multistations using analog ensembles. In CEUR Workshop Proceedings. Madrid. 2486, p. 215–229. ISSN 1613-0073
Editora
CEUR-WS