Hindcasting with multistations using analog ensembles

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.