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Weather prediction for locations without or scarce meteorological data available can be attempted by taking meteorological datasets from nearby stations. This hindcasting problem can be successfully solved using the Analog Ensemble (AnEn) method. This paper presents a parametric analysis of the AnEn method, and two variations (based on K-means and fuzzy C-means clustering methods), when used to search for analog ensembles in a historical dataset. The study allowed to identify the parameter combinations that yield the best prediction accuracy, improving 13% on the systematic error and 5% on the random error of the previous results obtained with the same dataset. In addition, important performance gains were achieved at the computational level.