  In order to help physicists to expand their knowledge of the climate in the
Lesser Antilles, we aim to identify the spatio-temporal configurations using
clustering analysis on wind speed and cumulative rainfall datasets. But we show
that using the L2 norm in conventional clustering methods as K-Means (KMS) and
Hierarchical Agglomerative Clustering (HAC) can induce undesirable effects. So,
we propose to replace Euclidean distance (L2) by a dissimilarity measure named
Expert Deviation (ED). Based on the symmetrized Kullback-Leibler divergence,
the ED integrates the properties of the observed physical parameters and
climate knowledge. This measure helps comparing histograms of four patches,
corresponding to geographical zones, that are influenced by atmospheric
structures. The combined evaluation of the internal homogeneity and the
separation of the clusters obtained using ED and L2 was performed. The results,
which are compared using the silhouette index, show five clusters with high
indexes. For the two available datasets one can see that, unlike KMS-L2, KMS-ED
discriminates the daily situations favorably, giving more physical meaning to
the clusters discovered by the algorithm. The effect of patches is observed in
the spatial analysis of representative elements for KMS-ED. The ED is able to
produce different configurations which makes the usual atmospheric structures
clearly identifiable. Atmospheric physicists can interpret the locations of the
impact of each cluster on a specific zone according to atmospheric structures.
KMS-L2 does not lead to such an interpretability, because the situations
represented are spatially quite smooth. This climatological study illustrates
the advantage of using ED as a new approach.

Machine Learning;Atmospheric and Oceanic Physics;Machine Learning