**3.1.5 Clusters formation for evaluation of the models**

For the evaluation of the applicability of SOM and two of its temporal extensions: Temporal Kohonen Map (TKM) and Recurrent Self-Organizing Map (RSOM) for the weather patterns recognition related to atmospheric instability factors, clusters were built using the K-means technique, which generated three clusters, containing 697, 484 and 593 examples, for the cluster 1, 2 and 3, respectively. Figure 6 shows the characteristics of the three clusters according to the four variables analyzed.

In describing some of the differential characteristics between the clusters, it is noticed that in cluster 1 CAPE and PWAT have their concentrations at low values, while in cluster 2 the concentration of the CAPE is in low values, however for PWAT the values are high. In cluster 3 both CAPE and PWAT have their concentrations at high values. Another distinctive feature among clusters is the gradual rise of the LFCT median value for the clusters 1, 2 and 3, respectively. It is also noticed that the cluster 1 has a SWET median value lower when compared with clusters 2 and 3.

Fig. 6. Characteristics of the clusters
