## 5 Conclusions  
An unsupervised clustering method based on the combination of the k-means and k-means++ algorithms was implemented on images of standardized \(\mathrm{T_{b}}\) anomalies derived from GOES-13 IR data for the period 1 December 2010 to 30 November 2016. The aim was to obtain a decomposition of each individual \(\mathrm{T_{b}}\) image into K clusters that capture the characteristics of different CC. The methodology that was carried out allowed the estimation of an optimal number of four clusters \(\mathrm{(K=4)}\), each one associated to different cloud types. Generally speaking, the lower the order of the cluster the higher the clouds it represents. Cluster 4 represented a cloud regime that spanned the lowest 2 km of the troposphere, and was the most dominant one representing, on average, 32% of the pixels. Likely, this cluster also captures cloud-like patterns that take place at surface levels or near it, i.e., fog [108], but this was not checked. Nevertheless, given that fogs can form up to 1000 m [108], cluster 4 may well represent this particular cloud type. Regarding clusters 2 and 3, there was an entanglement between them in the sense that both had their CTHs spanning the middle troposphere. Considering clusters 2 and 3 altogether they represented, on average, 62% of the pixels. The types of clouds that may populate these two clusters include altostratus, nimobostratus and stratocumulus. Finally, cluster 1 was the less dominant one (6% of the pixels on average), it represents clouds with the highest tops, and it showed the highest variabilities as well. Even though cluster 1 was populated with clouds having different microphysical properties, most notably Cis and Cbs, the cluster methodology alone could not properly resolve these types of clouds. Concerning this, the inclusion of additional parameters into the analysis (e.g., optical thicknesses) will help in refining the classification method. In this paper, a fractal-related analysis permitted distinguishing between Cis and Cbs. The former cloud types were the most abundant across the year, accounting for 83% of cluster 1's pixels on average. Cbs represented 6% of cluster 1's pixels on average. The results were satisfactory but, since this technique relies upon the pixel size, the current analysis will benefit from the incorporation of higher resolution images. The following are available online at [[http://www.mdpi.com/2072-4292/12/18/2991/s1](http://www.mdpi.com/2072-4292/12/18/2991/s1)]([http://www.mdpi.com/2072-4292/12/18/2991/s1](http://www.mdpi.com/2072-4292/12/18/2991/s1)), Figure S1: Brightness temperature monthly mean values and standard deviations. These values were used to standardize the study variable pixel-by-pixel, Figure S2: Geopotential height at ten different levels from 850 hPa to 100 hPa on 1 August 2011. The wind field and the kinetic energy per unit mass at all of these levels are also shown. This figure was prepared using daily data from the NCEP/NCAR reanalysis [64]. Conceptualization, A.E.Y, S.G.L., A.C. and P.O.C. ; methodology, A.C. and J.P.M. ; software, A.E.Y. and A.C.; validation, A.E.Y, S.G.L., A.C. and P.O.C. ; formal analysis, A.E.Y., S.G.L. and A.C.; investigation, A.C.; resources, A.E.Y. and A.C.; data curation, A.C.; writing-original draft preparation, A.E.Y. and A.C.; writing--review and editing, A.E.Y. and S.G.L. ; visualization, A.E.Y., S.G.L. and A.C.; supervision, A.E.Y. and S.G.L. ; project administration, A.E.Y. ; funding acquisition, A.E.Y., S.G.L. and P.O.C. All authors have read and agreed to the published version of the manuscript. This research was partly funded by Argentina's Ministerio de Defensa, grant number PIDDEF 26/14, and Universidad Tecnologica Nacional SCyT and Facultad Regional grant numbers PID MSUTNA0004570 and PID MSUTNA0004787. We gratefully acknowledge two anonymous reviewers for their comments and suggestions. The authors declare no conflict of interest.