## 1 Introduction
The deficient in situ cloud measurements in South America (SA), the lack of records in its surrounding oceans, and the fact that the GOES-E dataset covers both land and oceans spatially in a continuous fashion since the mid-1970s [37; 38; 30] point to the use of GOES retrievals to study CC evolution in the region. As computer technologies evolved data classification experienced a gradual transition from the traditional, time-consuming statistical modeling and pixel-by-pixel analyses to distinct mathematical methodologies that ingest and process satellite data in a timely manner. Concerning GOES imagery, the main setback of traditional point-by-point analyses, which establish if a pixel is cloud-covered or cloud-free by comparing its value with a prescribed threshold, is that it is far too inefficient in handling a large set of GOES images due to the huge amount of information a single image has (e.g., the number of pixels in each image of this study is above 5 million). There is a wide range of cloud classification algorithms applied to GOES imagery. Support vector machine classifiers [39], neural networks [40] and supervised algorithms ([41] and references therein) are just a few examples of them. Even though these methods can be adapted (possibly with modifications) to any region covered by GOES data, most of them were applied to CC analyses in areas located in the Northern Hemisphere, partly because of the number of validation opportunities the research community is presented with when compared with the Southern Hemisphere (SH). With a limited number of exceptions (e.g., [42]) no algorithms have hitherto been applied to, or particularly developed for the analysis of cloud systems in SA and adjacent oceans using GOES retrievals. The main reasons for undertaking an automated classification method for meteorological applications is thus linked to the huge volume of data that is available every day from the satellite and to the lack of studies in the region. In certain cases the analysis of several consecutive images is crucial to forecasting and nowcasting (e.g., severe weather events). As pointed out in [43] there is yet another motivation for this paper and it relies upon the identification of synoptic and subsynoptic cloud systems that follow the analysis of individual images. In order to provide further insights on CC classification in general, and for the SH in particular, this study is based upon an unsupervised k-means clustering algorithm with a k-means++ initializing algorithm that is individually applied on daily IR GOES-13 brightness temperature (T\({}_{\rm b}\)) data over Southern SA and the contiguous oceans for the 2010-2016 time span. The k-means algorithm has been extensively used to classify cloud top pressure/optical thickness joint histograms in order to obtain cloud regimes [44; 45; 46; 47]. This k-means classification was extended with the incorporation of IR information [48]. All these studies were applied to gridded data. The current paper paves the way for the analysis of sequential images in the study region on a pixel-by-pixel basis, as well as for the characterization of synoptic and subsynoptic processes from a CC perspective.