## 1 Introduction  
In the recent years the scientific community has focused on cloud cover (CC) because of its role in the Earth's radiative balance (ERB). In 2013, the Intergovernmental Panel on Climate Change (IPCC) included for the first time a dedicated chapter to this topic alongside aerosols [1]. Clouds critically influence the radiation budget since they reflect a substantial portion of the incoming solar radiation back to space, and partly trap the outgoing terrestrial radiation and re-emit it to space at the temperature of their tops [2]. Even though this influence can be theoretically determined by CC's density, height and thickness (among other parameters) the result can be disappointing since clouds scatter, absorb and reflect radiation in numerous complex ways. Furthermore, not only the presence of clouds but also their formation/evolution contributes to the ERB since the release of latent heat owing to condensation during the formation process heats the atmosphere [3]. Consequently, a number of research efforts have been devoted to address the connections between CC and climate and climate change [2; 4; 5; 6; 7; 8; 9; 10]. Time series of CC data from conventional information collected by meteorological observers all around the world are prone to subjective errors [11] and cannot be automatically performed. Subjective observations supplemented by data from ground equipment measurements using various technologies that can be implemented in an automated fashion allows for more detailed studies, but they are usually limited spatially, temporally or both [12; 13]. Identifying the presence and behavior of distinct clouds is a manifold task that requires the understanding of the different meteorological systems and their associated phenomena, and also involves the development of products and techniques that can help assessing their spatial and temporal behavior using remotely sensed data [14; 15; 16; 17; 18]. Object detection is the main application for satellite imagery (SI) ([19] and references therein). In particular, in the last four decades it became one of the most powerful and useful sources for obtaining meteorological information relevant to forecasting and to analyze different weather events [20; 21; 22; 23; 24; 25]. Clouds generally have both a higher reflectance and a lower temperature when compared with the Earth's surface and this distinction facilitates their detection by satellite sensors. SI has been used to establish cloud microphysical [26] and macrophysical properties [27] and cloud-base heights and temperatures [28; 29], among many other applications. The Geostationary Operational Environmental Satellite (GOES) is a geostationary satellite network consisting of a two-spacecraft constellation operated by the National Oceanic Atmospheric Administration (NOAA) [30]. At January 2005, GOES-12 and GOES-10 operated as GOES-E and GOES-W, respectively. They were positioned at 75 \({}^{\circ}\)W (over North-Western South America) and 105 \({}^{\circ}\)W (over the central Pacific), respectively, and observed 60% of the Earth [30]. GOES-13 replaced GOES-12 as GOES-E in April 2010 [31]. The Imager instrument on board the different GOES spacecraft from GOES-12 to GOES-15 sensed the atmosphere in five spectral bands, four of them with their central wavelengths within the infrared (IR) spectrum, and the remaining one in the visible (VIS) spectrum [32] (pp. 11-12). Considering specific CC studies, GOES-2 IR data was used to study its distribution across the Americas ([33] and references therein), GOES-8 and -9 IR data was processed in order to track the motion and evolution of stratiform clouds [34], and GOES-11 imager and sounder information was taken to describe the outbreak of a severe thunderstorm over the United States Great Plains [35]. CC was also analyzed for masking cloud imagery so as to detect fires [36].