## 4 Discussion  
A cluster classification of T\({}_{\rm b}\) as observed by the GOES-13 in SA and adjacent oceans was carried out using a single IR band. As mentioned in Section 2, the complexity of the cloud dynamics together with the underlying surface properties and the absorption in the atmosphere implies a challenge to remotely sensed cloud measurements. Cloud detection and classification based on satellite-retrieved IR observations is helpful since it provides valuable information of the presence of clouds with a good coverage both spatially and temporally, yet there are a number of constraints for the accuracy in the retrieval of cloud properties for all type of clouds, but particularly for Cis. These clouds are often semitransparent in the IR spectrum and data derived from a single IR channel have limitations for the retrieval of both their temperature and their emissivity [103]. The size of the ice crystals found in these particular clouds range from 10 to 1000 \(\upmu\)m [5]. Their effect in the calculation of T\({}_{\rm b}\) can be twofold. The size range of the ice particles is comparable to the wavelength of the emission of the Earth-atmosphere system [5], therefore altering T\({}_{\rm b}\) as calculated from the IR radiances and making their tops warmer and lower than they actually are. On the other hand, since ice particles can modify  
Figure 13: (**a**) Cluster 1’s pixels identified as part of cirrus (Ci; red) and cumulonimbus (Cb; blue) clusters on 1 August 2011 and (**b**) mean seasonal percentages of Ci and Cb in cluster 1.  
the upwelling radiation by scattering photons away from the satellite sensors' field of view [104] they may alter the actual T\({}_{\text{b}}\) values as well. The number of pixels represented by clusters 1 and 2 could also be greater than the ones informed as semitransparent Cis could be completely missed from the satellite retrievals [105] given that transmissive Cis may account for up to 42% [106]. Regarding this point, another aspect worth considering is the presence of multilayered cloud decks [51], such as the ones in Figure 7a,f where Cis were clearly visible. Yet another important shortcoming when using a single IR channel is that different types of clouds that have the same T\({}_{\text{b}}\) value cannot be properly resolved [107]. Despite this, there is at least one precedent in the literature that used a single IR channel to detect clouds [107]. The authors then used the T\({}_{\text{b}}\) gradient from the water vapor channel to achieve the classification of high-level clouds. In conjunction with actual physical processes, a possible explanation for the highest variability in cluster 1 relies upon the fact that there is a greater density of ice particles in the clouds that populate this cluster. The lower-order clusters could also be affected by the same effect even when the density of ice particles decreases. In spite of the aforementioned issues, which are inherent to the measurement process and can be mitigated but not eliminated, we managed to classify GOES IR imagery in four cloud regimes. In certain cases, if further partitions of the original clusters were intended the optimal number of new clusters as estimated using the VRC turned out to be 1. This suggested that no further actions could be taken from a k-means perspective and that alternative classification algorithms should be endeavored. Notwithstanding, the number of clusters being four is in agreement with the existing literature, in particular with the cloud regime classification carried out in another study region [76]. In principle, the four clusters can be associated to different types of clouds. For instance, cluster 4 seems to be associated to shallow clouds that span the lowest 2 km of the troposphere. On the other hand, cluster 1 tends to represent clouds whose tops populate the upper troposphere.