## Materials and Methods  
### Data  
The GOES-13 dataset used in this study was obtained from the NOAA's Comprehensive Large Array-Data Stewardship System (CLASS, available at [[http://www.class.noaa.gov](http://www.class.noaa.gov)]([http://www.class.noaa.gov](http://www.class.noaa.gov))). Spanning the period 1 December 2010 to 30 November 2016 (2192 days), daily NetCDF-formatted channel 4 IR images taken at 11:45 UTC were selected for each of the days of the study period. Each pixel in the selected imagery represents an area of 4 km by 4 km; the central IR wavelength corresponds to 10.70 \(\upmu\)m with a bandwidth of 1 \(\upmu\)m [32] (pp. 11-12). Availability was over 97%, totaling 2136 images. The basic IR quantities measured by the satellite sensors are raw digital counts, which are routinely converted to scaled radiances (SRs) packaged in 10-bit words in a format termed GVAR (for GOES Variable Format) [49]. Matlab's arcad function was used to read the GVAR values from the NetCDF files. A transformation of the GVAR values, i.e., calibration, is mandatory so as to express the SRs into a physical quantity. This calibration was carried out following NOAA's procedure to transform SR into T\({}_{\rm b}\)[49]. It represents the intensity of the radiation at a certain frequency, and in this paper is the working variable. The calibration process requires three intermediate steps. The first one calculates the scene radiance (R). Expressed in mW m\({}^{-2}\) sr\({}^{-1}\) cm, R is calculated from SR using the linear transformation  
\[\rm R=\frac{SR-b}{m} \tag{1}\]  
The relationship in (1) is extensive to all GOES IR channels; for the particular case of channel 4, the values of the scaling slope m and the scaling intercept b are 5.2285 and 15.6854, respectively [49]. The second calibration step transforms the R values into an effective temperature T\({}_{\rm eff}\) using the inverse of the Planck function as follows  
\[\rm T_{eff}=\frac{c_{2}v}{ln\left[1+\frac{c_{1}v^{3}}{R}\right]} \tag{2}\]  
In (2) v stands for the central wavenumber of the IR channel 4 \(\left(v=937.23449\;\rm cm^{-1}\right)\) and c\({}_{1}\) and c\({}_{2}\) are equal to 1.191066 \(10^{-5}\) mW m\({}^{-2}\) sr\({}^{-1}\) cm\({}^{4}\) and 1.438833 K cm, respectively [49]. The final transformation is the one from T\({}_{\rm eff}\) to T\({}_{\rm b}\), which is made through a quadratic function  
\[\rm T_{b}=\alpha+\beta T_{eff}+\gamma T_{eff}^{2} \tag{3}\]For the IR channel 4 \(\alpha\), \(\beta\) and \(\gamma\) in (3) are equal to \(-0.52227011\), \(1.0023802\) and \(-2.0798856\)\(10^{-6}\), respectively [49]. It should be noted that the retrieval of some cloud properties from IR measurements at selected channels is sensitive not only to these desired properties but also to a number of atmospheric and surface parameters that may vary both spatially and temporally [50]. As a consequence, the T\({}_{\rm b}\) values obtained through the aforementioned set of equations are subject to certain limitations due to the inherent sensitivity of the observed IR radiances. Measurements in the VIS and in the IR spectra can be overwhelmed by thicker/lower water clouds and so thin cirrus clouds have faint signals that are difficult to detect in both channels [51]. Following this, cirrus cloud top temperatures can be biased towards higher values and in consequence their top altitudes can be underestimated [52]. Moreover, subtle differences can emerge from the detection related to cloudy or cloud-free pixels depending on the band [53]. Further discussions regarding the use of a single IR channel are given below. Each of the original 2136 individual images was transformed into T\({}_{\rm b}\) images following the calibration process. Each resulting image was checked for consistency, and some of them were discarded for having either one or both of the following issues: (a) incomplete raw data information related to noise (49 images, 2.29%) and (b) images that have an anomalous scale of T\({}_{\rm b}\) values (51 images, 2.39%).