## 3 Results and Discussion
### Quality Control: Global Solar Irradiance and Temperature  
A step undertaken prior to the quality control procedure is data adjustment stemming from the calibration constant. In this case, there was no calibration constant for the _Bioppo_ AWS affecting the quality of the time series; likewise, this AWS also had the smallest number of recordings among all AWS. The total amount of data (47,612) for the period studied corresponded to 34.50% of the total data that should be recorded. The AWS registered information during 57.59% of the measured period 2005-2017. Table 6 shows the results of the global solar irradiance validation procedure. The first step, which evaluated the database structure, presents 5843 recordings on average with the incorrect structure. The AWS with the most recordings that had an incorrect data structure was _Viento Libre_; this step confirmed that 9715 data did not have the database structure, corresponding to 12.55% of the total data. The AWS with fewer recordings with the incorrect data structure was _Bioppo_; this step confirmed that 1055 data did have the database structure, corresponding to 2.22% of the total. The fixed range validation discarded 36.71% of data on average. _Bioppo_ lost the most data, corresponding to 47.62% of the total, whereas _Universidad de Narino_ lost the fewest data, amounting to about 22.09% of the total. The flexible range test results reveal information losses of 39.11%. The AWS with the lowest number of losses was _Bioppo_, about 15.14%, while _Guapi_ had the highest number of losses, about 5422%. Although the last test was not mandatory, it is useful to point out that 44.06% of the recordings did not overcome the time consistency level. Considering only the mandatory steps, about 35.27% of the data overcame these validation steps. _Universidad de Narino_'s AWS had the most data approving the validation process, whereas _Guapi_'s AWS had the fewest data appro  
Figure 2: Scheme for empirical model implementation. The first row of Table 7 represents the recorded data number between 06:00 and 18:00. The subsequent rows show the day numbers with the recorded data amount indicated in the heading--for instance, _Biotopo_ has 13 days with only one record. The columns highlighted in gray represent that each day has between 10 and 11 recordings per day. It is worth noting that only 1.26% of days had complete information, with a maximum of 4.25% in _Cerro Piaramo_ and a minimum of 0.28% in _Biotopo_. Consequently, the model implementation only considered the days with at least six recordings during the daytime period to avoid underestimating the resource--approximately 95.81% of the recordings that overcame the mandatory validation levels accomplished this requirement. The results confirm the importance of improving the maintenance and calibration procedures of the measurement instruments in order to increase the amount of useful information, in turn increasing reliability. We considered the [PERSON] et al. 's classification and modified the item concerning partially cloudy days, as Table 8 shows. Table 8 shows that, in the analyzed AWS, on average, 64.70% of days were classified as partially high cloudiness. This corresponds to losses between 20% and 40% of extraterrestrial solar irradiance when reaching ground level. This result is important not only because clouds affect global solar irradiance but also because, as a variable, it is not easy to model. Consequently, the resource estimation in this tropical and mountainous environment is more complicated than in other environments.