# Combination of Models to Generate the First PAR Maps for Spain
## 3 Results  
After applying the rejection criteria to all the data, the number of remaining records for each station is shown in Table 2.  
Table 2 reveals that in Salamanca-CIALE, all the original recordings passed filtering. By contrast, the highest number of rejected recordings was in Lugo-USC. The number of original recordings was 785 in all stations, except in Asturias-SERIDA, where one day of data was lost. The filtered data set for each station was randomly divided into two subsets, each containing the same number of recordings. The first subset was used to develop the PAR model, whereas the second subset was used to validate the model. The first subset was also used to calculate the estimates of the CM-SAF and MODIS models of [17] for the location of each station. The next step was to develop the PAR model itself. Therefore, the coefficients a, b, and c were calculated for each station by multilinear fitting according to Equation (2), where the PAR estimates of the CM-SAF and MODIS models are fitted to real PAR measurements from the seven stations. Table 3 shows the fitting coefficients for each station. The fitting coefficients vary from station to station. Therefore, to develop a model that covers the entire field of study, these coefficients were linearly extrapolated to the surface of mainland Spain on a grid with a resolution of 5 Km from 35.3\({}^{\circ}\)N to 44.0\({}^{\circ}\)N in latitude and from 9.5\({}^{\circ}\)W to 3.5\({}^{\circ}\)E in longitude. The coefficients for each point on the grid are illustrated in Figures 2-4, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Station** & **Number of Original** & **Number of Recordings** \\  & **Recordings** & **after Filtering** \\ \hline Alava-NEIKER & 785 & 784 \\ Albacete-ITAP & 785 & 783 \\ Asturias-SERIDA & 784 & 779 \\ Cordoba-IFAPA & 785 & 784 \\ Lugo-USC & 785 & 769 \\ Salamanca-CIALE & 785 & 785 \\ Zaragoza-Aula Dei & 785 & 782 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of recordings of each station before and after applying the rejection criteria. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Station** & **a** & **b** & **c (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\))** \\ \hline Alava-NEIKER & 0.26 & 0.79 & \(-\)9.58 \\ Albacete-ITAP & \(-\)0.25 & 1.36 & \(-\)18.46 \\ Asturias-SERIDA & \(-\)0.41 & 1.54 & \(-\)20.41 \\ Cordoba-IFAPA & \(-\)0.09 & 1.18 & \(-\)13.89 \\ Lugo-USC & 1.24 & \(-\)0.27 & \(-\)0.71 \\ Salamanca-CIALE & 0.34 & 0.66 & \(-\)8.02 \\ Zaragoza-Aula Dei & 0.29 & 0.79 & \(-\)9.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fitting coefficients a, b, and c for each station. Figure 3: Coefficients b of the PAR model for each point in the study territory. Figure 2: Coefficients a of the PAR model for each point in the study territory. These coefficients needed to be validated with another data set before developing the PAR model for the entire mainland of mainland Spain. Therefore, a validation test was carried out using the validation data subset of the stations. For this reason, the PAR estimates obtained using the validation data subset were compared to the real measurements from the stations. That is, the GHI data from the validation subset were utilized to calculate the assessments of the CM-SAF and MODIS models; then these assessments along with the corresponding coefficients a, b and c were used to calculate the estimates of the new PAR model for each station. Finally, the estimates of the new PAR model were compared with the PAR data measured at each station, as illustrated by the following scatterplots. Table 4 summarizes all the validation metrics at each station. Figure 5 reveals the linear fit between the estimates of the PAR model and the measured PAR data, where the red line represents the linear fit. Indeed, the slope is close to the unit in all the stations, and the intercepts are close to zero, being 3.70 the highest intercept (Salamanca-CIALE).