## 3 Results and Discussion
### Algorithm Performance for LST Forecasts
#### 3.1.1 The First Case-Study: The Pedemontana Motorway  
This section illustrates the Pedemontana motorway case-study. The dataset covers a period of twenty-one months from 1 April, 2017 to 31 December, 2018. As mentioned before, to estimate the algorithm performance, surface temperature data (measured by the LUFFT IRS31 Pro-UMB sensor) are compared with simulated data forced with observed quantities (measured by LUFFS WS600-UMB sensor), and with the forecasted ones using the WRF meteorological model as input. The following Figures 3 and 4 show the scatterplots over the Ponte Olona station point between observed and simulated/forecasted data during clear and cloudy days. For the sake of brevity, similar pictures for the other two station points along the motorway (the SP2 and Gorla) are not shown here, but a complete overview has been summarized in Tables 5-8. The algorithm performance has been tested in two configuration sets: the first one uses hourly data (observed and forecasted) as input, the second introduces a moving average value of the three previous hours as input for the incoming solar radiation only, maintaining the other hourly observed/forecasted input unchanged. This choice prevalently depends on the heat absorption by road pavement which is not instantaneous, but it has a certain inertia on surface temperature which is clearly affected by the heat received in the previous hours. As mentioned in Section 3, the results are shown both considering the minimum and maximum value of LST. In particular, red dots are representative of comparison between simulated/forecasted LST and the maximum value of corrected observed LST, while blue dots indicate the results obtained by the comparison between simulated/forecasted LST and the minimum value of corrected observed LST. Whether the simulations are carried out using the observed or forecasted hourly solar radiation or the 3-h moving average as input, a general underestimation of skin temperatures is found during clear sky days. However, if we focus the attention on those values close and below 0 \({}^{\circ}\)C (significant for ice risk), an overestimation of the simulated and forecasted LST is seen. On cloudy days, instead, a general overestimation is present in the LST, simulated and forecasted, and both using the hourly solar radiation or the 3-h moving average as input. The following Tables 5 and 6 indicate the skill scores between observed and simulated data during clear and cloudy days, respectively, while Tables 7 and 8 display the comparison between observed and forecasted data by the WRF model over the analyzed station points. Simulations in the SP2 station point are not available due to lack of a weather station in that track of motorway, hence the algorithm cannot be fed with observed meteorological values, but with forecasted ones only. Data summarized in Tables 5-8 show a higher performance when the algorithm receives as input the 3-h moving average of solar radiation in the subset of clear sky days, while no sensible differences are found during cloudy days. In particular, the LST simulations compared with observed data put in evidence these score values: NSE index between 0.89 and 0.92, RMSE between 4.3 and 5 \({}^{\circ}\)C, Pearson Correlation coefficient between 0.95 and 0.97, and determination coefficient between 0.89 and 0.93 during clear sky days. On the other hand, the NSE index between 0.90 and 0.93, RMSE between 1.4 and 1.7 \({}^{\circ}\)C, Pearson Correlation coefficient equal to 0.95, and R\({}^{2}\) equal to 0.94 are obtained during cloudy days. Figure 3: Scatterplot between observed vs. simulated Land Surface Temperature (LST) over the “Ponte Olona” station point during clear sky days (**top**). Scatterplot between observed vs. simulated LST over the “Ponte Olona” station point during cloudy days (**bottom**). Figure 4: Scatterplot between observed vs. forecasted LST over the “Ponte Olona” station point during clear sky days (**top**).