## 4 Experiments
### Results  
Figure 5a,b demonstrate the evaluation metrics (hourly error, daily maximum and minimum errors, and critical error rate as defined in Section 4.1) calculated on 6-month IMPROVER and MSB hourly data at a single site: Hobart (27 October 2022 to 27 April 2023). This site is selected to be presented because its results best represent the general data pattern to be discussed. For all the figures that follow, 'TMP' refers to temperature and 'DEW' refers to dew point. The results of other sites are summarized in Tables A1-A6. For hourly and daily extreme data, both RMSE and MAE are calculated and plotted with green and blue lines, respectively. Metrics are calculated on each forecast lead day separately. The results show that for this site, the employed ML model can reduce RMSE, MAE and critical error rate at all lead days. For RMSE of hourly data, the percentage of improvement on each lead day is similar, which shows that the ML model when trained on data with only lead day 0 can be applied to the prediction of all lead days. In most of the evaluated metrics and lead days for daily maximum and minimum predictions, the figures show that MSB has a better accuracy than IMPROVER. But exceptions also exist, such as the daily maximum dew point temperature on lead day 6 and 7. In addition, the results suggest that the improvements in RMSE and MAE have a similar trend, and therefore all subsequent discussions focus primarily on RMSE in this study. Figure 5: (**a**) Comparison of Multi-SiteBoost results with IMPROVER grid values on various metrics. Site: Hobart (Ellerslie Road). Temperature. (**b**) Comparison of Multi-SiteBoost results with IMPROVER grid values on various metrics. Site: Hobart (Ellerslie Road). Dew point temperature. Tables 2 and 3 summarize the RMSE of hourly and daily maximum and minimum and percentage of critical error calculated for each site from all lead days. The change of each metric from IMPROVER is also included in Tables 2 and 3, and a negative change means a lower RMSE or critical error percentage of MSB results compared with IMPROVER. The results show that MSB reduces hourly RMSE on all sites from up to 1.05 \({}^{\circ}\)C, but the effect varies widely with site location. In general, the sites where forecast error is already low in IMPROVER (e.g., Avalon Airport) benefit less from MSB than those with originally higher forecast error (e.g., Geraldton Airport). In addition, as shown in Figure 5a,b, the reduction in forecast error largely varies with forecast lead day, so the actual reduction on each lead day may be significantly different from the averaged reduction. Tables 4 and 5 summarize the improvement of prediction accuracy on all sites and all lead days, in terms of percentage of reduction of RMSE from MSB as compared to IMPROVER predictions. In this way, a positive percentage in the tables indicate a lower RMSE on MSB data and an improvement compared with IMPROVER. For the sake of brevity, other metrics as discussed in Section 4.1 are listed in the Appendix A. As shown in the tables, most of the calculated metrics are positive, which indicate a general forecast improvement on tested sites due to the applied ML models. However, the tables also show variation of ML skills over the selected sites. Even on hourly RMSE on lead day 0, which is the training objective, the percentage of RMSE reduction can range from 0.54% (Avalon airport, dew point temperature) to 37.13% (Geraldton airport, dew point temperature).