## 5 Discussion and Conclusions  
This study aimed to explore the capability of neural networks that rely on data from radiosonde measurement to predict daily temperature minimums and maximums. More specifically, the aim was to understand how the NN-based models utilize different types of input data and how the network design influences its behavior. The data utilization and behavior of the network depends on whether the NNs are used to do short-term or long-term forecasts--this is why the analysis was performed for a wide range of forecast lead times. The analysis using very simple NNs, consisting of only a few neurons, highlighted how the nonlinear behavior of the NN increases with the number of neurons. It also showed how different training realizations of the same network could result in different behaviors of the NN. The behavior in the part of the predictor phase space with the highest density of training cases was usually quite similar for all training realizations. In contrast, the behavior elsewhere was more variable and more frequently exhibited unusual nonlinearities. This has consequences for how the network behaves in part of the predictor phase space that is not sufficiently sampled with the training data--for example, in situations that could be considered outliers (such situations can occur but not very frequently). For such events, the NN behavior can be quite different for each training realization. The behavior can also be unusual, indicating that the results for such situations need to be used with caution. Analysis of selected NN hyperparameters showed that using larger batch sizes reduced training time without causing a significant increase in error; however, this was true only up to a point (in our case up to batch size 256), after which the error did start to increase. We also tested how the number of epochs influences the forecast error and training speed, with 100 epochs being a good compromise choice. Figure 7: Same as Figure 6 but for forecasts of Setup Z instead of Setup X. The values for input parameters \(T_{\text{max}}(t-1)\) and \(T_{\text{clim}}(t+i)\) are indicated by short vertical lines in the lower part of the graphs. We analyzed various NN setups that were used for the short- and long-term forecasts of temperature extremes. Some setups were more complex and relied on the profile measurements on 118 altitude levels or used additional predictors such as the previous-day measurements and climatological values of extremes. Other setups were much simpler, did not rely on the profiles, and used only the previous day extreme value or climatological extreme value as a predictor. The behavior of the setups was also analyzed via two XAI methods, which help determine which input parameters have a more significant influence on the forecasted value. For the setup based solely on the profile measurements, the short- to medium-range forecast (0-10 days) mainly relies on the profile data from the lowest layer--mainly on the temperature in the lowest 1 km. For the long-range forecasts (e.g., 100 days), the NN relies on the data from the whole troposphere. As could be expected, the error increases with forecast lead time, but at the same time, it exhibits seasonal periodic behavior for long lead times. The NN forecast beats the persistence forecasts but becomes worse than the climatological forecast already on day two or three (this depends on whether maximum or minimum temperatures are forecasted). It is also important to note the spread of error values of the NN ensemble (which consists of 50 members). The spread of the setups that use the profile data is significantly larger than the spread of the setups that rely only on non-profile data. For the former, the maximum error value in the ensemble was typically about 25% larger than the minimum error value. This again highlights the importance of performing multiple realizations of NN training.