## 1 Introduction  
The meteorological community is increasingly using modern machine learning (ML) techniques to improve specific aspects of weather prediction. It is conceivable that someday the data-driven approach will beat the numerical weather prediction (NWP) using the laws of physics, although several fundamental breakthroughs are needed before this goal comes into reach [1; 2; 3]. So far, the ML was mostly used to improve or substitute specific parts of the NWP workflow. For example, neural networks (NNs) were applied to describe physical processes instead of individual parametrizations [4; 5; 6], and to replace parts of the data assimilation algorithms [7]. NNs were also used to downscale the low-resolution NWP outputs [8], or to postprocess ensemble temperature forecasts to surface stations [9], whereas [PERSON] et al. [10] used them to improve quantification of forecast uncertainty and bias. In several studies, ML methods were utilized for the data analysis, e.g., detection of weather systems [11; 12] and extreme weather [13]. ML methods were also applied to emulate the NWP simulations using NNs trained on reanalyses [14; 15; 16; 17] or simulations with simplified general circulation models [18]. Thus far, not many attempts were made at constructing end-to-end workflows, i.e., taking the observations as an input and generating an end-user forecast [3]. Some examples of such approaches are [PERSON] et al. [19], which tried to predict wind speed and power, and [PERSON] et al. [20], which attempted to predict multiple weather variables from the data of the US weather balloon network. The NNs were shown to be particularly successful in precipitation nowcasting. For example, [PERSON] et al. [21] used radar data to perform short-range probabilistic predictions of precipitation, while [PERSON] et al. [22] combined radar data with the satellite data.  
Here we attempt to develop a model based on the NN that takes a single vertical profile measurement from the weather balloon as an input and tries to forecast the daily maximum (\(T_{\text{max}}\)) and minimum (\(T_{\text{min}}\)) temperatures at 2 m at the adjacent location for the following days. The aim of this work is not to develop an approach that would be better than the current state-of-the-art NWP models. Since only a single vertical profile measurement is used, it could hardly be expected that the NN model could perform better than an operational NWP model (which uses a fully fledged data assimilation system incorporating measurements of the global observation network). Our goal is rather to explore the capability of neural networks for these kinds of forecasts. More specifically, our aim was to understand how the NN-based models utilize different types of input data and how the network design influences their behavior. The data utilization and behavior of the network will be different whether the NNs are used for short- or long-term forecasts--this is why the analysis was performed for a wide range of forecast lead times going from 0 to 500 days into the future. By studying these networks, we hoped to gain some valuable insights that might help others develop more complex setups and neural-network-based data assimilation schemes.  
Section 2 presents the data and the methodology. Section 3 presents an analysis based on very simple NNs, consisting of only a few neurons, while Section 4 describes the analysis based on more complex NNs used for short- and long-term forecasts. Discussion and conclusions are given in Section 5.