# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
### The Essence, Significance, and Complexity of Weather Forecasting  
Weather forecasting is the use of science and technology to predict the state of the atmosphere and the associated meteorological phenomena, concerning a specific area and time period. Weather forecasts are performed based on scientific knowledge of atmospheric processes and historical quantitative data as typical meteorological conditions. The chaotic and complex nature of the atmosphere, the enormous computing power required by computers to solve atmospheric equations, and an incomplete understanding of atmospheric processes make predictions less accurate as the range of timely predictions increases [17].  
Meteorologists use different methods to forecast the weather. [PERSON] proposed the possibility of creating the first numerical weather prediction models in 1922 [18]. The practical use of numerical models began in 1955 due to the development of programmable electronic computers [19]. Data for forecasting come from observations of atmospheric pressure, temperature, wind speed, wind direction, humidity, and precipitation. Trained observers make these observations close to the ground surface or automatic weather stations are used for this purpose [16].  
At present, it is common and established to use numerical models for weather forecasting, which are uncompetitive but still not sufficiently effective at this stage of research regarding weather forecast models [20]. The basic condition for the usefulness of a weather forecast is its high verifiability. Users expect accurate local forecasts, broken down into hourly data and a very dense grid, i.e., for specific geographical points.  
Weather forecasting is one of the essential tools for planning, risk management, and decision making in sectors of the economy and everyday life. The sectors exposed to weather risk are energy, agriculture, food industry, construction, entertainment and tourism, transport, and defense--together, the lion's share of the national economy [21; 22].  
Therefore, despite the colossal costs generated by weather forecasting, technological infrastructure for testing alternative methods and creating better forecasting models is still being developed. These expenses are justified by ensuring the safety of people and their property from natural disasters. However, precise weather forecasting is extremely difficult. Weather is a chaotic phenomenon, characterized by temporal and spatial irregularity in which successive states vary and are difficult to predict [23].  
New technologies can change this situation. The world of meteorology is exploring Artificial Neural Networks for the generation of non-linear and non-parametric tools for modeling processes and genuine phenomena, i.e., problems that are difficult to solve with classical methods. Such networks have some brain properties. They learn from examples and apply this knowledge to problem solving, i.e., they can generalize. They are useful for tasks that are not very precisely defined formally.  
They can function properly at a certain level of damage and despite partially incorrect input. They also have a relatively high speed of operation (information processing). Their current level of development means that they are not yet competitive with numerical models. However, their speed is predicted to compete with standard supercomputers, whose current speed reaches \(2.8\times 10^{14}\) elementary operations per second [24; 25; 26].  
Currently, models based on machine learning using Artificial Neural Networks and based on historical data are being tested. According to global reports, these models produce forecast results that far exceed the precision and efficiency of numerical models based on current weather data. These models improve the forecasting efficiency by 40-50% for air temperature and 129-169% for precipitation [27].