This article discusses the issue of the features of measuring and predicting changes in the surface electric field strength in the atmosphere. The results of measurements of the atmospheric electric field strength are presented. The possibilities of forecasting changes in the surface electric field strength, including the use of numerical models, as well as the use of measurement results as an indicator of dangerous weather phenomena, are considered. The prospects of using the prediction of variations in the surface electric field intensity to predict adverse weather events and the importance of monitoring the intensity of the atmospheric electric field for understanding global climate change processes and the impact of the electric field on human health and the environment are discussed. For the research, a model was created that allows predicting electric field variations based on meteorological data. The developed neural network has shown good results. It is demonstrated that the use of neural networks can be an effective approach for predicting the parameters of the electric field of the surface layer of the atmosphere. In further research, it is planned to expand the measurement area by including additional parameters such as temperature, pressure and humidity in the analysis, as well as using more complex machine learning models to improve the accuracy of forecasts. In general, the results show that machine learning models can be effective in predicting variations of the electric field in the surface layer of the atmosphere. This can have practical applications in various fields such as aeronautics, meteorology, geology and others. Further research in this area contributes to the development of new methods and technologies in the field of electric power and communications and to improving our knowledge about the nature of the impact of atmospheric electrophysical phenomena on the environment and human health.

Keywords: electric field, surface layer of the atmosphere, measurements, methods, forecasting, modeling of variations in field strength