International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences
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Identification of Probable Precipitation Formation Zones using Data Mining and Control Actions by Local Injections of Moisture Concentrators

Authors: Andrei V. Chukalin, Ruslan V. Fedorov, Yury E. Chamchiyan, Dmitry S. Stepanov

DOI: https://doi.org/10.37082/IJIRMPS.ICTIMESH-23.10

Short DOI: https://doi.org/mgdv

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Abstract: The study of the atmosphere and the determination of probable precipitation formation zones is of practical interest for researchers in the fields of climatology, dynamic meteorology, in the tasks of aviation meteorology and wind power. This paper proposes a new approach to determining the areas with the highest probability of precipitation based on the use of neural network modeling. In addition, the scientific problem of knowing the influence of wind farms on the state of the atmospheric polydisperse boundary layer and their potential impact on the local meteorological situation is touched upon. This is due to the significant role of wind turbines in slowing down geostrophic wind, creating additional turbulence and increasing vertical mixing of momentum, heat and moisture. In order to effectively use local territories, the authors carried out a study a study of the possibility of controlling the meteorological situation. The paper presents the results of forecasting precipitation formation in a given area – the Ulyanovsk Wind Farm area. The results of a numerical study of the state of the atmospheric boundary layer are presented, and the impact of the wind farm on the local meteorological situation is assessed. An approach to controlled precipitation by influencing the atmospheric boundary layer with injections of moisture concentrators is proposed.

Keywords: Atmospheric Boundary Layer, Precipitation, Wind Farm, Neural Network


Paper Id: 3.110

Published On: 2024-01-23

Published In: Special Issue - International Conference on Trends & Innovations in Management, Engineering, Sciences and Humanities (ICTIMESH-23) (January 2024)

Cite This: Identification of Probable Precipitation Formation Zones using Data Mining and Control Actions by Local Injections of Moisture Concentrators - Andrei V. Chukalin, Ruslan V. Fedorov, Yury E. Chamchiyan, Dmitry S. Stepanov - IJIRMPS Special Issue - International Conference on Trends & Innovations in Management, Engineering, Sciences and Humanities (ICTIMESH-23) (January 2024). DOI 10.37082/IJIRMPS.ICTIMESH-23.10

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