Deployment of the Regional Climate Model for the South of Russia Based on RegCM 4.5

Analysis of the Climatic Factors Influence on the Overhead Transmission Lines Reliability

“…According to the results of research [9]- [15], the regional models INMCM48 and RegCM4.5 describe climate changes with different levels of approximation for such a parameter as wind quite similarly. But the forecast values of the number wind cases at a certain speed in the range of 15 m/s and more for the RegCM4.5 model are characterized by a small but increasing trend [14], [15].…”

“…According to the results of research [9]- [15], the regional models INMCM48 and RegCM4.5 describe climate changes with different levels of approximation for such a parameter as wind quite similarly. But the forecast values of the number wind cases at a certain speed in the range of 15 m/s and more for the RegCM4.5 model are characterized by a small but increasing trend [14], [15]. The obtained linear trends of changes in the number wind cases for certain groups of wind speeds confirm the calculation of hydrometeorological parameters using regional models, and allow to predict potential changes for 3-5 years of the future with a certain degree of significance based on retrospective data.…”

“…Using regional models INMCM48 [9]- [11], RegCM4.5 [12]- [15], research by Russian scientists on numerical modelling of a hydrometeorological parameter wind in central part of Russia has shown that in the next 20-30 years there will be a decrease in the average annual relative wind speed by 0.05-0.15, especially in areas with low wind loads. The outstanding feature of these models from global is that they take into account local characteristic of atmospheric circulation and consider the deviation of the parameters at a higher spatial resolution with reference to the analysis zone.…”

Estimates of the accuracy and rate of convergence of short-term meteorological forecasts using the regional climate model RegCM4

“…The development of control methods for the system of wind and solar power plants is a priority to reduce the intermittent mode of green energy [5], which is the main limiting factor for its development [6]. The explosive interest in machine learning algorithms for improving climate and meteorological forecasts is already leading to important results for forestry [7], agriculture [1], telecommunication systems [8], in addition to fundamental studies of climate dynamics [3,9,10].…”

“…The validity of the forecast of the temporal behavior of physical parameters characterizing the state of the meteorological system depends on complex unsteady three-dimensional movements in the atmosphere, radiation transfer in an inhomogeneous gaseous medium, interaction with the underlying surface of both air flows and radiation fluxes [4,10,11]. Various gasdynamic instabilities on different time scales and the associated unsteadiness of the system of vortex structures are important obstacles to accurate meteorological forecasts over long time intervals [11,12].…”