Correction of numerical forecasts of wind gusts using artificial neural networks and observations
I.V. Del1,2, A.V. Starchenko1,2
1National Research Tomsk State University, Tomsk, Russia
2V.E. Zuev Institute of Atmospheric Optics of Siberian Branch of the Russian Academy of Science, Tomsk, Russia
Keywords: wind gust, mesoscale model TSUNM3, ultrasonic meteosite, numerical forecast correction, artificial neural network
Abstract
In 2023, more than a third of dangerous meteorological events in the Siberian Federal District were associated with strong winds, which underscores the importance of improving the accuracy and timing of its forecasting. Modern numerical simulation and machine learning methods make it possible to improve forecasts, but the task of directly calculating wind gusts remains relevant due to the limited resolution of models. An original method is proposed for correcting the results of short-term forecast of wind gusts obtained on the basis of mesoscale models of numerical weather forecasting using advance measurements and artificial neural networks. The results show that the proposed correction method makes it possible to improve the forecast of wind gusts by various semi-empirical methods. The results can be applied in meteorology, energy, transportation, and other industries to minimize damage from dangerous weather events.
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