Rif. 6267_All. 3 - Development of high-resolution numerical techniques for applications in micrometeorology and dynamic downscaling

Global climate models fail to capture local phenomena due to their coarse spatial resolution—resolution that is nonetheless essential for assessing micro-scale climate change impacts, such as urban heat islands and pollutant dispersion. The research project aims to develop and optimize advanced numerical techniques for dynamic downscaling by integrating fluid dynamics data (LES/DNS) with mesoscale models (WRF). Its novelty lies in the use of advection schemes coupled with machine learning algorithms to reduce computational costs and overcome the limitations of traditional statistical methods by explicitly resolving turbulence. The expected outcome is the provision of predictive models for risk management and urban planning. The successful candidate will join the DIFA atmospheric physics research group and contribute to fostering synergies within ongoing research projects