RAL SEMINAR: Improving Model Performances for Decision-Making Activities: From Lagrangian Particle Modeling to Post-Processing for Renewable Energies

The presentation summarizes my career-long research efforts to enhance model performances for decision-making in environmental and energy systems. My work spans pollutant dispersion modeling, air quality forecasting, weather forecasting, and renewable energy prediction, focusing on developing innovative methodologies to improve accuracy and reliability. During my Ph.D., I investigated including chemical reactions in Lagrangian stochastic models to predict pollutant concentrations, such as NO2 and O3, in the presence of NO emissions. I modified and validated these models using wind tunnel and atmospheric data, addressing the challenges of chemical interactions in dispersion processes. I also studied dense gas and toxic industrial chemical dispersion, with a focus on chlorine releases, conducting intercomparison studies like the Jack Rabbit II experiments to evaluate model capabilities and limitations.

In the realm of air quality forecasting, I applied and expanded the analog ensemble (AnEn) statistical-dynamical method to improve predictions for PM2.5 and ozone concentrations. My recent work extends AnEn capabilities from point-based predictions to 2D gridded CMAQ forecasts, leveraging Copernicus Atmospheric Monitoring Service (CAMS) reanalysis data. This approach aims to reduce systematic and random errors, enabling more accurate and actionable forecasts across the U.S. 

Beyond air quality, I have contributed to renewable energy forecasting by incorporating meteorological variables into probabilistic wind and solar power predictions. Using AnEn, I combined numerical weather predictions with historical data, achieving superior forecast reliability compared to traditional techniques and reducing costs related to forecasting errors. In summary, my career has focused on enhancing model performance to tackle crucial decision-making challenges, connecting research innovation with practical applications in environmental and energy systems.