Reduced Representations of Physics-based Model Outputs for Observation-corrected Outputs

Music City Center 

Accurate weather and climate representations rely on effectively combining large-scale numerical climate models with fine-scale observational data. While weather and climate models capture broad-scale dynamics across various spatial and temporal scales, they often face challenges such as modeling biases, high computational costs, and difficulty in resolving local variability and extremes. On the other hand, fine-scale observations provide valuable, high-resolution insights into localized phenomena but are typically sparse and difficult to integrate into large-scale frameworks. This talk presents an innovative approach to address these challenges by utilizing reduced representations from physics-based model outputs and enhance them with observational information. By extracting reduced-order representations from large-scale models and integrating them with fine-scale data, this method refines model outputs, improving their resolution and reliability while allowing to work with dimensionally-reduced model outputs.