RAL SEMINAR: Applications of Numerical Weather Prediction for Renewable Energy and Fire Behavior Modeling

Limited area numerical weather prediction (NWP) models provide a physically consistent approach to downscale operational predictions and reanalysis products to achieve the fine grid spacing information required for a range of applications. In this presentation I will summarize the highlights of my career contributions to the topic. I will first introduce wind energy applications. I will focus on the value of regional NWP models for wind resource evaluation over complex terrain, model the effects of wind turbine wakes within offshore wind farms, and the impacts that wind farm wakes exert upon nearby wind farms. I will also discuss my contributions to enhance the value of WRF for solar energy applications, the WRF-Solar model, its cloud initialization system, MAD-WRF, and its probabilistic component, the WRF-Solar Ensemble Prediction System (WRF-Solar EPS). Beyond renewable energies, I will focus on the value of NWP models for wildland fire modeling. I will focus on applications with the WRF-Fire model, discuss opportunities to improve the realism of the simulations, and the value of satellite retrievals for fuel characterization for initialization of the models. I will also introduce the Community Fire Behavior model (CFBM). CFBM is the first fire behavior model explicitly designed for interoperability with atmospheric dynamical cores. It offers an attractive framework to foster collaborative developments to mitigate the adverse impacts of wildland fires, being a clear example of the societal benefits of applied NWP.