Deep Learning for Atmospheric Modeling: A Proof of Concept Using a Fourier Neural Operator on WRF Data to Accelerate Transient Wind Forecasting at Multiple Altitudes

This study addresses the problem of the computational cost of transient CFD simulations, which rely on iterative time-step calculations, by employing deep learning to generate optimized initial conditions for accelerating the Weather Research and Forecasting (WRF) model. To this end, we forecasted wind speed for short time frames over the Houston region using the WRF model data from 2019 to 2022, training the models to predict the X-component (U) wind speed. The so-called global FNO model, trained across all atmospheric heights, was first tested, achieving competitive results. A more refined approach was tested to improve it, training separate models for each altitude level, enhancing accuracy significantly. These ad hoc models outperformed surface and middle atmosphere persistence, achieving 27.64% and 20.46% nRMSE, respectively, while remaining competitive at higher altitudes. Variable selection played a key role, revealing that different physical processes dominate at various altitudes, necessitating distinct input features. The results highlight the potential of deep learning, particularly FNO, in atmospheric modeling, suggesting that tailored models for specific altitudes may enhance forecast accuracy. Thus, this study demonstrates that a deep learning model can be designed to start the iterations of a transient simulation, reducing convergence time and enabling faster, lower-cost predictions.