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

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 w...

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Bibliographic Details
Main Authors: Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé, Victor Oliveira Santos, Bahram Gharabaghi
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Atmosphere
Subjects:
deep learning
WRF
weather forecasting
computational fluid dynamics
artificial intelligence
Online Access:https://www.mdpi.com/2073-4433/16/4/394
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