Radiative transfer calculations in weather and climate models are notoriously complex and computationally intensive, which poses significant challenges. Traditional methods, while accurate, can be prohibitively slow, necessitating the development of more efficient alternatives. Recently, empirical emulators based on neural networks (NN) have been proposed as a solution to this problem. These emulators aim to replicate the radiation parametrization used in the models, at a fraction of the computational cost. However, a common issue with these emulators is that their accuracy has often been insufficiently evaluated, especially for extreme events for which the amount of training data is sparse. The current study proposes such a model for accelerating radiative heat transfer modeling in WRF, and validates the accuracy of the approach for an extreme weather scenario.
@article{arxiv.2504.14542,
title = {A Lightweight Neural Network for Accelerating Radiative Transfer Modeling in WRF},
author = {Erick Fredj and Iggy Segev Gal and Noam Lavi and Shahar Belkar and Mark Wasserman and Ding Zhaohui and Yann Delorme},
journal= {arXiv preprint arXiv:2504.14542},
year = {2025}
}