A Neural Network Approach for a Fast and Accurate Computation of a Longwave Radiative Budget

TLDR

The authors trained two neural‑network radiative transfer codes using a richly sampled TIGR dataset and benchmarked them against a line‑by‑line and a band model. The resulting NeuroFlux models match or surpass GCM radiative transfer accuracy while being 22–106× faster, enabling rapid longwave budget calculations.

Abstract

The authors have investigated the possibility of elaborating a new generation of radiative transfer models for climate studies based on the neural network technique. The authors show that their neural network–based model, NeuroFlux, can be used successfully for accurately deriving the longwave radiative budget from the top of the atmosphere to the surface. The reliable sampling of the earth’s atmospheric situations in the new version of the TIGR (Thermodynamic Initial Guess Retrieval) dataset, developed at the Laboratoire de Météorologie Dynamique, allows for an efficient learning of the neural networks. Two radiative transfer models are applied to the computation of the radiative part of the dataset: a line-by-line model and a band model. These results have been used to infer the parameters of two neural network–based radiative transfer codes. Both of them achieve an accuracy comparable to, if not better than, the current general circulation model radiative transfer codes, and they are much faster. The dramatic saving of computing time based on the neural network technique (22 times faster compared with the band model), 106 times faster compared with the line-by-line model, allows for an improved estimation of the longwave radiative properties of the atmosphere in general circulation model simulations.