Predicting atmospheric optical properties for radiative transfer computations using neural networks

Abstract

The radiative transfer equations are well known, but radiation parametrizations in atmospheric models are computationally expensive. A promising tool for accelerating parametrizations is the use of machine learning techniques. In this study, we develop a machine learning-based parametrization for the gaseous optical properties by training neural networks to emulate a modern radiation parametrization (RRTMGP). To minimize computa- tional costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimized BLAS functions to accelerate matrix computations. To generate training data, we use a set of randomly perturbed atmospheric profiles and calculate optical properties using RRTMGP. Predicted optical properties are highly accurate and the resulting radiative fluxes have average errors within 0.5 W m −2 compared to RRTMGP. Our neural network-based gas optics parametrization is up to four times faster than RRTMGP, depending on the size of the neural networks. We further test the trade-off between speed and accuracy by training neural networks for the narrow range of atmospheric conditions of a single large-eddy simulation, so smaller and therefore faster networks can achieve a desired accuracy. We conclude that our machine learning-based parametrization can speed-up radiative transfer computations while retaining high accuracy. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

Improved Weather Forecasting Using Neural Network Emulation for Radiation Parameterization

“…The radiative transfer for TOVS (RTTOV) has been developed using multiple linear regression since 1999, and it has been widely used in data assimilation in the NWP model (Saunders et al, 2018). Recent studies on radiative transfer modeling have extended the application of various AI techniques, including multiple linear regression, deep neural network (DNN), adaptive network-based fuzzy inference system, and convolutional neural network (CNN) for radiation processes over a clear sky (Bilgic & Mert, 2021;Liu et al, 2020;Ukkonen et al, 2020;Veerman et al, 2021) and 3-dimensional cloud radiative effects (Meyer et al, 2021). As these studies do not utilize repetition by time integration, such as in the numerical forecast model, errors caused by emulation do not accumulate.…”

Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting

“…Efforts have also been made to accelerate increased complexity (dubbed super) parameterization schemes (Rasp et al, 2018;Gentine et al, 2018). Veerman and Pincus (2021) emulate a radiation scheme and assess the computational cost relative to the existing scheme. Ukkonen et al (2020) emulate the gas optics scheme within a radiation scheme, providing acceleration for this kernel.…”

The History and Practice of AI in the Environmental Sciences

“…This approach combined, in the hybrid long wave radiation parameterization, calculations of cloudiness based on first principle equations with NN approximations for a partial or individual flux at each vertical level. Recently, this approach was applied by Veerman et al (2021), with the opposite combination of cloudiness computed with an NN and first principle equations to produce a hybrid parameterization based on a standard (Pincus et al 2019) radiation parameterization. Those authors applied an NN to emulate atmospheric optical properties and relied on radiative transfer equations to calculate the outputs of the RRTMGP) parameterization.…”