Accelerating Radiation Computations for Dynamical Models With Targeted Machine Learning and Code Optimization

Journal of Advances in Modeling Earth Systems

卷:12

Robert Pincus¹  Kristian Pagh Nielsen²  Peter Ukkonen²  Robin J. Hogan³  Eigil Kaas⁴ 

[1] Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder CO USA;

[2] Danish Meteorological Institute Copenhagen Denmark;

[3] European Centre for Medium‐Range Weather Forecasts Reading UK;

[4] Niels Bohr Institute University of Copenhagen Copenhagen Denmark;

关键词: machine learning;    radiation;    atmospheric model;    parameterization;   

DOI  :  10.1029/2020MS002226

来源: DOAJ

【 摘 要 】

Abstract Atmospheric radiation is the main driver of weather and climate, yet due to a complicated absorption spectrum, the precise treatment of radiative transfer in numerical weather and climate models is computationally unfeasible. Radiation parameterizations need to maximize computational efficiency as well as accuracy, and for predicting the future climate many greenhouse gases need to be included. In this work, neural networks (NNs) were developed to replace the gas optics computations in a modern radiation scheme (RTE+RRTMGP) by using carefully constructed models and training data. The NNs, implemented in Fortran and utilizing BLAS for batched inference, are faster by a factor of 1–6, depending on the software and hardware platforms. We combined the accelerated gas optics with a refactored radiative transfer solver, resulting in clear‐sky longwave (shortwave) fluxes being 3.5 (1.8) faster to compute on an Intel platform. The accuracy, evaluated with benchmark line‐by‐line computations across a large range of atmospheric conditions, is very similar to the original scheme with errors in heating rates and top‐of‐atmosphere radiative forcings typically below 0.1 K day−1 and 0.5 W m−2, respectively. These results show that targeted machine learning, code restructuring techniques, and the use of numerical libraries can yield material gains in efficiency while retaining accuracy.

【 授权许可】

Unknown