Balancing Accuracy, Efficiency, and Flexibility in Radiation Calculations for Dynamical Models

Pincus, Robert; Mlawer, E. J.; Delamere, Jennifer

doi:10.1029/2019ms001621

Cited by 85 publications

(83 citation statements)

References 51 publications

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“…The figure illustrates agreement with respect to changed greenhouse gas concentrations; perturbations in experiment rad-irf in which temperature and/or humidity changes are omitted. (Zhao et al, 2018), and for the newly developed RTE+RRTMGP code (Pincus et al, 2019) which is trained on calculations with LBLRTM v12.8. These parameterizations use recent spectroscopic information and so are likely to be among the parameterizations with the smallest error.…”

Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 99%

Benchmark Calculations of Radiative Forcing by Greenhouse Gases

et al. 2020

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Changes in concentrations of greenhouse gases lead to changes in radiative fluxes throughout the atmosphere. The value of this change, the instantaneous radiative forcing, varies across climate models, due partly to differences in the distribution of clouds, humidity, and temperature across models and partly due to errors introduced by approximate treatments of radiative transfer. This paper describes an experiment within the Radiative Forcing Model Intercomparision Project that uses benchmark calculations made with line-by-line models to identify parameterization error in the representation of absorption and emission by greenhouse gases. Clear-sky instantaneous forcing by greenhouse gases is computed using a set of 100 profiles, selected from a reanalysis of present-day conditions, that represent the global annual mean forcing from preindustrial times to the present day with sampling errors of less than 0.01 W m −2. Six contributing line-by-line models agree in their estimate of this forcing to within 0.025 W m −2 while even recently developed parameterizations have typical errors 4 or more times larger, suggesting both that the samples reveal true differences among line-by-line models and that parameterization error will be readily identifiable. Agreement among line-by-line models is better in the longwave than in the shortwave where differing treatments of the water vapor continuum affect estimates of forcing by carbon dioxide and methane. The impacts of clouds on instantaneous radiative forcing are estimated from climate model simulations, and the adjustment due to stratospheric temperature changes estimated by assuming fixed dynamical heating. Adjustments are large only for ozone and for carbon dioxide, for which stratospheric cooling introduces modest nonlinearity. The models participating in the previous phase of CMIP translated prescribed changes in atmospheric composition into a relatively wide range of effective radiative forcing, much of which remains even when model-specific adjustments are accounted for (e.g., Chung & Soden, 2015); initial results (Smith et al., 2020

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Section: Journal Of Geophysical Research: Atmospheresmentioning

confidence: 99%

Benchmark Calculations of Radiative Forcing by Greenhouse Gases

et al. 2020

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“…Given that RRTMGP uses linear interpolation from look-up tables to to compute optical properties [16], the computational efficiency of our neural network-based parametrization may be surprising. We attribute the speed-ups achieved by our parametrization to a large extent to the case-specific tuning, i.e.…”

Section: (B) Les-tuned Networkmentioning

confidence: 99%

“…For all network sizes of the NWP (blue) and Cabauw (red) sets of neural networks, the mean absolute errors with respect to RRTMGP of the radiative heating rates (a,d), upwelling radiative fluxes at the top of atmosphere (b,e) and downwelling radiative fluxes at the surface (c,f) for the longwave (a,b,c) and shortwave (d,e,f) spectrum. Radiative fluxes and heating rates are based on 1000 random profiles of Cabauw simulation5 ConclusionsWe developed a new parametrization for the gas optics by training multiple neural networks to emulate the gaseous optical properties calculations of RRTMGP[16]. The neural networks are able to predict the optical properties with high accuracy and errors of the radiative fluxes based on the predicted optical properties are generally within 1.2 W m −2 .…”

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confidence: 99%

Predicting atmospheric optical properties for radiative transfer computations using neural networks

Veerman¹,

Pincus²,

Leeuwen³

et al. 2020

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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 the Rapid Radiative Transfer model for General circulation Model applications -Parallel (RRTMGP). To minimize computational costs, we reduce the range of atmospheric conditions for which the neural networks are applicable and use machine-specific optimised 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 errors within 1.2 W m −2 for longwave and 0.12 W m −2 for shortwave radiation. Our parametrization is 3 to 7 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 a single LES case, so smaller and therefore faster networks can achieve a desired accuracy, especially for shortwave radiation. We conclude that our machine learningbased parametrization can speed-up radiative transfer computations whilst retaining high accuracy.

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“…The radiative transfer code used here, RRTMGP (Rapid Radiative Transfer Model for GCMs, Parallel) (Pincus et al, 2019), is a plane-parallel correlated-k two-stream model based on state-of-the-art spectroscopic data for gas and condensate optics. It is based on line parameters from Atmospheric and Environmental Research and the MT_CKD water vapor continuum absorption model (Mlawer et al, 2012).…”

Section: Radiative Transfer Calculationmentioning

confidence: 99%

“…the set of resulting profiles is then used as input to RRTMGP to derive upwelling and downwelling clear-sky radiative fluxes in the shortwave and longwave ranges of the spectrum. The calculation uses a spectrally-uniform surface albedo of 0.07 and a spectrally-uniform surface emissivity of 0.98, typical values for tropical oceans (Pincus et al, 2019).…”

Section: Radiative Transfer Calculationmentioning

confidence: 99%

Atmospheric radiative profiles during EUREC<sup>4</sup>A

Albright¹,

Fildier²,

Pincus³

et al. 2020

Preprint

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Abstract. The couplings among clouds, convection, and circulation in trade-wind regimes remain a fundamental puzzle that limits our ability to constrain future climate change. Radiative heating plays an important role in these couplings. Here we calculate the clear-sky radiative profiles from 2001 in-situ soundings (978 dropsondes and 1023 radiosondes) collected during the EUREC4A field campaign, which took place south and east of Barbados in January–February 2020. We describe the method used to calculate these radiative profiles and present preliminary results sampling variability at multiple scales, from the variability across all soundings to groupings by diurnal cycle and mesoscale organization state, as well as individual soundings associated with elevated moisture layers. This clear-sky radiative profiles data set can provide important missing detail to what can be learned from calculations based on passive remote sensing and help in investigating the role of radiation in dynamic and thermodynamic variability in trade-wind regimes. All data are archived and freely available for public access on AERIS (Albright et al. (2020), https://doi.org/10.25326/78).

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Balancing Accuracy, Efficiency, and Flexibility in Radiation Calculations for Dynamical Models

Cited by 85 publications

References 51 publications

Benchmark Calculations of Radiative Forcing by Greenhouse Gases

Benchmark Calculations of Radiative Forcing by Greenhouse Gases

Predicting atmospheric optical properties for radiative transfer computations using neural networks

Atmospheric radiative profiles during EUREC<sup>4</sup>A

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Balancing Accuracy, Efficiency, and Flexibility in Radiation Calculations for Dynamical Models

Cited by 85 publications

References 51 publications

Benchmark Calculations of Radiative Forcing by Greenhouse Gases

Benchmark Calculations of Radiative Forcing by Greenhouse Gases

Predicting atmospheric optical properties for radiative transfer computations using neural networks

Atmospheric radiative profiles during EUREC&lt;sup&gt;4&lt;/sup&gt;A

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Atmospheric radiative profiles during EUREC<sup>4</sup>A