Two fast radiative transfer methods to improve the temporal sampling of clouds in numerical weather prediction and climate models

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Abstract

The high computational cost of calculating the radiative heating rates in numerical weather prediction (NWP) and climate models requires that calculations are made infrequently, leading to poor sampling of the fast-changing cloud field and a poor representation of the feedback that would occur. This paper presents two related schemes for improving the temporal sampling of the cloud field. Firstly, the 'split time-stepping' scheme takes advantage of the independent nature of the monochromatic calculations of the 'correlated-k' method to split the calculation into gaseous absorption terms that are highly dependent on changes in cloud (the optically thin terms) and those that are not (optically thick). The small number of optically thin terms can then be calculated more often to capture changes in the grey absorption and scattering associated with cloud droplets and ice crystals. Secondly, the 'incremental time-stepping' scheme uses a simple radiative transfer calculation using only one or two monochromatic calculations representing the optically thin part of the atmospheric spectrum. These are found to be sufficient to represent the heating rate increments caused by changes in the cloud field, which can then be added to the last full calculation of the radiation code. We test these schemes in an operational forecast model configuration and find a significant improvement is achieved, for a small computational cost, over the current scheme employed at the Met Office. The 'incremental time-stepping' scheme is recommended for operational use, along with a new scheme to correct the surface fluxes for the change in solar zenith angle between radiation calculations. Copyright © Crown Copyright 2009. Reproduced with the permission of the Controller of HMSO. Published by John Wiley & Sons, Ltd.

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Manners, J., Thelen, J. C., Petch, J., Hill, P., & Edwards, J. M. (2009). Two fast radiative transfer methods to improve the temporal sampling of clouds in numerical weather prediction and climate models. Quarterly Journal of the Royal Meteorological Society, 135(639), 457–468. https://doi.org/10.1002/qj.385

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