Application of an adaptive radiative transfer scheme in a mesoscale numerical weather prediction model

The computational burden of radiative transfer parametrization is considerable, and hence operational atmospheric models use various sampling, coarsening and interpolation techniques to reduce this load; this, however, introduces new errors. An adaptive radiative transfer scheme takes advantage of the spatial and temporal correlations in the optical characteristics of the atmosphere to make the parametrization computationally more efficient. The adaptive scheme employed here generalizes the accurate radiation computations made in a fraction of the spatial and temporal space to the rest of the field. In this study, a previously developed scheme has been extended to atmospheric heating rates and implemented in the numerical weather prediction model COSMO. The performance of the adaptive scheme is compared with the performance of the currently operational COSMO-DE radiation configuration, in which radiation computations are performed quarter-hourly on 2 x 2 averaged atmospheric columns. The reference for both schemes is a set of frequent radiation computations for the full grid. We show that the adaptive scheme is able to reduce the sampling errors in the radiation surface fluxes by 1525% and to conserve the spatial variability, in contrast to the operational scheme. Deviations in the heating-rate profiles are reduced for larger averaging scales. Physical relationships between the radiative quantities and cloud water or rain rates are better captured. We demonstrate that these improvements also lead to improvements with respect to the dynamical development of the model simulation, showing a smaller divergence from the reference model run. Copyright (C) 2011 Royal Meteorological Society