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Improving the simulation of small-scale variability in radiation and land-surface parameterizations in a mesoscale numerical weather prediction model

For the simulation of subgrid-scale physical processes in mesoscale numerical weather prediction models various kinds of spatial and temporal sampling or averaging methods are employed to decrease their computational burden. These methods are applied both within the physical parameterizations, but also by restricting the number of calls to these parameterization schemes in time and space. This under-representation of small-scale variability can lead to systematic errors due to the nonlinearity of processes, and may cause inconsistencies between variables computed by the different parameterization schemes. In this work two methods are presented, which provide an efficient spatial and/or temporal sampling of heterogeneities, in the atmosphere itself and at the earth’s surface as lower boundary of atmospheric models. The first method, called adaptive radiative transfer parameterization, provides an efficient technique to compute the radiative effects in the atmosphere and at the soil surface. The second method allows for a scale-consistent coupling of atmospheric and soil-surface models, by running a high-resolution soil-vegetationatmosphere transfer model coupled to the coarser atmospheric model, connected by a novel atmospheric disaggregation scheme. Both developments incorporate small-scale variability in radiative and soil/surface processes in an efficient and consistent way. Furthermore, both methods improve the representation of the energy budget at the earth’s surface; the first by giving more accurate radiation surface net fluxes, the second by improving the turbulent exchange fluxes of sensible and latent heat. Both approaches have been implemented into the COSMO numerical weather prediction model, and tested in the COSMO-DE model configuration on a 2.8 km grid. The adaptive radiative transfer scheme takes advantage of the spatial and temporal correlations in the radiation characteristics of the atmosphere, and thus makes the parameterization computationally more efficient. The adaptive scheme generalizes the accurate radiation computations made in a fraction of the spatial and temporal space to the rest of the field. For validation three case studies with different synoptic conditions were carried out and the performance of the adaptive scheme is compared to the currently operational COSMO-DE radiation configuration, with quarter-hourly radiation computations on 2x2 averaged atmospheric columns. The reference for both schemes are frequent radiation computations on the full grid. The results show that the adaptive scheme is able to reduce the sampling errors in the surface radiation fluxes considerably and to conserve the spatial variability better, than to the operational scheme. Errors in the three-dimensional heating rates are reduced for larger averaging scales. Physical relations between the radiative quantities and cloud water or rain rates are captured better than with the operational scheme. It is shown, that these refinements also lead to improvements with respect to the dynamical development of the model simulation: the adaptive model runs show a smaller divergence from the reference model run than the currently operational scheme. One approach to deal with subgrid-scale variability at the surface in atmospheric models is the so-called mosaic approach, in which the soil and the surface are modelled on