A NEW PARADIGM fOR PARAMETERIZATIONS IN NUMERICAL WEATHER PREDICTION AND OTHER ATMOSPHERIC MODELS

The use of look-up-tables (LUTs) to represent parameterizations within numerical weather prediction and other atmospheric models is presented. We discuss several approaches as to how the use of LUTs can be optimized in order to retain the physical representation of the parameterization, yet be much more computationally efficient than the parent parameterization from which they are derived. trast, parameterizations, although partly based on fundamental concepts of physics, involve tunable coefficients and empirical functions. In atmospheric models, parameterizations are constructed, for example, for deep cumulus convection, stratiform cloud and precipitation processes, subgrid-scale mixing, shortand longwave radiative fluxes, and land-surface interactions (see Pielke 2002, Appendix B for summaries of the parameterizations used by a variety of numerical weather prediction and other atmospheric models). The computational costs of the parameterizations, however, are becoming much greater than for the dynamical core of a model, as parameterizations introduce greater complexity. Majewski et al. (2002) report the computational cost of different components of high-resolution global operational numerical weather prediction models. They show that parameterizations occupy 46.8% of the total when the radiation code was updated every 2 hours. If the radiation code (which represents 57.5% of the total parameterization cost) is updated on a more realistic