Sensitivity of precipitation and temperature over the Mount Kenya area to physics parameterization options in a high-resolution model simulation performed with WRFV3.8.1

Abstract

Several sensitivity experiments with the Weather Research and Forecasting (WRF) model version 3.8.1 have been performed to find the optimal parameterization setup for precipitation amounts and patterns around Mount Kenya at a convection-permitting scale of 1 km. Hereby, the focus is on the cumulus scheme, with tests of the Kain–Fritsch, the Grell–Freitas, and no cumulus parameterizations. In addition, two longwave radiation schemes and two planetary boundary layer parameterizations are evaluated, and different nesting ratios and numbers of nests are tested. The precipitation amounts and patterns are compared against a large amount of weather station data and three gridded observational data sets. The temporal correlation of monthly precipitation sums show that fewer nests lead to a more constrained simulation, and hence the correlation is higher. The pattern correlation with weather station data confirms this result, but when comparing it to the most recent gridded observational data set the difference between the number of nests and nesting ratios is marginal. The precipitation patterns further reveal that using the Grell–Freitas cumulus parameterization in the domains with resolutions >5 km provides the best results when it comes to precipitation patterns and amounts. If no cumulus parameterization is used in any of the domains, the temporal correlation between gridded and in situ observations and simulated precipitation is especially poor with more nests. Moreover, even if the patterns are captured reasonably well, a clear overestimation in the precipitation amounts is simulated around Mount Kenya when using no cumulus scheme in all domains. The experiment with the Grell–Freitas cumulus parameterization in the domains with resolutions >5 km also provides reasonable results for 2 m temperature with respect to gridded observational and weather station data.