Abstract
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.
Highlights
East Africa, including Kenya, has anomalously dry climate conditions compared to many other equatorial regions around the globe (e.g. Trewartha, 1981; Nicholson, 2017)
Note that because the gridded data sets are bilinearly interpolated to the respective Weather Research and Forecasting (WRF) grid, small differences can appear in the values of temporal correlations and root-mean-square error (RMSE) of each setup, and the shape of bars corresponding to these data sets in the box and whisker plots can look slightly different
Since the temporal correlation and the RMSE do not clearly define which parameterization option of WRF delivers the best results for precipitation in the region around Mount Kenya, we investigate the pattern correlation of the simulations compared to weather station data in a first step and to the gridded observational data set CHIRPS in a second step
Summary
East Africa, including Kenya, has anomalously dry climate conditions compared to many other equatorial regions around the globe (e.g. Trewartha, 1981; Nicholson, 2017). The precipitation patterns in East Africa are very heterogeneous, which can be attributed to the variety and complexity of large-scale controls, i.e. topography, influence from the ocean, the dynamics of the tropical circulation, and lakes (Nicholson, 2017). The Turkana jet has an influence on the local climate and especially on precipitation, and a study by Nicholson (2016a) suggests that it might even be responsible for the suppression of the summer rainy season in northwestern Kenya. The zonal circulation over the Indian Ocean further influences precipitation in Kenya, as it is located under subsiding air masses, leading to the aforementioned aridity over an equatorial region (Pohl and Camberlin, 2011; Nicholson, 2017).
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