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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-347
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-347
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model evaluation paper 12 Nov 2020

Submitted as: model evaluation paper | 12 Nov 2020

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This preprint is currently under review for the journal GMD.

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

Martina Messmer1,2,3, Santos J. González-Rojí1,2, Christoph C. Raible1,2, and Thomas F. Stocker1,2 Martina Messmer et al.
  • 1Climate and Environmental Physics, University of Bern, Bern, Switzerland
  • 2Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 3School of Earth Sciences, The University of Melbourne, Melbourne, Victoria, Australia

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 parameterization for the parent and all nested domains. Besides, two long wave radiation schemes and two planetary boundary layer parameterizations are evaluated. Additionally, different nesting ratios and numbers of nests are tested. The precipitation amounts and patterns are compared against a large number 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 are marginal. The precipitation patterns further reveal that the Grell-Freitas cumulus parameterization provides the best results, when it comes to precipitation patterns and amounts. If no cumulus parameterization is used, 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 quite well, a clear overestimation in the precipitation amounts is observed around Mount Kenya when using no cumulus scheme at all. The Grell-Freitas cumulus parameterization also provides reasonable results for 2-metre temperature with respect to gridded observational and weather station data.

Martina Messmer et al.

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Martina Messmer et al.

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Short summary
Sensitivity experiments with the WRF model are run to find an optimal parameterization setup for precipitation around Mount Kenya at a scale that resolves convection (1 km). Precipitation is compared against many weather stations and gridded observational data sets. Both the temporal correlation of monthly precipitation sums and pattern correlations show that fewer nests lead to a more constrained simulation with higher correlation. The Grell-Freitas cumulus scheme obtains most accurate results.
Sensitivity experiments with the WRF model are run to find an optimal parameterization setup for...
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