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

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.

Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1

“…Based on previous studies by the authors, the "Europe" experiment includes the updated parameterizations used over that region (Messmer et al, 2017), i.e., Noah-MP instead of Noah land surface scheme. Additionally, the "Kenya" experiment is the same as the one which is most successful in simulating precipitation and temperature in the surroundings of Mount Kenya (Messmer et al, 2021). The comparison against this experiment will help us to determine if the setups over equatorial regions are interchangeable.…”

“…The "Micro13" and the "No Cumulus" experiments are based on the parameterizations of the Kenya experiment, but with a different microphysics scheme and without any cumulus parameterization, respectively. The No Cumulus experiment is motivated by improvements obtained for precipitation when simulating convective precipitation explicitly at a spatial resolution of 25 km or below (Vergara-Temprado et al, 2020), and the good performance in simulating precipitation and temperature over Mount Kenya (Messmer et al, 2021).…”

Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning

“…With the availability of vast computational resources and observations, the accuracy in the specification of initial and lateral boundary conditions is improved to a great extent (Mohanty et al, 2010;Singh et al, 2021b). Many researchers have studied the sensitivity of physics schemes in simulating tropical cyclones over the BoB and invariably reported the performance of different combinations of physics schemes by comparing the tracks and intensities of cyclones (Pattanayak et al, 2012;Osuri et al, 2012;Rambabu et al, 2013;Kanase and Salvekar, 2015;Chandrasekar and Balaji, 2016;Sandeep et al, 2018;Venkata Rao et al, 2020;Mahala et al, 2021;Singh et al, 2021b;Messmer et al, 2021;Baki et al, 2021a). However, systematic studies on parameter sensitivity, to determine their optimal values, is yet to be explored for tropical cyclones over the BoB region.…”

Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning

“…Many researchers have studied the sensitivity of physics schemes in simulating tropical cyclones over the BoB and invariably reported the performance of different combinations of physics schemes by comparing the tracks and intensities of cyclones (Pattanayak et al, 2012;Osuri et al, 2012;Rambabu et al, 2013;Kanase and Salvekar, 2015;Chandrasekar and Balaji, 2016;Sandeep et al, 2018;Venkata Rao et al, 2020;Mahala et al, 2021;Singh et al, 2021b;Messmer et al, 2021). However, systematic studies on parameter sensitivity, to determine their optimal values is yet to be explored for tropical cyclones over the BoB region.…”