Abstract
Regional climate models (RCMs) are crucial for climate studies and may be an alternative source of meteorological data in data-scarce regions. However, the effectiveness of the numerical weather prediction (NWP) models applied in RCMs is hampered by the parameterization of unresolved physical processes in the model. A major source of uncertainties in NWP models is the parameterization of the planetary boundary layer (PBL). This study evaluates the influence of seven PBL parameterization schemes in the Weather Research and Forecasting (WRF) model on the retrieval of four meteorological variables over the Kenyan highlands. The seven PBL schemes consist of four local schemes: the Mellor-Yamada-Janjic (MYJ), Mellor-Yamada-Nakanishi-Niino (MYNN), Bougeault-Lacarrere (BouLac), quasinormal scale elimination (QNSE), and three nonlocal schemes: asymmetrical convective model version 2 (ACM2), Shin and Hong (SHIN) and Yonsei University (YSU). The forcing data for the WRF model was obtained from the fifth generation of the European ReAnalysis (ERA5) dataset. The results were validated against observational data from the Trans-African Hydro-Meteorological Observatory (TAHMO). WRF was found to simulate surface meteorological variables with spatial details coherent with the complex topography within the Kenyan highlands, irrespective of the PBL scheme. A comparison between 2-meter temperature (T2) derived from the YSU scheme and T2 from the land component of ERA5 (ERA5-Land) indicates that surface meteorological variables derived from WRF are better suited for applications over the Kenyan highlands. The choice of the PBL scheme was found to primarily influence the simulation of the 10-meter wind speed (WS10) and rainfall as opposed to T2 and the 2-meter relative humidity (RH2). The insensitivity of the 2-meter variables to the choice of the PBL scheme is attributed to the influence of the surface layer parameterization near the surface. Results from the rainfall simulation indicate that the YSU scheme provides a more realistic depiction of PBL dynamics within the study area. Hence, the YSU scheme is best suited for simulating surface meteorological variables over the Kenyan highlands.
Highlights
Regional climate models (RCMs) are increasingly being adopted in studies and applications at the regional and local scale to provide quality spatial-temporally refined weather and climate-related variables [1]
The RCM concept, known as dynamical downscaling, applies high-resolution numerical weather prediction (NWP) models over regional model domains to resolve the sub-grid variability in the climate fields derived from general circulation models (GCMs)
At the relatively higher spatial resolution of 1 km compared to the 9 km of ERA5-land, the Weather Research and Forecasting (WRF) model was able to capture the local scale physical processes related to topography and land use with more details
Summary
Regional climate models (RCMs) are increasingly being adopted in studies and applications at the regional and local scale to provide quality spatial-temporally refined weather and climate-related variables [1]. The RCM concept, known as dynamical downscaling, applies high-resolution numerical weather prediction (NWP) models over regional model domains to resolve the sub-grid variability in the climate fields derived from general circulation models (GCMs). Cognizant to the importance of local-scale processes, especially for land applications, GCM derived datasets with a relatively high resolution, e.g., the land component of the fifth generation of European ReAnalysis (ERA5) dataset [3] referred to as ERA5-land, have been developed. The dataset is still too coarse to effectively capture the local-scale processes, especially in regions characterized by complex terrain, emphasizing the need for RCMs
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