Abstract
There is an increasing need to improve the accuracy of extreme weather forecasts for life-saving applications and in support of various socioeconomic sectors in East Africa, a region with remarkable mesoscale systems due to its complex topography defined by sharp gradients in elevation, inland water bodies, and landuse conversions. This study sought to investigate the impacts of the Weather Research and Forecasting (WRF) model spatial resolution on resolving rainfall variability with topography utilizing nested domains at 12 and 2.4 km resolutions. The model was driven by the National Centers for Environmental Prediction (NCEP)-Global Data Assimilation System (GDAS) Global Forecast System (GFS) final (FNL) reanalysis to simulate the weather patterns over East Africa from 3rd April 2018 to 30th April 2018, which were evaluated against several freely available gridded weather datasets alongside rainfall data from the Kenya Meteorological Department (KMD) stations. The reference datasets and the model outputs revealed that the highlands had more rainfall events and higher maximum daily rainfall intensity compared to the surrounding lowlands, attributed to orographic lifting enhancing convection. Rainfall was inversely proportional to altitude from 500 m to 1,100 m above sea level (ASL) for both coarse and fine resolutions. The convection-permitting setup was superior in three aspects: resolving the inverse altitude-rainfall relationship for altitudes beyond 3000 m ASL, simulating heavy rainfall events over the lowlands, and resolution of the diurnal cycle of low-level wind. Although the coarse resolution setup reasonably simulated rainfall over large mountains, only the convection-permitting configuration could accurately resolve rainfall variability over contrasting topographical features. The study notes that high-resolution modeling systems and topography-sensitive bias correction techniques are critical for improving the quality of operational weather forecasts in East Africa.
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