The propagation of radio electric waves emitted from ground-based meteorological instruments is determined through stratification of the atmosphere. In super-refractive cases characterized by strong temperature inversions or strong vertical moisture gradients, the radar beam can be deflected towards the ground (trapping). This phenomenon often results in spurious returned echoes and misinterpretation of radar images such as erroneous precipitation, wind, and temperature detection. In this study, a 5-year Central and West Africa (CWA) climatology of the frequency of super-refractive and trapping-layer base height has been produced using refractivity computations from European Centre for Medium-Range Weather Forecasts (ECMWF) analyses at a 40-km horizontal resolution and 60 levels in the vertical direction. The aim of this climatology is to improve the understanding on how frequent such anomalous propagations conditions are, which is a prerequisite for fully benefiting from radar data information for the multiple purposes of model validation, precipitation analysis, and data assimilation. First, the main climatological features are summarized for the whole CWA: Sahara and inlands seldom experience super-refraction, whereas coastal areas are strongly affected, especially in regions where the temperature inversion and the trade winds are intense lying near the surface. Over land, seasonal averages of super-refraction frequencies reach 80% (40%) over moist areas year-round but remain below 40% (15%) in most other regions. Seasonal statistics exhibit a pronounced diurnal cycle of super-refraction occurrences, with averaged frequencies peaking at 60% in summer late afternoon over the areas located on the Atlantic Ocean border but inlands region are less affected with super-refractive cases by midday.

A Weather Research and Forecasting (WRF) model at a horizontal resolution of 12 km has been analysed for the month of July 2018 over the North region of Cameroon. For the first time, a high-resolution WRF version 3.7 is being run operationally over this part of the country for wet weather forecast. In such a study, detailed validation of the WRF model is crucial. Therefore, the validation of mean parameters including wind distribution, relative humidity and rainfall, over the entire region within all the forecast lead time, is essential. Validation is done by comparing WRF outputs to ERA5, ARC2 and observed upper air data. It is found that the model captures accurately relative humidity and low level wind events with sufficiently shorter lead times (3 days), while the same performance is observed for extreme precipitable water and rainfall but at longer lead time as well as the diurnal variability of these parameters associated with wet season at all lead times. Furthermore, the accuracy of WRF in predicting spatiotemporal changes of some atmospheric variables decreases with increase in lead time.