## 1 Introduction  
Africa is a continent that is vulnerable to the effects of global warming and climate change/variability, which will increase the incidences of abnormal weather, such as frequent flooding and prolonged drought [1]. Numerical Weather Prediction (NWP) in West Africa is still underdeveloped because of several problems (e.g., lack of data, inadequate forecast system, difficulties in simulating land/atmospheric processes, and lack of forecast verification exercises). NWP is mainly associated with the initial-boundary-values problem; _i.e._, given an estimate of the initial state of the atmosphere and land surface, the model forecasts the evolutions of both. A greater number of high quality observations that more fully represent the complete nature of the initial atmosphere and land surface would greatly improve the forecasting capabilities of NWP models.Therefore, the lack of reliable in situ data with which to initialize the NWP model is the most challenging problem regarding forecasting in underdeveloped regions. The capability of remote sensing to contribute to the prediction of the Earth's weather and climatic systems has been demonstrated in many scenarios. Although many studies have been based on the use of satellite data in West Africa, only a few have considered the use of satellite data for NWP modeling and validation [2]. Accordingly, this research investigates the use of passive microwave satellite observations with a mesoscale model in West Africa. In microwave frequencies, the dielectric property of materials allows the quantitative estimation of moisture quantities such as soil moisture, vegetation water, snow water content, and cloud condensate [3; 4; 5; 6]. Thus, the potential for the use of space-borne microwaveobservations in NWP has been advanced considerably by the launch of several sophisticated platforms (e.g., Terra, Aqua, and the Global Change Observation Mission). Furthermore, low-frequency microwave observations provide information on soil moisture that is very useful for enhancing NWP model forecasting through improved initialization [7,8]. However, the few studies that have been performed on the assimilation of soil moisture observations into NWP models have shown several limitations (e.g., retrieval and preprocessing of soil moisture products, Cumulative Distribution Function matching, difficulties in defining an observation operator that changes spatially and temporarily, and time constraints for the use of near-real-time applications). Thus, they recommended the application of assimilation schemes that are more advanced [9,10]. To overcome such problems, an on-line system (_i.e._, Land Data Assimilation System coupled with Atmospheric model (LDAS-A)) was developed to directly assimilate lower frequency microwave radiance from the Advanced Microwave Scanning Radiometer (AMSR) for updating realistic soil moisture content in a mesoscale model and the results were presented in [11]. However, because of limited satellite observations (e.g., AMSR-E and AMSR-2, twice daily maximum), the improved land surface conditions often suffered from significant errors and drift due to biases in the predicted forcing (e.g., rainfall and solar radiation) that misguided the subsequent forecast [11,12]. Thus, this particular problem rendered the on-line land data assimilation ineffective in NWP models. Clouds directly affect surface forcing (_i.e._, downward radiation and rainfall), and thus they affect the estimation of the Earth's surface water and energy budgets. Similar to microwave sounding observations (e.g., Advanced Microwave Sounding Unit (AMSU)), passive microwave images (e.g., AMSR-E and AMSR-2) also contain information on cloud fields at higher frequencies (e.g., 89 GHz). It is also noteworthy to mention the Global Precipitation Measurement mission, which is the first coordinated international satellite network to provide near-real-time observations of radar and microwave images across a range of frequencies (10-183 GHz) every 3 h anywhere on the globe.