## 4 Results and Discussions
### Distribution of Surface Soil Moisture  
To examine the reliability of assimilated land surface emission, model simulations of soil moisture content were compared with AMSR-E brightness temperatures at 6.9 GHz and satellite-derived soil moisture products immediately after land data assimilation. Although a linear relationship cannot be assumed between the surface soil moisture content and brightness temperatures at 6.9 GHz, visual classification can be performed based on lower brightness temperatures being related to higher soil moisture content. Figure 3a,b represent the spatial distributions of simulated surface soil moisture for both the ARPS and the CALDAS models, and Figure 3c represents the distribution of brightness temperatures at 6.9 GHz. The magnitude of the soil moisture content simulated by ARPS (derived from NCEP-FNL) was higher in most of the model grids (particularly in the south) and produced completely different spatial distributions when compared with the observed brightness temperatures. Conversely, the distribution of CALDAS soil moisture content was reasonably comparable with that of brightness temperature at 6.9 GHz, and the clusters of dry and wet regions observed at 6.9 GHz were well defined in the CALDAS simulation.  
Figure 3: Spatial distribution of volumetric surface soil moisture (m\({}^{3}\)/m\({}^{3}\)) at 0200 UTC on 6 June 2006; (**a**) ARPS; (**b**) CALDAS; (**c**) dvanced Microwave Scanning Radiometer (AMSR)-E brightness temperature (K) observed at 6.9 GHz; (**d**) Japan Aerospace Exploration Agency (JAXA) product; and (**e**) National Aeronautics and Space Administration (NASA) product, respectively.  
Quantitative validation of assimilated soil moisture content was not possible owing to lack of _in situ_ soil moisture data during this period. Therefore, JAXA and NASA (c-band) soil moisture products were obtained for independent verification of the assimilated soil moisture information. As shown in Figure 3d,e, the distribution of CALDAS assimilated soil moisture was comparable and correlated reasonably well with the products of JAXA (Pearson product-moment correlation coefficient R = 0.6) and NASA (R = 0.5). It is also worth noting that the absolute values of soil moisture obtained from the JAXA and NASA products differed greatly, even though they both used the same satellite information. A previous study [26] on the validation of the JAXA and NASA satellite-derived soil moisture products reported significant differences between them and that neither provided reliable estimates for all the conditions represented by the four watershed sites in the United States. The significant differences between both products in our domain also indicated that further coordinated validation studies are necessary to understand and resolve the problems associated with the retrieval algorithms for African regions. However, as CALDAS performed better than ARPS and spatially, its soil moisture correlated well with the satellite-derived soil moisture products, the improved soil moisture content from CALDAS (using the lower frequencies) was used to calculate land surface emission at higher frequencies to facilitate the integration of cloud microphysics data by assimilating AMSR-E higher-frequency observations over land surfaces.