## 4 Results and Discussions
### Comparisons of Cloud Condensate With Satellite IR Observations  
The quantitative validation of individual or integrated atmospheric moisture variables (_i.e._, cloud liquid water, ice, snow, and hail) is still difficult, even at point locations, because of the unavailability of the necessary data sets. Therefore, to investigate the retrieval capability of the land and atmosphere coupled data assimilation processes and to evaluate the reliability of the assimilated cloud related parameters, qualitative comparisons were performed of the spatial distributions between vertically integrated condensate (summation of liquid and solid phases) and IR satellite data. The results were investigated \(\sim\)1 h after the assimilation (_i.e._, from 0300 UTC on 6 June 2006).  
As shown in the first column of Figure 4, the IR observation reflected very active cloud activity over the model domain, which was successfully introduced by the assimilation of passive microwave observations in CALDAS. The assimilated cloud distributions from CALDAS were coherent and compared well with the observed IR cloud cell distributions. Conversely, the ARPS and LDAS-A models predicted almost no cloud activity at this time. In addition, hourly evolutions of cloud activity (each column) showed that the seeding of cloud information using the 89-GHz observation within the model enhanced the forecasting capability of the model. Thus, the clouds predicted by CALDAS correlated well with IR observations 6 h after the assimilation, whereas ARPS and LDAS-A showed much lower cloud activity during this period. The identical results of cloud condensate obtained from ARPS and LDAS-A during the model forecasting showed that the assimilation of soil moisture did not improve the cloud simulation, and therefore, the reliable initialization of cloud information is shown to be crucial for improving weather forecasting during this particular period.  
**Figure 4.** Hourly variation of integrated condensate (kg/m\({}^{2}\)) and IR data (K); first row: ARPS, second row: Land Data Assimilation System coupled with Atmospheric model (LDAS-A), third row: CALDAS, and fourth row: infrared (IR) brightness temperature.  
Figure 5 depicts the diurnal variation in spatial correlations calculated between cloud condensate simulated by the models and IR observations for 6 June 2006. To eliminate the shift between the AMSR-E and IR observations, the IR data and the model's cloud condensate were scaled up to 0.25\({}^{\circ}\). Negative values in the spatial correlation indicate mismatches between the IR data and model simulations of cloud positions, whereas positive values indicate coherence between the IR observations and model simulations. As shown, the spatial correlations derived from ARPS and LDAS-A were very similar and showed mostly negative or very low spatial correlations with the IR observations. However, the CALDAS results showed a rapid increase (+0.49) in the spatial correlation at 0300 UTC. Two hours after the assimilation, the correlation reached a maximum of +0.52. This result indicates that CALDAS was reinitialized with improved cloud distributions (which coincided well with the IR data) and that the assimilation of cloud condensate enhanced the simulated cloud activities over the model domain. Moreover, the correlation of CALDAS was larger than ARPS and LDAS-A for about 6 h from the assimilation before decreasing. Therefore, CALDAS, as a multi-frequency assimilation system, has the potential to improve cloud information over land surfaces. However, quantitative information on each atmospheric moisture variable and microphysical property must be investigated in the future, using reliable data sets, to assess the full capabilities of the CALDAS model.