*2.3.1 Result of the analyses*

Firstly, the difference of relative humidity between CTRL and TEST after the first data assimilation cycle is shown in **Figure 3**. The impact of surface data is clearly appeared, and positive values are widespread. In this case, numerical model underestimated the surface relative humidity; in other words, it predicted drier condition. However, owing to the dense surface data assimilation, the lower relative humidity was well modified.

Next, the rain mixing ratios [g kg−1] at the lowest model level (approximately 20-m altitude) at 1200JST and 1800 JST on September 9, 2015, are shown in **Figure 4** This period corresponds to observing the intense precipitation by gauge observation around the disaster area. In both cases, peak rainfall mixing ratios reached the order of 1 g kg−1, indicating that intense precipitation is occurring. However, in a quantitative comparison, particularly intense precipitation areas above 2 g kg−1 are only found in TEST. Focusing on the distribution of rain mixing ratio, the CTRL shows

*On the Use of the Ensemble Kalman Filter for Torrential Rainfall Forecasts DOI: http://dx.doi.org/10.5772/intechopen.107916*

#### **Figure 2.**

*An example of the observation sites of NCEP PREPBUFR. The valid time is 1200 UTC on December 1, 2019. (a) Aircraft observations, (b) satellite-assigned atmospheric tracking winds, (c) satellite scattered measured winds, (d) surface observations, (e) ship and buoy observations, and (f) radiosonde observations, respectively.*


#### **Table 1.**

*Observation elements in the NCEP PREPBUFR. The elements enumerate all the data stored in the files, including those that are diagnostically calculated.*

#### **Figure 3.**

*The difference of surface relative humidity between TEST and CTRL after the first data assimilation cycle at 0100 JST, September 7.*

#### **Figure 4.**

*Rain mixing ratio of the ensemble mean analyses at (a1, b1) 1200 JST, September 9, and (a2, b2) 1800 JST, September 9. Top and bottom rows correspond to TEST and CTRL, respectively.*

*On the Use of the Ensemble Kalman Filter for Torrential Rainfall Forecasts DOI: http://dx.doi.org/10.5772/intechopen.107916*

#### **Figure 5.**

*Six-hour accumulated precipitation amount [mm] in the forecasts initialized at (a) 1200 JST, September 9 and (b) 1800 JST, September 9.*

a northwesterly tilt of the precipitation zone. In CTRL, the path of typhoon No. 18, which brought rich moisture into the disaster area, had westerly bias, so the moist air mass from the typhoon No. 18 flowed into the different area from the actual observation. In TEST, the issue of shifting to the west remains; however, precipitation area extended in meridional direction, and it has consistency with the actual phenomenon. The dense surface data assimilation contributed to reproduce the distribution and amount of rain mixing ratio closer to reality than CTRL.

#### *2.3.2 Forecast experiment*

Here, 6-hour forecasts initialized at 1200 JST and 1800 JST were performed. The initial conditions come from the analyses of the ensemble mean of CTRL and TEST at each initial time. **Figure 5** shows 6-hour accumulated precipitation amount [mm] of the forecasts. For reference, analysis precipitation, which is the best estimation of surface precipitation intensity at 1-km resolution by JMA, is also shown. In CTRL, regardless of the initial time, the precipitation area is shifted about 100 km to the west compared with the JMA analyses. Moreover, accumulated precipitation amount in CTRL was underestimated. At 1800 JST, even though the peak precipitation amount was 80% level of the JMA analysis, a quite narrow rainband was represented. In TEST, dislocation of the precipitation areas was improved and quantitatively consistent with JMA analyses.
