**2. The dense surface observation data assimilation for a forecast of a torrential rainband**

### **2.1 Introduction**

In the torrential rainfall event in Japan, band-shaped rainfall areas are often observed. In Japan, it is widely called "Senjo-Kousui-Tai," an example of translation is "linear rainband." According to an explanation by JMA, this type of rainband is explained as follows: a rainfall area with intense precipitation extending in 50–300 km length and 20–50 km width, created by organized cumulonimbus clouds, which are formed by a series of developing rain clouds passing or lingering in the same location for several hours [10]. As the development of observational networks, torrential rainbands are frequently captured, and they are one of the most essential factors in severe meteorological disasters. In this section, a record-breaking rainfall event in Kanto, Tohoku region, which is located in eastern Japan, in September 2015, is focused. In this event, the southerly winds from Typhoon No. 18, made landfall in

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

the central region of mainland of Japan and moved into the Sea of Japan. At about the same time, the southeasterly winds from Typhoon No. 17 moved northward in Pacific Ocean over the eastern Honshu region. This moist air mass flowed into the Kanto and southern Tohoku regions. The air mass maintained a pronounced convergence zone for a long time, resulting in the development of a series of torrential rain bands. Eventually, the accumulated precipitation since the beginning of the rainfall reached 647.5 mm, more than twice the monthly precipitation normal for September. In addition, 16 of the JMA's 1300 surface observation stations (Automated Meteorological Data Acquisition System; AMeDAS) recorded the highest maximum 24-hour precipitation in observation history [11, 12].

The active rain band itself had a scale of several hundred kilometers; however, the peak precipitation, which directly related to severe disasters, was concentrated in a narrow region. From the viewpoint of heavy rainfall forecasting and disaster prevention, it is desirable not only to improve the accuracy of simulation of a 100 km-scale phenomenon, but also to be able to simulate particularly intense local precipitation with pinpoint accuracy in terms of both location and amount. In this section, as a modern NWP study, a series of data assimilation experiments with dense surface observation data for the heavy rainfall event in September 2015 are reported. The surface observation data comes from the Environmental Sensor Network provided by NTT DoCoMo, which is a major mobile phone company in Japan, and it has about 4000 stations throughout Japan. This observation network has approximately 5-km special intervals and 1-minute temporal resolution at minimum. This study focuses on the impact of this dense surface observation data on the forecast for the rainfall.

### **2.2 Experimental design**

This series of experiments was performed by an NWP system called SCALE-LETKF [13]. It combines the Local Ensemble Transform Kalman Filter (LETKF) [14] with a regional numerical model Scalable Computing for Advanced Library and Environment (SCALE) [15].

The workflow is visualized in **Figure 1**. Firstly, 18-km-mesh model with 271x243x50 grid points, 6-hour-update, 50-member SCALE-LETKF was performed from 0000 JST on September 7, 2015, to 0000 JST on September 10, 2015. Initial condition of ensemble mean and boundary condition came from National Center for Environmental Prediction, Global Forecast System (NCEP GFS). The initial perturbation for generating initial ensemble state came from the perturbation components of NCEP Global Ensemble Forecast System (GEFS). The assimilated observation data are NCEP PREPBUFR. It consists conventional observation by airplanes, ships, buoys, satellites, surface stations, radiosondes. The locations and elements of the observation are summarized in **Figure 2** and **Table 1**; however, some observation systems such as the orbital satellites, airplanes, and ships are not stationary. Thus, it needs to pay attention that the locations in **Figure 2** are just an example at a single time. Next, 4-km-mesh, hourly update data assimilation experiments were performed. The initial ensemble states were interpolated from 18-km-mesh data at 0000 JST on September 7, 2015. The hourly boundary conditions came from a simple forecast at 18-km resolution initialized at 0000 JST on September 7, 2015. To investigate impacts of rich surface observation data, two experiments were performed. One experiment only assimilates NCEP PREPBUFR (CTRL). Although NCEP PREPBUFR is delivered every 6 hours, it has been divided into hourly segments with reference to time stamps for the hourly update LETKF cycles. The other assimilates rich surface observation

### **Figure 1.**

*The workflow of the data assimilation experiments. NCEP PREPBUFR was input both in CTRL and TEST, and surface observation was input in TEST only.*

data in addition to NCEP PREPBUFR (TEST). The surface data have been interpolated to the nearest numerical model grid point by bilinear scheme in horizontal and been applied an altitude correction using moist-adiabatic lapse rate in vertical as an observation operator for data assimilation. The surface stations observe wind velocity, pressure, temperature, relative humidity, rainfall amount, solar radiation, and detection of precipitation. In this experiment, wind velocity, pressure, temperature, and relative humidity were assimilated.
