**3. Applications to research**

#### **3.1 Mountain flow**

As described in section 2.1, NHM was first developed as a research tool at MRI. Ikawa and Nagasawa (1989) conducted a numerical experiment for a dynamically induced foehn event

convection, perturbation depending on relative humidity was added in the trigger function to reduce the overestimation of convective rain induced by orography. For the turbulent model, a Mellor and Yamada level-3 closure model (MYNN3; Nakanishi and Niino, 2004; 2006) was implemented first as the operational NWP model to reduce model bias in the boundary layer (Hara, 2007). In addition to the prognostic turbulent kinetic energy (*TKE*),

cloudiness, partial condensation computed by the probability density function in MYNN3

A nonhydrostatic 4D-Var data assimilation system (JNoVA; JMA Nonhydrostatic model based Variational data assimilation system) (Honda et al., 2005) was implemented in April 2009 to supply MSM more accurate initial conditions (Honda and Sawada, 2008). The horizontal resolution of the 4D-Var inner-loop model was enhanced from 20 km of Meso

Figure 1 plots the quantitative precipitation forecast (QPF) performance of MSM since it began actual operation (March 2001) to November 2011. In this figure, threat scores averaged for FT=3 to 15 for moderate rain with a threshold value of 5 mm in 3 hours are indicated. The verification grid size is 20 km. The averaged score in 2001 was about 0.2, but the score improved year by year, and the latest score approaches 0.4. Given the fact that statistical PQF performance of high resolution regional models is sometimes notoriously bad due to the difficulty of predicting mesoscale precipitation and the *double penalty* problem,

Table 1 lists the main modifications added to the operational mesoscale NWP at JMA from 2001 to 2011. In addition to the modifications discussed in the former subsections, the global positioning system (GPS)-derived total precipitable water vapor (TPWV) data (Ishikawa, 2010) has been assimilated since October 2009, and the 1D-Var retrieved water vapor data from radar reflectivity (Ikuta and Honda, 2011) has been used since June 2011. These modifications have contributed to the recent improvement of the QPF performance of MSM

The GPV of the operational MSM is used as input to the atmospheric transport model at MRI and JMA. This mesoscale ATM takes a Lagrangian scheme (Seino et al., 2004) with many tracer particles that follow advection, horizontal and vertical diffusion, fallout, and dry and wet deposition processes. JMA incorporated photochemical oxidant information in June 2007

As described in section 2.1, NHM was first developed as a research tool at MRI. Ikawa and Nagasawa (1989) conducted a numerical experiment for a dynamically induced foehn event

(Takano et al., 2007) and the tephra fall forecast in March 2008 (Shimbori et al., 2010).

was considered. Results of these modifications are given in Saito et al. (2007)

*<sup>l</sup>*'*qw*') were treated as prognostic variables. To evaluate the degree of

*<sup>l</sup>*'2), total water content (*qw*'2), and their

fluctuations of liquid water potential temperature (

correlation (

4D-Var to 15 km in JNoVA.

**2.2.4 QPF performance of MSM** 

this threat score improvement is remarkable.

through improving water vapor analysis.

**2.2.5 Mesoscale tracer transport model** 

**3. Applications to research** 

**3.1 Mountain flow** 

Fig. 1. Threat score of MSM for three-hour precipitation averaged for FT = 3 to 15 with a threshold value of 5 mm/3 hour from March 2001 to November 2011. The red broken line denotes the monthly value, while the black solid line indicates the 12-month running mean. Courtesy of NPD/JMA.


Table 1. Modifications for operational mesoscale NWP at JMA.

The JMA Nonhydrostatic Model and Its Applications to Operation and Research 93

Limited-Area Assimilation and Prediction System (BMRC LAPS). The left panel of Fig. 2 presents the visible satellite images over Melville and Bathurst Islands, Northern Territory of Australia, on 27 November 1995. At 1200 CST, shallow convective clouds corresponding to Rayleigh–Benard convection covered the interior of the islands. Along the southern coasts of the two islands, line-shaped clouds suggest organization associated with the sea breeze front (SBF). At 1300 CST, the clouds merged and organized at the central part of the islands in the form of an east–west line. An hour later (1400 CST), deep convection (*Hector*) developed at the southwestern part of Melville Island and along the southern coast of Bathurst Island. The right panel of Fig. 2 indicates the corresponding simulation with a 1 km NHM. Details of the observed evolution of the clouds on this day (Rayleigh–Benard convection, cloud merger along the convergence zone between the two SBFs, and

Fig. 2. Left: Visible image on 27 Nov 1995: (a) at 0230 UTC (1200 CST), (b) at 0330 UTC (1300 CST), and (c) at 0430 UTC (1400 CST). Right: Fields derived from the 1 km NHM simulation. (a) Cloud water mixing ratio at *z* = 1.46 km and *t* = 180 min (1300 CST). Contour interval is 0.1 g Kg-1. (b) Vertically accumulated cloud water at *t* = 240 min (1400 CST). Contour

interval is 0.1 Kg m-2. (c) Simulated cloudtop temperature at *t* = 300 min (1500 CST). Contour

On 26 July 2005, an intense rainfall system caused heavy rain in excess of 900 mm at Mumbai, on the west coast of India. This system was simulated by Seko et al. (2008) using the global analysis data of JMA as the initial condition of NHM. A maximum rainfall exceeding 1,100 mm in 17 hours was reproduced by the simulation with a horizontal

interval is 5 K. After Saito et al. (2001b).

succeeding explosive growth of deep convection) were very well reproduced.

observed in Hokkaido, northern Japan, using the 2-dimensional AE model. Inspired by their works, Saito and Ikawa (1991) conducted 2-dimensional simulation of the local downslope wind *Yamaji-kaze* in Shikoku, western Japan. The averaged orography of Shikoku Island in the east-west direction was regarded as the typical orography, and the development and propagation of an internal hydraulic jump were simulated by a numerical experiment using the observed thermal stratification and time-changing wind profile.

Saito (1993) conducted numerical experiments using the real orography of Shikoku Island with the surface friction, and studied the geographical characteristics of the Yamaji-kaze. Smith's (1980) linear analytic solution of the mountain flow over an isolated mountain was extended to the flow over a mountain range with a col, and compared with the non-linear aspect of the simulated flow.

Saito (1994) developed a double-nested model to reproduce the Yamaji-kaze of the 27 September 1991 windstorm. A realistic simulation of the observed phenomena was first conducted in Japan using a nested nonhydrostatic model with a horizontal resolution of 2.5 km. Good agreement between the simulation and the observed time evolution of surface wind was obtained when a strong typhoon approached western Japan.

The Kii Peninsula, central Japan, is famous for its abundant rainfall which reaches 3000 to 4000 mm a year. Airflow over the Kii Peninsula and its relation to the orographic enhancement of rainfall has been studied by Saito et al. (1994) and Murata (2009). Saito et al. (1998) compared the Deutscher Wetterdienst nonhydrostatic regional model(DWD LM)and MRI-NHM for numerical solutions of the 3-dimensional mountain waves, focusing on the computational efficiency of HI-VI and HE-VI schemes. Fujibe et al. (1999) studied diurnal wind variation in the lee of a mountain range using MRI-NHM and demonstrated agreement with the daytime advance of downslope wind in the Canterbury Plains in New Zealand.

A model intercomparison of mountain flow over a steep mountain was conducted by Satomura et al. (2003) as the Steep Mountain Model Intercomparison Project (St-MIP). To examine the accuracy of the terrain-following coordinates, mountain waves over twodimensional bell-shaped mountains with various half-widths and heights were compared with theoretical calculations and among models, including NHM.
