**3.5.3 Mesoscale ensemble prediction**

98 Atmospheric Model Applications

Although JNoVA contributed to improve the QPF performance of operational MSM, its horizontal grid spacing in the inner loop model is 15 km and only large-scale condensation is considered in the adjoint model. Kawabata et al. (2007) developed a cloud-resolving 4DVAR system based on the JMA-NHM (NHM-4DVAR), and applied it to reproduce the deep convection observed in Tokyo on 21 July 1999. The cost function to be minimized was

T 1 T 1

*b bo o J x x x x x Hx y Hx y* **B R** (18)

1 1 ( ) ( ) ( ) ( ) ( ), 2 2

where *x* denotes the model prognostic variables, subscript 0 means those at the beginning time of the assimilation window, and *x0b* is the first guess of *x0*. **B** represents the background error covariance matrix. *H* is the observation operator, *yo* is the observations, and **R** the observation error covariance matrix. Lateral boundary conditions were also included in the analysis. The adjoint model included only dry dynamics and advection of water vapor, but they successfully reproduced observed cumulonimbi by assimilating Doppler radar radial winds and GPS TPWV data. This study was the first to demonstrate the feasibility of shortrange forecasting of local heavy rainfall brought about by deep convection, using a full-scale

Kawabata et al. (2011) implemented the warm rain process into NHM-4DVAR and applied it to a data assimilation experiment of a heavy rainfall event in Tokyo on 5 September 2005 with a horizontal resolution of 2 km. GPS-TPWV data was assimilated at 5-min intervals within the 30-min assimilation windows, and surface in-situ data and wind profiler data were assimilated at 10-min intervals. Doppler radial winds and radar-reflectivity data were assimilated at 1-min intervals. The 4DVAR assimilation reproduced a line-shaped mesoscale convective system (MCS) with a shape and intensity consistent with the observation (Fig. 3). Assimilation of radar-reflectivity data intensified the MCS and suppressed false convection.

Fig. 3. Three-dimensional image of the MCS in Tokyo on 5 September 2005 reproduced by

0 00 00

numerical model and a dense observation network.

**3.5.2 Storm scale 4DVAR** 

formulated as

NHM-4DVAR.

Another reason of difficulty in predicting local heavy rainfall is the inherent low predictability of severe small-scale phenomena, that occur under convectively unstable atmospheric conditions. To consider forecast errors due to uncertainties in initial conditions and numerical models, ensemble prediction systems (EPSs) are widely used for medium range NWP, and there is now an increasing need to develop mesoscale EPSs.

Seko et al. (2009) conducted 11-member mesoscale ensemble experiments using NHM with a horizontal resolution of 15 km for two tornado events in Japan. Initial conditions were produced by adding the normalized perturbation of operational one-week ensemble forecast of JMA. Probabilities that the Energy Helicity Index (EHI) exceeds some criteria were examined, and feasibility of the probability forecast of tornadoes using the EPSderived potential parameters was indicated.

As discussed in 3.2.3, Saito et al. (2010a) conducted ensemble predictions of Nargis and the associated storm surge. NHM with a horizontal scale of 10 km was used for the 21 member ensemble prediction. In addition to the initial perturbations, the effect of lateral boundary perturbations (LBPs) on tropical cyclone ensemble prediction was examined. When LBPs were implemented, the ensemble spread increased by 50%, and root mean square errors (RMSEs) of the ensemble mean forecast became smaller than without LBPs.

A mesoscale singular vector (MSV) method was developed by Kunii (2010a). The tangent linear model and the adjoint model of NHM developed for JNoVA (Honda et al., 2005) were used in the Lanczos method with Gram-Schmidt re-orthogonalization to obtain the singular vector. Kunii (2010b) tested the MSV for a heavy rainfall event to assess MSV performance as the initial perturbation method. A torrential rainfall that occurred over central Japan on 29 August 2008 (Fig. 3a) was chosen. Ensemble forecast with a horizontal resolution of 15 km was conducted, where probabilistic values were determined by the proportion of members that predicted precipitation above a certain threshold. A greater-than-30% probability of precipitation was estimated even with the large threshold of 50 mm/3 hours (Fig. 3c), whereas the control forecast (Fig. 3b) predicted rainfall of only about 20 mm/3 hours.

Fig. 4. a) 3-hour accumulated precipitation at 1800 UTC 28 August 2008 (Radar AMeDAS analyzed rainfall). b) Rainfall predicted by a control forecast (FT = 06). c) Probability forecasts of 3-hour accumulated precipitation with a threshold of 50 mm. After Kunii (2010b).

Kunii and Saito (2009) conducted a sensitivity analysis experiment using MSV to support the THORPEX Pacific Asian Regional Campaign (T-PARC). For TY0813 (SINLAKU), MSVs-

The JMA Nonhydrostatic Model and Its Applications to Operation and Research 101

applied four-dimensional expansion of LETKF to NHM and performed data assimilation experiments in a perfect model scenario with 5-km grid spacing. The analysis equation for

<sup>1</sup> ( ) ( ( )) . *a a To f f f f x e*

Here, the overbar means the ensemble mean, and **X**f the ensemble perturbation matrix. **H** is the tangent linear of the observation operator, and *e* is an *m*-dimensional row vector. **P**a with tilde is the analysis error covariance matrix in the space spanned by forecast ensemble

This system, NHM-LETKF, was modified by Seko (2010) and was tested as the initial

Saito et al. (2012) examined the effects of LBPs on the MBD method and LETKF for mesoscale ensemble prediction. Introducing LBPs in the data assimilation cycles of LETKF improved the ensemble spread, the ensemble mean accuracy, and the performance of precipitation forecast. The accuracy of the LETKF analyses was compared with those of the Meso 4D-VAR analyses. With LBPs in the LETKF cycles, the RMSEs of the forecasts from the LETKF analyses improved, and some became comparable to those of the Meso 4D-VAR

Seko et al. (2011) performed data assimilation experiments with NHM-LETKF for an intense local rainfall event near the city of Kobe, western Japan, on 28 July 2008. They assimilated GEONET GPS TPWV data with conventional observation data. Adding GPS TPWV data tended to increase low-level water vapor and improve the precipitation forecast. The experiment with 5-km resolution generated a rainfall band in western Japan that was not reproduced using conventional data alone. The experiment with 1.6-km resolution

The next-generation supercomputer project, "Strategic Programs for Innovative Research (SPIRE)", is being carried out under a MEXT initiative. A supercomputer center was built in the city of Kobe by the RIKEN Advanced Institute for Computational Science (AICS). The supercomputer 'K' achieved the benchmark performance of 10.51 petaflops in November

The SPIRE project consists of five strategic research fields (life science and medicine, new material and energy, disaster prevention, engineering, and matter and universe). A five-year research plan of high performance NWP with cloud resolving ensemble data assimilation has been endorsed as a sub-subject of Field 3 in SPIRE (Saito et al., 2011c). The sub-subject

b. Development and validation of a cloud resolving ensemble analysis forecast system,

perturbation generator for the mesoscale ensemble prediction in the B08RDP project.

*y Hx e*

**X XP HX R X T** (19)

 

perturbations obtained by eigenvalue decomposition.

effectively reproduced the observed band of intense rainfall.

**4.1 Next generation supercomputer project in Japan** 

2011 with a total of 88,128 CPUs of the FUJITSU SAPARC64 processor.

c. Basic research using very high resolution atmospheric models.

a. Development of a cloud resolving 4 dimensional data assimilation system,

LETKF is:

analyses.

**4. Ongoing plans** 

and

on mesoscale NWP has three goals:

based sensitivity areas for the typhoon were located on the right side of the moving direction of the typhoon, which was dominated by potential energy components in the midlower troposphere. Compared with global model singular vectors (GSVs), MSVs reflected small scale structures that affect mesoscale disturbances.

A mesoscale EPS system using MSV and GSV is under development at JMA (Ono et al., 2011), assuming operational application.
