The Meteorological Research Institute of the Japan Meteorological Agency has developed a cloudresolving nonhydrostatic 4-dimensional variational assimilation system (NHM-4DVAR), based on the Japan Meteorological Agency Nonhydrostatic Model (JMA-NHM), in order to investigate the mechanism of heavy rainfall events induced by mesoscale convective systems (MCSs). A horizontal resolution of the NHM-4DVAR is set to 2 km to resolve MCSs, and the length of the assimilation window is 1-hour. The control variables of the NHM-4DVAR are horizontal wind, vertical wind, nonhydrostatic pressure, potential temperature, surface pressure and pseudo relative humidity. Perturbations to the dynamical processes, and the advection of water vapor are considered, but these to the other physical processes are not taken into account.The NHM-4DVAR is applied to the heavy rainfall event observed at Nerima, central part of Tokyo metropolitan area, on 21 July 1999. Doppler radar's radial wind data, Global Positioning System's precipitable water vapor data, and surface temperature and wind data are assimilated as high temporal and spatial resolution data. The Nerima heavy rainfall is well reproduced in the assimilation and subseCorresponding author: Takuya Kawabata, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: tkawabat@mri-jma.go.jp 1 Present affiliation: Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.( 2007, Meteorological Society of Japan quent forecast, with respect to time sequence of 10-minute rainfall amount. The formation mechanism of the Nerima heavy rainfall is clarified from this study. A surface convergence line of horizontal winds was made of a southerly sea breeze and north-easterly winds over the Kanto plain around Nerima. Since the rise of temperature over the northern part of the Kanto plain was suppressed, due to a shield of clouds against sunshine, the difference of temperature between the convergence line and its northern side became large. Consequently, the wind convergence was enhanced around Nerima. An air with high equivalent potential temperature was lifted over this enhanced convergence line to generate cumulonimbi that caused the Nerima heavy rainfall.

A cloud-resolving nonhydrostatic four-dimensional variational data assimilation system (NHM-4DVAR) was modified to directly assimilate radar reflectivity and applied to a data assimilation experiment using actual observations of a heavy rainfall event. Modifications included development of an adjoint model of the warm rain process, extension of control variables, and development of an observation operator for radar reflectivity.The responses of the modified NHM-4DVAR were confirmed by single-observation assimilation experiments for an isolated deep convection, using pseudo-observations of rainwater at the initial and end times of the data assimilation window. The results showed that the intensity of convection could be adjusted by assimilating appropriate observations of rainwater near the convection and that undesirable convection could be suppressed by assimilating small or no reflectivity.An assimilation experiment using actual observations of a local heavy rainfall in the Tokyo, Japan, metropolitan area was conducted with a horizontal resolution of 2 km. Precipitable water vapor derived from global positioning system data was assimilated at 5-min intervals within 30-min assimilation windows, and surface and wind profiler data were assimilated at 10-min intervals. Doppler radial wind and radar-reflectivity data below the elevation angle of 5.48 were assimilated at 1-min intervals.The 4DVAR assimilation reproduced a line-shaped rainband with a shape and intensity consistent with the observation. Assimilation of radar-reflectivity data intensified the rainband and suppressed false convection. The simulated rainband lasted for 1 h in the extended forecast and then gradually decayed. Sustaining the low-level convergence produced by northerly winds in the western part of the rainband was key to prolonging the predictability of the convective system.

A mesoscale ensemble prediction system (EPS) employing the Japan Meteorological Agency's (JMA's) highresolution global analysis and forecast for initial and boundary conditions of the control run and perturbations from JMA's one-week global EPS for initial and boundary perturbations is developed and applied to numerical simulations of cyclone Nargis. Using the JMA nonhydrostatic model (NHM) with a horizontal resolution of 10 km, the system reproduces Nargis' development and the associated storm surge in southwestern Myanmar with plausible ensemble spreads.In the ensemble prediction with initial boundary perturbations, predicted positions of cyclone centers are distributed in an elliptic area whose major axis is oriented east-northeast, suggesting that track forecast errors tend to increase in the moving direction of Nargis. The location of the minimum surface pressure of the ensemble mean is closer to the best track than the control run, and root mean square errors (RMSEs) of the ensemble mean against analyses are smaller than those of the control run in all forecast variables. However, ensemble spreads tend to decrease in the latter half of the forecast period, and the cyclone center does not disperse enough compared with the track forecast error without the lateral boundary perturbation.When lateral boundary perturbations are implemented in addition to the initial perturbations, dispersion of the cyclone center and spread of the center pressure increase by about 50% at forecast time (FT) ¼ 42. The location of the minimum surface pressure in the ensemble mean shifts westward, reducing the track error. RMSEs of ensemble means become smaller than the ensemble prediction without lateral boundary perturbations.Ensemble forecasts of storm surge were conducted using the Princeton Ocean Model (POM). When surface wind and sea level pressure from JMA's global EPS were input, the maximum surge was no more than 0.6 m even in the highest ensemble member. The POM simulation driven by the mesoscale ensemble prediction with NHM predicted a storm surge near 4 m in southwestern Myanmar, where the timings of the peak surge were dispersed widely from FT ¼ 33 to FT ¼ 56. When the ensemble mean was input to POM, the maximum surge was 1.5 m, despite the better accuracy of the ensemble mean in terms of RMSE. This result shows that the scenario is more important than the ensemble mean when applying the mesoscale ensemble prediction to disaster prevention.

This work quantifies the benefits of using a high-resolution atmosphere–ocean coupled model in tropical cyclone (TC) intensity forecasts in the vicinity of Japan. To do so, a large number of high-resolution calculations were performed by running the Japan Meteorological Agency (JMA) nonhydrostatic atmospheric mesoscale model (AMSM) and atmosphere–ocean coupled mesoscale model (CMSM). A total of 281 3-day forecasts were compiled for 34 TCs from April 2009 to September 2012 for each model. The performance of these models is compared with the JMA global atmospheric spectral model (GSM) that is used for the operational TC intensity guidance. The TC intensities are better predicted by CMSM than the other models. The improvement rates in CMSM relative to GSM and AMSM generally increase with increasing forecast time (FT). CMSM is better than GSM and AMSM by 27.4% and 21.3% at FT = 48 h in terms of minimum sea level pressure, respectively. Regarding the maximum wind speed, CMSM is better than GSM and AMSM by 12.8% and 19.5% at FT = 48 h, respectively. This is due to smaller initial intensity errors and sea surface cooling consistent with in situ observations that suppress erroneous TC intensification. Thus, a high-resolution coupled model is promising for TC intensity prediction in the area surrounding Japan, where most of the TCs are in a decay stage. In contrast, coupling to the upper-ocean model yields only a negligible difference in the TC track forecast skill on average.