We investigated the impact of wave-ocean interaction on numerical predictions for Typhoon Hai-Tang in 2005 using a nonhydrostatic atmosphere model coupled with a third-generation ocean wave model and a mixed-layer ocean model. Here we address the effect of breaking surface waves on entrainment induced at the mixed-layer base, assuming that the turbulent transport due to breaking surface waves is expressed as a function of both waveinduced and surface wind stresses. The introduction of breaking surface waves into the coupled model enables us to reproduce the evolution of Hai-Tang's central pressure and sea-surface temperature (SST) distribution more realistically. SST decreases significantly (rarely) along Hai-Tang's track where the mixed-layer depth is relatively shallow (deep). Hai-Tang tends to intensify where the initial mixed layer is relatively deep along Hai-Tang's track. Introducing the wave-ocean interaction may lead to more precise tropical cyclone intensity prediction through more realistic reproduction of the SST distribution.

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.

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Numerical simulations of the 2008 Myanmar cyclone Nargis and the associated storm surge were conducted using the Japan Meteorological Agency (JMA) Nonhydrostatic Model (NHM) and the Princeton Ocean Model (POM). Although the JMA operational global analysis (GA) and the global spectral model (GSM) forecast underestimated Nargis' intensity, downscale experiments by NHM with a horizontal resolution of 10 km using GA and GSM forecast data reproduced the development of Nargis more properly.Sensitivity experiments to study the e¤ects of ice phase, sea surface temperature (SST), and horizontal resolutions to Nargis' rapid development were conducted. In a warm rain experiment, Nargis developed earlier and the eye radius became larger. It was shown that a high SST anomaly preexistent in the Bay of Bengal led to the rapid intensification of the cyclone, and that SST at least warmer than 29 C was necessary for the development seen in the experiment. In a simulation with a horizontal resolution of 5 km, the cyclone exhibited more distinct development and attained a center pressure of 968 hPa.Numerical experiments on the storm surge were performed with POM whose horizontal resolution is 3.5 km. An experiment with POM using GSM forecast data could not reproduce the storm surge, while a simulation using NHM forecast data predicted a rise in the sea surface level by over 3 m. A southerly sub-surface current driven by strong surface winds of the cyclone caused a storm surge in the river mouths in southern Myanmar facing the Andaman Sea.Our results demonstrate that the storm surge produced by Nargis was predictable two days before landfall by a downscale forecast with a mesoscale model using accessible operational numerical weather prediction (NWP) data and application of an ocean model.

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