Numerical Simulation of Myanmar Cyclone Nargis and the Associated Storm Surge Part II: Ensemble Prediction

A mesoscale ensemble prediction system (EPS) employing the Japan Meteorological Agency’s (JMA’s) high-resolution 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.