Abstract/Summary

Recent work has demonstrated that skillful hybrid statistical-dynamical forecasts of heavy rainfall events in Southeast Asia can be made by combining model forecasts of the phases and amplitudes of Kelvin, Rossby and Westward Moving Rossby Gravity waves with climatological rainfall statistics conditioned on these waves. This study explores the sensitivity of this hybrid forecast to its parameter choices and compares its skill in forecasting extreme rainfall events in the Philippines, Malaysia, Indonesia and Vietnam to that of the Met Office Global and Regional Ensemble Prediction System (MOGREPS). The hybrid forecast is found to outperform both the global and convection-permitting ensemble in some regions when forecasting the most extreme events, however for less extreme events the ensemble is found more skillful. A weighted blend of the MOGREPS forecasts and the hybrid forecast was found to have the highest skill of all for almost all definitions of extreme event and in most regions. To quantify the influence of errors in the predicted wave state on the skill of the hybrid forecast, the skill of a hypothetical best case forecast was also calculated using reanalysis data to specify the wave amplitudes and phases. This best case forecast indicates that errors in the forecasts of all wave types reduce the skill of hybrid forecast, however the reduction in skill is largest for Kelvin waves. The skill in convection-permitting models is greater than for global models in the regions where Kelvin waves dominate, but the added-value of limited area high resolution forecasts is hampered by the poor representation of Kelvin waves in the parent global model.