The DOTSTAR Observations in Improving Tropical Cyclones Forecast using Ensemble-based Data Assimilation

An ensemble Kalman filter data assimilation system for the Weather Research and Forecasting model is used to explore the Dropsonde Observation for Typhoon Surveillance near the TAiwan Region (DOTSTAR) observations in improving the tropical cyclones forecast. The case selected for this study is Typhoon Morakot (2009), a Western Pacific storm that brought record-breaking rainfall to Taiwan. A 50-member convection-permitting ensemble predicts that observations located in the southwest quadrant of the typhoon will have the highest impact on reducing the forecast uncertainty of the track, intensity and rainfall of Morakot. Several experiments demonstrate that assimilating DOTSTAR observations located in regions with higher predicted observation impacts will lead to a better rainfall and track forecast than in regions with smaller predicted impacts. However, not all the observation located in high impact regions lead to the same improvement in terms of the observations locations and observation errors. The inconsistency may be due to strong nonlinearity in the governing dynamics of the typhoon (e.g., moist convection), the accuracy of the sample ensemble background covariance, and the adaptive localization technique .