Performance of convection-permitting hurricane initialization and prediction during 2008-2010 with ensemble data assimilation of inner-core airborne Doppler radar observations

This study examines a hurricane prediction system that uses an ensemble Kalman filter (EnKF) to assimilate high‐resolution airborne radar observations for convection‐permitting hurricane initialization and forecasting. This system demonstrated very promising performance, especially on hurricane intensity forecasts, through experiments over all 61 applicable NOAA P‐3 airborne Doppler missions during the 2008–2010 Atlantic hurricane seasons. The mean absolute intensity forecast errors initialized with the EnKF‐analysis of the airborne Doppler observations at the 24‐ to 120‐h lead forecast times were 20–40% lower than the National Hurricane Center's official forecasts issued at similar times. This prototype system was first implemented in real‐time for Hurricane Ike (2008). It represents the first time that airborne Doppler radar observations were successfully assimilated in real‐time into a hurricane prediction model. It also represents the first time that the convection‐permitting ensemble analyses and forecasts for hurricanes were performed in real‐time. Also unprecedented was the on‐demand usage of more than 23,000 computer cluster processors simultaneously in real‐time. Key Points Systematic use of airborne Doppler radar data Ensemble based analysis and prediction for cloud‐resolving hurricane prediction Significant improvement in hurricane intensity hurricane