Impact of Enhanced Atmospheric Motion Vectors on HWRF Hurricane Analyses and Forecasts with DifferentData Assimilation Configurations

The impacts of enhanced satellite-derived atmospheric motion vectors (AMVs) on the numerical prediction of intensity changes of Hurricanes Gonzalo (2014) and Joaquin (2015) during ONR TCI Experiment are examined. Enhanced AMVs benefit from special data processing strategies and are examined for impact on model forecasts via assimilation experiments by employing the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting (HWRF) model using a gridpoint statistical interpolation (GSI)-based ensemble-variational hybrid system. Two different data assimilation (DA) configurations, one with and one without the use of vortex initialization (VI) are compared.

It is found that the assimilation of enhanced AMVs can improve the HWRF track and intensity forecasts of Gonzalo and Joaquin during their intensity change phases. The degree of data impact depends on the DA configuration used. Overall, assimilation of enhanced AMVs in the innermost HWRF domains (hurricane core region) outperforms other DA configurations, both with and without VI, as it results in better track and intensity forecasts. Compared to the experiment with VI, assimilation of enhanced AMVs without VI reveals more notable data impact on the hurricane forecasts. Even in the configuration with VI, assimilation of enhanced AMVs in the inner-core region can mitigate the unrealistic vortex weakening in the simulation of Hurricane Gonzalo by removing unrealistic outflow structure and unfavorable thermodynamic conditions, thus leading to improved intensity forecasts.