Abstract
The impacts of enhanced satellite-derived atmospheric motion vectors (AMVs) on the numerical prediction of intensity changes during Hurricanes Gonzalo (2014) and Joaquin (2015) 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 analysis system (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 domain (e.g., storm inner-core region and its immediate vicinity) 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 forecasts of Hurricane Gonzalo, as the VI before DA alters the first guess and reduces the actual number of AMV observations assimilated into the DA system. Even with VI, assimilation of enhanced AMVs in the inner-core region can at least partially mitigate the negative effect of VI on the intensity forecast of Hurricane Gonzalo and alleviate the unrealistic vortex weakening in the simulation by removing unrealistic outflow structure and unfavorable thermodynamic conditions, thus leading to improved intensity forecasts.
Ensemble Data Assimilation and Forecast Experiments for the September 2015 Heavy Rainfall Event in Kanto and Tohoku Regions with Atmospheric Motion Vectors from Himawari-8
