Tropical Cyclone Data Impact Studies from T-PARC

The impacts of dropsondes and enhanced atmospheric motion vectors on tropical cyclone track forecasts are examined in a series of experiments using the Navy global operational data assimilation and forecasting systems. Two tropical cyclones, Nuri and Jangmi, are examined in detail, with a focus on how model error may influence or lessen data impact. For Nuri, it is found that dropsondes and enhanced atmospheric motions vectors are at least as likely to degrade as to improve forecasts. Examination of the synoptic forecast fields indicates that a persistent forecast bias resulting in a weakening of the subtropical anticyclone over the western North Pacific results in erroneous recurvature that is not corrected with additional data. For Jangmi, additional data, particularly enhanced satellite winds, is found to improve the track forecasts in most cases. Examination of model biases indicate that a persistent weakening of the subtropical high is also contributing to the track errors for Jangmi, but there is evidence that assimilation of the enhanced satellite winds mitigates this error to some extent. Experiments in which the error assigned to synthetic tropical cyclone observations is increased lead to improvements in track forecasts on average for both Jangmi and Nuri, and more generally for all basins, suggesting that a reformulation of the synthetic tropical cyclone observation scheme may lead to improved forecasts as more in-situ and remote observations become available.