Dynamical Tropical Cyclone Track Forecast Errors. Part II: Midlatitude Circulation Influences

Abstract

All highly erroneous (>300 n mi or 555 km at 72 h) Navy Operational Global Atmospheric Prediction System (NOGAPS) and U.S. Navy version of the Geophysical Fluid Dynamics Laboratory model (GFDN) tropical cyclone track forecasts in the western North Pacific during 1997 are examined. Error mechanisms that are more related to midlatitude circulations are described in this paper and those errors that predominantly occur while the tropical cyclone (TC) is still in the Tropics are addressed in a companion paper. Responsible error mechanisms are described by conceptual models that are all related to known tropical cyclone motion processes that are being misrepresented in the dynamical models. As in the companion paper, characteristics and symptoms in the forecast tracks and model fields that accompany these frequently recurring error mechanisms are documented and illustrative case studies are presented. Whereas 21 GFDN forecasts were degraded by an improper prediction of a midlatitude system evolution, only seven NOGAPS track forecasts had 72-h errors exceeding 300 n mi. The NOGAPS model is more prone to excessive responses to vertical shear, and a useful indicator is that the 500-mb circulation of the TC becomes noticeably displaced downshear of the low-level center in the 48?72-h forecast fields. Both the NOGAPS and GFDN forecasts are degraded by the baroclinic cyclone interaction, and none of the five dynamical models provided consistently accurate or time-consistent TC track forecasts in these situations. These midlatitude cases are more difficult than the tropical errors to diagnose as multiple error sources may be present. When a sudden deviation from previous track guidance or a track outlier from the other dynamical model guidance appears, the forecaster should diagnose whether this is an error, or is indicative of a real track change. If the conceptual models of large-error mechanisms proposed from this retrospective study can be applied in real time, track forecasting will be improved.