Dynamical tropical cyclone track forecast errors. Part II: Midlatitude circulation influences

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 Geophy sical Fluid Dynamics Laboratory model (GFDN) tropical cyclone track forecas ts in the western North Pacific during 1997 are examined. Error mechanisms that are more related to midlatitude circulations are described in this pap er and those errors that predominantly occur while the tropical cyclone (TC ) is still in the Tropics are addressed in a companion paper. Responsible e rror mechanisms are described by conceptual models that are all related to known tropical cyclone motion processes that are being misrepresented in th e dynamical models. As in the companion paper, characteristics and symptoms in the forecast tracks and model fields that accompany these frequently re curring error mechanisms are documented and illustrative case studies are p resented. Whereas 21 GFDN forecasts were degraded by an improper prediction of a midlatitude system evolution, only seven NOGAPS track forecasts had 7 2- 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 ci rculation of the TC becomes noticeably displaced downshear of the low- leve l center in the 48- 72- h forecast fields. Both the NOGAPS and GFDN forecas ts are degraded by the baroclinic cyclone interaction, and none of the five dynamical models provided consistently accurate or time- consistent TC tra ck forecasts in these situations. These midlatitude cases are more difficul t than the tropical errors to diagnose as multiple error sources may be pre sent. When a sudden deviation from previous track guidance or a track outlier fro m the other dynamical model guidance appears, the forecaster should diagnos e whether this is an error, or is indicative of a real track change. If the conceptual models of large- error mechanisms proposed from this retrospect ive study can be applied in real time, track forecasting will be improved.