Meeting Abstracts
Lagrangian Predictive Skill Assessment of a Navy Coastal Ocean Model Ensemble in the Northern Gulf of Mexico
B. L. Lipphardt, H. S. Huntley, M. Sulman, A. D. Kirwan
(Abstract received 05/24/2012 for session C)
ABSTRACT
The explosion and sinking of the Deepwater Horizon drilling platform produced enormous human, ecological, and economic impacts. At the same time this disaster provided an unprecedented amount of Lagrangian information on ocean processes, including a large number of surface and near-surface drifters deployed in the northeastern Gulf of Mexico as well as remotely sensed images of the surface oil slick. In addition several global and regional ocean model predictions were used to forecast the spill movements. These models generally exhibited large variations in the mesoscale flow near the Deepwater Horizon site, even though they all assimilated similar sets of ocean observations. This provides a unique opportunity to thoroughly assess model Lagrangian predictive skill.
Here, the predictive skill of a regional implementation of the Navy Coastal Ocean Model (NCOM) is evaluated using data from more than 160 drifters in the northern Gulf of Mexico during May through August 2010. The model produced daily ensemble forecasts with sixteen ensemble members. The drifter tracks were divided into non-overlapping uniform three-day segments, with "launches" at three-day intervals, at midnight. This scheme produced 1197 trajectory segments. Observed trajectories over three days are compared with ensemble trajectory predictions. Statistics from a number of skill metrics are used to quantify the ensemble's Lagrangian predictive skill.