Lagrangian predictability of high-resolution regional models: the special case of the Gulf of Mexico

Abstract

Abstract. The Lagrangian prediction skill (model ability to reproduce Lagrangian drifter trajectories) of the nowcast/forecast system developed for the Gulf of Mexico at the University of Colorado at Boulder is examined through comparison with real drifter observations. Model prediction error (MPE), singular values (SVs) and irreversible-skill time (IT) are used as quantitative measures of the examination. Divergent (poloidal) and nondivergent (toroidal) components of the circulation attractor at 50 m depth are analyzed and compared with the Lagrangian drifter buoy data using the empirical orthogonal function (EOF) decomposition and the measures, respectively. Irregular (probably, chaotic) dynamics of the circulation attractor reproduced by the nowcast/forecast system is analyzed through Lyapunov dimension, global entropies, toroidal and poloidal kinetic energies. The results allow assuming exponential growth of prediction error on the attractor. On the other hand, the q-th moment of MPE grows by the power law with exponent of 3q/4. The probability density function (PDF) of MPE has a symmetrical but nonGaussian shape for both the short and long prediction times and for spatial scales ranging from 20 km to 300 km. The phenomenological model of MPE based on a diffusion-like equation is developed. The PDF of IT is non-symmetric with a long tail stretched towards large ITs. The power decay of the tail was faster than 2 for long prediction times.

Evaluation of trajectory modeling in different dynamic regions using normalized cumulative Lagrangian separation

“…Lagrangian trajectory evolution is a subject of many investigations [e.g., Özgökmen et al , 2000, 2001; Chu et al , 2004], one finding being that the prediction error tends to grow with time at a rate proportional to the square root of the velocity variance. Piterbarg [2001], in a study on short‐term Lagrangian trajectory prediction, shows that the prediction error is most sensitive to the ratio of the velocity correlation radius and the initial cluster radius.…”

On model validation for meso/submesoscale currents: Metrics and application to ROMS off Central California

“…Three possible scenarios of model-data comparison can be introduced (Chu et al, 2004a). First, the model reproduces the pattern of the real circulation attractor (the attractor is a robust dynamical regime of a flow) including its small-scale details, and may also predict flow behavior for long time intervals (global predictability).…”

Necessity and Proportionality in the Operation Enduring Freedom VII Campaign

“…The long-term (extreme long) predictability is not an "outlier" and shares the same statistical properties as the shortterm predictions (Chu et al 2002c). The FPT is also used to verify the model ability to predict Lagrangian drifter trajectories (Chu et al 2004) and the regional ocean model predictability to stochastic perturbations in initial conditions, winds, and open boundary conditions (Chu et al 2005).…”