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Analysis and Prediction of a Noisy Nonlinear Ocean. With: L. Ehret, M. Maltrud, J. McClean, and G. Vernieres. Modeling and Analysis. Ocean models predict the evolution of the ocean from approximate initial conditions according to incompletely resolved dynamics forced by approximate inputs.
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Analysis and Prediction of a Noisy Nonlinear Ocean With: L. Ehret, M. Maltrud, J. McClean, and G. Vernieres
Modeling and Analysis • Ocean models predict the evolution of the ocean from approximate initial conditions according to incompletely resolved dynamics forced by approximate inputs. • We treat noise according to the formalism of random processes. • Data assimilation is our best hope for resolving physical questions in terms of models and data • Most data assimilation schemes are based on linearized theory
TheDynamicalSystems Approach • Linear systems are characterized by their steady solutions and stability characteristics... • But the ocean is nonlinear, and nonlinear systems may have multiple stable solutions • Stable solutions may not be steady • Stability characteristics tell you about essential local behavior.
Example: The Kuroshio • The Kuroshio exhibits multiple states. Is it • A system with multiple equilibria? • A complex nonlinear oscillator? • Both? Neither? • Many plausible models give output that resembles the observations. • We show results from a QG model and a 1/10 degree PE model (J. McClean, M. Maltrud) • Use a variational method to assimilate satellite data (G. Vernieres) • Dynamical systems techniques and data assimilation will help us decide among models
Fine Resolution Model • 1/10o North Pacific model • Courtesy J. McClean & M. Maltrud
2-Level QG Model • 2-layer QG model on curvilinear grid • Assimilate SSH data at one point at three times • Strong constraint: adjust initial condition only • First guess contains a transition • Use representer method
Typical steady states of the Kuroshio (adapted from Kawabe, 1995)
Typical steady states of the Kuroshio (adapted from Kawabe, 1995) nNLM: nearshore non large meander ssh [cm] and geostrophic velocities [m/s] (AVISO merged TOPEX/POSEIDON products)
Typical steady states of the Kuroshio (adapted from Kawabe, 1995) oNLM: offshore non large meander
Typical steady states of the Kuroshio (adapted from Kawabe, 1995) tLM: typical large meander ssh [cm] and geostrophic velocities [m/s] (AVISO merged TOPEX/POSEIDON products)
Contours of ssh nNLM LM nNLM tLM
Contours of ssh nNLM LM Unstable nNLM tLM
Contours of ssh nNLM LM Stable nNLM tLM
Qualitative comparison with satellite data ~4.0km/day
Qualitative comparison with satellite data ~4.0km/day ~4.0 km/day
Qualitative comparison with satellite data ~ 800km ~ 800 km
3 data points for the assimilation!
Data / Forward model We want to assimilate ssh data that spans the last transition from the nNLM to the tLM state
Summary • Model nonlinear systems can behave as real world noisy nonlinear systems • Simplified systems share enough with detailed models, and both resemble observations sufficiently to make comparisons useful • Consequences of applying linearized techniques to intrinsically nonlinear systems are yet to be explored