analysis and prediction of a noisy nonlinear ocean
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
Analysis and Prediction of a Noisy Nonlinear OceanAnalysis and Prediction of a Noisy Nonlinear Ocean
With:L. Ehret, M. Maltrud, J. McClean, and
G. Vernieres
With:L. Ehret, M. Maltrud, J. McClean, and
G. Vernieres
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
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
Modeling and AnalysisModeling and Analysis
The Dynamical Systems Approach
The Dynamical Systems 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.
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 KuroshioExample: 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
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 ModelFine Resolution Model
1/10o North Pacific model
Courtesy J. McClean & M. Maltrud
1/10o North Pacific model
Courtesy J. McClean & M. Maltrud QuickTime™ and a
decompressorare needed to see this picture.
Model-Data ComparisonModel-Data Comparison
Model-Data ComparisonModel-Data Comparison
2-Level QG Model2-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
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 KuroshioTypical steady states of the Kuroshio(adapted from Kawabe, 1995)
Typical steady states of the KuroshioTypical steady states of the Kuroshio(adapted from Kawabe, 1995)
nNLM: nearshore non largemeander
ssh [cm] and geostrophic velocities [m/s] (AVISO merged TOPEX/POSEIDON products)
Typical steady states of the KuroshioTypical steady states of the Kuroshio(adapted from Kawabe, 1995)
oNLM: offshore non largemeander
Typical steady states of the KuroshioTypical steady states of the Kuroshio(adapted from Kawabe, 1995)
tLM: typical largemeander
ssh [cm] and geostrophic velocities [m/s] (AVISO merged TOPEX/POSEIDON products)
tLM
nNLM
nNLMLM
Contours of ssh
tLM
nNLM
nNLMLM
Contours of ssh
Unstable
tLM
nNLM
nNLMLM
Contours of ssh
Stable
Qualitative comparison with satellite data
Qualitative comparison with satellite data
~4.0 km/day
Qualitative comparison with satellite data
~4.0 km/day
~4.0 km/day
Qualitative comparison with satellite data
~ 800 km
Qualitative comparison with satellite data
~ 800 km
~ 800 km
3 data points forthe assimilation!
Data / Forward model
We want to assimilate ssh data that spans the last transition from the nNLM to the tLM state
QuickTime™ and a decompressor
are needed to see this picture.
SummarySummary
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
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