U.S. GODAE: Sustained Global Ocean State Estimation for Scientific and Practical Application

2004 ◽  
Author(s):  
Carl Wunsch ◽  
Ichiro Fukumori ◽  
Tong Lee ◽  
Dimitris Menemenlis ◽  
David W. Behringer ◽  
...  
Keyword(s):  
State Estimation ◽  
Global Ocean ◽  
Practical Application ◽  
Ocean State Estimation

U.S. GODAE: Sustained Global Ocean State Estimation for Scientific and Practical Application

2007 ◽  
Author(s):  
Carl Wunsch ◽  
Ichiro Fukumori ◽  
Tong Lee ◽  
Dimitris Menemenlis ◽  
David W. Behringer ◽  
...  
Keyword(s):  
State Estimation ◽  
Global Ocean ◽  
Practical Application ◽  
Ocean State Estimation

scholarly journals Improvement of Ocean State Estimation by Assimilating Mapped Argo Drift Data

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Shuhei Masuda ◽  
Nozomi Sugiura ◽  
Satoshi Osafune ◽  
Toshimasa Doi
Keyword(s):  
State Estimation ◽  
Current Data ◽  
Global Ocean ◽  
Ocean Current ◽  
Oceanic Circulation ◽  
Synthesis System ◽  
Wind Stress Curl ◽  
Data Synthesis ◽  
Ocean State Estimation ◽  
The Impact

We investigated the impact of assimilating a mapped dataset of subsurface ocean currents into an ocean state estimation. We carried out two global ocean state estimations from 2000 to 2007 using the K7 four-dimensional variational data synthesis system, one of which included an additional map of climatological geostrophic currents estimated from the global set of Argo floats. We assessed the representativeness of the volume transport in the two exercises. The assimilation of Argo ocean current data at only one level, 1000 dbar depth, had subtle impacts on the estimated volume transports, which were strongest in the subtropical North Pacific. The corrections at 10°N, where the impact was most notable, arose through the nearly complete offset of wind stress curl by the data synthesis system in conjunction with the first mode baroclinic Rossby wave adjustment. Our results imply that subsurface current data can be effective for improving the estimation of global oceanic circulation by a data synthesis.

scholarly journals Assessing ENSO Simulations and Predictions Using Adjoint Ocean State Estimation

2004 ◽  
Vol 17 (22) ◽  
pp. 4301-4315 ◽  
Author(s):  
Dietmar Dommenget ◽  
Detlef Stammer
Keyword(s):  
State Estimation ◽  
Positive Impact ◽  
Coupled Model ◽  
Estimation Procedure ◽  
Time Interval ◽  
Seasonal Forecasts ◽  
Predictive Skill ◽  
Sst Anomaly ◽  
Ocean State Estimation

Abstract Simulations and seasonal forecasts of tropical Pacific SST and subsurface fields that are based on the global Consortium for Estimating the Circulation and Climate of the Ocean (ECCO) ocean-state estimation procedure are investigated. As compared to similar results from a traditional ENSO simulation and forecast procedure, the hindcast of the constrained ocean state is significantly closer to observed surface and subsurface conditions. The skill of the 12-month lead SST forecast in the equatorial Pacific is comparable in both approaches. The optimization appears to have better skill in the SST anomaly correlations, suggesting that the initial ocean conditions and forcing corrections calculated by the ocean-state estimation do have a positive impact on the predictive skill. However, the optimized forecast skill is currently limited by the low quality of the statistical atmosphere. Progress is expected from optimizing a coupled model over a longer time interval with the coupling statistics being part of the control vector.

scholarly journals Ocean State Estimation for Climate Research

Oceanography ◽  
2009 ◽  
Vol 22 (3) ◽  
pp. 160-167 ◽  
Author(s):  
Tong Lee ◽  
Toshiyuki Awaji ◽  
Magdalena Balmaseda ◽  
Eric Greiner ◽  
Detlef Stammer
Keyword(s):  
State Estimation ◽  
Climate Research ◽  
Ocean State Estimation

scholarly journals Controllability, not chaos, key criterion for ocean state estimation

2017 ◽  
Vol 24 (3) ◽  
pp. 351-366 ◽  
Author(s):  
Geoffrey Gebbie ◽  
Tsung-Lin Hsieh
Keyword(s):  
Boundary Conditions ◽  
State Estimation ◽  
Lagrange Multiplier ◽  
Weather Prediction ◽  
Lagrange Multiplier Method ◽  
Multiplier Method ◽  
Estimation Methods ◽  
Surface Boundary ◽  
Highly Nonlinear ◽  
Ocean State Estimation

Abstract. The Lagrange multiplier method for combining observations and models (i.e., the adjoint method or 4D-VAR) has been avoided or approximated when the numerical model is highly nonlinear or chaotic. This approach has been adopted primarily due to difficulties in the initialization of low-dimensional chaotic models, where the search for optimal initial conditions by gradient-descent algorithms is hampered by multiple local minima. Although initialization is an important task for numerical weather prediction, ocean state estimation usually demands an additional task – a solution of the time-dependent surface boundary conditions that result from atmosphere–ocean interaction. Here, we apply the Lagrange multiplier method to an analogous boundary control problem, tracking the trajectory of the forced chaotic pendulum. Contrary to previous assertions, it is demonstrated that the Lagrange multiplier method can track multiple chaotic transitions through time, so long as the boundary conditions render the system controllable. Thus, the nonlinear timescale poses no limit to the time interval for successful Lagrange multiplier-based estimation. That the key criterion is controllability, not a pure measure of dynamical stability or chaos, illustrates the similarities between the Lagrange multiplier method and other state estimation methods. The results with the chaotic pendulum suggest that nonlinearity should not be a fundamental obstacle to ocean state estimation with eddy-resolving models, especially when using an improved first-guess trajectory.

Temperature profiling measurements by sea turtles improve ocean state estimation in the Kuroshio-Oyashio Confluence region

2018 ◽  
Vol 69 (2) ◽  
pp. 267-282 ◽  
Author(s):  
Yasumasa Miyazawa ◽  
Akira Kuwano-Yoshida ◽  
Takeshi Doi ◽  
Hatsumi Nishikawa ◽  
Tomoko Narazaki ◽  
...  
Keyword(s):  
State Estimation ◽  
Sea Turtles ◽  
Ocean State Estimation ◽  
Confluence Region ◽  
The Kuroshio
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