scholarly journals Uncertainty quantification in ocean state estimation

2013 ◽  
Author(s):  
Alexander G. Kalmikov
Keyword(s):  
Uncertainty Quantification ◽  
State Estimation ◽  
Ocean State Estimation

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

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

scholarly journals A reduced-order Kalman smoother for (paleo-)ocean state estimation: assessment and application to the LGM

2019 ◽  
Author(s):  
Charlotte Breitkreuz ◽  
André Paul ◽  
Stefan Mulitza ◽  
Javier García-Pintado ◽  
Michael Schulz
Keyword(s):  
Data Assimilation ◽  
State Estimation ◽  
Adjoint Method ◽  
Computing Time ◽  
Oxygen Isotopic Composition ◽  
Proxy Data ◽  
Overturning Circulation ◽  
Control Variables ◽  
State Reduction ◽  
Ocean State Estimation

Abstract. Combining ocean models and proxy data via data assimilation is a powerful means to obtain more reliable estimates of past ocean states, but studies using data assimilation for paleo-ocean state estimation are rare. A few studies used the adjoint method, also called 4D-Var, to estimate the state of the ocean during the Last Glacial Maximum (LGM). The adjoint method, however, requires the adjoint of the model code, which is not easily obtained for most models. The method is computationally very demanding and does not readily provide uncertainty estimates. Here, we present a new and computationally very efficient technique to obtain ocean state estimates. We applied a state reduction approach in conjunction with a finite difference sensitivity-iterative Kalman smoother (FDS-IKS) to estimate spatially varying atmospheric forcing fields and to obtain an equilibrium model simulation in consistency with proxy data. We tested the method in synthetic pseudo-proxy data experiments. The method is capable of very efficiently estimating 16 control variables and reconstructing a target ocean circulation from sea surface temperature (SST) and oxygen isotopic composition of seawater data at LGM coverage. The method is advantageous over the adjoint method regarding that it is very easy to implement, it requires substantially less computing time and provides an uncertainty estimate of the estimated control variables. The computing time, however, depends linearly on the size of the control space limiting the number of control variables that can be estimated. We used the method to investigate the constraint of data outside of the Atlantic Ocean on the Atlantic overturning circulation. Our results indicate that while data from the Pacific or Indian Ocean aid in correctly estimating the Atlantic overturning circulation, they are not as crucial as the Atlantic data. We additionally applied the method to estimate the LGM ocean state constrained by a global SST reconstruction and data on the oxygen isotopic composition of calcite from fossil benthic and planktic foraminifera. The LGM estimate shows a large improvement compared to our first guess, but model-data misfits remain after the optimization due to model errors that cannot be corrected by the control variables. The estimate shows a shallower North Atlantic Deep Water and a weaker Atlantic overturning circulation compared to today in consistency with previous studies. The combination of the FDS-IKS and the state reduction approach is a step forward in making ocean state estimation and data assimilation applicable for complex and computationally expensive models and to models where the adjoint is not available.

scholarly journals Ocean state estimation from hydrography and velocity observations during EIFEX with a regional biogeochemical ocean circulation model

2014 ◽  
Vol 129 ◽  
pp. 437-451 ◽  
Author(s):  
Martin Losch ◽  
Volker Strass ◽  
Boris Cisewski ◽  
Christine Klaas ◽  
Richard G.J. Bellerby
Keyword(s):  
State Estimation ◽  
Ocean Circulation ◽  
Circulation Model ◽  
Ocean Circulation Model ◽  
Ocean State Estimation
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