Improved ocean prediction skill and reduced uncertainty in the coastal region from multi-model super-ensembles

Rixen, M.; Book, Jeffrey W.; Carta, Alessandro; Grandi, V.; Gualdesi, L.; Stoner, R.; Ranelli, Peter; Cavanna, Andrea E.; Zanasca, Pietro; Baldasserini, Gisella; Trangeled, Alex; Lewis, Craig V.W.; Trees, C.; Grasso, Rafaelle; Giannechini, Simone; Fabiani, Alessio; Merani, Diego; Berni, Alessandro; Léonard, Michel; Martin, Paul J.; Rowley, Clark; Hulbert, Matthew H.; Quaid, Andrew; Goode, Wesley; Preller, Ruth H.; Pinardi, Nadia; Oddo, Paolo; Guarnieri, A.; Chiggiato, Jacopo; Carniel, Sandro; Russo, Aniello; Tudor, Martina; Lenartz, Fabian; Vandenbulcke, Luc

doi:10.1016/j.jmarsys.2009.01.014

Cited by 29 publications

(25 citation statements)

References 40 publications

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“…So, we are forced to compromise working to produce the most accurate models utilizing a minimal set of resources or utilizing additional resources to augment the computation of model reliability and variability. In current research, several assimilation techniques, e.g., Kalman filter-based ensemble forecasts (Rixen et al, 2009), and variational schemes, e.g., 4DVar (Powell et al, 2008), are under investigation in the modeling community, with the goal to get the model output closer to the observed values.…”

Section: Introductionmentioning

confidence: 99%

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

2016

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Effective study of ocean processes requires sampling over the duration of long (weeks to months) oscillation patterns. Such sampling requires persistent, autonomous underwater vehicles that have a similarly, long deployment duration. The spatiotemporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. In this paper, we consider the combination of two methods for reducing navigation and localization error: a predictive approach based on ocean model predictions and a prior information approach derived from terrain-based navigation. The motivation for this work is not only for real-time state estimation but also for accurately reconstructing the actual path that the vehicle traversed to contextualize the gathered data, with respect to the science question at hand. We present an application for the practical use of priors and predictions for large-scale ocean sampling. This combined approach builds upon previous works by the authors and accurately localizes the traversed path of an underwater glider over long-duration, ocean deployments. The proposed method takes advantage of the reliable, short-term predictions of an ocean model, and the utility of priors used in terrain-based navigation over areas of significant bathymetric relief to bound uncertainty error in dead-reckoning navigation. This method improves upon our previously published works by (1) demonstrating the utility of our terrain-based navigation method with multiple field trials and (2) presenting a hybrid algorithm that combines both approaches to bound navigational error and uncertainty for long-term deployments of underwater vehicles. We demonstrate the approach by examining data from actual field trials with autonomous underwater gliders and demonstrate an ability to estimate geographical location of an underwater glider to <100 m over paths of length >2 km. Utilizing the combined algorithm, we are able to prescribe an uncertainty bound for navigation and instruct the glider to surface if that bound is exceeded during a given mission.

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Section: Introductionmentioning

confidence: 99%

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

2016

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“…A data-assimilative numerical circulation model can overcome the spatio-temporal sampling limitations of in situ measurements and remote sensing and can, in addition, provide simulation and nowcast/forecast capabilities (Rixen et al 2009, Kantha 2005. However, it is rather difficult to assess the skill of a numerical model in producing accurate currents and circulation features, mostly because the verification data available are severely limited (Rixen et al 2009, Kantha 2005, Taillandier et al 2008.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is rather difficult to assess the skill of a numerical model in producing accurate currents and circulation features, mostly because the verification data available are severely limited (Rixen et al 2009, Kantha 2005, Taillandier et al 2008. The dearth of in situ data forces modelers to rely instead on more readily available remotely sensed data for model skill assessment.…”

Section: Introductionmentioning

confidence: 99%

On the use of a simple primary productivity model to assess the skill of a physical ocean model

Kantha

Carniel

Clayson

et al. 2011

Oceanological and Hydrobiological Studies

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Ecosystem models, used mainly in studying the interactions between different trophic levels, can also be used for ocean circulation model skill assessment, with the help of satellite ocean color data. This paper presents how the use of a simple NPZ primary productivity ecosystem model, coupled to a hydrodynamical model, can help assessing the skill of the physical ocean model in depicting realistically the prevailing mesoscale features of the upper layers of the Gulf of Mexico. Results indicate that the physical model effectively reproduces the mesoscale features of circulation underlying the resulting chlorophyll concentrations, especially when circulation fronts exist.

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“…Tropical precipitation forecasts (Krishnamurti et al, 2000a) and tracking of tropical cyclones in the Pacific (Kumar et al, 2003) also benefited from the application of SE techniques. During the last few years, the method has been further investigated with dynamical linear models, from the Kalman Filter (Shin and Krishnamurti, 2003a) to probabilistic approaches (Shin and Krishnamurti, 2003b) for short-to medium-range precipitation forecasts using satellite products. In oceanography, the use of multi-model statistics has been shown to improve the prediction of temperature (Logutov and Robinson, 2005;Rixen et al, 2009) and acoustic properties (Rixen and Ferreira-Coelho, 2006) in the water column.…”

Section: Introductionmentioning

confidence: 99%

“…During the last few years, the method has been further investigated with dynamical linear models, from the Kalman Filter (Shin and Krishnamurti, 2003a) to probabilistic approaches (Shin and Krishnamurti, 2003b) for short-to medium-range precipitation forecasts using satellite products. In oceanography, the use of multi-model statistics has been shown to improve the prediction of temperature (Logutov and Robinson, 2005;Rixen et al, 2009) and acoustic properties (Rixen and Ferreira-Coelho, 2006) in the water column. More recently, Rixen and Ferreira-Coelho (2007) introduced the concept of hyper-ensemble, combining models of different nature, to improve surface drift prediction; a method also evaluated in Vandenbulcke et al (2009).…”

Section: Introductionmentioning

confidence: 99%

Super-ensemble techniques applied to wave forecast: performance and limitations

et al. 2010

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Abstract. Nowadays, several operational ocean wave forecasts are available for a same region. These predictions may considerably differ, and to choose the best one is generally a difficult task. The super-ensemble approach, which consists in merging different forecasts and past observations into a single multi-model prediction system, is evaluated in this study. During the DART06 campaigns organized by the NATO Undersea Research Centre, four wave forecasting systems were simultaneously run in the Adriatic Sea, and significant wave height was measured at six stations as well as along the tracks of two remote sensors. This effort provided the necessary data set to compare the skills of various multi-model combination techniques. Our results indicate that a super-ensemble based on the Kalman Filter improves the forecast skills: The bias during both the hindcast and forecast periods is reduced, and the correlation coefficient is similar to that of the best individual model. The spatial extrapolation of local results is not straightforward and requires further investigation to be properly implemented.

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Improved ocean prediction skill and reduced uncertainty in the coastal region from multi-model super-ensembles

Cited by 29 publications

References 40 publications

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

On the use of a simple primary productivity model to assess the skill of a physical ocean model

Super-ensemble techniques applied to wave forecast: performance and limitations

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