Super-ensemble techniques: Application to surface drift prediction

Vandenbulcke, Luc; Beckers, Jean-Marie; Lenartz, Fabian; Barth, Alexander; Poulain, Pierre‐Marie; Aïdonidis, M.; Meyrat, J.; Ardhuin, Fabrice; Tonani, Marina; Fratianni, Claudia; Torrisi, L.; Pallela, D.; Chiggiato, Jacopo; Tudor, Martina; Book, Jeffrey W.; Martin, Paul J.; Peggion, Germana; Rixen, M.

doi:10.1016/j.pocean.2009.06.002

Cited by 59 publications

(40 citation statements)

References 36 publications

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“…As shown by the statistics, the Ibi36 system is the closest to observations with very good timing of mixing and re-stratification events and a good estimate of the mixed-layer depth. This is not surprising as this system benefits from the highest-resolution horizontal mesh, the highest-resolution atmospheric forcing, and also from the GLS scheme for the vertical mixing (Umlauf and Burchard, 2003;Reffray et al, 2014). For instance, on 13 May (Fig.…”

Section: Evaluation Of the Hindcastsmentioning

confidence: 88%

“…Moreover the spring stratification is a crucial phenomenon for the onset of phytoplankton blooms in this area (Mahadevan et al, 2012). Other studies quantifying the uncertainties in the ocean forecast for several oceanic fields (Lermusiaux et al, 2006) made use of superensemble techniques (Vandenbulcke et al, 2009;Lenartz et al, 2010;Pistoia, 2012;Scott et al, 2012) or have quantified the impact of medium-range atmospheric forecasting on the ocean (Drillet et al, 2009). An ensemble approach is also used in oceanography for estimating variability at a more climatic scale, for example in Zhu et al (2012), Xue et al (2012) and more recently in the Clivar Exchange special issue (http://www.clivar.org/node/1507).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

et al. 2014

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Abstract. Operational systems operated by Mercator Ocean provide daily ocean forecasts, and combining these forecasts we can produce ensemble forecast and uncertainty estimates. This study focuses on the mixed-layer depth in the Northeast Atlantic near the Porcupine Abyssal Plain for May 2013. This period is of interest for several reasons: (1) four Mercator Ocean operational systems provide daily forecasts at a horizontal resolution of 1/4, 1/12 and 1/36 • with different physics; (2) glider deployment under the OSMOSIS project provides observation of the changes in mixed-layer depth; (3) the ocean stratifies in May, but mixing events induced by gale force wind are observed and forecast by the systems. Statistical scores and forecast error quantification for each system and for the combined products are presented. Skill scores indicate that forecasts are consistently better than persistence, and temporal correlations between forecast and observations are greater than 0.8 even for the 4-day forecast. The impact of atmospheric forecast error, and for the wind field in particular (miss or time delay of a wind burst forecast), is also quantified in terms of occurrence and intensity of mixing or stratification events.

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Section: Evaluation Of the Hindcastsmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

et al. 2014

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“…How to combine models results is not obvious and some recent works have been done on this issue. One methodology is the hyper-ensemble technique (Rixen and Ferreira-Coelho 2007;Rixen et al 2008;Vandenbulcke et al 2009), which combines multiple model of different physical processes (ocean currents, winds, and waves) and then performs the trajectory simulations using the combined product. The optimal combination is obtained during a training period by minimizing the distance between modeled and observed trajectories and requires comprehensive observational networks.…”

Section: ) Wme (D-f) and Tyrr (G-i) Oil Concentration Is Visualizementioning

confidence: 99%

A relocatable ocean model in support of environmental emergencies

et al. 2014

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During the Costa Concordia emergency case, regional, subregional, and relocatable ocean models have been used together with the oil spill model, MEDSLIK-II, to provide ocean currents forecasts, possible oil spill scenarios, and drifters trajectories simulations. The models results together with the evaluation of their performances are presented in this paper. In particular, we focused this work on the implementation of the Interactive RElo- Concordia emergency and on its validation using drifters released in the area of the accident. It is shown that thanks to the capability of improving easily and quickly its configuration, the IRENOM results are of greater accuracy than the results achieved using regional or subregional model products. The model topography, the initialization procedures, and the horizontal resolution are the key model settings to be configured. Furthermore, the IRENOM currents and the MEDSLIK-II simulated trajectories showed to be sensitive to the spatial resolution of the meteorological fields used, providing higher prediction skills with higher resolution wind forcing.

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“…Persistence has been extensively used in meteorology ( Van den Dool, 2007) but less in oceanography (Farrara et al, 2013;Oey et al, 2005;Rowe et al, 2016;Shriver et al, 2007;Solabarrietaa et al, 2016;Vandenbulcke et al, 2009) as a performance benchmark. For quantifying improvements in ocean current forecasts, particularly the Lagrangian predictability, persistence has been typically taken as either a constant last known position (Barron et al, 2007;Kuang et al, 2012;Schmidt & Gangopadhyay, 2013;Ullman et al, 2006) or constant last known velocity (Paldor et al, 2004;Rixen & Ferreira-Coelho, 2007) of a surface-drifting buoy held constant.…”

Section: Introductionmentioning

confidence: 99%

The Crossover Time as an Evaluation of Ocean Models Against Persistence

Phillipson

Toumi

2018

Geophysical Research Letters

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A new ocean evaluation metric, the crossover time, is defined as the time it takes for a numerical model to equal the performance of persistence. As an example, the average crossover time calculated using the Lagrangian separation distance (the distance between simulated trajectories and observed drifters) for the global MERCATOR ocean model analysis is found to be about 6 days. Conversely, the model forecast has an average crossover time longer than 6 days, suggesting limited skill in Lagrangian predictability by the current generation of global ocean models. The crossover time of the velocity error is less than 3 days, which is similar to the average decorrelation time of the observed drifters. The crossover time is a useful measure to quantify future ocean model improvements.

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Super-ensemble techniques: Application to surface drift prediction

Cited by 59 publications

References 36 publications

Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

Forecasting the mixed-layer depth in the Northeast Atlantic: an ensemble approach, with uncertainties based on data from operational ocean forecasting systems

A relocatable ocean model in support of environmental emergencies

The Crossover Time as an Evaluation of Ocean Models Against Persistence

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