Impact of data assimilation on ocean current forecasts in the Angola Basin

Phillipson, Luke; Toumi, Ralf

doi:10.1016/j.ocemod.2017.04.006

Cited by 15 publications

(16 citation statements)

References 52 publications

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“…Finally, the work of Phillipson and Toumi (2017) assesses the added value of assimilating OSCAR velocity fields in Figure 15. Surface salinity field (daily average) corresponding to 14 November 2010 without assimilation (a) and after assimilation (b).…”

Section: Dvarmentioning

confidence: 99%

“…In the context of remotely sensed velocity fields Santoki et al (2013) were able to reduce the errors of the surface currents in a simulation of the Indian Ocean by assimilating 5-day, 1 • × 1 • OSCAR currents. More recently, Phillipson and Toumi (2017) found that adding OS-CAR velocities in their assimilation scheme did not improve the forecasting skill obtained when drifters were assimilated alone. One of the reasons pointed out by the authors was the low-frequency sampling (5 days) of the OSCAR currents, together with the variable coverage of the satellite data used to derive OSCAR.…”

Section: Introductionmentioning

confidence: 99%

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Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Isern‐Fontanet

Ballabrera-Poy

Turiel

et al. 2017

Nonlin. Processes Geophys.

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Abstract. Ocean currents play a key role in Earth's climate -they impact almost any process taking place in the ocean and are of major importance for navigation and human activities at sea. Nevertheless, their observation and forecasting are still difficult. First, no observing system is able to provide direct measurements of global ocean currents on synoptic scales. Consequently, it has been necessary to use sea surface height and sea surface temperature measurements and refer to dynamical frameworks to derive the velocity field. Second, the assimilation of the velocity field into numerical models of ocean circulation is difficult mainly due to lack of data. Recent experiments that assimilate coastal-based radar data have shown that ocean currents will contribute to increasing the forecast skill of surface currents, but require application in multidata assimilation approaches to better identify the thermohaline structure of the ocean. In this paper we review the current knowledge in these fields and provide a global and systematic view of the technologies to retrieve ocean velocities in the upper ocean and the available approaches to assimilate this information into ocean models.

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Section: Dvarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Isern‐Fontanet

Ballabrera-Poy

Turiel

et al. 2017

Nonlin. Processes Geophys.

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“…In addition to the model forecasts, a baseline of persistence was included. Since the SMOS dataset represents a 9-day moving average, good persistence skill in the plume statistics are highly likely, especially in an area with slowly evolving dynamics [23,36], where persistence forecast excels over 16 days [37]. Consequently, for many plume metrics over most months, the SMOS PERSIST forecast did indeed perform the best with the lowest errors and largest average improvement over the CNTRL (85-95%).…”

Section: Forecastmentioning

confidence: 95%

“…The observation error for satellite altimetry measuring sea surface height is assigned as 0.04 m as in [23].…”

Section: Assimilation Schemementioning

confidence: 99%

Assimilation of Satellite Salinity for Modelling the Congo River Plume

Phillipson

Toumi

2019

Remote Sensing

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Satellite salinity data from the Soil Moisture and Ocean Salinity (SMOS) mission was recently enhanced, increasing the spatial extent near the coast that eluded earlier versions. In a pilot attempt we assimilate this data into a coastal ocean model (ROMS) using variational assimilation and, for the first time, investigate the impact on the simulation of a major river plume (the Congo River). Four experiments were undertaken consisting of a control (without data assimilation) and the assimilation of either sea surface height (SSH), SMOS and the combination of both, SMOS SSH. Several metrics specific to the plume were utilised, including the area of the plume, distance to the centre of mass, orientation and average salinity. The assimilation of SMOS and combined SMOS SSH consistently produced the best results in the plume analysis. Argo float salinity profiles provided independent verification of the forecast. The SMOS or SMOS SSH forecast produced the closest agreement for Argo profiles over the whole domain (outside and inside the plume) for three of four months analysed, improving over the control and a persistence baseline. The number of samples of Argo floats determined to be inside the plume were limited. Nevertheless, for the limited plume-detected floats the largest improvements were found for the SMOS or SMOS SSH forecast for two of the four months.

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“…An example of a reasonable choice of M is the commonly used Lagrangian separation distance (Barron et al, 2007;Berta et al, 2015;Castellari et al, 2001;Liu et al, 2014;Phillipson & Toumi, 2017;Rixen & Ferreira-Coelho, 2007;Muscarella et al, 2015), that is, the separation distance between simulated trajectories (computed via an advection scheme) and observed drifters (km):…”

Section: Methodsmentioning

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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Impact of data assimilation on ocean current forecasts in the Angola Basin

Cited by 15 publications

References 52 publications

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

Assimilation of Satellite Salinity for Modelling the Congo River Plume

The Crossover Time as an Evaluation of Ocean Models Against Persistence

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