An Overview of Operational Oceanography

Schiller, A.; Mourre, Baptiste; Drillet, Yann; Brassington, Gary B.

doi:10.17125/gov2018.ch01

Cited by 12 publications

(13 citation statements)

References 24 publications

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“…The observational approach includes the systematic and long-term routine measurements of the oceans and their rapid interpretation and dissemination. Ocean forecasting is based on the near-real-time collection of ocean observations that are assimilated into numerical models to provide short-term forecasts (5 to 10 days) (Schiller et al [24]). It is only recently that operational biogeochemical forecasting systems have been developed by extending existing physical forecasting ocean systems, combining them with atmospheric models and also with biogeochemical models developed either for climate research or for ecological modeling (Ford et al [25]).…”

Section: Earth System Modeling: Physical Drivers For Oil Spill Modelingmentioning

confidence: 99%

Progress in Operational Modeling in Support of Oil Spill Response

Barker

Kourafalou

Beegle-Krause

et al. 2020

JMSE

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Following the 2010 Deepwater Horizon accident of a massive blow-out in the Gulf of Mexico, scientists from government, industry, and academia collaborated to advance oil spill modeling and share best practices in model algorithms, parameterizations, and application protocols. This synergy was greatly enhanced by research funded under the Gulf of Mexico Research Initiative (GoMRI), a 10-year enterprise that allowed unprecedented collection of observations and data products, novel experiments, and international collaborations that focused on the Gulf of Mexico, but resulted in the generation of scientific findings and tools of broader value. Operational oil spill modeling greatly benefited from research during the GoMRI decade. This paper provides a comprehensive synthesis of the related scientific advances, remaining challenges, and future outlook. Two main modeling components are discussed: Ocean circulation and oil spill models, to provide details on all attributes that contribute to the success and limitations of the integrated oil spill forecasts. These forecasts are discussed in tandem with uncertainty factors and methods to mitigate them. The paper focuses on operational aspects of oil spill modeling and forecasting, including examples of international operational center practices, observational needs, communication protocols, and promising new methodologies.

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Section: Earth System Modeling: Physical Drivers For Oil Spill Modelingmentioning

confidence: 99%

Progress in Operational Modeling in Support of Oil Spill Response

Barker

Kourafalou

Beegle-Krause

et al. 2020

JMSE

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“…Overall, this study shows the utility of our state-ofthe-art assimilative ocean models in providing insights into the drift of particles in the ocean surface. An overview of current and future developments of various assimilative models is described in detail in Schiller et al (2018). We showed the importance of including Stokes drift, and the need for better quantitative understanding of its magnitude and effect, and also highlighted the high level of accuracy in the numerical schemes which are able to follow the particles over ocean-basin scales (O(1000) km) and gyre timescales (several years).…”

Section: Discussionmentioning

confidence: 99%

Strategies for simulating the drift of marine debris

Durgadoo

Biastoch

New

et al. 2019

Journal of Operational Oceanography

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Modelling the drift of marine debris in quasi-real time can be of societal relevance. One pertinent example is Malaysia Airlines flight MH370. The aircraft is assumed to have crashed in the Indian Ocean, leaving floating wreckage to drift on the surface. Some of these items were recovered around the western Indian Ocean. We use ocean currents simulated by an operational ocean model in conjunction with surface Stokes drift to determine the possible paths taken by the debris. We consider: (1) How important is the influence of surface waves on the drift? (2) What are the relative benefits of forward-and backward-tracking in time? (3) Does including information from more items refine the most probable crash-site region? Our results highlight a critical contribution of Stokes drift and emphasise the need to know precisely the buoyancy characteristics of the items. The differences between the tracking approaches provide a measure of uncertainty which can be minimised by simulating a sufficiently large number of virtual debris. Given the uncertainties associated with the timings of the debris sightings, we show that at least 5 items are required to achieve an optimal most probable crash-site region. The results have implications for other drift simulation applications. ARTICLE HISTORY

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“…Rapidly increasing activities in the ocean, such as marine transportation, oil, and gas industry, fishery, and maritime search and rescue operations, require highquality oceanic reanalyses, estimates of the current state, and short-range predictions of both global and regional domains (Schiller et al 2018). Because of the smaller Rossby deformation radius in the ocean than in the atmosphere, the scales of oceanic mesoscale motions are at least an order smaller than those of atmospheric ones.…”

Section: Introductionmentioning

confidence: 99%

“…The first operational global-or basin-scale forecast system was built in the late 1990s. Many regional forecast systems have been made while high-resolution satellite observations, supercomputers, and advanced methods of ocean data assimilations are routinely available (Schiller et al 2018). Ocean forecast systems have been significantly improved in the last two decades (Tonani et al 2015;Schiller et al 2018).…”

Section: Introductionmentioning

confidence: 99%

A global eddy-resolving ocean forecast system in China – LICOM Forecast System (LFS)

Liu

Lin

Zheng

et al. 2021

Journal of Operational Oceanography

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A global eddy-resolving forecast system LFS is developed based on a primitive ocean general circulation model. The system's configuration, forecast experiments, and a preliminary evaluation of the preoperational system are shown. In the preoperational stage, since the full data assimilation has not yet been set up for LFS, the initial state is obtained by nudging the ocean temperature and salinity from an ocean analysis dataset. Despite this, the LFS demonstrates a generally good performance in short-term oceanography forecasting, except for sea level anomaly (SLA). The median values of the 1-day forecast leading root mean square error (RMSE) for the sea surface temperature (SST), SLA, upper 2000 m temperature, and salinity are approximately 0.52°C, 0.10 m, 0.57°C and 0.13 psu, respectively. Although there are slight warm biases in the forecasted SST, the forecasts of temperature and salinity in the thermocline by the LFS are comparable with the results of operational oceanography systems under the framework of the Intercomparison and Validation Task Team. However, the forecast SLA has a relatively large RMSE related to the absence of direct observational constraints in the initial state. Further investigations are needed to improve the performance of LFS.

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An Overview of Operational Oceanography

Cited by 12 publications

References 24 publications

Progress in Operational Modeling in Support of Oil Spill Response

Progress in Operational Modeling in Support of Oil Spill Response

Strategies for simulating the drift of marine debris

A global eddy-resolving ocean forecast system in China – LICOM Forecast System (LFS)

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