Consensus forecasting for oil spill response



Eoin Howlett, Applied Science Associates, Rhode Island, USA


Blowout in the Timor Sea

In August 2009, the Montara wellhead platform in the Timor Sea suffered a blowout. Oil leaked continuously at the seabed for the next three months.

Australia’s national oil spill response plan was activated immediately, triggering the Australian Maritime Safety Authority (AMSA), an agency of the Australian government, to mobilize. AMSA, supported by the marine modeling company Applied Science Associates (ASA), provided metocean data analysis, integration of remotely-sensed oil observations and oil spill trajectory forecasts on a daily basis. Through their subcontract agreement for these services, ASA’s Asia-Pacific office (APASA) supported AMSA during the response, and together the team was able to apply new techniques and technologies to managing the problem of approximately 300 to 400 barrels of oil spilled each day (as estimated by PTTEP, September 2009).

Metocean Data challenge

The movement of oil (and other substances) on the water surface is driven by a combination of winds and ocean currents. One of the key challenges to providing oil spill trajectories is assimilating the wide variety of oceanographic and meteorological data. While atmospheric weather forecasting is now a routine part of everyday life for all of us, ocean forecasting is still a rapidly developing technology, offering the potential for a better understanding of our offshore marine environment. Many countries now operate ocean forecasting models with regional or global coverage, such as NCOM, operated by the US Navy (global), and BLUElink by a partnership of the Royal Australian Navy, Australia’s CSIRO and the Australian Bureau of Meteorology. Given the number of wind forecast datasets and now multiple ocean forecast datasets that are available, how does one choosewhich forecast to trust when planning a spill response or a search and rescue operation? One possible solution would be to test the various wind and ocean forecasts over time, and decide which performs best for the particular problem being addressed. However, oil spills and chemical spills are infrequent events, and there is no guarantee that a particular data source will provide the best forecast at the time of the spill.

New approach: consensus forecasting

Dr. Brian King, a senior oceanographer with APASA, says that oceanographers should follow the best-practice methodology used by weather forecasters, to take full advantage of the multiple wind and ocean forecasting datasets available.

“Weather forecasters use all available datasets, and assess each of them to develop a consensus of opinion from the various weather forecast models on what might occur,” says Dr. King. “With multiple ocean forecasting datasets available now, the same approach can be applied.”

 “For example”, says Dr. King, “oil spill models rely on good forecasts of both currents and weather, to accurately predict the oil’s future drift and potential impact zones. We use both winds and currents as input data to ASA’s OILMAP and CHEMMAP spill models, and have been able to successfully predict the movement of oil or chemicals over time, if our winds and currents have been accurate.”

To enable forecasters to take full advantage of the wealth of metoceandata available, ASA developed the Environmental Data Server (EDS) that provides ocean observations and forecasts to OILMAP, CHEMMAP, and other systems such as the U.S. Coast Guard’s SAROPS system. This allows maritime responders and scientists to undertake consensus forecasting for oil spills, chemical spills, and search and rescue incidents.

The EDS ensures that each of the multiple forecast datasets is readily available in a form that can be utilized immediately, so that model predictions using different metoceandata may be evaluated quickly during high-pressure response planning missions.

Dr. King offers the example of how this works in practice: “When you have access to multiple ocean and weather forecast datasets, the latest approach is to run the same spill scenario with different datasets. When you do this, you sometimes get very close agreement in the predictions even though you used different forecast datasets of winds and currents, and this gives you a higher level of confidence in the spill predictions.”

“If different forecast datasets result in disparate trajectories and outcomes, then you have multiple viable outcomes, but a low level of confidence in any one prediction. You can thus issue your spill forecasts with a confidence indicator, based on the degree of consensus obtained from the multiple analyses performed. We can also use field observations such as aircraft overflights, drifting buoys, or satellite-derived observations to help us estimate errors in the forecast data.”

New technology: the integrated EDS/OILMAP system

The OILMAP system connected to the EDS was used to respond to the Montara well blowout spill. Dr. King used the consensus forecasting approach with a matrix of different meteorological and oceanographic model forecasts to provide daily predictions of oil spill trajectories to the Australian response team. The system allowed ASA scientists to analyze hundreds of spill trajectories during the three month monitoring and response phase, and provided decision makers valuable information in the successful response to this major spill.

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