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Study Quantifies Influence of Data Input on Confidence in Loop Current Forecasts

April 11, 2017

Researchers described in a recent study a surrogate-based technique to quantify the uncertainty in forecasting the oceanic circulation. The authors focused on the time period during the Deepwater Horizon oil spill when an extended Loop Current increased the risk of carrying the oil slick towards the eastern seaboard of the U.S. The new methodology, which accounts explicitly for the inherent uncertainty in forecasts, may help improve the planning of emergency responses to weather and marine pollution events. The authors’ paper was published in the Journal of Geophysical Research: Oceans: Quantifying uncertainty in Gulf of Mexico forecasts stemming from uncertain initial conditions.

The accuracy and usefulness of material transport models depend on the quality of oceanic and atmospheric forecasts. However, the input data needed to run the forecast models are incomplete because observations are limited in space and time and may include measurement errors. The uncertainties in the model input lead to uncertainties in the model output. Useful forecasts should include a quantitative assessment of these uncertainties to better inform decisions such as evacuations or deploying resources. Probabilistic forecasts allow policy makers and emergency responders to consider a range of possible scenarios instead of only one best-guess scenario whose certainty is unclear.

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