Abstract
In view of the fact that oil spill remote sensing could only generate the oil slick information at a specific time and that traditional oil spill simulation models were not designed to deal with dynamic conditions, a dynamic data-driven application system (DDDAS) was introduced. The DDDAS entails both the ability to incorporate additional data into an executing application and, in reverse, the ability of applications to dynamically steer the measurement process. Based on the DDDAS, combing a remote sensor system that detects oil spills with a numerical simulation, an integrated data processing, analysis, forecasting and emergency response system was established. Once an oil spill accident occurs, the DDDAS-based oil spill model receives information about the oil slick extracted from the dynamic remote sensor data in the simulation. Through comparison, information fusion and feedback updates, continuous and more precise oil spill simulation results can be obtained. Then, the simulation results can provide help for disaster control and clean-up. The Penglai, Xingang and Suizhong oil spill results showed our simulation model could increase the prediction accuracy and reduce the error caused by empirical parameters in existing simulation systems. Therefore, the DDDAS-based detection and simulation system can effectively improve oil spill simulation and diffusion forecasting, as well as provide decision-making information and technical support for emergency responses to oil spills.
Highlights
In recent years, frequent oil spills have affected the environment, economy and quality of life for coastal inhabitants [1]
We introduced the dynamic data-driven application system (DDDAS) into oil spill remote sensing detection and numerical simulation in order to minimize the influence caused by inaccurate parameters and to improve the predictive accuracy of oil spill simulations
The DDDAS can be viewed as a methodology to counterbalance incompleteness in the model and the capability to enhance the simulation models by imparting additional information into the model, as at runtime, additional data are used to selectively enhance or refine the original model
Summary
Frequent oil spills have affected the environment, economy and quality of life for coastal inhabitants [1]. For the first time, the combination of satellite images of surface oil slicks with Lagrangian trajectory models has been implemented in the operational oil spill trajectory hindcast/forecast for the Deepwater Horizon oil spill [2,14,15]; Zodiatis et al (2012). As for the combined model, the location and size of the surface oil slick are inferred from satellite images, and they should be frequently re-initialized to reduce the forecast errors, which could be accumulated from the initial locations (conditions), as learned from the rapid response to the Deepwater Horizon oil spill in the Gulf of Mexico [2,14,15]. We introduced the DDDAS into oil spill remote sensing detection and numerical simulation in order to minimize the influence caused by inaccurate parameters and to improve the predictive accuracy of oil spill simulations. For conveniently describing the introduced methodology completely, we chose the Penglai 19-3B platform oil spill incident as the key experimental case, and the other two cases were selected as method validation cases
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