A new method is presented to provide automatic sequencing of multiple hydrodynamic models and automated analysis of model forecast uncertainty on a Linux based multi-processor workstation. A Hydrodynamic and oil spill model Python (HyosPy) wrapper was developed to run a sequence of hydrodynamic models, link with an oil spill model, and visualize results. HyosPy completes the following steps automatically: (1) downloads wind and tide data (nowcast, forecast and historical); (2) converts data to hydrodynamic model input; (3) initializes a sequence of hydrodynamic models starting at predefined intervals on a multi-processor workstation. Each model starts from the latest observed data, so that the multiple models provide a range of forecast hydrodynamics with different initial and boundary conditions reflecting different forecast horizons. The GNOME oil spill model and a Runge-Kutta 4th order (RK4) particle transport tracer routine are applied for oil spill transport simulation. As an advanced visualization strategy, the Google Maps/Earth GIS API is employed. The HyosPy integrated system with wind and tide force is demonstrated by introducing an imaginary oil spill in Corpus Christi Bay. The model forecast uncertainty is estimated by the difference between forecasts in the sequenced model runs and quantified by using simple statistical processing. This research show that challenges in operational oil spill modeling can be met by leveraging existing models and web-visualization methods to provide tools for emergency managers.
Visualizing Hydrodynamic Uncertainty in Operational Oil Spill Modeling
Xianlong Hou, Ben R. Hodges; Visualizing Hydrodynamic Uncertainty in Operational Oil Spill Modeling. International Oil Spill Conference Proceedings 1 May 2014; 2014 (1): 299013. doi: https://doi.org/10.7901/2169-3358-2014-1-299013.1
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