Forecasting search areas using ensemble ocean circulation modeling
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We investigate trajectory forecasting as an application of ocean circulation ensemble modeling. The ensemble simulations are performed weekly, starting with assimilation of data for various variables from multiple sensors on a range of observational platforms. The ensemble is constructed from 100 members, and member no. 1 is designed as a standard (deterministic) simulation, providing us with a benchmark for the study. We demonstrate the value of the ensemble approach by validating simulated trajectories using data from ocean surface drifting buoys. We find that the ensemble average trajectories are generally closer to the observed trajectories than the corresponding results from a deterministic forecast. We also investigate an alternative model in which velocity perturbations are added to the deterministic results and ensemble mean results, by a first-order stochastic process. The parameters of the stochastic model are tuned to match the dispersion of the ensemble approach. Search areas from the stochastic model give a higher hit ratio of the observations than the results based on the ensemble. However, we find that this is a consequence of a positive skew of the area distribution of the convex hulls of the ensemble trajectory end points.
KeywordsModel validation Lagrangian forecasts Ensemble simulations Ocean modeling Search and rescue
This work has been performed under the MyOcean R&D project Exploring the potential for probabilistic forecasting in MyOcean. We are grateful for the funding from MyOcean/EU Project no. FP7-SPACE-2007-1. Support has also been available from the Norwegian Research Council under contract no. 196685/S40. Computer resources for the ensemble assimilation and model simulations were provided by the NOTUR supercomputing project, which is financed by the Research Council of Norway. Observations of satellite-tracked surface drifting buoys from The Global Drifter Program (http://www.aoml.noaa.gov/phod/dac/index.php) were downloaded from the real-time Coriolis Portal to the Data Buoy Cooperation Panel (DBCP). The analysis was performed using scripts developed for the R programming language (http://www.r-project.org). All figures were made using tools from the NCAR Command Language (NCL, http://ncl.ucar.edu). We thank the anonymous reviewers for their constructive comments.
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