Reduced Order Modeling for Stochastic Prediction Onboard Autonomous Platforms at Sea
Heuss, J.P., P.J. Haley, Jr., C. Mirabito, E. Coelho, M.C. Schönau, K. Heaney, and P.F.J. Lermusiaux, 2020. Reduced Order Modeling for Stochastic Prediction Onboard Autonomous Platforms at Sea. In: OCEANS '20 IEEE/MTS, 5-30 October 2020, pp. 1-10, doi:10.1109/IEEECONF38699.2020.9389149
We describe and investigate several Dynamic Mode Decomposition (DMD) and reduced order projection methods for regional stochastic ocean predictions. We then showcase some of their results as applied to a 300-member set of ensemble forecasts from the POSYDON-POINT sea experiment in the Middle Atlantic–New York Bight region for the period 23–27 August 2018 as well as to a 42-day data-driven reanalysis from the AWACS–SW06 sea experiment in the Middle Atlantic Bight region for the period 14 August to 24 September 2006. We discuss these results for use by autonomous platforms in uncertain scenarios as well the combination of DMD with ideas from large-ensemble forecasting and Dynamically-Orthogonal (DO) differential equations.
