Entry Date:
March 5, 2013

Stochastic Forcing for Ocean Uncertainty Prediction

Principal Investigator Pierre Lermusiaux

Co-investigator Patrick J Haley

The research vision is to develop and transform ocean modeling and data assimilation to quantify regional ocean dynamics on multiple scales. Our group creates and utilizes new models and methods for multiscale modeling, uncertainty quantification, data assimilation and the guidance of autonomous vehicles. We then apply these advances to better understand physical, acoustical and biological interactions. We seek both fundamental and applied contributions to build knowledge and benefit naval operations.

A main focus of this research is the role of stochastic forcing on ocean uncertainty and variability predictions. The work includes collaborations with NRL-Stennis to prepare the transfer of a subset of the capabilities and software developed by our Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) group. This applied research in stochastic modeling and ocean uncertainty prediction is linked to two growing fundamental fields: prediction and reduction of uncertainties; and, estimation of properties by combining models with data. From a fundamental viewpoint, uncertainty is characterized by a probability density function (pdf). One of the aims of the applied research and collaborations with NRL will be to improve the prediction of such pdfs.

The research thrusts for this effort include:
(*) Stochastic forcing and uncertainty/variability predictions
(*) Sensitivity analysis for forecast quality control, data-model comparisons and data error models
(*) Multiscale covariance modeling and mapping
(*) Ensemble initialization and generation, towards non-Gaussian ensemble initialization

Specific objectives are to:
(*) Develop, demonstrate and transfer techniques for stochastic error modeling and stochastic boundary forcing for improved ensemble uncertainty predictions with NCOM and COAMPS
(*) Develop and transfer software for ocean data management, quality control and automated robust distribution, including data error-models and data-model comparison codes
(*) Demonstrate and transfer techniques for multiscale covariance modeling and level-set-based objective analysis codes for mapping data in complex coastal/archipelago domains
(*) Develop and demonstrate ensemble initialization and generation schemes, towards non-Gaussian ensemble initialization
(*) Apply the above advances in collaborative sea exercises of opportunity
(*) Strengthen existing and initiate new collaborations with NRL, using and leveraging the MIT Naval Officer education program