Many task computing for multidisciplinary ocean sciences: real-time uncertainty prediction and data assimilation

Error Subspace Statistical Estimation (ESSE), an uncertainty prediction and data assimilation methodology employed for real-time ocean forecasts, is based on a characterization and prediction of the largest uncertainties. This is carried out by evolving an error subspace of variable size. We use an ensemble of stochastic model simulations, initialized based on an estimate of the dominant initial uncertainties, to predict the error subspace of the model fields. The dominant error covariance (generated via an SVD of the ensemble-generated error covariance matrix) is used for data assimilation. The resulting ocean fields are provided as the input to acoustic modeling, allowing for the prediction and study of the spatiotemporal variations in acoustic propagation and their uncertainties. The ESSE procedure is a classic case of Many Task Computing: These codes are managed based on dynamic workflows for the: (i) perturbation of the initial mean state, (ii) subsequent ensemble of stochasti...

• Type: Conference
• Year: 2009
• Where: SC
• Authors: Constantinos Evangelinos, Pierre F. J. Lermusiaux, Jinshan Xu, Patrick J. Haley, Chris N. Hill
• Keywords: Applied Computing, Data Assimilation, Error Covariance, Error Subspace, SC 2009