Evangelinos, C., P.F.J. Lermusiaux, J. Xu, P.J. Haley, and C.N. Hill, 2009. Many Task Computing for Multidisciplinary Ocean Sciences: Real-Time Uncertainty Prediction and Data Assimilation. Conference on High Performance Networking and Computing, Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers (Portland, OR, 16 November 2009), 10pp. doi.acm.org/10.1145/1646468.1646482.
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 stochastic PE model runs, (iii) continuous
generation of the covariance matrix, (iv) successive
computations of the SVD of the ensemble spread until a
convergence criterion is satisfied, and (v) data assimilation.
Its ensemble nature makes it a many task data intensive application
and its dynamic workflow gives it heterogeneity.
Subsequent acoustics propagation modeling involves a very
large ensemble of short-in-duration acoustics runs.