NASDAC · iNnovative Approaches for Scalable Data Assimilation in oCeanography

iNnovative Approaches for Scalable Data Assimilation in oCeanography

The project focus is on the Predictive Science as the paradigm shift of the emerging CSE (Computational Science and Engineering) which tightly integrates the numerical simulations of Computational Science and Engineering with Validation and Verification and Uncertainty Quantication (UQ). UQ is an essential ingredient of Predictive Science, whose aim is to not only reproduce with high fidelity an observed phenomenon, but also to predict the reality in absence of measurements. To this end, reliable numerical predictions require complex nonlinear physical models as well as a systematic and comprehensive treatment of calibration and validation procedures, including the quantication of inherent uncertainties. Furthermore, because the equations governing physical model contain multiscale, multilevel nonlinear spatio-temporal interactions that use ever more data and ner model grid resolutions, due to the inherent ill conditioning of the underlying mathematical models, increasing size of the computational data leads to increasing amount of uncertainties. Therefore, it is crucial to assess the impact of these uncertainties on future predictions. The planned result was the redesign of the software stack, the main innovations being situated at the medium and low level of the stack, ranging from the simultaneous introduction of space-and-time decomposition approaches (i.e. parallel in time (PINT) methods coupled with Hybrid Data Assimilation models (Ensemble and Variational), composition (additive or multiplicative) of Block Communication Avoiding Algorithms for preconditioned high-order nonlinear solvers that perform more computation to obtain greater accuracy for each computational degree of freedom; and additionally, at the lowest level, the reuse of recent scalable linear algebra solvers developed by other EU-funded and still active projects. The planned results aimed at changing the way we think about the computational approach to simulation problems. Rather than applying more resources to an existing formulation to obtain a more accurate solution or to solve a larger problem, the proposed activity has provided an opportunity to loosen the grip of, or even remove, computationally-imposed simplications.

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