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Enhancing Ocean Modeling for Predictive Capabilities

Learn about the recipe for developing ocean models to predict oceanic behavior, test models in hindcast mode, and achieve high performance through grid discretization and message-passing harness.

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Enhancing Ocean Modeling for Predictive Capabilities

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  1. Introduction and rationale Ocean models are needed to “fill-in the gaps” and provide a predictive capability Recipe for an ocean model: Derive mathematical equations describing the ocean’s evolution from an initial state subject to surface forcing. Discretize equations on a spatial grid (3-dimensional). Obtain initial state from observations. Obtain time varying surface forcing from observations or Numerical Weather Prediction program. Integrate equations forward in time from initial state. Test and develop model in hindcast mode.

  2. 1/12o 1/4o 1o 1o 1/4o 1/12o

  3. Recipe for a High Performance ocean model:. Discretize equations on a spatial grid (3-dimensional). Decompose grid into multiple overlapping tiles Introduce a message-passing harness to exchange information between tiles. Obtain time varying surface forcing from observations or Numerical Weather Prediction program. Integrate equations forward in time from initial state. Test and develop model in hindcast mode. A separate processor computes values in each differently coloured patch

  4. NEMO example 16x16 domain decomposition requiring 221 processors

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