Harvard Ocean Prediction System (HOPS)

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Presentation transcript:

Harvard Ocean Prediction System (HOPS) Technical Overview Patrick J. Haley, Jr. 13 November 2002

Patrick J. Haley, Jr.

HOPS Modules Core Additional Up and Coming Patrick J. Haley, Jr. Domain Set-Up Domain Definition Topography Conditioning Data Manipulation Conversion Quality Control Data Preparation Mapping (Objective Analysis) Preparation for Model Ingestion Dynamical Model Primitive Equation Terrain Following Coordinates Data Assimilation (OI) Biological Models 2-way Nesting External Forcing (atmospheric, tidal, river) Additional Error Sub-Space Statistical Estimation (ESSE) Data Assimilation Multi-Scale Energy and Vorticity Analysis (MS-EVA) Up and Coming Free Surface Patrick J. Haley, Jr.

HOPS PE Model Based on GFDL Bryan & Cox Arakawa B-Grid 2nd order finite difference Leap Frog Time Stepping Additions Open Boundary Conditions Shapiro Filter Subgrid Scale Parameterization Terrain Following Coordinates , hybrid, 2- robust pressure gradient algorithm OI Assimilation 2-way Nesting External Forcing (atmospheric, tidal, river) Coupled Biological Models Patrick J. Haley, Jr.

2-Way Nesting At each time step, independent estimates are made in each domain. Nesting algorithm “aligns” the estimates at each time step. Fine grid data is averaged to update coarse grid estimates. Collocation simplifies navigation between coarse nodes and supporting fine grid nodes. 3:1 ratio for scale matching Coarse grid data is interpolated onto fine grid boundaries Bi-Cubic interpolation. Compromise between: Smoothness across coarse grid boundaries “Footprint” of the interpolation Patrick J. Haley, Jr.

Focus Domains Support Domains Patrick J. Haley, Jr.

Issues Set Up OSSE Domain Set-Up Process Selection Model Tuning Baseline Topography Process Selection California Current Influence Rivers Tides OSSE Domain Definitions Extents Resolutions Vertical Distributions Topography Conditioning Model Tuning Parameters Assimilation Methodology Patrick J. Haley, Jr.