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A Hybrid Coordinate Ocean Model (HYCOM) For Data-Assimilative Ocean Modeling

A Hybrid Coordinate Ocean Model (HYCOM) For Data-Assimilative Ocean Modeling. A Hybrid Coordinate Ocean Model (HYCOM) For Data-Assimilative Ocean Modeling. A multi-institutional effort on the development and eval-

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A Hybrid Coordinate Ocean Model (HYCOM) For Data-Assimilative Ocean Modeling

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  1. A Hybrid Coordinate Ocean Model (HYCOM) For Data-Assimilative Ocean Modeling A Hybrid Coordinate Ocean Model (HYCOM) For Data-Assimilative Ocean Modeling

  2. A multi-institutional effort on the development and eval- • uation of a data-assimilative hybrid isopycnal-sigma-z (generalized coordinate) ocean model (called Hybrid • Coordinate Ocean Model or HYCOM.) • The partnering organizations are the University of Miami/ • RSMAS, the Naval Research Laboratory, NOAA/AOML, • the University of Minnesota, the Los Alamos National Labor- • atory, Planning Systems Inc., Orbital Image Corp., and the • U.S.Coast Guard.

  3. The hybrid coordinate, in the context of this consortium, • is one that is isopycnal in the open, stratified ocean, but • smoothly reverts to a terrain-following coordinate in • shallow coastal regions, and to z-level coordinates in the • mixed layer and/or unstratified seas. • The primary computational goal is the establishment of a • global real-time ocean forecast system with sophisticated • data assimilation techniques that can be efficiently executed • on massively parallel computers.

  4. The global model configuration is the one adopted by the • Los Alamos National Laboratory in a comparison between • HYCOM and POP (Parallel Ocean Program) and by the • National Center for Atmospheric Research Climate System • Model. This ensures that HYCOM’s set-up and forcing par- • ameterizations conform to the latest consensus on climate • modeling. • Coarse resolution model-based reanalysis of archived ob- • servational data will provide a comprehensive picture of • the dynamics and thermodynamics of the global ocean • during recent decades.

  5. Expertise on the model’s behavior with an eddy-resolving • grid will be gained by running the model in basin-scale • configurations using lateral boundary conditions provided • by the global simulations. • A new web-based tool that allows quicker access to both • data and software will facilitate the availability of those • products to the user community.

  6. Example of hybrid coordinates placement JULY JANUARY

  7. MICOM/HYCOM comparison with Kraus-Turner mixed layer physics MICOM HYCOM

  8. The capability of assigning additional coordinate surfaces • to the oceanic mixed layer gives us the option of replacing • the slab-type Kraus-Turner mixed layer with a more sophis- • ticated closure scheme, such as the K-Profile Parameter- • ization (KPP).

  9. HYCOM with KPP mixed layer SEPTEMBER MARCH

  10. Implementation Plan Phase I • This implementation plan summarizes the goals of • the consortium for • a) the release of HYCOM 1.0, • b) the global and basin scale simulations, • c) the data assimilation approach, • d) the evaluation of the results.

  11. HYCOM 1.0 • A single scalable source code implementing both MICOM • and HYCOM (replacing all previous versions) that runs on • a wide range of machines • K-Profile Parameterization (KPP) mixing model (already • implemented, tests underway). A Kraus-Turner option will • be provided • A global grid which smoothly connects the conventional • Mercator projection grid, covering all of the southern and • northern hemispheres to 50°N, to a “bipolar” grid generated • by placing two poles on opposite sides of the 50°N parallel • ("Pan Am" grid). The correct phase speed and latitudinal • structure of higher modes of equatorial waves will also be • facilitated by a reduction of the meridional mesh size in the • equatorial belt

  12. Two types of boundary conditions for areas not • bounded by land: • 1) the traditional "buffer zone" approach in which open • ocean boundaries are treated as closed, but are outfitted • with buffer zones in which temperature T and salinity S • are linearly relaxed toward climatological (or any other • observed) values • 2) "open" in the sense that relaxation to mass fluxes, • interface depths, T, S, and density is prescribed in a fin- • ite-width sponge zone, and that the barotropic pressure • and velocities are advected into/out of the domain via • characteristics

  13. Global HYCOM • HYCOM will be configured globally for the reanalysis • The first configuration will be a repeat of the “classic • MICOM” 1.4° simulation described in Sun and Bleck (1999) • forced with a climatology based primarily on COADS • A comparison to the MICOM solution as well as to • observed data will be carried out for a 50-year climato- • logical run • This simulation will be followed by a 20-year integration • forced with the twice-daily ECMWF forcing from 1979-1999

  14. North Atlantic HYCOM • This series of basin experiments will build on the DYNAMO • experience (1/3° grid spacing). The domain configuration will be • from 70°N to 20°S with relaxation to observed monthly values • (modified Levitus) at the northern boundary and to monthly • climatology (Levitus) at the southern boundary. • Three 20-year climatological simulations will be performed • with forcing from a) COADS, b) ECMWF, and c) NCEP. • The ECMWF and NCEP runs will then be integrated for • an additional 20 years with daily winds from the 1979-1999 • period. Freshwater flux will consist of observed precipitation • and computed evaporation + a small relaxation to monthly • climatological surface salinity. • Analysis: Performance metrics • Comparison to DYNAMO and CME results

  15. Data Assimilation: Global Domain • The global simulation will be used to provide boundary conditions • at the open boundaries since the high resolution model will be init- • ially limited to the Atlantic basin. Before assimilation, the global • simulations will be compared to data. • Results with climatological forcing will be first compared to the • Levitus climatology. Simulations with ECMWF forcing for 1979- • 1999 will then be closely compared to XBT data, in order to detect • systematic model-data differences that might be corrected and to • establish the nature of the error-covariances that will provide the • basis for assimilation. • Codes for assimilating data will be prepared in such a way that • simple methods can be replaced by more sophisticated methods • as they become available.

  16. Data Assimilation: Atlantic Basin • The basic configuration will be the 1/3° DYNAMO domain • forced by the 1979-1999 ECMWF. • Four assimilation routines will be evaluated: nudging, PMOA, • adaptive and Markov Random Field implementations of • Kalman filter. • The evaluation will consist of two primary experiments, twin • experiments with simulated Sea Surface Height (SSH) data and • the assimilation of measured SSH. The SSH anomalies from • TOPEX/Poseidon, ERS 1 and 2 will be used.

  17. The mean sea surface height (SSH) to be added to the altimetric • anomalies will be determined by comparing the mean SSH from • different products: • a) mean SSH from the 1/3° HYCOM over the time period • of the satellite data (1993-1999), • b) mean dynamic height calculated from BTs over the same period, • c) mean from a high resolution (MICOM) HYCOM (1/12°) model, • d) mean from a high resolution QG model. • The most realistic position of the Gulf Stream from these products • will be combined with the mean from the 1/3° HYCOM using a • rubber-sheeting technique. This mean will be used in the assimilation • of the real altimetry data.

  18. The four data assimilation techniques are: Nudging: A nudging assimilation technique will be imple- mented to assimilate satellite altimetry observations track by track. The implementation will be based on the nudging technique used in the Navy Layered Ocean Model (NLOM). It will include an optimal interpolation of the model/data SSH differences, a vertical projection of the surface informa- tion and a geostrophic correction to the velocities outside the equatorial region. The Parameter Matrix Objective Analysis (PMOA) algorithm: An OI scheme, with correlation parameters that vary in space and time for space-time interpolation of SSH, a vertical projection of the surface information, and a geo- strophic correction to the velocities outside the equatorial region.

  19. Adaptive Filter: The algorithm estimates the unknown para- meters at each data assimilation step by minimizing the fore- cast errors under some more-demanding hypothesis on both model and observation errors. The estimation process requires the use of the adjoint of a linearized version of the MICOM (HYCOM) model. The simple formulation of the filter is an Optimal Interpolation-like structure of the gain. The filter will be used to estimate vertical correlation coefficients of the fore- cast error covariance matrix. The Markov Random Field Information filter (MRFIF): The horizontal (cross-) covariance functions of SSH and ve- locities in an extended information filter are parameterized as second-order spatial Markov Random Fields. A vertical pro- jection scheme is used to correct lower-layer thicknesses and velocities.

  20. Performance Metrics for GODAE 1. Operational utility measures 2. Model climatology, mean and variability statistics, including spectral character 3. Dynamical performance 4. Forecast skill 5. Fit to assimilated data, including error statistics of the assimilation 6. Discrepancy between the model and data just prior to assimilation 7. Comparison to unassimilated data a. Same quantity (field) i. Same data type ii. Different data type b. Ability to constrain a sparsely observed field or a field for which the data are not assimilated 8. Real-time metrics vs. metrics applied to applications using historical data 9. Metrics measuring the ability to represent oceanic features vs. point by point statistics 10. Observing system assessment vs. data-assimilative model assessment 11. Assessment of data impact, atmospheric and oceanographic

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