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Coupling%20COSMO%20with%20the%20WAM%20model

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Coupling COSMO with the WAM model Aron Roland (TUD, Darmstadt), Mathieu Dutour (IRB, Zagreb), Luigi Cavaleri (ISMAR, Venice), Luciana Bertotti (ISMAR, Venice) – PowerPoint PPT presentation

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Title: Coupling%20COSMO%20with%20the%20WAM%20model


1
Coupling COSMO with the WAM model
Aron Roland (TUD, Darmstadt), Mathieu Dutour
(IRB, Zagreb), Luigi Cavaleri (ISMAR, Venice),
Luciana Bertotti (ISMAR, Venice) and Lucio
Torrisi (CNMCA Rome).
2
Content
  • Motivation
  • Physics
  • The coupling library and its methodology
  • Validation of the coupling library
  • Conclusion

3
Motivation
  • Wind generate waves influence the atmosphere and
    determine the fluxes from the ocean to the
    atmosphere.
  • Waves are driving currents and currents are
    modulating the waves.
  • In order to have the full cycle we couple in this
    project the operationally used
  • COSMO (Atmosphere)
  • WAM (Waves)
  • ROMS (Currents) models
  • The leading model is here COSMO organizing the
    output, providing the forcing for the other two
    models and receiving the surface conditions from
    them.

4
Motivation
5
Some recent results of Hurricane Isabel using
coupled Ocean-Wave model (SELFE-WWMII) on
unstructured meshes driven by NARR (North
American Regional Reanalysis) winds
6
Some recent results of Isabel using coupled
Ocean-Wave model on unstructured meshes
7
Comparison of the sign. Wave height with the buoy
measurements (blue no currents, black with
currents)
8
Comparison of the average period with the buoy
measurements
9
Estimation Isabell storm surge
10
Atmospheric conditions during Xynthia courtesy
to Xavier Bertin, submitted to OM
11
Xynthia !Influence of wave induced surface drag
on the sea surface elevation courtesy to Xavier
Bertin, submitted to OM
12
Physical formulations potential for improvement
when coupling COSMO WAM
  • When looking at the sea surface it becomes
    evident that the sea surface roughness depends on
    the sea state.
  • When looking at a typical balance of energy
    fluxes for a growing wind sea at a constant wind
    speed of 20m/s (Janssen et al. 2002)
  • At the same time Fig. 1 illustrates the role
    ocean surface waves play in the interaction of
    the atmosphere and the ocean, because on the one
    hand ocean waves receive momentum and energy from
    the atmosphere through wind input (controlling to
    some extent the drag of air flow over the
    oceans), while on the other hand, through wave
    breaking, the ocean waves transfer energy and
    momentum to the ocean thereby feeding the
    turbulent and large-scale motions of the oceans.
    (Janssen et al. 2002)

13
Impact of the atmosphere-wave coupling at ECMWF
Surface winds
  • When the two-way interaction of winds and waves
    was introduced in operations on the 29th of June
    1998 there was a pronounced improvement of the
    quality of the surface wind field. (Janssen et
    al. 2002).
  • The reduction of the surface wind RMS-error was
    around 10.
  • It was found that with increased spatial
    resolution of the atmospheric model the influence
    of the coupling to the waves also increases.

14
The Situation at the beginning
  • Source codes
  • COSMO 250.000 l.o.c (simple partitioning)
  • WAM 60.000 l.o.c (optimized partitioning with
    respect to the LAND/SEA mask)
  • ROMS 340.000 l.o.c. (simple partitioning, more
    complicated when using nesting)
  • After the study of the codes we tried to couple
    the models using the MCT (Model Coupling Toolkit)
    library according to the work of Warner et al.
    However, the library proved complicated, has no
    good manual, and does not satisfy the needs for
    realizing the interpolation in the background in
    a transparent manner.
  • Therefore, we decided to develop a custom made
    MPI library that is tailored especially for these
    three models.
  • We called this Library at this stage PGMCL
    (Parallel Geophysical Model Coupling Library).
    The PGMCL library has at this stage 3500 l.o.c.
    and is well documented. Easy to understand and
    nice to follow within all source code due to
    usage of CPP (C Preprocessor Pragma e.g. grep
    bwn WAT2ATM, ATM2WAV, OCN2WAV, OCN2ATM).

15
Methodology of the coupling
  • The technique is that, if we have N processors we
    decompose them as
  • Nocn Nwav Natm N
  • Computationally, this means to split the
    MPI_COMM_WORLD into subsets by using the
    MPI_COM_SPLIT command.
  • Hence, after that each model is using a
  • OCN_COMM_WORLD,
  • ATM_COMM_WORLD and
  • WAV_COMM_WORLD.
  • The coupling is done at instantaneous times and
    provide instantaneous values of the fields, i.e.
    no averaging is done. In other words the models
    are fully synchronized.

16
Interpolation Algorithm
  • We want to allow different grids for each model.
    Hence some degree of interpolation is needed.
  • We used linear interpolation by using the
    longitude/latitude of the grid points. Thus we
    compute a sparse matrix at the beginning of the
    run that contains the weights of the
    interpolation.
  • We take care of the land/sea mask of the models
    using a direct mapping.

17
Partition methodology
  • Each node of the model has access to the global
    longitude/latitude index of each model.
  • Each node knows which point are computational
    nodes and which are ghost points for each node.
  • As a consequence each node knows exactly what it
    gets/sends from/to the other node.
  • So, we avoid a global gathering of all data on 1
    computational node and we have instead some
    MPI_INTERP_Send and corresponding
    MPI_INTERP_Recv function. This makes the
    coupler efficient on massive parallel platforms.
  • Once all declarations are done, this is the only
    thing that show up in the code of the fully model
    coupled. We preferred this solution to the MCT
    library.
  • This makes clear, clean, efficient and easy to
    apply exchange operations between all models.

18
Difficulties in the Development
  • Beside the amount of l.o.c (lines of codes)
    750.000 we found certain weaknesses that made
    the developing/debugging of the coupled model
    more difficult
  • COSMO has certain weak points in the code e.g.
  • The models works even if NaN is present in the
    solution
  • When switching on Netcdf output the prognostic
    arrays become NaN
  • Netcdf is not working due to some errors in the
    code e.g. the same variable name is written
    frequently to the same file, which is not
    allowed.
  • Unallocated arrays are initialized
  • More small bugs are present in the code
  • WAM has also some e.g.
  • At certain place of the code same buffers have
    been used for communication, very difficult to
    trace long story
  • Memory allocation/initializations weaknesses
  • Certain small bugs
  • However, with the help of Jean Bidlot and the
    ECWMF team most of these issues have been fixed.
  • It would be very nice to have somebody from the
    COSMO team to interact with.

19
Validation of the PGMCL
Blue Cosmo Red WAM
20
Validation of the PGMCL
21
Present situation and future steps
  • The COSMO model was coupled to the WAM model
    using a custom made model coupling library
    (PGMCL)
  • The COSMO model receives the roughness length
    from the wave model.
  • The WAM model receives air density, air
    temperature and wind velocity.
  • The PGMCL library was verified up to 8 CPUs
  • The next step is to investigate the impact of the
    coupling, 1st on a 0.25 grid followed by a
    operational setting at CNMCA with a spatial
    resolution of 7km for both COSMO and WAM.
  • Following this we will also include the ROMS
    model in the coupling cycle and exchange more
    variables among the models e.g.
  • Sea surface temperature
  • Water density
  • Water level elevation
  • Surface currents
  • As final step we will try to homogenize the
    formulation of the boundary layer, which is
    presently inconsistent among the three models.

22
Conclusions
  • We have setup up the technical framework for the
    coupling of
  • COSMO
  • WAM and
  • ROMS
  • using a custom made coupling library.
  • We have shown the benefits of the coupling of
    waves to the ocean and waves to the atmosphere.
  • COSMO will benefit from a better representation
    of the surface roughness.
  • WAM will benefit from a better representation of
    the driving wind.
  • Finally, the coupling to ROMS will close the big
    circle.
  • At the end we will have 1 model COSMOWAMROMS.
    This will allow to take into account the full
    interaction at the interface.
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