Title: Coupling%20COSMO%20with%20the%20WAM%20model
1Coupling 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).
2Content
- Motivation
- Physics
- The coupling library and its methodology
- Validation of the coupling library
- Conclusion
3Motivation
- 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.
4Motivation
5Some recent results of Hurricane Isabel using
coupled Ocean-Wave model (SELFE-WWMII) on
unstructured meshes driven by NARR (North
American Regional Reanalysis) winds
6Some recent results of Isabel using coupled
Ocean-Wave model on unstructured meshes
7Comparison of the sign. Wave height with the buoy
measurements (blue no currents, black with
currents)
8Comparison of the average period with the buoy
measurements
9Estimation Isabell storm surge
10Atmospheric conditions during Xynthia courtesy
to Xavier Bertin, submitted to OM
11Xynthia !Influence of wave induced surface drag
on the sea surface elevation courtesy to Xavier
Bertin, submitted to OM
12Physical 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)
13Impact 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.
14The 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).
15Methodology 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.
16Interpolation 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.
17Partition 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.
18Difficulties 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.
19Validation of the PGMCL
Blue Cosmo Red WAM
20Validation of the PGMCL
21Present 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.
22Conclusions
- 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.