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ICON
The next-generation global model for numerical weather prediction and
climate modeling of DWD and MPI-M
Current development status and the way towards preoperational testing
Günther Zängl and the ICON development team
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General overview of ICON and its current development status
Selected results of idealized mountain tests and of (a very first) real-data forecast
Further planning towards preoperational testing Summary
Outline
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ICON = ICOsahedral Nonhydrostatic model Joint development project of DWD and Max-Planck-Institute for
Meteorology for the next-generation global NWP and climate modeling system
Nonhydrostatic dynamical core for triangles and hexagons as primal grid; C-grid discretization; coupling with physics parameterizations in principle completed
Two-way nesting for triangles as primal grid, capability for multiple nests per nesting level; vertical nesting, one-way nesting mode and limited-area mode are also available
Work in progress: Thorough testing (and debugging) of physics parameterizations and physics-dynamics coupling, GRIB2 I/O, further optimization of MPI parallelization, data assimilation, ocean model on triangular grid
ICON
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Primary development goals Better conservation properties (air mass, mass of trace gases
and moisture, consistent transport of tracers; hexagonal core also conserves energy)
Grid nesting in order to replace both GME (global forecast model, mesh size 30 km) and COSMO-EU (regional model, mesh size 7 km) in the operational suite of DWD
Applicability on a wide range of scales in space and time ("seamless prediction"); therefore, the dynamical core was requested to be nonhydrostatic
Scalability and efficiency on massively parallel computer architectures with O(104+) cores
ICON
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G. Zängl Project leader DWD two-way nesting, parallelization, optimization, numerics
H. Asensio external parametersM. Baldauf NH-equation setK. Fröhlich physics parameterizationsM. Köhler physics
parameterizationsD. Liermann post processing,
preprocessing IFS2ICONF. Prill numerics, optimization,
parallelizationD. Reinert advection schemesP. Ripodas test cases, power
spectraB. Ritter physics parameterizationsA. Seifert cloud microphysicsU. Schättler software designMetBwT. Reinhardt physics parameterizations
M. Giorgetta Project leader MPI-M physics
parameterizations
M. Esch software maintenance
A. Gaßmann NH-equations, numerics
P. Korn ocean model
L. Kornblueh software design, hpc
L. Linardakis parallelization, grid generators
S. Lorenz ocean model
C. Mosley regionalization
R. Müller pre- and postprocessing
T. Raddatz external parameters
F. Rauser adjoint version of the SWM
W. Sauf Automated testing (Buildbot)
U. Schulzweida external post processing (CDO)
H. Wan 3D hydrostatic model version, physics parameterizations
External: R. Johanni: MPI-Parallelization
Project teams at DWD and MPI-M
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The horizontal grid
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Primary (Delaunay, triangles) and dual grid (Voronoi, hexagons/pentagons)
The horizontal grid
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latitude-longitude windows circular windows
Grid structure with nested domains
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vv
vpdnn
vpdn
tn
vt
vt
gz
czwwvwwv
tw
nc
zv
wnKvf
tv
vn,w: normal/vertical velocity component
: density
v: Virtual potential temperature
K: horizontal kinetic energy
: vertical vorticity component
: Exner function
blue: independent prognostic variables
Nonhydrostatic equation system
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• Two-time-level predictor-corrector time stepping scheme• 2D Lamb transformation for nonlinear momentum advection• Flux form for continuity equation and thermodynamic equation;
Miura 2nd-order upwind scheme (centered differences) for horizontal (vertical) flux reconstruction
• implicit treatment of vertically propagating sound waves, but explicit time-integration in the horizontal (at sound wave time step; not split-explicit); larger time step (default 4x) for tracer advection / fast physics
• Mass conservation and tracer mass consistency
Numerical implementation
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• Finite-volume tracer advection scheme (Miura) with 2nd-order and 3rd-order accuracy for horizontal advection; 2nd-order MUSCL and 3rd-order PPM for vertical advection with extension to CFL values larger than 1; monotonous and positive-definite flux limiters
Special measures to improve the numerical stability over steep terrain:
• Option for truly horizontal temperature diffusion over steep slopes• Option for truly horizontal discretization of the horizontal pressure
gradient over steep slopes
Numerical implementation
• Precompute for each edge (velocity) point at level the grid layers into which the edge point would fall in the two adjacent cells
Discretization of horizontal pressure gradient (apart from conventional method)
dashed lines: main levelspink: edge (velocity) pointsblue: cell (mass) points
A
S
• Reconstruct the Exner function at the mass points using a quadratic Taylor expansion, starting from the point lying in the model layer closest to the edge point
Discretization of horizontal pressure gradient
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vpce
ccc zz
zcgzz
z
• Note: the quadratic term has been approximated using the hydrostatic equation to avoid computing a second derivative
• Treatment at slope points where the surface is intersected:
)(2 ASA
v
vpAS
zzxc
gxx
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• Cloud microphysics from COSMO-EU (hydci_pp)• New mass-conserving saturation adjustment scheme (U. Blahak/
A. Seifert)• Turbulence schemes from COSMO-model (M. Raschendorfer) and
ECHAM• Tiedtke-Bechtold convection scheme (from ECMWF)• COSMO cloud cover scheme; new scheme under development by
M. Köhler• Ritter-Geleyn and RRTM radiation schemes• TERRA surface scheme
Physics parameterizations
• Isolated circular Gaussian mountain, e-folding width 2000 m, various mountain heights (see subsequent slides)
• Isothermal atmosphere at rest or with u0 = 20 m/s
• Horizontal mesh size 300 m• 50 vertical levels, top at 40 km, gravity-wave damping layer starts
at 27.5 km• dry dynamical core with turbulence scheme for vertical mixing• Results are shown after 6 h of integration time
Idealized steep-mountain test
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vertical wind speed (m/s) potential temp. (c.i. 4 K), hor. wind speed (m/s)
mountain height: 2.25 km, isothermal atmosphere at restmaximum slope 0.95 (43.5°)
conventional pressure gradient discretization, no z-diffusion of temperature
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conventional pressure gradient discretization, with z-diffusion of temperature
vertical wind speed (m/s) potential temp. (c.i. 4 K), hor. wind speed (m/s)
mountain height: 2.25 km, isothermal atmosphere at restmaximum slope 0.95 (43.5°)
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conventional pressure gradient discretization, with z-diffusion of temperature
vertical wind speed (m/s) potential temp. (c.i. 4 K), hor. wind speed (m/s)
mountain height: 3.0 km, isothermal atmosphere at restmaximum slope 1.27 (52°)
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z-based pressure gradient discretization, and z-diffusion of temperature
vertical wind speed (m/s) potential temp. (c.i. 4 K), hor. wind speed (m/s)
mountain height: 3.0 km, isothermal atmosphere at restmaximum slope 1.27 (52°)
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vertical wind speed (m/s) potential temp. (c.i. 4 K), hor. wind speed (m/s)
mountain height: 3.75 km, isothermal atmosphere at restmaximum slope 1.59 (58°)
z-based pressure gradient discretization, and z-diffusion of temperature
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vertical wind speed (m/s) potential temp. (c.i. 4 K), hor. wind speed (m/s)
mountain height: 4.5 km, isothermal atmosphere at restmaximum slope 1.90 (62°)
z-based pressure gradient discretization, and z-diffusion of temperature
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vertical wind speed (m/s) potential temp. (c.i. 4 K), hor. wind speed (m/s)
mountain height: 3.5 km, isothermal atmosphere with u0 = 20 m/s
maximum slope 1.49 (56°)
z-based pressure gradient discretization, and z-diffusion of temperature
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vertical wind speed (m/s) potential temp. (c.i. 4 K), hor. wind speed (m/s)
mountain height: 4.5 km, isothermal atmosphere with u0 = 20 m/s
maximum slope 1.90 (62°)
z-based pressure gradient discretization, and z-diffusion of temperature
• Global mesh size 40 km, refinement to 20 km over Europe• Model top at 45 km, 60 levels (another expt.: 67.5 km / 90 levs)• Time step in global domain: 60s / 240s• Initialization with interpolated IFS analysis data for 01.01.2011• Total integration time: 20 days• Results shown on subsequent slides: 3-day forecast in
comparison with corresponding analysis data
Real-data test
Temperature at 3 km AGL
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Specific humidity at 3 km AGL
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Pressure at 3 km AGL
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Further steps towards preoperational testing
• Thorough investigation of still exisiting numerical instabilities; bug fixes or improvement of algorithms
• Test suites for ICON and GME with interpolated IFS analysis data; systematic tuning of physics parameterizations and their mutual interaction (maybe also physics-dynamics coupling)
• Work on GRIB2 I/O, interpolation to lat-lon grid and pressure/ height levels, meteogram output, …
• Data assimilation
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Conclusions and further remarks
• We are on a good way, but there is still a lot to do…
• Efficiency and scalability: significantly better than for GME; roughly comparable to the COSMO model
• Time planning: start with preoperational testing late 2011 / early 2012; operational use (hopefully) by mid-2013