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8/17/2014 Zängl

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. 8/17/2014 Zängl. Outline.

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8/17/2014 Zängl

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  1. ICONThe 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 8/17/2014 Zängl

  2. Outline • 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 17.08.2014 Zängl

  3. ICON 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 17.08.2014 Zängl

  4. ICON 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 17.08.2014 Zängl

  5. Project teams at DWD and MPI-M M. GiorgettaProject 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 G. ZänglProject leader DWD two-way nesting, parallelization, optimization, numerics H. Asensio external parameters M. Baldauf NH-equation set K. Fröhlich physics parameterizations M. Köhler physics parameterizations D. Liermann post processing, preprocessing IFS2ICON F. Prill numerics, optimization, parallelization D. Reinert advection schemes P. Ripodas test cases, power spectra B. Ritter physics parameterizations A. Seifert cloud microphysics U. Schättler software design MetBw T. Reinhardtphysics parameterizations External: R. Johanni: MPI-Parallelization 17.08.2014 Zängl

  6. The horizontal grid

  7. The horizontal grid Primary (Delaunay, triangles) and dual grid (Voronoi, hexagons/pentagons)

  8. Grid structure with nested domains latitude-longitude windows circular windows 17.08.2014 Zängl

  9. Nonhydrostatic equation system vn,w: normal/vertical velocity component : density v: Virtual potential temperature K: horizontal kinetic energy : vertical vorticity component : Exner function blue: independent prognostic variables 17.08.2014 Zängl

  10. Numerical implementation • 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

  11. Numerical implementation • 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

  12. Discretization of horizontal pressure gradient (apart from conventional method) • Precompute for each edge (velocity) point at level the grid layers into which the edge point would fall in the two adjacent cells A S dashed lines: main levels pink: edge (velocity) points blue: cell (mass) points

  13. Discretization of horizontal pressure gradient • 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 • 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:

  14. Physics parameterizations • 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

  15. Idealized steep-mountain test • 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

  16. conventional pressure gradient discretization, no 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 rest maximum slope 0.95 (43.5°) 17.08.2014 Zängl

  17. 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 rest maximum slope 0.95 (43.5°) 17.08.2014 Zängl

  18. 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 rest maximum slope 1.27 (52°) 17.08.2014 Zängl

  19. 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 rest maximum slope 1.27 (52°) 17.08.2014 Zängl

  20. 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: 3.75 km, isothermal atmosphere at rest maximum slope 1.59 (58°) 17.08.2014 Zängl

  21. 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: 4.5 km, isothermal atmosphere at rest maximum slope 1.90 (62°) 17.08.2014 Zängl

  22. 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: 3.5 km, isothermal atmosphere with u0 = 20 m/s maximum slope 1.49 (56°) 17.08.2014 Zängl

  23. 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: 4.5 km, isothermal atmosphere with u0 = 20 m/s maximum slope 1.90 (62°) 17.08.2014 Zängl

  24. Real-data test • 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

  25. Temperature at 3 km AGL

  26. Specific humidity at 3 km AGL

  27. Pressure at 3 km AGL

  28. 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

  29. 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

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