ICON The new global nonhydrostatic model of DWD and
MPI-M
Daniel Reinert1, Günther Zängl1, and the ICON-team1,2
1Deutscher Wetterdienst / 2Max-Planck-Institute for Meteorology
13th EMS Annual Meeting09 – 13 September 2013, Reading, United Kingdom
ICON – ICOsahedral Nonhydrostatic Model
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Joint development project of DWD and Max-Planck-Institute for Meteorology for building a next-generation global NWP and climate modelling system
Atmosphere and ocean model
Outline
I. Project goalsII. Horizontal grid structure and accompanying problemsIII. ICON NWP physics suiteIV. Selected resultsV. Roadmap and Summary
DWDMPI
I. Primary development goals
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At DWD: • Replace current global model GME • Replace regional model COSMO-EU by a high-
resolution window over Europe.
At MPI-M: • Use ICON as dynamical core of an Earth System
Model (MPI-ESM2) Horizontal grid with nest over Europe
Improved conservation properties (at least mass) and consistent tracer transport (tracer air-mass consistency)
Applicability on a wide range of scales from 100 km to 1 km
Scalability and efficiency on massively parallel computer architectures with O(104 +) cores
Local refinement/nesting capability
II. ICON’s unstructured grid
Primal cells: triangles uses icosahedron for macro triangulation
C-type staggering:
local subdomains (“nests”)
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local domain(s)
global domain
velocity at edge midpoints mass at cell circumcenter
Triangular C-Grid
Equations (dry adiabatic) and solver
Fully compressible nonhydrostatic vector invariant form, shallow atm.
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Solver: Finite volume/finite difference discretization (mostly 2nd order) Two-time level predictor-corrector time integration Vertically implicit (vertical sound-wave propagation) Fully explicit time integration in the horizontal (at sound wave time step; not split
explicit!) Mass conserving
Checkerboard noise on triangular C-Grid
Main problem with triangular C-grid: suffers from spurious computational mode (e.g. Danilov (2010)), triggered by the discretized divergence operator (Wan (2013))
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Divergence operator: applies the Gauss theorem
Truncation error (Wan (2013)):
Only 1st order accurate on triangular C-grid Error changes sign from upward- to downward
pointing triangle checkerboard
Example for synthetic velocity field (Wan, 2013)
Controlling the checkerboard noise
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Goal: Eliminate 1st order error Basic idea: Divergence averaging
I: Compute standard 1st order divII: Compute divergence estimate
based on immediate neighbors (2nd order bilinear interpolation)
III Averaging:
2nd order accurate for isosceles triangles
Example: Baroclinic wave
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Jablonowski-Williamson (2006) baroclinic wave test case
PS T
Example: Baroclinic wave
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Standard divergence operator Divergence averaging
Jablonowski-Williamson (2006) baroclinic wave test case
divdiv
“checkerboard” noise
PS T
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III. ICON NWP-physics
Process Author Scheme Origin
Radiation Mlawer et al. (1997)Barker et al. (2002) RRTM ECHAM6
Non-orographic gravity wave drag
Scinocca (2003)Orr, Bechthold et al. (2010) wave dissipation at critical level IFS
Cloud cover Köhler et al. (new development) diagnostic (later prognostic) PDF ICON
Microphysics Doms and Schättler (2004)Seifert (2010)
prognostic: water vapour, cloud water, cloud ice, rain, snow COSMO
Saturation adjustment Blahak (2010) isochoric adjustment COSMO
Convection Tiedtke (1989)Bechthold et al. (2008) mass-flux shallow and deep IFS
Sub-grid scale orographic drag Lott and Miller (1997) blocking, GWD IFS
Turbulent transfer / diffusion Raschendorfer (2001) prognostic TKE COSMO
Soil/surfaceHeise and Schrodin (2002)Mironov and Ritter (2004)Mironov (2008)
TERRA (tiled + multi-layer snow)SEAICE FLAKE(fresh water lake scheme)
GME/COSMO
every 30min
Reduced grid for radiation
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upscaling
downscaling
Rad
iativ
e tr
ansf
er c
ompu
tatio
ns
Hierarchical structure of the triangular mesh is very attractive for calculating physical processes (e.g. radiative transfer) with different spatial resolution compared to dynamics.
Radiation step
Empi
rical
co
rrec
tions
Proof of concept
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net surface shortwave flux (reduced – full grid) average over 30 x 48h forecast runs in June 2012
Reduced radiation grid currently generates positive bias in
Avg: 1.57
Flat-MPI performance
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Test setup: ICON RAPS 2.0, IBM Power 720/10/5 km, 8h forecast, reduced radiation grid
(S. Körner, DWD, 03/2013)
Recall goal: scalability up to O(104+) cores
40961024 1024 4096
time
(s)
MPI tasks
IV. Selected results of NWP test suite
Real-case 7-day forecasts with interpolated IFS analysis data WMO standard verification against IFS analysis on 1.5° lat/lon grid. Comparison against GME reference experiment with interpolated IFS analysis data.
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Basic requirement for operational use of ICONICON must outperform GME in terms of forecast quality/scores
ICON40L90 GME40L60
hor. resolution 40 km 40 km
vertical levels 90 60
top height 75 km 36 km
analysis data IFS IFS
Verification: Surface Pressure, January 2012
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ICONGMEagainst IFS
Region: Northern hemisphere (NH)
SH: 21%
Verification: G. Zängl, U. Damrath, 08/2013 (DWD)
Verification: Geopot 500 hPa, January 2012
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ICONGMEagainst IFS
Region: Northern hemisphere (NH)
SH: 9.4%
Verification: G. Zängl, U. Damrath, 08/2013 (DWD)
Verification: Rh 700 hPa, January 2012
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ICONGMEagainst IFS
Region: Tropics (Tr)
Verification: G. Zängl, U. Damrath, 08/2013 (DWD)
ICON shows strong positive moisture bias in the tropics
V. Roadmap towards operational application
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Summary
Verification results are mostly exceeding those of GME, but there are still some weaknesses/biases e.g. moisture field
Technical parts scale on massively parallel systems (I/O still needs performance improvements)
Optimization of forecast quality still ongoing
Tests with own 3D-Var data assimilation have started recently.
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ICON is entering the home stretch for becoming operational
Thank you for your attention !!