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 Meeting

09 – 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 goals

II. Horizontal grid structure and accompanying problems

III. ICON NWP physics suite

IV. Selected results

V. 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

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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

Em

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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

tim

e (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 ICON

ICON 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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ICONGME

against 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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ICONGME

against 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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ICONGME

against 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 !!