Centre report - DWD. 27 th WGNE meeting Oct. 2011, Boulder. M. Baldauf, M. Lange, H. Frank, G. Zängl, B. Früh (DWD)

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1 Centre report - DWD 27 th WGNE meeting Oct. 2011, Boulder M. Baldauf, M. Lange, H. Frank, G. Zängl, B. Früh (DWD)

2 Supercomputing environment at DWD (Sept. 2011) One production and one research computer NEC SX-9, each with: 30 nodes with16 CPUs / node = 480 vector processors Peak Vector Performance / CPU: 100 GFlops Peak Vector Performance total: 48 TFlops main memory / node: 512 GByte main memory total: 15.4 TByte Internode crossbar switch (NEC IXS): 128 GB/s bidirectional replacement in 2013, but only with the same computing power For climate runs: NEC SX-8r 2 * 7 nodes with 8 CPUs / node = 112 vector processors Peak Vector Performance / CPU: 35.2 GFlops main memory / node: 64 GByte Internode crossbar switch (NEC IXS): 16 GB/s bidirectional Login nodes: SUN X nodes with 8 processors (AMD Opteron QuadCore)/node = 120 processors (2 nodes for interactive login) main memory / node: 128 GB Database server: two SGI Altix 4700

3 The operational Model Chain of DWD: GME, COSMO-EU and -DE GME COSMO-EU (LME) COSMO-DE (LMK) hydrostatic parameterised convection Δx 30 km * 60 GP Δt = 100 sec., T = 7 days non-hydrostatic parameterised convection Δx = 7 km 665 * 657 * 40 GP Δt = 66 sec., T = 78 h non-hydrostatic convection-permitting Δx = 2.8 km 421 * 461 * 50 GP Δt = 25 sec., T = 21 h

4 Variational soil moisture analysis applied in the GME 30 km /L60 model M. Lange (DWD) Cost function penalizes deviations from initial soil moisture and from synop observations Analysed soil moisture depends on T2m fc-error and sensitivity T2m/ w

5 Bias and Stdev T2m over Europe, avg(12, 15 GMT) Europe 1. March 5. July 2009 Stdv. T2m Bias is effectively reduced by SMA Soil is wetted during summer Bias Soil moisture content over Europe, top layers Running time (2009) Soil moisture content over Europe, bottom layers with SMA with SMA Running time (2009) Running time (2009)

6 Current state GME 20 km / L60 Helmut Frank (DWD) Reduction of grid mesh size from 30 km (ni=256) to 20 km (ni=384). but regular grid remains at 0,25. Advection of prognostic rain and snow content, QR und QS Newest experiments use an enhanced sedimentation velocity compared to that used in GME 30/L60 Outlook Autumn 2011 Start of GME20L60 in parallel routine Start of migration meetings January 2012 Operational production of GME20L60

7 monthly mean Nov. 2010, 00 UTC runs: precipitation 0 78 h

8 upper air verification: RMS Geopot. 500 hpa northern hemisphere tropics GME20L60 GME30L60 southern hemisphere Europe

9 upper air verification: ANOC Geopot. 500 hpa GME20L60 GME30L60 northern hemisphere tropics southern hemisphere Europe

10 Summary GME20L60 runs stably GME20L60 slightly better scores than GME30L60 (over hemispheres and for longer forecast times) precipitation fields are too smooth ( advection too diffusive) problems with runtime of GME (and SMA-GME) due to not optimal parallelization to do: I/O with GRIB API

11 ICON The next-generation global model for numerical weather prediction and climate modeling of DWD and Max Planck Institute for Meteorology (MPI-M) ICON = ICOsahedral Nonhydrostatic model Günther Zängl and the ICON development team

12 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. Nonhydrostatic dynamical core for triangles and hexagons as primal grid; C- grid discretization; Scalability and efficiency on massively parallel computer architectures with O (10 4 +) cores 11/7/11 M. Baldauf Zängl (DWD)

13 Grid structure with nested domains latitude-longitude windows circular windows 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

14 Nonhydrostatic equation system Two-time-level predictor-corrector time stepping scheme 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 2D Lamb transformation for nonlinear momentum advection Flux form for continuity equation and thermodynamic equation; Miura 2 nd - order upwind scheme (centered differences) for horizontal (vertical) flux reconstruction Mass conservation and tracer mass consistency v n,w ρ: d θ v : K: h ζ: v π: E blu

15 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 Total integration time: 20 days Results shown on subsequent slides: 3-day forecast in comparison with corresponding analysis data

16 Temperature at 3 km AGL

17 Specific humidity at 3 km AGL

18 Pressure at 3 km AGL

19 Further steps towards preoperational testing Work in progress: coupling with physics parameterizations in principle completed. Thorough testing (and debugging) of physics parameterizations and physics-dynamics coupling, GRIB2 I/O, further optimization of MPI parallelization, ocean model on triangular grid still existing 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 (LETKF) pre-operational use probably in ~2012, operational ~2013

20 COSMO-EU - main changes in 2010/2011 Tracer-advection of moist variables: replace Semi-Lagrangian scheme (tricubic interpolation, 2nd order backward trajectory) by a Finite Volume (Bott, 1989, Skamarock 2006) advection scheme with better conservation and mass consistency properties Assimilation of radiosondes of the type Vaisala RS92: correction of the dry bias around noon (due to solar radiation) and a moist bias during night by the method of Miloshevich, Vomel, et al. (2009) JGR introduction of a sea ice scheme (analog to that of GME) introduction of a lake model (FLake)

21 COSMO-DE - main changes in 2010/2011 Assimilation of radiosondes of the type Vaisala RS92 (see above) change of the upwind advection of T and p' in the staggered grid avoid 'Theta-peaks' small artificial horizontal 4th order hyperdiffusion to increase numerical stability (near future: additional horizontal Smagorinsky diffusion)

22 obs forecast Fraction Skill Score Roberts and Lean (2005)

23 FSS July 2011, start date: 00 UTC, forecast time h von U. Damrath (FE15)

24 FSS July 2011, start date: 00 UTC, forecast time h von U. Damrath (FE15) 24

25 Model area of COSMO-DE in 2011 and grid mesh size: 2.8 km number of levels: 50 number of grid points: 421 x grid mesh size: 2.8 km number of levels: 65 number of grid points: 421 x grid mesh size: 2.0 km number of levels: 80 number of grid points: 724 x 780

26 COSMO-DE- Ensemble Prediction system Susanne Theis, Christoph Gebhardt, Zied Ben Bouallègue, Michael Buchhold motivation: simulations with a convection-permitting model (COSMO-DE with 2.8 km horizontal grid size) have a strong non-deterministic behaviour use an EPS to assess the model uncertainties since 9 Dec the pre-operational phase has started (20 members)

27 PREC 1h accumulation RANK HISTOGRAM JUNE 2011 observation... - treated as Ensemble Member - ranked according to prec amount at each grid point and forecast hour Frequency 0.05 How frequent is each rank? If ensemble underdispersive U-shaped rank histogram Rank Observation

28 PREC 1h accumulation RANK HISTOGRAM JANUARY 2011 observation... - treated as Ensemble Member - ranked according to prec amount at each grid point and forecast hour Frequency 0.05 How frequent is each rank? If ensemble underdispersive U-shaped rank histogram Rank Observation Susanne Theis, Christoph Gebhardt, Zied Ben Bouallègue, Michael Buchhold

29 PREC 1h accumulation, threshold: 0.1 mm DETERMINISTIC SCORES for Individual Members Equitable Threat Score IFS GME GFS GSM JUNE Forecast Time [h] } 20 members Do the ensemble members have different long-term statistics? (multi-model / multi-configuration) Are there many cases with the same best member or wettest member? - look at Equitable Threat Score - look at Frequency Bias Index (results similar, not shown) Susanne Theis, Christoph Gebhardt, Zied Ben Bouallègue, Michael Buchhold

30 COSMO-DE-EPS plans ( ) under consideration: including past production cycles in product generation 2011 upgrade to 40 members, redesign reach operational status 2012 statistical postprocessing 2013 initial conditions by LETKF lateral boundary conditions by ICON EPS

31 Deutscher Wetterdienst Status and plans for regional climate modelling at DWD Barbara Früh, Susanne Brienen, Kristina Trusilova, Andreas Walter

32 1. Evaluation of COSMO-CLM in NWP mode referenceconfiguration of COSMO-CLM for IPCC-AR5 climate projection comparison of the forecast skill Routineconfiguration of COSMO-EU. Analysis of error increase (drift) of the COSMO-CLM with increasing forecast time compared to COSMO-EU find an optimal configuration for the climate runs (until now: by try & error) Barbara Früh, DWD

33 2. implementation of the urban climate parameterisation TEB in COSMO-CLM Urban parameterisation: Town Energy Budget (TEB) (V. Masson, Météo-France) depends from - vegetation fraction in the town - density distribution of buildings - shape of buildings - materials - temperature inside of the building W H Barbara Früh, DWD

34 3. climate scenarios: planned simulations forcing Europe Europe Germany Evaluation ERAinterim 80 km 14 km ,8 km Evaluation ERAinterim 80 km 14 km ,8 km Control run Scenario ECHAM6 200 km ECHAM6 200 km 50 km km km km ,8 km ,8 km Barbara Früh, DWD

35 Hans Ertel center for weather research Projects and research groups 1. Project "Object-based Analysis and Seamless prediction" 2. Project "Ensemble based convective scale data assimilation and the use of remote sensing observations" 3. Project "Boundary layer clouds, sub-cloud layer heterogenities and stochastic parameterizations" work on the Grey zone project (A. Seifert, collaborators) 4. Project "Retrospective analysis of regional climate" 5. Project "Improving the process of weather warnings and extreme weather information in the chain from the meteorological forecasts to their communication for the Berlin conurbation (WEXICOM)" Web-site: search for 'Hans Ertel Zentrum' or go to -->...

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37 Outline GME 20 km L 60 ICON developments COSMO COSMO-DE-EPS Climate runs HERZ

38 New formulation of minimum stomata reduces deep layer soil water during summer Europe 1. March 5. Juli 2009 Soil moisture content over Europe, bottom layers Soil moisture content over Europe, bottom layers Running time (2009)

39 Orography: mesh size 30 km 20 km 30 km max. height: 2796 m 20 km max. height: 2916 m

40 GME20L60 REGNIE precipitation h Oct-Dec 2010 GME30L60 COSMO-EU

41 synop verification: ANOC pmsl northern hemisphere tropics GME20L60 GME30L60 southern hemisphere Europe

42 synop verification: RMSV Wind in 200 hpa northern hemisphere tropics GME20L60 GME30L60 southern hemisphere Europe

43 COSMO-DE precipitation climatology Winter Accumulated precipitation, 1 Nov Jan Oct. Sept

44 Precipitation sum in January 2010 Observations COSMO-DE COSMO-EU General overestimation during winter time

45 COSMO-DE precipitation climatology Winter precipitation statistics for 24h-sums: P(R) = N(R) R PDF(R) R: Intensity in mm/h N(R): number of events PDF(R): probability density function COSMO-DE overestimates in the range 5-15 mm/24h Oct. Sept

46 COSMO-DE precipitation climatology Summer Accumulated precipitation, 1 Mai Sept Oct. Sept

47 Precipitation sum in August 2010 Observations COSMO-DE COSMO-EU Clear underestimation of precipitation in both models

48 Fraction Skill Score A scale dependent precipitation skill score for convective-scale NWP models observation forecast Δ = 1: 0 vs 1 bad forecast on 1Δ-scale Δ = 3: 4/9 vs 3/9 OK forecast on 3Δ-scale Δ = 5: 6/25 vs 6/25 perfect forecast on 5Δ-scale Roberts and Lean (2005)

49 Generation of Ensemble Members Variations in Forecast System for the Representation of Forecast Uncertainty Initial Conditions Boundaries Model Physics multi-model COSMO-DE initial conditions modified by different global models multi-model driven by different global models multi-configuration different configurations of COSMO-DE model Susanne Theis, Christoph Gebhardt, Zied Ben Bouallègue, Michael Buchhold

50 Generation of Ensemble Members Variations in Forecast System for the Representation of Forecast Uncertainty Initial Conditions Boundaries Model Physics Susanne Theis, Christoph Gebhardt, Zied Ben Bouallègue, Michael Buchhold

51 Generation of Ensemble Members plus variations of initial conditions model physics Ensemble Chain COSMO-DE-EPS 2.8km GME, IFS, GFS, GSM COSMO 7km BC-EPS BC-EPS is running as a time-critical application at ECMWF Susanne Theis, Christoph Gebhardt, Zied Ben Bouallègue, Michael Buchhold

52 Probability Maps + 13h + 10h probability of snow > 5cm + 7h produced by ensemble

53 Average T_2m ( ) with TEB scheme: night (0:00-03:00) day (11:00-13:00)