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Current status and plans at DWD

D. Majewski

Deutscher Wetterdienst

• Global 3D-Var assimilation

• SSO-Scheme for regional COSMO-EU model

• Prognostic precipitation

• New headquarters, new supercomputer

• GME 20 km / L60

• Global soil moisture assimilation

Numerical Weather Prediction at DWD

COSMO-EUGrid spacing: 7 km

Layers: 40

Forecast range:

78 h at 00 and 12 UTC

48 h at 06 and 18 UTC

1 grid element: 49 km2

COSMO-DEGrid spacing: 2.8 km

Layers: 50

Forecast range:

21 h at 00, 03, 06, 09,

12, 15, 18, 21 UTC

1 grid element: 8 km2

Global model GMEGrid spacing: 40 km

Layers: 40

Forecast range:

174 h at 00 and 12 UTC

48 h at 06 and 18 UTC

1 grid element: 1384 km2

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Global 3D-Var data assimilation

• Replacement of the OI in the global (GME) data assimilaton system• Operational since 17 September 2008• Technical characteristics:

• PSAS (Physical Space Assimilation System)• 3-h assimilation window• Wavelet representation of the B-Matrix

• Observation usage currently similar to OI, but:• Direct assimilation of radiances (currently AMSU-A)• Temperature (TEMP, AMDAR) instead of geopotential (TEMP)• No use of “Pseudo-TEMPS” (profiles derived from IFS analysis)

• Scores still very similar to OI (which includes Pseudo-Temps)• Current work:

• Migration to NEC SX-8R (11 / 2008) and NEC SX-9 (01 / 2009)• Extended use of remote sensing data• B-Matrix without separability constraint

PSAS 3D-Var Data Assimilation for GME, Part I

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PSAS 3D-Var Data Assimilation for GME, Part II

Anomaly correlation MSL pressure 00 and 12 UTC, northern hemisphere

• Observations used– Conventional (in situ) observations (3h interval) : 80 000– Radiances (AMSU-A) : 120 000

• Usage of remote sensing data will be extended:– Scatterometer 2008/2009– Radio occultations 2008/2009– AMSU-B 2009/2010– AIRS/IASI 2009/2010

PSAS 3D-Var Data Assimilation for GME, Part III

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DWD 3D-Var – B-Matrix Representation

• Sparse matrix representation of wavelet transformed covariances– Representation of arbitrary (NMC or ensemble-derived) B-Matrices– No constraints regarding isotropy, symmetry, separability

• Accuracy– 1% (due to sparse matrix representation)– Noise due to limited ensemble size is an issue

• Status– Separability assumption still used

• Current work– Full 3-D non-separable formulation– Localization approach

DWD 3D-Var – Plans in the long term

• Hybrid 3D-Var / EnKF system for GME / ICON• Goals:

– Improve global assimilation system– Provide boundaries for local ensemble forecast system (COSMO-DE)– Share Resources with local ensemble forecast system

• Schedule:– 2008 1st version of LETKF for GME– 2009 3D wavelet representation in 3D-Var– 2009 Hybrid EnKF & 3D-Var– 2010 3D-Var for ICON– 2011 Operational EnKF for ICON

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SSO scheme for 7-km regional model COSMO-EU

The problem …

Verification at DWD shows that the surface pressure in forecasts of the COSMO-EU model is systematically biased. In particular, in wintertime high pressure systems tend to develop a positive pressure bias, by 1-2 hPa after 48h, low pressure systems a negative bias ("highs too high, lows too low").

At the same time the wind speed tends to be overestimated by up to 1 m/s throughout the entire troposphere. The wind direction near the surface shows a positive bias of some degrees.

SSO Scheme for the 7-km COSMO-EU Model

The solution …

Implement SSO scheme of GME (Lott and Miller, 1997) in COSMO-EU.

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Bias of wind speed [m/s]COSMO-EU experiment period: 26 Feb. – 17 Mar. 2008, 00 UTC

REF

SSO

Bias of wind direction [deg]COSMO-EU experiment period: 26 Feb. – 17 Mar. 2008, 00 UTC

REF

SSO

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RMSE of vector wind [m/s]COSMO-EU experiment period: 26 Feb. – 17 Mar. 2008, 00 UTC

REFSSO

RMSE of MSL pressure [hPa]COSMO-EU experiment period: 26 Feb. – 17 Mar. 2008, 00 UTC

REFSSO

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Prognostic precipitation in GME

Processes considered in the grid-scale precipitation scheme.

Currently, rain and snow are treated diagnostically.

Prognostic treatmentof rain and snow(including advection)is now being tested.

Prognostic precipitation

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Cloud cover oflow clouds July 2008

96 h fct.

20 km

40 km

OLR intercomparison, July 14, 2007

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New DWD Headquarters by June 2008

Offenbach, Frankfurter Strasse 135

New DWD Supercomputer

• NEC SX-9, Sun Login nodes and SGI Altix data base server

• Installation phase 0 in June 2008 (because of delay of SX 9): 2 x 0.3 TFlop/s sustained (SX-8R)

• Installation phase I in October 2008 until February 2009: 2 x 4.5 TFlop/s sustained (SX-9)

• Installation phase II in 2010: Upgrade by a factor of 3

• Main cost driver: Ensembles (data assimilation, forecasting)

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GME 20 km / L60

Pre-operational trial of GME 20 kmTopography in the Alpine region

40 km, ni=192, Max: 2551 m 20 km, ni=384, max: 2918m

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GME L40, GME L60, ECMWF L91 and ECMWF L60

GME scales well with the number of CPUs

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First results based on ECMWF analysis

Schedule of introduction of GME 20 km / L60

Since summer 2007

Test runs of GME 20 km / L60 based on ECMWF analyses

February 2008

Daily test runs of GME 20 km / L60 based on ECMWF analyses

October 2008 until February 2009

New NEC SX-9 supercomputer (2 x 4.5 TFlop/s sustained) at DWD

March 2009

Pre-operational test runs of GME 20 km / L60 with 3D-Var at DWD

End of Q2 2009

GME 20 km / L60 with 3D-Var and soil moisture assimilation operational at DWD

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Global soil moisture analysis

T0=0:00 LT, vv=12, 15 hrs

T0=3:00 LT, vv=9, 12 hrs

Analyses in different time zones need background fieldwith different forecast lead time

Data coverage active synops over land, 2007/10/07,12:00 UTC

Analysis for main run at 0:00 UTC, Observations at 12:00, 15:00 LT

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2D-Var (z,t) soil moisture analysis (SMA)

)()()()( 221

221 obs

mmTobs

mmbT

b TTOTTwwBwwJ −−+−−= −−

))(()( 221

211

21

2 bmmT

mTmTT

mTbana wTTOBOww obs−

−−−− Γ+ΓΓ+=

Cost function penalizes deviations from observations and initial soil moisture content

Analysed soil moisture depends on T2m forecast error and sensitivity ∂T2m/∂w

0=∇J

)00:0,()00:15,00:12(2

kwT m

∂∂

Soil moisture increments compensate for T_2M forecast errors

Bias T_2M (K) Soil moisture increment (mm)

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T_2M forecast errors are effectively reduced

Bias T_2M (K) with SMA Bias T_2M (K) without SMA