1. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress 22th North America-Europe Data Exchange Meeting Reading, UK, Dec. 09-11, 2009 Deutscher Wetterdienst (DWD) status report Alexander Cress With contributions from Rheinhold Hess, Andreas Rhodin, Marco Schwaerz, Klaus Stephan

2. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress DWD’s new building 2008 New central computer facilities: Computer area 1100 m 2 Uninterruptible power supply up to 3000 kW

3. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress 102.4 GFlops / CPU 1.6 TFlops / Node 512 GB Main Memory per node 16 CPUs per node Frequenzy 3.2 GHz Ultra-High bandwidth shared memory subsystem (256 GB/s per CPU) Internode crossbar switch (IXS), 128 GB/s bidirectional per node DWD 14 nodes per cluster, 2 clusters, 1 test node New computer system at DWD Vector Computer NEC SX-9 System specification

4. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Global Model GME Operational NWP Model of DWD gridpoint model, hexagonal triangular grid 40 km mesh size, 36870 grid points/layer 40 layers (hyprid, sigma/pressure) prognostic variables: p s, u, v, T, q v, q c, q i, o 3 3DVAR (PSAS) system incremental digital filter initialization (P.Lynch) At 00 UTC and 12 UTC: forecasts for 174 hours At 18 UTC: forecasts for 48 hours

5. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress 3-D Variational Data assimilation (PSAS)  Replacement of the Optimum Interpolation (OI) scheme  Operational since Sep. 2008 and operational on NEC SX9 since Sep. 2009  Minimization in observation space  Allows assimilation of observations with highly nonlinear dependence on the background variables (e.g. remote sensing)  Wavelet representation of the B-Matrix Separable 1D+2D approach vertical: NMC derived covariances (64 levels) horizontal: Wavelet representation (512x256 Gaussian grid)  Goals: Flexible representation of B-Matrix 3D non-separable representation NMC or ensemble derived covariances

6. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Observational Use in 3DVAR Aircraft Synop Buoy RadiosondePilotAMSU A AMVScatterometer

7. Deutscher Wetterdienst Alexander Cress Anomaly correlation coefficient of geopotential height in 500 hPa 2007051400 – 2007053100 (18 forecasts)

8. Deutscher Wetterdienst Alexander Cress Use of AMSU-A auf NOAA-15, 16, 18, AQUA und METOP-A (seit 21.11.07)

9. Deutscher Wetterdienst Using Metop ATOVS radiances: Clear positive impact for all areas (00 UTC)

10. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Use of radio occulation measurements A electromagnetic signal, transmitted by a GPS satellite, is delayed due to the presence of free electrons in the ionosphere an the refractivity of the atmosphere The refractivity is directly linked to horizontal and vertical variation of temperature, pressure and water vapour The refraction of the GPS signal corresponds to a shift in its phase, recorded at the receiving LEO satellite. Additionally, the signal path undergoes a bending in the atmosphere, resulting in a bending angle Relative geometry of the GPS and LEO and the Earth changes during the occultation event, the signal path intersects the atmosphere vertically, thus providing a vertical profile of bending angle. Observation error can be derived from the signal-noise-ratio of the amplitude of the signal Observed quantity to be assimilated can be bending angle (DWD) or refractivity (Met Office) profiles Benefits: High vertical resolution, independence of cloud conditions, lack of fundamental biases, uniform global coverage Observation coverage

11. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress

12. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Read SCATT (25 km BUFR data) Data from data base or file or ECMWF MARS System Pre SCATT - read in original Bufr - data selection within analyses time window - eliminates overlapping orbits - computes KNMI rain flag; data flaging - computes direction check; data flagging - computes bias correction - data selection based on quality control - thinning - output options: Bufr; netCDF, ascii OI - Analyses - (u/v) most likely wind - coded as Pseudo-Buoy (Bufr) 3DVAR - Analyses - (u/v) more than one wind - coded as Bufr/netCDF Offline Monitoring - colloc. with GME -FG/Ana - analyses of data quality Use of scatterometer data at DWD 10 m wind vectors (most likely wind) QuikScat and ASCAT Global and regional Use of multiple wind solutions (planned) Experiments with OI and 3DVAR

13. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Scatterometer Data Coverage 2008022500 +/- 1.5 H ASCAT (red) QuikScat (blue)

14. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Scatterometer data in the Parallelroutine Tropical Storm 02B 2009052412 sea level pressure [hPa] Routine Parallelroutine Roup - Rou ECMWF

15. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Scatterometer data in the Parallelroutine Tropical Storm 02B 2009052412 vv=12h sea level pressure [hPa] / max windspeed [m/s] RoutineParallelroutine

16. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Crtl Crtl plus SCAT Crtl Crtl plus SCAT Anomaly correlation coefficient for sea level pressure Northern HemisphereSouthern Hemisphere Europe VV=72h Crtl Crtl plus SCAT

17. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Use of the polar AVHRR wind vectors in the global analyses system of DWD Polar regions with small observation density Polar regions show still large observation errors Polar lows have influence on weather regimes in Europe and North America Polar AMV winds from Terra and Aqua only Experimental No operational satellite programm planned for Derivation of AMV wind vectors in polar regions Deriation of wind vectors from polar orbiting NOAA satellites (15/16/17/18) and Metop Only infrared winds Height assigment more problematic than for Modis winds

18. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress MetopNOAA 17 NOAA 18Modis/Terra AVHRR OBS – FG Statistik 2008081400 – 2008083121 All Data mit QI > 65 700 hPa – 400 hPa

19. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Anomaly correlation coefficient of the 500 hPa geopotential height 2008100200 – 2008102300 00 UTC 22 cases Control (red)Exp. with AVHRR Winden (blau)

20. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Time series of anomaly correlation coefficients 96-h forecast of the 500 hPa geopotential height field 2008100200 – 2008102300 00 UTC NHTR SH EU Routine Exp. Mit AVHRR

21. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Direct Broadcast MODIS Winds MODIS polar winds are not available in time to be used in assimilation of main run. Only available in assimilation run Direct broadcasting winds can be received much earlier ~ 100 minutes or more Winds from a variety of stations  Tromso - Terra Modis  Sodankyla - Terra Modis  Fairbanks - Terra Modis  McMurdo, Antartica - Terra/Aqua Modis Provide only partial coverage and only Terra can be received in the NH At DWD, no MODIS winds could be used in the main runs. Using DB winds, some polar winds can be used also in the main run. Additionally, more polar winds can be used in the assimilation Motivation

22. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Alexander Cress Data coverage 00 UTC 12 UTC

23. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress StoreTime Number of Observations

24. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress OBS –FG Statistics Terra QI > 65 20090111 - 20090119 IR 700 – 400 hPa IR 400 – 0 hPa WV 700 – 400 hPa WV 400 – 0 hPa Mean: -0.05 Mean: -0.26 Mean: -0.13 Mean: 0.38

25. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Alexander Cress

26. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Alexander Cress

27. Deutscher Wetterdienst w Lokal-Model COSMO-EU (LME) und COSMO-DE (LMK) COSMO-EU (regional model): non-hydrostatic, rotated lat-lon grid, mesh-size: 7km terrain-following hyprid coordinate with 40 layers up to 20 hPa forecast range: 78 h every 6 hours prognostic cloud ice, prognostic rain schemes boundary values from GME Analysis:continuous nudging scheme observations:radiosonde, pilots, wind profiler, aircraft, synops, buoys, ships cut-off: 2h30min variational soil moisture analysis COSMO-DE (lokal model): similar to COSMO-EU forecast range 18 h every 3 h mesh-size: 2.8 km, explicit convection latent heat nudging of radar reflectivities boundary values of COSMO-EU GME COSMO-EU COSMO-DE

28. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Assimilation of Scatterometer Wind in COSMO-EU Heinz-Werner Bitzer (MetBw), Alexander Cress, Christoph Schraff (DWD)  nudging of scatterometer wind data technically implemented, taking into account all quality control / bias correction steps developed for use in GME  idealised case studies: model rejects largest part of 10-m wind info unless mass field is explicitly balanced  derive surface pressure analysis correction in geostrophic balance with 10-m wind analysis increments (implies need to solve Poisson equation): implemented, model now accepts data  first real case study computed QSCAT 19 June 2007, 6 – 9 UTC 48N 50N 15 W Opr (no QSCAT) – Exp (QSCAT) PMSL 19 June 2007, 9 UTC hPa

29. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress with ASCAT / QuickScat pmsl (model – obs) too low too strong gradient COSMO-EU 9-h forecasts, valid for 6 March 2008, 9 UTC No scatt

30. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress COSMO-EU ana with ASCAT/QuickScatCOSMO-EU ana, no scatt ECMWF analysis 29 Feb 08 ASCAT 28 Feb 08, 21 UTC ± 1.5h 984 hPa max. 30 kn ~15 m/s 10-m wind [m/s] 975 hPa

31. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Compariosn of surface weather elements between COSMO-EU Routine and COSMO-EU experiment including Scatterometer data 27/02/2008 – 09/03/2008 00 UTC Routine Exp. with Scatt

32. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Assimilation of IASI Measurements into the COSMO-EU Michael Schwärz EUMETSAT Fellow Infrared atmospheric sounding interferometer onboard Metop IFOV: 3.33 o (48 km nadir) Swath: +/- 1026 km 8461 channels ⇨ 300 channels selected by IC Use of RTTOV 9 within 1DVAR Bias correction (Harris and Kelly 2001) Cloud detection a)IASI level 2 cloud flags b)after McNelly and Watts (2003) Use of temperature and humidity profiles in COSMO EU

33. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Experiment design 215 temperature channels from 15 µm band 6.25 µm wv band IASI level 2 flags for cloud detection COSMO-EU + IASI 1DVAR profiles Results Data processing and nudging works Positive results in upper air verification Stronger for RMS than Bias Heighest for geopotential height in the upper troposphere and humidity in the middle troposphere Outlook Better channel selection Thinning in COSMO – EU Use of cloud detection by McNelly and Watts

34. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress COSMO-DE  2.8 km solution with 50 vertical layers  Runge Kutta time integration  Explicit deep convection/ param. shallow conv.  Rain, snow, graupel  Started every 3 hours (30 min cut off)  Forecast range 21 h  Nudging of conventional data  Latent Heat Nudging of radar derived rain rates (16 German stations, will be extended to about 30 stations, soon) Domain of COSMO-DE and German Radar Network COSMO-DE To provide the nowcasters a appreciate guidance on severe weather events related to deep moist convection (super- and multi-cell thunderstorms) or to small scale orography

35. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Skill scores for a convective period (14 days) for 0.1 mm/h about midnight (00 UTC) about noon (12 UTC) Comparison of forecast starting with and without LHN LHN noLHN LHN noLHN LHN noLHN LHN noLHN Forecast starting from LHN analysis are better LHN via noLHN ETS FBI Events

36. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress LHN noLHN LHN noLHN LHN noLHN LHN noLHN Forecast starting from LHN analysis are better LHN via noLHN ETS FBI Events Skill scores for a convective period (14 days) for 1.0 mm/h about midnight (00 UTC) about noon (12 UTC) Comparison of forecast starting with and without LHN

37. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Room for improvement: data base  Up to now used withtin LHN:  All 16 German stations  Shortly to be extended with  3 Dutch stations  2 Belgian stations  10 France stations  3 Swiss stations  Quality Control  Clutter filter  Cross error detection  Bright band correctio  blacklisting (comparison to satellite picture)

38. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Impact of extended data: Hourly Precipitation on 25.05.2009 12 UTC +5h New Radar CompositeForecast with orig. data Forecast with new data Forecast is improved by extended data base Extended Data Base

39. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Skill scores for a convective period (23 days) for 0.1 mm/h about midnight (00 UTC) about noon (12 UTC) Forecast is improved by extended data base Extended Data Base ETS FBI Events

40. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Summary 3DVAR outperforms OI and is operational since mid Sep. 2008 Positive impact of ATOVS radiances from Metop and NOAA 19 Use of radio occulation in 3DVAR showed some promising results Positive impact of scatterometer data in global and regional forecasts Substantial benefit of AVHRR and DB MODIS polar winds First results of using IASI data in COSMO-EU promising LHN of radar derived rain rates is beneficial for the forecast, esp.for nowcasting purposes

41. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Plans for the next years Continuation of experiments using Radio Occultation data including Metop Use of AMSU B & MHS data (first experiments running) Use of AIRS & IASI data in GME (already started) Prepare for ADM mission Use of VAD and radar radial winds in COSMO Models Use of GPS humidity data (COPS reanalysis) Development of a new non-hydrostatic Global Model with local zooming (ICON) Development of an Ensemble Kalman Filter (LETKF) for the new model ststem

42. Deutscher Wetterdienst 22 th NAEDEX Meeting Reading Alexander Cress Thank you for your attention