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Joint APSDEU-12/NAEDEX-24 Data Exchange Meeting (Exeter 2012)

Joint APSDEU-12/NAEDEX-24 Data Exchange Meeting (Exeter 2012) Deutscher Wetterdienst (DWD) status report Alexander Cress Deutscher Wetterdienst, Frankfurter Strasse 135, 6003 Offenbach am Main, Germany alexander.cress@dwd.de.

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Joint APSDEU-12/NAEDEX-24 Data Exchange Meeting (Exeter 2012)

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  1. Joint APSDEU-12/NAEDEX-24 Data Exchange Meeting (Exeter 2012) Deutscher Wetterdienst (DWD) status report Alexander Cress Deutscher Wetterdienst, Frankfurter Strasse 135, 6003 Offenbach am Main, Germany alexander.cress@dwd.de and Christof Schraff, Klaus Stephan, Annika Schomburg, Robin Faulwetter, Olaf Stiller, Andreas Rhodin, Harald Anlauf, Christina Köpken-Watts etc…

  2. Numerical Weather Prediction at DWD Global model GME Grid spacing: 20 km Layers: 60 Forecast range: 174 h at 00 and 12 UTC 48 h at 06 and 18 UTC 1 grid element: 778 km2 COSMO-EU Grid 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-DE Grid 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 COSMO-DE EPS Pre-operational 20 members Grid spacing: 2.8 km Variations in: lateral boundaries, initial conditions, physics

  3. Assimilation schemes • Global: 3DVAR PSAS • Minimzation in observation space • Wavelet representation of B-Matrix • seperable 1D+2D Approach • vertical: NMC derived covariances • horizontal: wavelet representation • Observation usage: Synop, Temp/Pilot, Dropsonde, AMV, Buoy, Scatterometer, AMUSU-A/B, Aircraft, Radio Occultation • Time window: 3 hours • Local: • Continous nudging scheme and latent heat nudging • Time windows: 0.5 – 1 hour • Observation usage: Synop, Temp/Pilot, Dropsonde, Buoy, • Aircraft, Scatterometer, Windprofiler, • Radar precipitation

  4. Future assimilation systems • Currently ( 2010-2015) moving to an Ensemble Data Assimilation on all scales • Global data assimilation (VarEnKF) • Run a global EnKF with 40 members, low resolution (40km/30/km) with • regional refinements over Europe (10km/5km) • Run a global high resolution analysis (20km/5km refinements) with a • covariance matrix which is fed in from the global EnKF in combination • with model error/climatological terms (multiplicative, additive) • Local data assimilation (LETKF) • Development of an Ensemble Kalman Filter for the convection resolving • scale • LETKF version using conventional data is implemented and running at • DWD, Uni Munich, Meteo Swiss, Italy … • Many research projects running to implement and test particular • observation operators for the LETKF e.g. • volume radar operator, GNSS total and slant delay operator, • cloud analysis operator etc.

  5. New developments since last meeting • Global: • Change from 30 km / 60 L to 20 km / 60 L • Revised background error correlations for new model • Use of RARS radiances • Monitoring of ATMS and AMSU-A/MHS of METOP-B • Use of Radio Occultation (bending angles) from SAC/C and C/NOFS. Monitoring of METOP-B ROs • Monitoring of AMVs from GOES 14 • AMVs over land • Use of wind profiler networks (Europe, USA, Japan, Canada) • Monitoring of Oceansat-2 scatterometer data • Temperature bias correction of aicraft • Local: • Humidity bias correction for radiosondes • Use of doppler radar wind data • Cloud analyses based on NWCSAF products

  6. Use of RARS data RARS – Regional Advanced Retransmission Service Number of data in main runs 8% more assimilated radiances in main runs Satellite data coverage Main run 2012012900

  7. Verification: surface obs. Europe 12UTC • More data available in main runs. • Verification against analyses: neutral (NH slightly positive, SH worse) • Verification against surface obs: globally neutral, Europe positive • Verification against TEMPs: positive

  8. Monitoring of ATMS radiances obs-fg

  9. Monitoring of METOP-B AMSU-A radiances

  10. Data quality AMVs over land Meteosat 9 wvCloudy Level: 400 hPa – 100 hPa NH sea NH land • AMVs over land comparable to AMVs over sea for upper troposphere • For the lower troposphere, AMVs over land above deep orography problematic • On average bias for AMVs over land 0.5 m/s higher in upper troposphere • increasing to 1 m/s in lower troposhere. RMS comparable

  11. AMV over land Normalized rms difference Experiment period: 2011040200 - 2011052400 NH EU • Experiment with AMVs over land but without Asian AMVs • Verified agains own analyses • Forecast impact positiv for all forecast times on Northern Hemisphere and Europe • Neutral impact on Southern Hemisphere

  12. Scatterometer

  13. Oscat data quality ASCAT OSCAT

  14. Hurricane Maria Station Lat Lon Obs obs-fg (gpm) Status Routine OSCAT Routine OSCAT 71600 43.93 299.99 997.4 -40. -22. REJECTED ACCEPTED 44141 43.00 302.00 985.4 -151. -140. REJECTED REJECTED 44139 44.20 302.90 997.4 -45. -38. REJECTED REJECTED Die Schranke für den FG-Check liegt bei ca. 30 gpm (3* sqrt(e_obs^2+e_fg^2)).

  15. Use of GPS - radio occultation (bending angles) in the 3DVar-Assimilation of GME (since 03. Aug. 2010) • Advantages of GPS radio occultations (bending angles) • high vertical resolution  even vertical thinning of data required! • globally accessible, approximately equally spaced • not influenced by clouds • measurement of the bending angle is almost bias free, temporally stable, independent from the instrument • number of profiles is proportional to the product of the sending GNSS-satellites (GPS, Galileo, GLONASS) and receiving LEOs: • CHAMP, GRACE-A (research satellites) • FORMOSAT-3 / COSMIC ( 6 research satellites) • GRAS (Metop-A) • TerraSar, C/NOFS, SAC/C (H. Anlauf, DWD)

  16. Radio Occultation of Metop-B/Gras Cal/Val study of Metop-B/Gras RO quality Time Period: 2012092900 – 2012100921 UTC Good correspondence between METOP-A and METOP-B RO quality

  17. Assimilation of cloud information into COSMO-DE • NWCSAF cloud products based on satellite • data: • - geostationary satellite Meteosat • - Instrument: SEVERI • Dx ~ 5km over Europe • dt ~ 15 min • - cloud products: • cloud type • cloud top height Source: EUMETSAT • Use nearby radiosondes within the same cloud typeto correct • (or approve) cloud top height from satellite cloud height retrieval 17 17 17

  18. Determine cloud top height model equivalent Assimilated variables if cloud observed: Cloud top height Model: height of model layer k which is close to observation and has high relative humidity Relative humidity at cloud top height obs = 100% Model: relative humidity of layer k If observed cloud is low: Cloud cover for high (and medium) clouds obs=0 Model: maximum cloud cover in vertical range If no cloud observed: Cloud cover for high, medium, low clouds Obs= 0 Model equivalent: maximum cloud cover in vertical range  use all this information for weighting the ensemble members in the LETKF Z [km] Z [km] 12 12 “no cloud“ „no cloud“ 9 9 model profile model profile Cloud top “no cloud“ 6 6 - no data - 3 3 “no cloud“ Relative humidity Cloud cover 18 18 18

  19. Results of first assimilation experiment Cloud top height OBS-FG ANA deviations – FG dev OBS-ANA Here: results of deterministic run: Kalman gain matrix applied to a standard model integration 19

  20. Results of first assimilation experiment Relative humidity at cloud level FG ANA ANA – FG Here: results of deterministic run: Kalman gain matrix applied to a standard model integration 20

  21. A so called Doppler Radar is able to measure the phase of the radio wave. Moving targets will produce a phase shift due to the Doppler effect This shift can be detected and the velocity along the beam can be measured (radial component of the wind vector) Radial Wind Component measured by Doppler Radar PPI of radial wind (lowest elevation) • Radial wind volumes can be used for: • clutter filtering (stationary ground clutter, but not wind mills) • 2nd trip detection • Estimation of vertical profile of horizontal wind (VAD) • directly used for DA • Hazard warning: meso cyclone detection towards

  22. High resolution in space ( 1 km in range, 1° in azimuth, ~1° in elevation) High observation frequency ( 5-15 min) Precise measurement of radial wind (about ± 0.5m/s) Dense networks in northern hemisphere (mainly over land) Expensive observation system (building, maintenance) Huge amount of data Measurements relay on observable particles (~ 1 mm) Pro and Cons

  23. Observation Model Range in m Azimuth Azimuth Increment (obs-mod)

  24. Radar Verification May 20121h Precipitation FSS at 11 GP 12 UTC 00 UTC Assimilation 0.1 mm/h Control RadWindAss noWindAss 5.0 mm/h

  25. Future Plans • Use of IASI/CriS data in global and regional model • Use of SSM/I-SSM/IS data • Preparation for AEOLUS wind lidar observations • Develop a 3D radar oberator for radar reflectivities / radial velocities • Use of ground-based GNSS total and slant delay observations • Develop cloud analysis based on conventional and satellite • observations • Use of radiances over land and/or cloudy conditions

  26. Thank you for your attention!Questions?

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