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Transcript of APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress Joint APSDEU-12/NAEDEX-24 Data Exchange...
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Joint APSDEU-12/NAEDEX-24 Data Exchange Meeting (Exeter 2012)
Deutscher Wetterdienst (DWD) status report
Alexander CressDeutscher Wetterdienst, Frankfurter Strasse 135, 6003 Offenbach am Main, Germany
and Christof Schraff, Klaus Stephan, Annika Schomburg, Robin Faulwetter, Olaf Stiller, Andreas Rhodin, Harald Anlauf, Christina Köpken-Watts etc…
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
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
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
Numerical Weather Prediction at DWD
COSMO-DE EPSPre-operational 20 membersGrid spacing: 2.8 kmVariations in:lateral boundaries, initial conditions, physics
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
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
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
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.
Future assimilation systems
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
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
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Use of RARS data
8% more assimilated radiances in main runs
Satellite data coverage Main run 2012012900
Number of data in main runs
RARS – Regional Advanced Retransmission Service
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
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
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Monitoring of ATMS radiances
obs-fg
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Monitoring of METOP-B AMSU-A radiances
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Meteosat 9 wvCloudyLevel: 400 hPa – 100 hPa
NH NHsea 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
Data quality AMVs over land
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
AMV over landNormalized rms difference
NH EU
Experiment period: 2011040200 - 2011052400
• 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
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Scatterometer
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Oscat data quality
ASCAT OSCAT
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Hurricane Maria
Station Lat Lon Obs obs-fg (gpm) Status Routine OSCAT Routine OSCAT
71600 43.93 299.99 997.4 -40. -22. REJECTED ACCEPTED44141 43.00 302.00 985.4 -151. -140. REJECTED REJECTED44139 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)).
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
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)
Use of GPS - radio occultation (bending angles) in the 3DVar-Assimilation of GME (since 03. Aug. 2010)
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Radio Occultation of Metop-B/Gras
Cal/Val study of Metop-B/Gras RO qualityTime Period: 2012092900 – 2012100921 UTC
Good correspondence between METOP-A and METOP-B RO quality
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress 1717
Assimilation of cloud information into COSMO-DE
17
Source: EUMETSAT
NWCSAF cloud products based on satellitedata:
- geostationary satellite Meteosat- Instrument: SEVERI
• Dx ~ 5km over Europe• dt ~ 15 min
- cloud products:• cloud type • cloud top height
Use nearby radiosondes within the same cloud type to correct(or approve) cloud top height from satellite cloud height retrieval
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress 1818
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 18
3
6
9
12
Z [km]
- no data -
„no cloud“
Cloud top
Z [km]
12
“no cloud“
“no cloud“
“no cloud“
3
6
9
Relative humidity
model profile
model profile
Cloud cover
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress 19
OBS-FG OBS-ANA ANA deviations – FG dev
Here: results of deterministic run: Kalman gain matrix applied to a standard model integration
Cloud top height
Results of first assimilation experiment
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress 20
FG ANA ANA – FG
Here: results of deterministic run: Kalman gain matrix applied to a standard model integration
Relative humidity at cloud level
Results of first assimilation experiment
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Radial Wind Component measured by Doppler Radar
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 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
PPI of radial wind (lowest elevation)
towards
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Pro and Cons
+ 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)
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Observation
Azimuth
Ran
ge in
m
Model
AzimuthIncrement (obs-mod)
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Radar Verification May 20121h Precipitation FSS at 11 GP
Assimilation 00 UTC
Control
RadWindAss
noWindAss
12 UTC
0.1 mm/h
5.0 mm/h
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
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
APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress
Thank you for your attention!
Questions?