APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress Joint APSDEU-12/NAEDEX-24 Data Exchange Meeting (Exeter 2012) Deutscher Wetterdienst (DWD) status

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

[email protected]

and Christof Schraff, Klaus Stephan, Annika Schomburg, Robin Faulwetter, Olaf Stiller, Andreas Rhodin, Harald Anlauf, Christina Köpken-Watts etc…


COSMO-EU

Grid spacing: 7 km

Layers: 40

Forecast range:

78 h at 00 and 12 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


Global model GME

Grid spacing: 20 km

Layers: 60

Forecast range:




Numerical Weather Prediction at DWD

COSMO-DE EPSPre-operational 20 membersGrid spacing: 2.8 kmVariations in:lateral boundaries, initial conditions, physics


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


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


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


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


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


Monitoring of ATMS radiances

obs-fg


Monitoring of METOP-B AMSU-A radiances


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


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


Scatterometer


Oscat data quality

ASCAT OSCAT


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)).


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)


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


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


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


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


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


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)


Observation

Azimuth

Ran

ge in

m

Model

AzimuthIncrement (obs-mod)


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


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


Thank you for your attention!

Questions?

Documents

APSDEU-12/NAEDEX-24 Data Exchange Meeting Alexander Cress Joint APSDEU-12/NAEDEX-24 Data Exchange Meeting (Exeter 2012) Deutscher Wetterdienst (DWD) status