1. Global and regional impact studies at the Deutscher Wetterdienst (DWD)
Alexander Cress, Andreas Anlauf, Robin Faulwetter, Klaus Stephan, Andreas Rhodin, Christoph Schraff, Hendrik Reich, Michael Bender
Deutscher Wetterdienst, Frankfurter Strasse 135, Offenbach am Main, Germany
Martin Weissmann, Tijana Janic-Pfander, Leonhard Scheck, Florian Harnisch, Hainer Lange, Yuefei Zheng
Hans-Ertel-Centre for Weather Research, LMU
Theresa Bick
University Bonn
Introduction
Global impact studies with the new ICON system
Regional impact studies using convection resolving ensemble system
Summary

2. The deterministic NWP-System of DWD
Global-Modell ICON
grid size: 13 km
vertical levels: 90
forecasts:
180 h von 00 und 12 UTC
120 h von 06 und 18 UTC
30 h von 03, 09, 15 und
21UTC
Grid area: 173 km2
ICON-EU Nest over Europa
grid size: 6.5 km
Vertical levels: 60
forecasts:
120 h von 00, 06, 12 und 18 UTC
30 h von 03, 09, 15 und 21UTC
Grid area: 43 km2
COSMO-DE (convection resolving)
grid size: 2.8 km
vertical levels: 50
forecasts:
27/45 h von 00, 03, 06, 09,
12, 15, 18, 21 UTC
421x461 grid size
Gitterfläche: 8 (5) km2 2

3. The probabilistic NWP-System of DWD
ICON-EPS; M40
grid size: 40 km
vertical levels: 90
forecasts:
180 h von 00 und 12 UTC
120 h von 06 und 18 UTC
30 h von 03, 09, 15 und
21UTC
grid area: 1638 km2
ICON-EU Nest over Europa
grid size: 20 km
vertical levels: 60
forecasts:
120 h von 00, 06, 12 und 18 UTC
30 h von 03, 09, 15 und 21 UTC
grid area: 407 km2
COSMO-DE-EPS; M20
Grid size: 2.8 km
Vertical levels: 50
forecasts:
27 h von 00, 03, 06, 09,
12, 15, 18, 21 UTC
421x461 grid points
grid area: 8 km2 3

4. Ensemble Data Assimilation (EDA)
Global
Ensemble Data Assimilation (EDA)
Implementation following the LETKF method based on Hunt et al. (2007).
VarEnKF. Flow dependent B: BVarEnKF = αBLETKF + (α-1)B3DVAR
Boundary conditions for KENDA-COSMO.
Natural initialization for global EPS.
Prior for particle filters.
Using a variety of conventional and satellite based observing systems
Deterministic DA
13km 3D-VAR.
SST, SMA and snow ana.
Incremental analysis update.
Hybrid DA
13km VarEnKF(hyprid )
Operational since January 2016
Ensemble DA
40 member 40/20 km LETKF.
Horizontal localization radius 300km.
Relaxation to prior perturbations ( 0.75).
Adaptive inflation ( ).
SST perturbations.
Soil moisture perturbations

5. Kilometer ScaleEnsemble Data Assimilation (KENDA)
Implementation following the LETKF method based on Hunt et al. (2007)
(because of ist relatively low computational costs)
Should replace the nudging scheme for COSMO-DE in 2017
Advantages against nudging
provide perturbed IC for EPS
improved analysis / forecast quality by use of
multi-variate, flow-dependent error covariances
better suitable than current operational nudging scheme for use of
indirect observations (satellite, radar, etc.):
nudging requires retrievals (e.g. T-, q- profiles from satellite radiances)
EnKF: apply forward observation operator ( simulated radiances)
Full System with conventional data including LHN is running
(pre-operational since May 2016)

6. HPC System (Cray XC40) of DWD
Intel Haswell Partition 432 compute knots 864 processors compute 415 Tera-Flop Peak Intel Broadwell Partition 544 Rechenknoten 1088 Prozessoren Rechenkerne 660 Tera-Flop Peak

7. Global impact experiment with the
new ICON model at DWD
Evaluating the impact of radio occultation data
Scatterometer impact studies
Impact of AMVs derived from the MISR
instrument onboard of Terra

8. Radiooccultation experiments
Experiment design
Control experiment
Experiment without Radio Occultation data
Experiment using Cosmic data only in tropics and Metop A/B data
Experiment not using radiosonde data above 100 hPa
Period: – /summer period)
All experiments use a 40 km resolution 3dvar + ICON
Radio Occultation data used in Crt: Cosmic, Grace, Gras, Terrasar
Tamdem-X, C-Nofs , SAC-C

9. Radiosonde statistics
No Radiooccultations
Radiosonde statistics –
Crtl NoGPSRO NH TR SH
Large impact in stratosphere on both hemispheres and tropics
Bias not changed significantly
No significant change in number of radiosondes used

10. (Crtl without GPSRO) - CTRL
Normalized rms difference
(Crtl without GPSRO) - CTRL
Period: 17. July 2016 – 20 Sept (67 forecasts) NH SH
500 hPa geopotential height
500 hPa geopotential height TR
200 hPa temperature

11. No extra-tropical Radio Occultations but with Metop A/B
Radiosonde statistics –
Crtl NoextrGPSRO NH TR SH
No impact in stratosphere on both hemispheres and tropics
Bias not changed significantly
No significant change in number of radiosondes used

12. Normalized rms difference
(Crtl without extra tropical GPSRO but including
METEOP A/B) – CTRL)
Period: 17. July 2016 – 20 Sept (67 forecasts) NH SH
500 hPa geopotential height
500 hPa geopotential height TR
200 hPa temperature

13. Normalized rms difference
(Crtl exluding radiosonde observations above 100 hPa)
Period: 17. July 2016 – 20 Sept (67 forecasts)
100 hPa geopotetial height NH SH
Strong negative impact in the upper troposhere on both hemispheres
Largest in the first 48 hours

14. Normalized 48-h geopotential height error difference
((Crtl - radiosonde observations above 100 hPa) minus Ctrl)
Large negative impact in the upper troposphere and stratosphere
Negative impact highest over Asia and North America
Substantial impact in the upper stratosphere also over the southern hemisphere
Use of Radiances and Radio Occulation can not compensate for loss of radiosondes

15. Scatterometer experiments
Experiment design
Control experiment
Experiment using additionally HY-2A scatterometer data
Exp: Crtl discarding OceanSat-2 but including HY-2A
Exp: Crtl without OceanSat-2 data
Exp: Crtl without any scatterometer data
Period: – (winter period)
All experiments use a 40 km resolution 3dvar + ICON
Scatterometer data used in Crt: ASCAT onboard Metop A/B and
OceanSat-2

16. Scatterometer experiments
NoScat shows a clear degration of forecast quality on both hemispheres
Using a fourth scatterometer (HY-2A) shows some improvements
Removing the Oceansat-2 scatterometer shows small degration
Using HY-2A alone can not fully compensate for loss of Oceansat-2

17. Scatterometer experiments
sea level pressure [hPa] NH SH
Crtl + HY-2A
Crtl + HY-2A NH SH
Crtl - Scat
Crtl - Scat

18. Evaluation of MISR winds
Multi-angle Imaging SpectroRadiometer (MISR) instrument (TERRA)
Employing nine fixed cameras pointing at fixed angles
Provides wind speed and direction in visible channel
Monitoring of wind product on behalf of the Int. Wind Working Group and following SWG suggestion
Use of the global assimilation and forecasting system of DWD
Two monitoring periods:
Summer 2010: 15th August – 30th September 2010
Winter 2010/11: 01th December 2010 – 15th January 2011
Operational data flow since spring 2015

19. Observation Coverage MIRS Winds
Number of MISR Winds
15 days
Most MISR winds found in the
lower troposphere over Sea

20. Anomaly correlation coefficient 500 hPa geopotential height
Crtl
Crtl + Misr
winter
summer
winter
summer

21. Regional impact experiments with the KENDA/COSMO system at DWD
Impact of GPS total/slant delays
Assimilation of radar reflectivities
Use of MODE-S data
Evaluationg the impact of SEVIRI measurements
Aircraft humidity observations

22. high-resolution obs: GNSS Slant Total Delay (STD)
 Michael Bender (DWD/IAFE)
GNSS (GPS) Slant Path Delay : humidity integrated over path
from ground station to GNSS (GPS) satellite, all weather obs
many stations  3-D information on humidity, but !
at 5° (7°), path reaches height of 10 km at ~ 100 (80) km distance
vert. + horiz. non-local obs (not point measurements)
(45) GPS obs from 1 station / 9 satellites in 15 min.
elevation angles 90° - 5

23. GNSS-STD, first trial for use in KENDA
high-resolution obs:
GNSS-STD, first trial for use in KENDA
20 days
10. –
1-hrly precip
FSS
( 30 km )
12-UTC forecast runs
0.1 mm/h
1 mm/h
STD
assimilated
CNTR:
only conventional
(no LHN)
preliminary (!) results (no LHN):
improved precip forecast in first 8 – 16 hours
precip forecasts tend to be degraded for longer lead times

24. Assimilating Radar Reflectivity Data
(see also Poster of Yuefei Zheng /WS )
3D Reflectivity:
volume reflectivity measurements (10 PPI scans) of 17 C-Band radar stations
Radar forward operator:
Simulate synthetic 3D radar scan based on COSMO-DE model fields (EMVORADO)
No-reflectivity: threshold data at 5 dBZ
Superobbing: achieve homogeneous horizontal data distribution:
Assimilation experiment (20 – 29 May 2015)
Conv. Obs. Only
Conv obs + LHN
Conv. Obs + radar reflectivietes

25. ensemble mean and std dev.
high-resolution obs: radar reflectivity
 Theresa Bick et al., QJRMS 2016
7 days / 29 forecasts
(22 – 29 May 2014)
FSS precipitation
ensemble mean and std dev.
precip
precip
CONV
CONV + RAD
CONV + LHN
CONV + RAD
rather large, long-lived positive impact from use of radar reflectivity in LETKF
use of radar reflectivity slighltly more impact than Latent heat nudging in first 4 hours

26. Use of MODE-S data Aircraft related meteorological information
originates from tracking and ranging radar
for air traffic control provided by KNMI
From aircraft information temperature and
wind vector can be inferred
Mode-S original resolution: every aircraft
every 4 sec
Mode-S averaged along flight tracks in
AMDAR-fashion
Averaging distance between consecutive
observations: 15 km
15 x times more flights in Mode-S than in
AMDAR
Much better coverage with Mode-S EHS.
(note the scale !!!!!)
However AMDAR also provides humidity in
addition to temperature and velocity!

27. Experiments Test period 7th till 17th of May 2014
NoDA the control experiment without data assimilation.
Aconv assimilates all conventional data except radiosondes.
MAconvTh10, MAconvTh50 and MAconv in addition assimilate Mode-S data randomly thinned to contain 10, 50 and 100 percent of full data set.
Mconv does not assimilate AMDAR.
The areas of the circles correspond to the average daily number of single observations of the wind variable per station: PROF, 5813 SYNOP, and 1571 TEMP.

28. Results 3 hour forecasts
Forecast RMS evaluated during the last hour of 3-hour forecast windows from 9 to 12 UTC and 21 to 00 UTC (also for NoDA).
A two-colored diamond on a level means that the RMSs of the left experiment are significantly smaller than of the right experiment.

29. Assimilation of MSG SEVIRI satellite Observations in KENDA-COSMO
Observations for convective-scale data assimilation
Geostationary satellite observations:
high temporal (~min) and spatial (~km) resolution
provide information: temperature, water vapour, clouds
→ potential for convective-scale data assimilation
Many challenges for optimal use:
fast and accurate forward operators
assimilation of cloud-affected data (e.g., biases, non-Gaussian errors)
using ensemble data assimilation systems
(e.g., vertical localization, ensemble derived background errors)
Developments
Fast forward operator for visible and near infrared observations
(Scattering makes the radiative process complex, 1D-process, log-up tables)
Error model for the assimilation of cloud-affected infrared satellite data
All sky approach; adapts the observation errors in cloudy situations
observation errors are usually much larger than the ensemble spread in cloudy conditions

30. Assimilation of MSG SEVIRI satellite Observations in KENDA-COSMO
Assimilation set-up
Summer time over central Europe, June 2014
1-h DA cycle with a 40 ensemble member LETKF (KENDA)
Standard conventional observations assimilated (Crtl)
Assimilation of SEVIRI BT observations channel 7.3 μm (Exp:73)
- RTTOV 10.2
- Observation error: dynamic error model
- No bias correction; adjusted RTTOV settings)
Results
Using all BT data with dynamic observation error model draws the analysis / first guess closer to the observations
Fit against conventional observations is overall neutral
Using BT observations improves the IWV field

31. Assimilation of MSG SEVIRI vis/near-IR Observations in KENDA-COSMO
Assimilation results
June 2014
Crt: only conventional observation
Exp: Crtl + SEVIRI visible data
Setup:
40 member LETKF
1h assimilation interval
Observation error 0.2
Superobbing (radius 3 pixels)
Horiz. localization 100km
No vertical localization
Only lower reflectance
Reduction of Bias and RMS compared to SEVIRI reflectances

32. Experiment design Eucos Aircraft humidity and TAMDAR study over the US
Run GME and Cosmo over the U.S.
Time period: –
Control run (both GME and Cosmo)
Exp. using AMDAR humidity over U.S.
Exp: using TAMDAR data over U.S.
Exp. Using TAMDAR/AMDAR humidity
Experiments for GME and COSMO finished
EUMETNET Meeting Alexander Cress

33. COSMO-US ETS scores Results
Verified against NCEP stage IV radar rain rate product
Results
All experiments show am improvement
compared to the Crtl
Strong improvement for the first 3-h
Largest improvement of AMDAR data
for longer forecast ranges
Tamdar data always equal or slightly
worse than Amdar data
Using both data sets showed often
largest improvements
EUMETNET Meeting Alexander Cress

34. Summary Global impact studies
Radio Occulation data are a very important observation system
Restricting the COSMO constallation on the tropics have a small
negative impact
Having no stratospheric radiosonde measurements lead to a substantial
reduction in forecast quality in the upper troposhere and stratosphere
More than two scatterometer flying have a positive impact on the
forecast quality; thereby quality of the scatterometer data very
important
AMV observations derived from the MISR instrument show a positive
impact on the analyses and forecast quality in our system
EUMETNET Meeting Alexander Cress

35. Regional impact studies
Summary
Regional impact studies
GPS total/slant delays show positive impact in the first 12 hours
Radar reflectivities show clear positive impacts, comparable latent
heat nudging
Mode-S data show positive imapcts similar to other aircraft data
but no humnidity observations
Use of high resolution radiances depict some positive impacts and
can be very impart for high impact weather events
Use of aircraft humidity depict some positive impacts in the first
forecast hours
EUMETNET Meeting Alexander Cress

36. Thank you for your attention! Questions?
E-SAT Meeting Alexander Cress