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, 63067 Offenbach am Main, Germany alexander.cress@dwd.de 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

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

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

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 (0.9 - 1.5). SST perturbations. Soil moisture perturbations

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)

HPC System (Cray XC40) of DWD Intel Haswell Partition 432 compute knots 864 processors 10368 compute 415 Tera-Flop Peak Intel Broadwell Partition 544 Rechenknoten 1088 Prozessoren 19584 Rechenkerne 660 Tera-Flop Peak

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

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: 15.07.2015 – 10.09.2015 /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

Radiosonde statistics No Radiooccultations Radiosonde statistics 2015080100 – 2015083121 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

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

No extra-tropical Radio Occultations but with Metop A/B Radiosonde statistics 2015080100 – 2015083121 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

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

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

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

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: 01.12.2013 –31.12.2014 (winter period) All experiments use a 40 km resolution 3dvar + ICON Scatterometer data used in Crt: ASCAT onboard Metop A/B and OceanSat-2

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

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

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

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

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

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

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

GNSS-STD, first trial for use in KENDA high-resolution obs: GNSS-STD, first trial for use in KENDA 20 days 10. – 29.06. 2014 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

Assimilating Radar Reflectivity Data (see also Poster of Yuefei Zheng /WS6-2016-043) 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

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

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!

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:11 851 PROF, 5813 SYNOP, and 1571 TEMP.

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.

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

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

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

Experiment design Eucos Aircraft humidity and TAMDAR study over the US Run GME and Cosmo over the U.S. Time period: 01.03.2011 – 31.05.2011 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 2016 Alexander Cress

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 2016 Alexander Cress

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 2016 Alexander Cress

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 2016 Alexander Cress

Thank you for your attention! Questions? E-SAT Meeting 2011 Alexander Cress