IV WMO Impact Workshop 2008Alexander Cress Regional impact studies performed in the COSMO community Alexander Cress, Reinhold Hess Christoph Schraff German Weather Service, Offenbach am Main, Germany Introduction Use of radar reflectivity measurements Assimilation of satellite radiances with 1D-VAR and Nudging Use of VAD profiles, GPS tomography data and scatterometer winds E-AMDAR humidity data Experiment testing a reduced radiosonde network over Europe (EUCOS proposal) Summary

w Lokal-Model COSMO-EU (LME) und COSMO-DE (LMK) COSMO-EU (regional model): non-hydrostatic, rotated lat-lon grid, mesh-size: 7km terrain-following hyprid coordinate with 40 layers up to 20 hPa forecast range: 78 h every 6 hours prognostic cloud ice, prognostic rain schemes boundary values from GME Analysis:continuous nudging scheme observations:radiosonde, pilots, wind profiler, aircraft, synops, buoys, ships cut-off: 2h30min variational soil moisture analysis COSMO-DE (lokal model): similar to COSMO-EU forecast range 18 h every 3 h mesh-size: 2.8 km, explicit convection latent heat nudging of radar reflectivities boundary values of COSMO-EU

Data base Reflectivity from „Precipitation scan“ (DX-Produkt): –Elevation angle: 0.5°-1.8° –Spatial resolution: 1km x 1°, max. Range 128 km –Temporal resolution: 5 Minuten –Quality control mechanism: Clutter, spikes, rings, AnaProp, … Data processing: –Correction of orographic shading –variable Z-R-relations –Composit of 16 German locations –Bright band correction

IV WMO Impact Workshop 2008Alexander Cress Basic principles of latent heat nudging LHN: specific form of Nudging to assimilate radar reflectivities respectivly the derived precipitation Problem: precipitation is a „derived“ model parameter with limited feedback on the thermodynamic structure of the model Aim: Adjust the dynamic of the model, that it produces the observed precipitation at the right place and time ⇨ relation to a thermodynamic relevant model variable (temperature, humidity) is required Solution: Adjustment of the latent heat in the model yields to a temperature (humidity) change

Results of an operational application Animation of hourly precipitation 26. August 2006 Assimilation: 09 UTC Radar Without LHNWith LHN

Results of an operational application Animation of hourly precipitation sum 26. August 2006 Assimilation: 10 UTC Radar Without LHNWith LHN

Results of an operational application Animation of hourly precipitation 26. August 2006 Assimilation: 11 UTC Radar Without LHNWith LHN

Results of an operational application Animation of hourly precipitation 26. August 2006 Assimilation: 12 UTC Radar Without LHNWith LHN

Results of an operational application Animation of hourly precipitation 26. August 2006 Free forecast 12 UTC + 1 H Radar Without LHNWith LHN

Results of an operational application Animation of hourly precipitation 26. August 2006 Free forecast 12 UTC + 2 H Radar Without LHNWith LHN

Results of an operational application Animation of hourly precipitation 26. August 2006 Free forecast 12 UTC + 3 H Radar Without LHNWith LHN

Results of an operational application Animation of hourly precipitation 26. August 2006 Free forecast 12 UTC + 4 H Radar Without LHNWith LHN

Results of an operational application Animation of hourly precipitation 26. August 2006 Free forecast 12 UTC + 5 H Radar Without LHNWith LHN

Results of an operational application Animation of hourly precipitation 26. August 2006 Free forecast 12 UTC + 6 H Radar Without LHNWith LHN

Results of an operational application 24H precipitation sum: (6 UTC – 6 UTC) Assimilation Radar Without LHNWith LHN

Results of an operational application Skill scores for 32 runs in AUGUST 2006 Threshold 1.0 mm/h ASS Free forecast ASS Free forecast ETSFBI LHN NoLHN LHN NoLHN

Results of an operational application Skill scores for 32 runs in AUGUST 2006 Threshold 5.0 mm/h ASS Free forecast ASS Free forecast ETSFBI LHN NoLHN LHN NoLHN

Summary and outlook Radar reflectivities are assimilated within the COSMO-DE to improve the forecasts of convective systems Latent Heat Nudging (LHN) of radar reflectivities prove to be a proper method Assumption: Precipitation rate at the surface is proportional to the vertical integral of latent heat release within a model column A positive impact of the LHN during the assimilation could be demonstrated The positive benefit of LHN can be verified up to nine hours in the free forecast Additional investigations are needed to extent the positive benefit beyond nine hours (additional use of radar winds to adjust the dynamic structure in the model) Inclusion of European radar sites

COSMO-Project : Assimilation of satellite radiances with 1D-Var and Nudging Germany, Italy, Poland 1DVAR + Nudging i.e. RETRIEVE temperature and humidity profiles and then nudge them as “pseudo”- observations Goals of Project: Assimilate radiances (SEVIRI, ATOVS, AIRS/IASI) in COSMO-EU Explore the use of nonlinear observation operators with Nudging

no thinning of 298 ATOVS30 ATOVS by old thinning (3)‏30 ATOVS, correl. scale 70% 40 ATOVS by thinning (3)‏82 ATOVS by thinning (2)‏82 ATOVS, correl. scale 70%  T-‘analysis increments’ from ATOVS, after 1 timestep (sat only), k = 20 Reinhold Hess, 20 Athens, 2007

no thinning of 298 ATOVS30 ATOVS by old thinning (3)‏30 ATOVS, correl. scale 70% 40 ATOVS by thinning (3)‏82 ATOVS by thinning (2)‏82 ATOVS, correl. scale 70%  T-‘analysis increments’ from ATOVS, after 30 minutes (sat only), k = 20

Reinhold Hess, 22 GME forecast for 48 hours Athens, 2007

mean sea level pressure & max. 10-m wind gusts analysis+ 48 h, REF (no 1DVAR)‏ valid for 20 March 2007, 0 UTC + 48 h, 1DVAR-THIN3+ 48 h, 1DVAR-THIN2 m/s

Reinhold Hess, 24w COSMO Priority Project: Assimilation of Satellite Radiances with 1DVAR and Nudging Status of Developments September 2007  technical implementation ready (ATOVS/SEVIRI, including debugging)‏  basic monitoring of radiances (day by day basis)‏  basic set up, first case studies available  tuning required  nudging coefficients/thinning of observations  bias correction/stratosphere  background errors/humidity correlations  long term evaluation  already studies on spatially localised background error covariance matrices (SREPS for SEVIRI)‏  implementation for AIRS/IASI has just started Use of 1D-Var developments already for other activities: GPS tomography Radar reflectivities To be done: tuning, testing, tuning Athens, 2007

IV WMO Impact Workshop 2008Alexander Cress Use of VAD Wind profiles in the Lokal Modell of DWD Vertical Wind Profiles provided by Doppler weather radars using the Velocity-Azimuth Display (VAD) technique Before using VAD wind profiles extensive monitoring has to be done Bad measurement sides are blacklisted Use of VAD wind profiles in LM in the same way as pilot wind profiles

IV WMO Impact Workshop 2008Alexander Cress VAD statistics of background (LME) - observations Mean over used VAD stations ( – ) Left: BIAS Right: RMS BIAS and RMS comparable to radiosonde statistics Almost no bias in wind velocity and direction

IV WMO Impact Workshop 2008Alexander Cress

IV WMO Impact Workshop 2008 Alexander Cress Station: blacklisted

IV WMO Impact Workshop 2008 Alexander Cress Verification of LME forecasts against radiosondes Results Mainly neutral Slightly positive for + 48 h RMS geopotential height

GPS tomography: –comprehensive monitoring (14 months) of quasi-operational tomography profiles (at CSCS) against Payerne radiosonde and COSMO fields done  results: tomographic refractivity profiles have rather large errors unless COSMO forecasts are included as background info –start working on assimilating humidity profiles derived from tomography retrievals –new PhD (Perler) at ETH started working on tomography method itself 1.2Multi-Sensor Humidity Analysis (incl. GPS-obs) Daniel Leuenberger (MCH) BIAS – wet bias below 1500 m, large dry bias around 2000 m Summer: 10 – 15 ppm (~ g/kg) or ~35% Winter: 5 ppm (~ 0.75 g/kg) or 20% (much) larger than NWP model (+12h / +24h fc) STD – Summer: up to 12 ppm (~1.8 g/kg) or 10% – 30% in PBL Winter: up to 7 ppm (~1 g/kg) or 20% – 40% in PBL slightly smaller than NWP model (+12h / +24h fc)

1.4Assimilation of Screen-level Observations PBL Analysis Jean-Marie Bettems, Oliver Marchand, Andre Walser (MCH), Andrea Rossa + collaborators (ARPA-Veneto), Antonella Sanna (ARPA-Piemonte) main objects: data selection, extrapolation to 10 m, vertical + horizontal structure functions Diploma work at MCH (Lilian Blaser): 9 case studies, standard assimilation parameters –10-m wind ass.: analysis impact: positive at surface, also for upper-air wind speed forecast impact: neutral, except 1 positive case (+8 h) –2-m temperature & humidity additionally (1 convective case): clear positive impact on analysis of surface parameters, negative for upper-air wind speed –surface pressure (1 winter case): slight negative impact on 10-m wind, not due to geostrophic correction –need to select representative stations, need appropriate vertical structure functions (impact of screen-level obs reaching high) »up to now: only case studies done

1.4Assimilation of Screen-level Observations PBL Analysis Antonella Sanna, M. Milelli, D. Cane, D. Rabuffetti (ARPA-Piemonte) Sensitivity study (1 case with floodings, 2.8 km resolution) on assimilation of non-GTS data and soil moisture initialisation (PREVIEW framework): –clear positive impact from ass of high-res 10-m wind and 2-m temperature data and with nudging parameters adjusted to fit denser obs network –no benefit from replacing IC soil moisture by FEST-WB (hydrological model for floods) 5 June 2002, 12 UTC diurnal cycle T 2m 5 June 2002, 12 UTC - 18-h precipitation sum analysis 12-h forecast T -profiles CTRL: interpol. ana SET2: standard nudging SET3: + nudge T 2m, v 10m adjusted parameters SET4: + init. soil moisture

1.5Assimilation of Scatterometer Wind Heinz-Werner Bitzer (MetBw), Alexander Cress, Christoph Schraff (DWD) nudging of scatterometer wind data as buoy observations technically implemented, taking into account all quality control / bias correction steps developed for use in GME idealised case studies: model rejects largest part of 10-m wind info unless mass field is explicitly balanced  derive surface pressure analysis correction in geostrophic balance with 10-m wind analysis increments (implies need to solve Poisson equation): implemented, model now accepts data first real case study computed QSCAT 19 June 2007, 6 – 9 UTC 48N 50N 15 W Opr (no QSCAT) – Exp (QSCAT) PMSL 19 June 2007, 9 UTC hPa

1.5Assimilation of Scatterometer Wind Heinz-Werner Bitzer (MetBw), Alexander Cress, Christoph Schraff (DWD) m/s 10-m wind gusts analysis (21 June 2007, 12 UTC)+ 48-h, no QSCAT+ 48-h, with QSCAT minor impact, central pressure error reduced from – 5 hPa to – 3 hPa

IV WMO Impact WorkshopAlexander Cress E-AMDAR humidity measurements Three Lufthansa airplanes are implemented with humidity sensor First humidity data in bufr format became available at the beginning of 2007 DWD started a data monitoring (obs – bg) using its global (GME) and regional model (LME) What additional humidity data from airplanes will be of use to regional NWP ? What potential savings to the radiosonde network (complemt or reduction) will such a programme bring?

IV WMO Impact WorkshopAlexander Cress 00 UTC12 UTC All aircrafts

IV WMO Impact Workshop 2008Alexander Cress EU UTC12 UTC

IV WMO Impact Workshop 2008Alexander Cress EU UTC 12 UTC

IV WMO Impact Workshop 2008Alexander Cress Summary of the preliminary results Monitoring and blacklisting is very important for a successful use of VAD wind profiles. Quality differs substantially from station to station VAD wind measurements produce no additional noise in the analyses The wind and geopotential height fields are in balance for different pressure levels => wind observations are successfully used in the Lokal Modell Overall impact of VAD wind profiles on forecast quality is neutral GPS tomography refractivity profiles have rather large errors unless COSMO forecasts are included as background

IV WMO Impact Workshop 2008Alexander Cress Summary of the preliminary results Assimilation of screen-level observations show positive impact on surface analysis but neutral to slightly negative for upper-air analysis Small positive impact of using QuikScat scatterometer winds in nudging assimilation of COSMO-EU Monitoring of AMDAR humidity profiles showed a strong negative humidity bias (higher than radiosondes) decreasing with height and also a warm temperature bias

Experiment testing a reduced radiosonde network over Europe Experimentdesign Following a EUCOS Redesign proposal Only 32 Radiosondenstations in Europe (EUCOS Area) With emphasis on the western borders of Europe and offshore islands v No changes over eastern Europe All other observing systems unchanged Time range: to Assimilation und forecasts (00 und 12 UTC) with COSMO-EU IV WMO Impact Workshop 2008Alexander Cress

00 UTC

12 UTC

Verification area: 40 o N – 60 o N and -5 o W – 25 o E Sea surface pressure bias – UTC EUCOS Experiment Routine 12 UTC IV WMO Impact Workshop 2008Alexander Cress

Frequency Bias of low level clouds – UTC Verification area: 40 o N – 60 o N and -5 o W – 25 o E EUCOS Experiment Routine 12 UTC IV WMO Impact Workshop 2008Alexander Cress

Bias of wind direction – Verification area: 40 o N – 60 o N and -5 o W – 25 o E 00 UTC EUCOS Experiment Routine 12 UTC IV WMO Impact Workshop 2008Alexander Cress

12 UT12 UT

12 UT12 UT

Summary Reducing the radiosonde network has a negative impact on the quality of COSMO-EU analysis Negative impact was largest on humidity fields Negative impact diminished after 12 hours Slightly negative impact on sea surface pressure On average scattered (broken) clouds are overestimated and strong clouds (overcasting situations) are underestimated Increasing wind direction bias during forecast Negative impact is higher at 00 UTV compared to 12 UTC (Compensation by aircraft data ?)