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Developments at DWD christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany

Developments at DWD christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany. Klaus Stephan: Latent Heat Nudging. Mariella Tomassini: Integrated Water Vapour (IWV) from ground-based GPS. Heinz-Werner Bitzer, Alexander Cress: Scatterometer Wind.

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Developments at DWD christoph.schraff@dwd.de Deutscher Wetterdienst, D-63067 Offenbach, Germany

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  1. Developments at DWDchristoph.schraff@dwd.deDeutscher Wetterdienst, D-63067 Offenbach, Germany • Klaus Stephan: Latent Heat Nudging • Mariella Tomassini: Integrated Water Vapour (IWV) from ground-based GPS • Heinz-Werner Bitzer, Alexander Cress: Scatterometer Wind • Martin Lange, Werner Wergen: Variational Soil Moisture Analysis (SMA)

  2. Latent Heat NudgingKlaus Stephan (DWD) • DWD: Done: – revised definition of reference precipitation → reduced overestimation of precipitation during LHN • bright band detection inside COSMO model Outlook: – extend use of radar data to foreign radars • revise reference precipitation to account for (min.) radar beam height • better understand how (nature/) model develops convection (role of environment, (moisture) balance, …)

  3. Z vertically averaged precipitation flux (temporal delay not completely eliminated) z0 cloud Pearly Pmature RR RRmo= 0 RRref≈RRmo RRref > 0 Modified definition of the reference precipitation prognostic precipitation: generated precipitation reaches the ground with some delay:  wanted: an immediate information on how much precipitation the temperature increment has initialised already (refer this info to observed precip.) use of a ’reference precipitation’RRref: z0new z0→ k = ? old: first layer from above with P≥ 0.1 mm/h new: first layer from above with P≥ 0,4 *Pmax in the column aim: reduction of bias betw. model precip RRmo and reference precip RRref

  4. October 2007 negative bias of RRref removed RRref new old RRmo

  5. October 2007 RRobs positive bias of RRmo reduced RRmo reduced overestimation of model precipitation (against radar)

  6. assimilation (August 2007) new old ETS 0.1 mm/h 5.0 mm/h old new FBI reduced overestimation of strong precipitation, improved scores

  7. free forecasts (00 & 12 UTC; 62 runs (August 2007) ETS 0.1 mm/h 5.0 mm/h FBI no significant impact on free forecasts

  8. Bright Band detection (inside COSMO model) H_zero: height of freezing level in the model H_radar: height of radar beam RR_RAD: hourly sum of precip. observed by radar H_zero Ḣ_radar Bright Band criteria: • H_zero – H_radar  [-300;600] RR_RAD(i,j) 2. > 8.5 <RR_RAD> Quelle: wikipedia

  9. Synop-Regnie Radar g.pts. with BB (≥1x/day) ASS, LHN, no BB detect. ASS with BB detection ASS without LHN

  10. FORECAST FORECAST ASS ASS LHN and prognostic precipitation shows impact of LHN refinements in 2005 / 06 (reference precip / LHN restricted to ‘cloudy layers’ / grid point search / limits) mean skill scores over 32 forecast (00 and 12 UTC) AUGUST 2006 threshold 0.1 mm/h FBI ETS Stephan, K., S. Klink, C. Schraff, 2008: Assimilation of radar-derived rain rates into the convective-scale model COSMO-DE at DWD. Q. J. R. Meteorol. Soc.,134, 1315 – 1326.

  11. IWV gps < IWV mod qmodel ‘quality weights’ for ( ~ 1 betw. 700 – 800 hPa) : q gps Experiment • IWV from 169 Sta. every 15 min. (verify well with RS92-humidity, except for 12-UTC dry bias of RS92 in summer) • GPS assimilated like radiosonde humidity profiles, but with smaller horizontal influence ( ~120 km → ~ 50 km) • 1 – 13 June 2007, anticyclonic air-mass convection • 21-h forecasts from 0, 6, 12, 18 UTC ass cycle • comparison: ‘CNT’ : like opr (with RS + LHN) ‘GPS’ : CNT + GPS ‘noRSq : CNT – RS-humidity Use of Integrated Water Vapour (IWV) from Ground-Based GPSMariella Tomassini, Klaus Stephan, Christoph Schraff (DWD) (W.P. 1.2) IWV derived from observed TZD (with p, T from Synop or COSMO) pseudo-obs profile of specific humidity

  12. Analysis 00 UTC 06 UTC 12 UTC 18 UTC Obs GPS 12 CNT 12 CNT 00 GPS 00 daily cycle of: IWV NoRSq CNT → COSMO-DE too moist → 12-UTC RS dries GPS → GPS dries except at 12-UTC

  13. CNT GPS NoRSq RS verification : BIAS (model - obs) 00-UTC runs 12-UTC runs + 0 h + 0 h + 6 h + 6 h

  14. Synop verification 00 UTC Forecast 06 UTC Forecast 12 UTC Forecast 18 UTC Forecast Correct Cloud Cover Percent : GPS oooo CNT ****

  15. Obs CNT GPS NoRSq + + + + + hourly mean of precipitation (forecasts compared to radar) 0.1 mm/h 0.1 mm/h increase of precip without RS-q reduction of precip by GPS 12 UTC runs 00 UTC runs 2.0 mm/h 2.0 mm/h

  16. CNT GPS NoRSq + + + + + radar verification –ETS 0.1 mm/h 0.1 mm/h great improvement by GPS 00 UTC runs 12 UTC runs 1.0 mm/h 1.0 mm/h GPS: worse because too little strong precip in early evening

  17. GPS – IWV : Conclusions & Outlook • GPS IWV obs from GFZ have good quality → further comparison / assimilation with GPS data from ~ 1000 European stations (Eumetnet Project E-GVAP) main objects: data selection, extrapolation to 10 m, vertical + horizontal structure functions • GPS data have shown 12-UTC dry bias of RS92 (in 2007) → validate new version of RS92 • GPS data useful for verification of daily cycle of humidity in the model → test future development in data assimilation / physics with these data • GPS IWV assimilation reduces overestimation of precip at night and has significant positive impact in first 8 hours of 0-UTC forecasts, but tends to suppress strong precip in afternoon → test again, when model physics improve daily cycle of precip, and test in winter

  18. 1.5 Assimilation of Scatterometer WindHeinz-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 • use of ASCAT in addition to QuickScat implemented

  19. too strong gradient too low Assimilation of Scatterometer WindHeinz-Werner Bitzer (MetBW), Alexander Cress, Christoph Schraff (DWD) (W.P. 1.5) Experiment 28 Feb – 9 March 2008 , with QuickScat & ASCAT data no scatt with ASCAT / QuickScat COSMO-EU 9-h forecasts, valid for 6 March 2008, 9 UTC pmsl (model – obs)

  20. Assimilation of Scatterometer Wind 29 Feb 08, 0 UTC COSMO-EU ana , no scatt COSMO-EU ana with ASCAT/QuickScat 10-m wind [m/s] ASCAT 28 Feb 08, 21 UTC ± 1.5h ECMWF analysis 29 Feb 08 984 hPa max. 30 kn ~15 m/s

  21. Soil Moisture InitialisationMartin Lange, Werner Wergen (DWD) (W.P.1.8.1) • aim: replace additional model runs by parameterized regressions to the determine the gradient of the cost function in the variational scheme (absolutely required for GME (long term dry drift), welcome for COSMO model) Cost function penalizes deviations from observations and initial soil moisture content Analysed soil moisture depends on T2m forecast error and sensitivity T2m/w current scheme: by additional model runs with slightly different w(k,0:00) new scheme: parameterised as a function of predicted latent heat flux at noon

  22. Deutscher Wetterdienst Deutscher Wetterdienst Deutscher Wetterdienst comparison of parameterised SMA with operational SMA: experiment May – November 2006 BiasT2m on LM1 domain, avg 12:00, 15:00 RMSET2m on LM1 domain, avg 12:00, 15:00 no SMA opr. SMA param. SMA no SMA opr. SMA param. SMA T2m(12 & 15 UTC) :good performance in summer , degredation in winter

  23. opr. SMA : top layer param. SMA: top layer opr. SMA : bottom layer param. SMA: bottom layer opr. SMA param. SMA Deutscher Wetterdienst Deutscher Wetterdienst Deutscher Wetterdienst comparison of parameterised SMA with operational SMA: experiment May – November 2006 RMS of SMA increments, at layer 4 (9-27cm) ( SMA incr. at layer 5 = 3 * (SMA incr. at layer 4) ) soil moisture content Small change in top layers, higher wetness in bottom layers • small differences in upper layers (until Nov.) • stronger moistening of lower layers (further reduces positive T2m bias in summer) parameterised SMA : almost zero increments during winter, starting mid September

  24. sensitivity of T2m to w2 → is different in operational and parameterised SMA in winter total differential: parameterised (in winter: near zero due to inactive plants) not parameterised, but how does it look like in the model (i.e. in the operational SMA)

  25. soil water content: Lindenberg observations 15 Oct 2006 – 1 Jan 2007 (2.5 months) 5 – 7 Nov 2006 (2 days) 8 cm 15 cm 15 cm: reacts after 6 hours 30 cm: reacts after 4 days 45 cm: reacts after 2 weeks 60 cm 90 cm → expect model layer 27 – 81 cm to take about 1 week to react → expect model layer 9 – 27 cm to take few hours at most to react

  26. soil water content: model at Lindenberg model layer 9 – 27 cm expected to take few hours at most to react → ok model layer 27 – 81 cm expected to take about 1 week to react → ok → gravitational drainage (sedimentation) appears roughly realistic in COSMO → soil moisture increments of operational SMA appear reasonable

  27. Outlook → parameterise also can be derived analytically from Richards eq. used in COSMO (TERRA) parameterisation already exists in current version of param. SMA → parameterised SMA for GME: full experiment started, operational in spring 2009 → parameterised SMA for COSMO: operational in 2009 (spring (?): simple version, autumn: with gravitational drainage) → cheap, efficient Note: SMA parameterisation needs some maintenance to account for future changes in the parameterisation of surface fluxes e.g. modification of root water uptake → include RH2m as additional obs (param. implemented, increments reasonable in first case) possible further extensions: • Analyse the top 5 soil layers separately instead of 2 aggregated layers (DWD). • Inclusion of precipitation analysis when good product is available (Suisse). • Improvement of model error statistics (Italy).

  28. Deutscher Wetterdienst New T2mdiagnostics affects the whole PBL through SMA Bias T2m, C-EU on LM1-domain, avg12:00, 15:00 Accumulated soil moisture increments both runs done with operational version of SMA Rmse T2m, C-EU on LM1-domain, avg12:00, 15:00 COSMO-EU 20070427 00:00 +15 hours 2250 m Dew point temperature Germany 10 m analysis operational new T2m diagnostics

  29. Thank you for your attention

  30. ~ 1 by 1 converter simple + portable applicable to WMO or non-WMO BUFR IT section SKY / archive any kind of BUFR bufr 2 wmo_bufr WMO BUFR bufr2netcdf standard WMO templates, i.e. unique descriptors + dimensions of elements + code tables unique BUFR format for each obs type NWP section NetCDF obs 3DVar NetCDF feedback verification COSMO model Under discussion at DWD can keep AOF as alternative data input as long as needed NetCDF 2 ODB ODB monitoring DWD switches to NetCDF on 17 Sept. 2008 thereafter, DWD will no longer support AOF interface DWD plans: envisaged set-up observation formats, pre- and post-processing Advantages of NetCDF: • widely used and portable • a variety of software exists to plot, analyse and evaluate the data.

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