COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 1 WG1 Overview Deutscher Wetterdienst, D Offenbach, Germany current DA method: nudging to be developed: PP KENDA for km-scale EPS PP Sat-Cloud: use of AMSU-A over land use of cloud info from IR-rad radar reflectivity (precip): latent heat nudging 1DVar radar radial wind: for nudging: VAD, SAR, nudg. V r ground-based GPS  humidity tomography (profiles) vertically integrated WV scatterometer 10-m wind improved use of surface-level obs LETKF for HRM var. soil moisture analysis snow (cover)  PP COLOBOC

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 2 DWD: Done: – microphysics change (2006) reduced evaporation below cloud base  ratio RR surf / RR upper-air and hence RR surf / RR ref increased  (need to) revise definition of reference precipitation  reduced overestimation of precipitation during LHN  very small impact on forecasts – 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, …) Use of Radar-derived Surface Precipitation: Latent Heat Nudging Klaus Stephan (DWD), Daniel Leuenberger (MetCH)  Talk: On the Value of Radar-Derived Rainfall Assimilation on High-Resolution QPF MetCH: LHN introduced operationally 20 June 2008, extensive verification done

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 3 Bright Band detection (inside COSMO model) Quelle: wikipedia 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 H ̇ _radar Bright Band criteria: 1.H_zero – H_radar  [-300;600] RR_RAD(i,j) 2. > 8.5

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 4 Synop-Regnie Radar g.pts. with BB (≥1x/day) ASS, LHN, no BB detect. ASS with BB detection ASS without LHN

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 5 Use of Radar-derived Surface Precipitation Virginia Poli (ARPA-SMR)  Poster: Assimilation of radar derived surface rain rate into the regional COSMO model through a 1D-Var+nudging scheme: analysis of results ARPA-SMR: 1DVAR to retrieve T, q –profiles from RR (using linearised parameterisations of large-scale condensation and convection) then nudge T, q –profiles Model space Observation space no yes Analysis x a =x MINIMIZATION Background x b =(T b, q b, ps b ) x=(T, q, ps) Initialization x=x b

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview Example of RR assimilation Very encouraging results! Shades: Radar observation Contours: COSMO-I2 forecasted RR Control run – Forecast +6 hours Control run – Forecast +1 hour Experimental run – Forecast +1 hour Experimental run – Forecast +6 hours Assimilation of RR is able to dry off precipitation and also to create structures in the right place

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 7 Simple Adjoint Retrieval (SAR) of 3-D Wind Vector Jerzy Achimowicz (IMGW) (W.P.1.1.2) input data: 3 consecutive scans of 3-d reflectivity and radial velocity at 10’-intervals, interpolated to Cartesian grid (1km x 1km x 500m, 20 levels) ‘predicted’ by SAR method: very sensitive to errors in radial velocity input data  new software package developed for QC of radar Doppler data (incl. de-aliasing, interpolation from polar to cartesian coord.) Doppler radial velocity wind retrieval

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 8 IWV derived from observed TZD (with p, T from Synop or COSMO) Use of Integrated Water Vapour (IWV) from Ground-Based GPS Mariella Tomassini, Klaus Stephan, Christoph Schraff (DWD) (W.P. 1.2) q model q gps IWV gps < IWV mod pseudo-obs profile of specific humidity ‘quality weights’ for ( ~ 1 betw. 700 – 800 hPa) : IWV from 169 Sta. every 15 min. (verify well with RS92-humidity, except for 12-UTC dry bias of RS92 in summer) 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 GPS assimilated like radiosonde humidity profiles, but with smaller horizontal influence ( ~120 km → ~ 50 km) Experiment

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 9 12 UTC Analysis 00 UTC 06 UTC Obs 18 UTC CNT NoRSq GPS daily cycle of: IWV CNT 00 GPS 00 CNT 12 GPS 12  COSMO-DE too moist  12-UTC RS dries  GPS dries except at 12-UTC

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 10 RS verification : BIAS (model - obs) + 0 h + 6 h CNT GPSNoRSq 00- UTC runs + 0 h + 6 h 12- UTC runs

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 11 Synop verification 00 UTC Forecast 06 UTC Forecast 12 UTC Forecast 18 UTC Forecast Correct Cloud Cover Percent : GPS oooo CNT ****

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 12 hourly mean of precipitation (forecasts compared to radar) Obs CNT GPS NoRSq 0.1 mm/h 2.0 mm/h 00 UTC runs 12 UTC runs 2.0 mm/h reduction of precip by GPS increase of precip without RS-q

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 13 radar verification – ETS 0.1 mm/h 1.0 mm/h 00 UTC runs 12 UTC runs CNT GPS NoRSq great improvement by GPS GPS: worse because too little strong precip in early evening

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 14 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 GPS – IWV : Conclusions & Outlook

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 15 Experiment 28 Feb – 9 March 2008, with QuickScat & ASCAT data with ASCAT / QuickScatno scatt pmsl (model – obs) too low too strong gradient COSMO-EU 9-h forecasts, valid for 6 March 2008, 9 UTC Assimilation of Scatterometer 10-m Wind Heinz-Werner Bitzer (MetBW), Alexander Cress, Christoph Schraff (DWD) (W.P. 1.5) (10-m wind nudging with surface pressure correction which is in geostrophic balance with 10-m ana. incr.)

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 16 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) Soil Moisture Initialisation Martin Lange, Werner Wergen (DWD) (W.P.1.8.1) Cost function penalizes deviations from observations and initial soil moisture content Analysed soil moisture depends on T 2m forecast error and sensitivity  T 2m /  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

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 17 Deutscher Wetterdienst T 2m (12 & 15 UTC) : good performance in summer, degredation in winter Bias T 2m on LM1 domain, avg 12:00, 15:00 RMSE T 2m on LM1 domain, avg 12:00, 15:00 comparison of parameterised SMA with operational SMA: experiment May – November 2006 no SMA opr. SMA param. SMA no SMA opr. SMA param. SMA

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 18 Deutscher Wetterdienst Small change in top layers, higher wetness in bottom layers soil moisture content RMS of SMA increments, at layer 4 (9-27cm) ( SMA incr. at layer 5 = 3 * (SMA incr. at layer 4) ) opr. SMA : top layer param. SMA: top layer opr. SMA : bottom layer param. SMA: bottom layer opr. SMA param. SMA small differences in upper layers (until Nov.) stronger moistening of lower layers (further reduces positive T 2m bias in summer) comparison of parameterised SMA with operational SMA: experiment May – November 2006 parameterised SMA : almost zero increments during winter, starting mid September

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 19 total differential: sensitivity of T 2m to w 2  is different in operational and parameterised SMA in winter 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)

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 20 8 cm 15 cm 60 cm 90 cm soil water content: Lindenberg observations 15 cm: reacts after 6 hours 5 – 7 Nov 2006 (2 days)15 Oct 2006 – 1 Jan 2007 (2.5 months) 30 cm: reacts after 4 days 45 cm: reacts after 2 weeks → 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

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 21 soil water content: model at Lindenberg model layer 27 – 81 cm expected to take about 1 week to react → ok model layer 9 – 27 cm expected to take few hours at most to react → ok → gravitational drainage (sedimentation) appears roughly realistic in COSMO → soil moisture increments of operational SMA appear reasonable

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 22  parameterised SMA for COSMO: operational in 2009 (spring (?): simple version, autumn: with gravitational drainage) 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  include RH 2m 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). Note: SMA parameterisation needs some maintenance to account for future changes in the parameterisation of surface fluxes e.g. modification of root water uptake  cheap, efficient

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 23 Thank you for your attention

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 24 Advantages of NetCDF: widely used and portable a variety of software exists to plot, analyse and evaluate the data. DWD plans: envisaged set-up observation formats, pre- and post-processing can keep AOF as alternative data input as long as needed DWD switches to NetCDF on 17 Sept thereafter, DWD will no longer support AOF interface ~ 1 by 1 converter simple + portable applicable to WMO or non-WMO BUFR standard WMO templates, i.e. unique descriptors + dimensions of elements + code tables unique BUFR format for each obs type NetCDF 2 ODB monitoring NetCDF obs 3DVar NetCDF feedbac k COSMO model verification NWP section any kind of BUFR bufr 2 wmo_buf r WMO BUF R bufr2netcdf IT section SKY / archive Under discussion at DWD

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 25 analysisoperationalnew T2m diagnostics Deutscher Wetterdienst COSMO-EU : hours New T 2m diagnostics affects the whole PBL through SMA Bias T2m, C-EU on LM1-domain, avg12:00, 15:00Accumulated soil moisture increments Rmse T2m, C-EU on LM1-domain, avg12:00, 15:00 10 m 2250 m Dew point temperature Germany both runs done with operational version of SMA

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 26 mean skill scores over 32 forecast (00 and 12 UTC) AUGUST 2006 threshold 0.1 mm/h ETSFBI ASS FORECAST ASS FORECAST LHN and prognostic precipitation shows impact of LHN refinements in 2005 / 06 (reference precip / LHN restricted to ‘cloudy layers’ / grid point search / limits) 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.

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 27 New PP: Km-scale Ensemble-based Data Assimilation (KENDA) Discussion with input from Chris Snyder 18 Sept 2007 on EnKF –no new obstacles seen for the EnKF –to get a system to evaluate, need 2 people (with good background) for 2 years –do EnKF first without radar data (quality control problems), gain experiences, detect bugs / flaws in the scheme, later include radar data

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview 28 Assimilation of Scatterometer Wind 29 Feb 08, 0 UTC COSMO-EU ana with ASCAT/QuickScatCOSMO-EU ana, no scatt ECMWF analysis 29 Feb 08ASCAT 28 Feb 08, 21 UTC ± 1.5h 984 hPa max. 30 kn ~15 m/s 10-m wind [m/s]

COSMO General Meeting, Cracow, 15 – 19 Sept Overview on Data Assimilation WG1 Overview Variational assimilation Model space Observation space no yes Analysis x a =x MINIMIZATION Background x b =(T b, q b, ps b ) x=(T, q, ps) Initialization x=x b Convert observations (Rain Rates - RR) in profiles of temperature and humidity and nudge them as “pseudo”-observations.