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Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 1.

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Presentation on theme: "Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 1."— Presentation transcript:

1 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 1 Data Assimilation for Very Short-Range Forecasting in COSMO Christoph Schraff Deutscher Wetterdienst, Offenbach, Germany operational : radar-derived precipitation rates by latent heat nudging in development : LETKF NWP for nowcasting : 2 examples

2 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 2 Germany Greece Italy Poland Romania Russia Switzerland operational configurations :  x = 2.2 – 2.8 km COSMO-DE:  x = 2.8 km (deep convection explicit, shallow convection param.) ~ 2014 :  x  2 km, LETKF COSMO consortium / convection permitting COSMO configurations

3 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 3 Method: Dynamic Relaxation against observations (  : model state vector) G  determines the characteristic time scale for the relaxation current COSMO DA: Observation Nudging +assimilates high-frequency obs + continuous analyzed state  indirect obs  need retrievals  limited background error cross-covariances

4 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 4 Assumption: vertically integrated latent heat release  precipitation rate Approach: modify latent heating rates such that the model responds by producing the observed precipitation rates  Latent Heat Nudging (LHN) Required: relation: precipitation rate  model variables (observed) (info required by nudging) precipitation  condensation  release of latent heat current COSMO DA: use of radar-derived precipitation by Latent Heat Nudging (LHN)

5 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 5 LHN - temperature increment (in K/h) Scaling factor  : Vertical profiles: cloud liquid water content (in g/kg) latent heat release (in K/h) current COSMO DA: Latent Heat Nudging, implementation Assumption: vertically integrated latent heat release  precipitation rate

6 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 6 LHN: modify temperature (latent heating) + adjust specific humidity to maintain relative humidity COSMO-DE:  x = 2.8 km (deep convection explicit, shallow convection param.) radar composite as used since June 2011: 16 D, 2 NL, 2 B, 9 F, 3 CH, 2 CZ stations current COSMO DA: Latent Heat Nudging, general info computationally efficient, applicable to complex microphysics composite of precip rates every 5 min adjustment applied locally in areas with precipitation, not in environment  strong, but short-lived positive impact

7 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 7 analysis+ 1 h+ 2 h+ 3 h+ 4 h+ 5 h  x = 2.8 km, no convection parameterisation, LHN with humidity adjustment + 6 h 1-hour sum of precipitation current COSMO DA: Latent Heat Nudging, impact study

8 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 8 15 June – 15 July 2009, 0- UTC COSMO-DE forecast runs threshold 0.1 mm/h opr (LHN) no LHN FSS, 280 km (101 g.p.) FSS, 30 km (11 grid pts.) ETS, 2.8 km 2.0 mm/h 5 10 15 20 forecast lead time [h] 5 10 15 20 current COSMO-DE DA: LHN, scale-dependent verification

9 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 9 future (km-scale) COSMO DA: strategy  ensemble-based data assimilation component required convection-permitting NWP: after ‘few’ hours, a forecast of convection is a long-term forecast  deliver probabilistic (pdf) rather than deterministic forecast  need ensemble forecast and data assimilation system  forecast component: COSMO-DE EPS pre-operational perturbations: LBC + IC + physics GME, IFS, GFS, GSM perturb.  products (precip beyond warning threshold) used by bench forecasters for lead times  3 hrs

10 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 10 COSMO priority project KENDA (Km-scale ENsemble-based Data Assimilation) implementation following Hunt et al., 2007 basic idea: do analysis in the space of the ensemble perturbations –computationally efficient, but also restricts corrections to subspace spanned by the ensemble –explicit localization (doing separate analysis at every grid point, select only obs in vicinity) –analysis ensemble members are locally linear combinations of first guess ensemble members LETKF (COSMO) : method

11 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 11 Analysis for a deterministic forecast run : use Kalman Gain K of analysis mean L : interpolation of analysis increments from grid of LETKF ensemble to (possibly finer) grid of deterministic run deterministic ensemble deterministic analysis recently implemented Kalman gain / analysis increments not optimal, if deterministic background x B (strongly) deviates from ensemble mean background deterministic run must use same set of observations as the ensemble system !

12 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 12 ensemble size N ens = 32  40 covariance inflation (adaptive multiplicative, additive) localisation(multi-scale data assimilation, successive LETKF steps with different obs / localisation ? adaptive, dep. on obs density ? ) update frequency  a t ? 3 hr  RUC 1 hr   a t  15 min ! non-linearity vs. noise / lack of spread / 4D property ? perturbed lateral BC (ICON hybrid VAR-EnKF / EPS) noise control ? LETKF (km-scale COSMO) : scientific issues / refinement non-linear aspects, convection initiation (outer loop, latent heat nudging ?) technical aspects: efficiency, system robustness  2014 (quasi-)operational

13 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 13 radar : direct 3-D radial velocity & 3-D reflectivity (start summer 2010) develop sufficiently accurate and efficient observation operators, soon available Particular issues for use in LETKF: obs error variances and correlations, superobbing, thinning, localisation LETKF (km-scale COSMO) : some important observations at km scale

14 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 14 LETKF (km-scale COSMO) : some important observations at km scale ground-based GPS slant path delay (start Jan. 2012) –direct use in LETKF, or tomography –implement non-local obs operator in parallel model environment Particular issue: localisation for (vertic. + horiz.) non-local obs GPS stations (ZTD resp. IWV)

15 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 15 cloud information based on satellite and conventional data (start March 2011) –derive incomplete analysis of cloud top + cloud base, using conventional obs (synop, radiosonde, ceilometer) and NWC-SAF cloud products from METEOSAT SEVIRI use obs increments of cloud or cloud top / base height or derived humidity LETKF (km-scale COSMO) : some important observations at km scale

16 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 16 fractional water clds high semitransparent very highclouds highclouds mediumclouds lowclouds very lowclouds cloud-free water cloud-free land undefined cloud type CTcloud top height CTH NWC-SAF SEVIRI cloud products: example COSMO: cloud water q c > 0, or cloud ice q i > 5. 10 -5 kg/kg  clc = 100 % subgrid-scale clouds  clc = f(RH; shallow convection; q i, q i,sgs ) < 100 % LETKF (km-scale COSMO) : some important observations at km scale

17 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 17 cloud information based on satellite and conventional data (start March 2011) –derive incomplete analysis of cloud top + cloud base, using conventional obs (synop, radiosonde, ceilometer) and NWC-SAF cloud products from Meteosat SEVIRI use obs increments of cloud or cloud top / base height or derived humidity –use SEVIRI brightness temperature directly in LETKF in cloudy (+ cloud-free) conditions (in view of improving the horizontal extent of cloud / cloud top height) –compare approaches Particular issues:non-linear observation operators, non-Gaussian distribution of observation increments LETKF (km-scale COSMO) : some important observations at km scale

18 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 18 displacement forecast: weighted mean using data from –KONRAD:radar-derived detection of storm cells + displacement vectors –CellMOS:displacement forecast based on radar / lightning data –RADVOR-OP:radar-derived forecast of precip + displacement –COSMO-DE:upper-air wind (?) storm category using fuzzy logics –gust: COSMO-DE V-max (700 – 950 hPa), displacement –rain: radar + fuzzy set based on KONRAD cell categ., COSMO-DE PW, radar VIL –hail: radar VIL, KONRAD –lightning (yes / no) DWD nowcasting product with use of NWP : NowCastMIX, for storm prediction

19 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 19 example : forecast for next 90 min. DWD nowcasting product with use of NWP : NowCastMIX thunderstorms with : gusts Bft 7 gusts Bft 8-10 gusts Bft 8-10, hail, heavy rain gusts Bft 8-10, hail, very heavy rain

20 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 20

21 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 21 study on blending probabilistic nowcasting & NWP (EPS) Kober et al., 2011 radar reflectivity at initial time of ‘forecast’ probability of reflectivity > threshold (19 dBZ) nowcasting: by neighbourhood method (area grows at 1 km / minute,  240 km) + displacement (pyramidal optical flow technique, Keil and Craig, 2007) nowcast of probability valid for 14 July 2009, 2300 UTC 2300 UTC: radar obs

22 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 22 Kober et al., 2011: blending probabilistic nowcasting & NWP (EPS) NWP probability: COSMO-DE-EPS N(Z>thr) / N ens (fraction method) (calibration with reliability diagram statistics)

23 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 23 Kober et al., 2011: blending probabilistic nowcasting & NWP (EPS) seamless probabilistic blending additive combination in probability space

24 christoph.schraff@dwd.de Data Assimilation for Very Short-Range Forecasting in COSMO WMO WS on Use of NWP for Nowcasting, Boulder, 24 – 26 Oct. 2011 24 Data Assimilation for very short-range forecasting in COSMO thank you for your attention

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