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Enhancing Forecasting with Data Assimilation in Very Short Range - COSMO

Explore advanced data assimilation techniques like Latent Heat Nudging (LHN) in COSMO-DE for short-range forecasting. Leveraging radar-derived precipitation rates and LETKF methods for accurate nowcasting. Learn about the impact studies, strategies, and future developments in this operational configuration.

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Enhancing Forecasting with Data Assimilation in Very Short Range - COSMO

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  1. Data Assimilation for Very Short-Range Forecasting in COSMO Christoph SchraffDeutscher Wetterdienst, Offenbach, Germany • operational : radar-derived precipitation rates by latent heat nudging • in development : LETKF • NWP for nowcasting : 2 examples

  2. COSMO-DE: x = 2.8 km (deep convection explicit, shallow convection param.) ~ 2014 : x  2 km , LETKF COSMO consortium /convection permitting COSMO configurations Germany Greece Italy Poland Romania Russia Switzerland operational configurations : x = 2.2 – 2.8 km

  3. current COSMO DA:Observation Nudging Method: Dynamic Relaxation against observations ( : model state vector) G determines the characteristic time scale for the relaxation + assimilates high-frequency obs + continuous analyzed state  indirect obs  need retrievals  limited background error cross-covariances

  4. Required: relation: precipitation rate  model variables (observed) (info required by nudging) precipitation  condensation  release of latent heat • Approach: modify latent heating rates such that the model responds by producing the observed precipitation rates  Latent Heat Nudging (LHN) current COSMO DA: use of radar-derivedprecipitation by Latent Heat Nudging (LHN) • Assumption: vertically integrated latent heat release  precipitation rate

  5. LHN - temperature increment (in K/h) Scaling factor : Scaling factor : current COSMO DA: Latent Heat Nudging , implementation • Assumption: vertically integrated latent heat release  precipitation rate Vertical profiles: cloud liquid water content (in g/kg) latent heat release (in K/h)

  6. current COSMO DA: Latent Heat Nudging , general info LHN: modify temperature (latent heating) COSMO-DE: x = 2.8 km (deep convection explicit, shallow convection param.) + adjust specific humidity to maintain relative humidity • 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 radar composite as used since June 2011: 16 D, 2 NL, 2 B, 9 F, 3 CH , 2 CZ stations

  7. analysis + 1 h + 2 h + 3 h + 4 h + 5 h + 6 h current COSMO DA: Latent Heat Nudging , impact study x = 2.8 km , no convection parameterisation , LHN with humidity adjustment 1-hour sum of precipitation

  8. current COSMO-DE DA: LHN, scale-dependent verification 15 June – 15 July 2009 , 0-UTC COSMO-DE forecast runs FSS,30km (11 grid pts.) FSS,280km (101 g.p.) ETS,2.8km threshold 0.1 mm/h opr (LHN) no LHN 2.0 mm/h 5 10 15 20 5 10 15 20 5 10 15 20 forecast lead time [h]

  9. perturbations: LBC + IC + physics perturb. GME, IFS, GFS, GSM future (km-scale) COSMO DA:strategy 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  products (precip beyond warning threshold) used by bench forecasters for lead times  3 hrs  ensemble-based data assimilation component required

  10. LETKF (COSMO) :method • 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

  11. deterministic run must use same set of observations as the ensemble system ! • Kalman gain / analysis increments not optimal, if deterministic background xB (strongly) deviates from ensemble mean background Analysis for a deterministic forecast run :use Kalman Gain K of analysis mean deterministic analysis recently implemented L : interpolation of analysis increments from grid of LETKF ensemble to (possibly finer) grid of deterministic run ensemble deterministic

  12. LETKF (km-scale COSMO) : scientific issues / refinement • ensemble size Nens = 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at ? 3 hr  RUC 1 hr  at  15 min ! non-linearity vs. noise / lack of spread / 4D property ? • perturbed lateral BC (ICON hybrid VAR-EnKF / EPS) noise control ? • non-linear aspects, convection initiation (outer loop , latent heat nudging ?) • technical aspects: efficiency, system robustness  2014 (quasi-)operational

  13. LETKF (km-scale COSMO) : some important observations at km scale • 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

  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. LETKF (km-scale COSMO) : some important observations at km scale • 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

  16. LETKF (km-scale COSMO) : some important observations at km scale NWC-SAF SEVIRI cloud products: example cloud type CT cloud top height CTH fractional water clds high semitransparent very high clouds high clouds medium clouds low clouds very low clouds cloud-free water cloud-free land undefined COSMO: cloud water qc > 0 , or cloud ice qi > 5 .10-5 kg/kg clc = 100 % subgrid-scale clouds  clc = f(RH; shallow convection; qi ,qi,sgs) < 100 %

  17. LETKF (km-scale COSMO) : some important observations at km scale • 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

  18. DWD nowcasting product with use of NWP : NowCastMIX , for storm prediction • 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)

  19. DWD nowcasting product with use of NWP : NowCastMIX example : forecast for next 90 min. 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. study on blendingprobabilistic nowcasting & NWP (EPS) Kober et al., 2011 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) 2300 UTC: radar obs radar reflectivity at initial time of ‘forecast’ nowcast of probability valid for 14 July 2009, 2300 UTC

  21. Kober et al., 2011: blendingprobabilistic nowcasting & NWP (EPS) NWP probability: COSMO-DE-EPS N(Z>thr) / Nens (fraction method) (calibration with reliability diagram statistics)

  22. Kober et al., 2011: blendingprobabilistic nowcasting & NWP (EPS) seamless probabilistic blending additive combination in probability space

  23. Data Assimilation for very short-range forecasting in COSMO thank you for your attention

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