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M. Baldauf , K. Helmert, B. Hassler, K. Stephan, S. Klink,

M. Baldauf , K. Helmert, B. Hassler, K. Stephan, S. Klink, C. Schraff, A. Seifert, J. Förstner, T. Reinhardt, P. Prohl, C.-J. Lenz, U. Damrath Deutscher Wetterdienst, Offenbach, Germany F. Theunert Amt für Wehrgeophysik, Traben-Trarbach, Germany.

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M. Baldauf , K. Helmert, B. Hassler, K. Stephan, S. Klink,

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  1. M. Baldauf, K. Helmert, B. Hassler, K. Stephan, S. Klink, C. Schraff, A. Seifert, J. Förstner, T. Reinhardt, P. Prohl, C.-J. Lenz, U. Damrath Deutscher Wetterdienst, Offenbach, Germany F. Theunert Amt für Wehrgeophysik, Traben-Trarbach, Germany LMK (Lokal-Modell-Kürzestfrist)3rd COPS and GOP WorkshopUniversity of Hohenheim, Stuttgart10./11.04.2006

  2. Outline • LMK - Project • Case study examples of precipitation; verifications • Developments • Physical Parameterisations • Dynamics, Numerics • Latent Heat Nudging • Radar quality product • Time table

  3. LMK - (Lokal-Modell-Kürzestfrist) Subproject P2 of the ‘Aktionsprogramm 2003’ (DWD) Goals • Development of a model-based NWP system for very short range (‘Kürzestfrist’) forecasts (2-18 h) of severe weather events on the meso- scale, especially those related to • deep moist convection (super- and multi-cell thunderstorms, squall-lines, MCCs, rainbands,...) • interactions with fine-scale topography(severe downslope winds, Föhn-storms, flash floodings, fog, ...)

  4. LMK-Configuration • grid length: x = 2.8 km • direct simulation of the coarser parts of deep convection • interactions with fine scale topography • timestep t=30 sec. • 421 x 461 x 50 grid points~ 1200 * 1300 * 22 km³lowest layer in 10 m above ground • forecast duration: 18 h • center of the domain 10° E, 50° N • boundary values from LM / LME(x = 7 km)

  5. LMK - Subprojects (‚Measurements‘) Radar (M7) • 5-min DX-radar composit, European composit • pattern recognition of false echoes --> DXQ - quality product Latent Heat Nudging (M8) • Thermodynamic feedback and interactions of LHN • Adaptation of LHN to prognostic precipitation LMK 2.8 km & explicit convection (M9) • Numerical schemes • Physical parameterizations • Lateral and upper boundaries • Case studies and intercomparison • LMK Testsuites Verification (M10) • Traditional verifikation (Syn/Temp) • Use of synthetic satellite- and synthetic radar tools • New methods (pattern recognition, upscaling, ...)

  6. Physical Parameterisations (I) LM LMK Deep Convection none! -> no distinction between convective and stratiform precipitation! Tiedtke (1989) Shallow Convection Tiedtke (1989) shallow conv., only for Hcloud < 2 km Soil-Vegetation- Model TERRA, 7 levels, additional freezing/melting

  7. Monthly Mean of diurnal cycle of area averaged precipitation, July 2004

  8. precipitation: diurnal cycle of area average 02.07.2004 ...... radar ___ LMK, 00 UTC ___ LM, 00 UTC 07.07.2004 12.07.2004 17.07.2004 18.07.2004

  9. Monthly precipitation mean of ‚July 2004‘ Mean: 103 mm Mean: 80 mm Mean: 95 mm ground obs. radar obs. LM LMK w/o DA LMK + DA LMK + DA Mean: 67 mm Mean: 78 mm Mean: 72 mm

  10. precipitation: diurnal cycle of area average 02.07.2004 ...... radar ___ LMK, 00 UTC ___ LM, 00 UTC 07.07.2004 12.07.2004 17.07.2004 18.07.2004

  11. 24h precipitation sum ‚12.07.2004, 06...30 UTC‘ ground obs. radar obs. LM LMK w/o DA LMK + DA LMK + DA

  12. precipitation: diurnal cycle of area average 02.07.2004 ...... radar ___ LMK, 00 UTC ___ LM, 00 UTC 07.07.2004 12.07.2004 17.07.2004 18.07.2004

  13. 24h precipitation sum ‚17.07.2004, 06...30 UTC‘ ground obs. radar obs. LM LMK w/o DA LMK + DA LMK + DA

  14. precipitation: diurnal cycle of area average 02.07.2004 ...... radar ___ LMK, 00 UTC ___ LM, 00 UTC 07.07.2004 12.07.2004 17.07.2004 18.07.2004

  15. Example: 18.07.2004, 00 UTC + 10 h LMK Radar LM

  16. Example: 18.07.2004, 00 UTC + 12 h LMK Radar LM

  17. Example: 18.07.2004, 00 UTC + 18 h LMK Radar LM

  18. =TS 2.2 =TS 2.2b =TS 1.7 =TS 1.6 =TS 1.5 > 0.1 mm/h > 2 mm/h True skill statistics (TSS) for precipitation rates July 2004, 12-UTC-runs LMK: averaging to the 9 neighbouring grid points > 10 mm/h

  19. LM and LMK: ETS for gusts > 20 m/s, January 2004, 00-UTC-runs

  20. Bias (mean error) of 2m-temperature, July 2004, 00-h-runs =TS 1.5 =TS 1.6 =TS 1.7 =TS 2.2 =TS 2.2b

  21. Conclusions, Verification results: • Precipitation: • LMK delivers partly better verification results than LM/LME • Diurnal cycle (especially time of maximum) is often more realistic • problem up to now: general underestimation of the precipitation amount in convective events solution proposals: improvement in physics-dynamics-coupling, cloud physics, shallow convection, subscale cloud coverage • Gusts:better quality of LMK-forecasts compared to LM/LME because of finer numerical (vertical and horizontal) and no convection param. • 2m-temperature:stronger bias than in LM, because no soil moisture analysis (SMA) is used up to now; can LHN help?

  22. LMK - Subprojects (‚Measurements‘) Radar (M7) • 5-min DX-radar composit, European composit • pattern recognition of false echoes --> DXQ - quality product Latent Heat Nudging (M8) • Thermodynamic feedback and interactions of LHN • Adaptation of LHN to prognostic precipitation LMK 2.8 km & explicit convection (M9) • Numerical schemes • Physical parameterizations • Lateral and upper boundaries • Case studies and intercomparison • LMK Testsuites Verification (M10) • Traditional verifikation (Syn/Temp) • Use of synthetic satellite- and synthetic radar tools • New methods (pattern recognition, upscaling, ...)

  23. Physical Parameterisations (I) LM LMK Deep Convection none! -> no distinction between convective and stratiform precipitation! Tiedtke (1989) Shallow Convection Tiedtke (1989) shallow conv., only for Hcloud < 2 km Soil-Vegetation- Model TERRA, 7 levels, additional freezing/melting of snow

  24. Shallow convection based on Tiedtke-scheme N-S-crosssection rh(with shallow convection) Diff.: rh(with sh. conv) - rh(without sh. conv.) Dr. F. Theunert (AGeoBW)

  25. Physical Parameterisations (II) LM LMK Cloud Microphysics 5-class-scheme 6-class (Graupel)-scheme 6-class/2 moments-scheme (Seifert, Beheng, 2000) (for research/benchmark purposes) Radiation 2-flux-scheme (Ritter, Geleyn,1992) upscaling, higher frequency Turbulence • 3-dim., full coordinate transforms (Herzog et al., 2002; Baldauf, 2005) • 1 eq. model (progn. TKE) • moist turbulence (=condensation alters buoyancy-prod. of TKE) • 1-dim. • 1 eq. model (progn. TKE)

  26. Underestimation of precipitation in LMK • systematic underestimation of ~20 % in many cases • even enhanced in strong convective situations Possible solution approaches: • enhancement of subscale coverage in the boundary layer cloud diagnostic scheme • change of entrainment-/detrainment-coefficients in the parameterisation of shallow convection • Tuning of cloud physics: reduced evaporation of rain below cloud base Side effects: • all changes influence cloud coverage and 2m-temperature

  27. Sensitivity study for the problem of underestimation of precipitation case study ‚26.08.2004‘ LMK 3.17 + changes in moisture turbulence (enhanced subscale coverage) + changes in shallow convection (enhanced entrainment/detrainment)

  28. Physical Parameterisations (II) LM LMK Cloud Microphysics 5-class-scheme 6-class (Graupel)-scheme 6-class/2 moments-scheme (Seifert, Beheng, 2000) (for research/benchmark purposes) Radiation 2-flux-scheme (Ritter, Geleyn,1992) upscaling, higher frequency Turbulence • 3-dim., full coordinate transforms (Herzog et al., 2002; Baldauf, 2005, 2006) • 1 eq. model (progn. TKE) • moist turbulence (=condensation alters buoyancy-prod. of TKE) • 1-dim. • 1 eq. model (progn. TKE)

  29. LES-3D-turbulence model from ‘Litfass-LM’ Herzog et al. (2003) COSMO Techn. rep. 4 Metric terms of 3D-turbulence scalar flux divergence: terrain following coordinates vertical horizontal(cartesian) earth curvature scalar fluxes: analogous: ‚vectorial‘ diffusion of u, v, w Baldauf (2005), COSMO-Newsl. Nr. 5

  30. Real case study: LMK (2.8 km resolution) ‚12.08.2004, 12 UTC-run‘ ‚3D-turb., with metric‘ - ‚1D‘ total precip. in 18 h

  31. Numerics LM LMK horizontal: Arakawa-C vertical: Lorenz Grid Time integration 3-timelevels: Leapfrog 2-timelevels: Runge-Kutta 2. order, 3. order, 3. order TVD Advection u, v, w, T, p‘ horizontal: centered diff. 2. order vertical: implicit 2. order horizontal: upwind 3., 5. order centered diff. 4., 6. order vertical: implicit 2. order implicit 3. order Advection qv, qc, qi, qr, ..., TKE qv, qc: centered diff.2. order qi: Lin, Rood qr, qs: Semi-Lagrange (trilin.) Bott-2, conservation form or Semi-Lagrange, tricubic/trilin. Divergence damping Smoothing 4. order diffusion 4. order diffusion (?) Asselin-filtering filtering of orography stronger filtering of the Alps filtering of orography

  32. Current work in Dynamics: • diabatic terms in pressure equation • influence of deep/shallow atmosphere Current work in Numerics: • unrealistic cold temperatures in narrow valleys (‚cold pool problem‘) • is due to pressure gradient in terrain following coordinates --> solution by • Dynamic lower boundary condition (Gassmann, 2004) • slope dependent orographic filtering • modified physics-dynamics-Coupling • improved 3. order vertical advection for dynamic variables (u,v,w,T,p‘) • radiative upper boundary condition

  33. LMK - Subprojects (‚Measurements‘) Radar (M7) • 5-min DX-radar composit, European composit • pattern recognition of false echoes --> DXQ - quality product Latent Heat Nudging (M8) • Thermodynamic feedback and interactions of LHN • Adaptation of LHN to prognostic precipitation LMK 2.8 km & explicit convection (M9) • Numerical schemes • Physical parameterizations • Lateral and upper boundaries • Case studies and intercomparison • LMK Testsuites Verification (M10) • Traditional verifikation (Syn/Temp) • Use of synthetic satellite- and synthetic radar tools • New methods (pattern recognition, upscaling, ...)

  34. Data assimilation LM LMK ground obs.: Synop, ship, buoy upper air obs.: AMDAR, Radiosonde (TEMP) PILOT Nudging Radar precipitation scan resolution: 1 km * 1° Latent Heat Nudging T2m Soil moisture SMA SMA (?) (not used up to now)

  35. Basics of Latent Heat Nudging Basic assumption of LHN: this relation is valid in a vertical model column vertical structure of latent heating <--> temperature increments (optional: moisture increments, e.g. by conservation of relative humidity) Differences (or ratios) between (radar) measured and simulated precipitation rates are interpreted as a lack/surplus of latent heat along the trajectory of a condensed particle.

  36. 12-UTC forecasts >0.1mm threshold values >2.0mm ETS FBI scores for hourly precipitation :with latent heat nudging / without latent heat nudging Testsuite ‚Juli 2004‘ (7.-16.7.04)

  37. 18-UTC forecasts >0.1mm threshold values >2.0mm ETS FBI scores for hourly precipitation :with latent heat nudging / without latent heat nudging Testsuite ‚Juli 2004‘ (7.-16.7.04)

  38. 0-UTC forecasts >0.1mm threshold values >2.0mm ETS FBI scores for hourly precipitation :with latent heat nudging / without latent heat nudging Testsuite ‚Juli 2004‘ (7.-16.7.04)

  39. Conclusions; Latent Heat Nudging: • An assimilation of spatially and temporally highly resolved data is needed to trigger convection on the LMK scale • Latent Heat Nudging of radar reflectivities can be used for this task • Forecasts especially for 12h-, 18h-runs can be improved.

  40. LMK - Subprojects (‚Measurements‘) Radar (M7) • 5-min DX-radar composit, European composit • pattern recognition of false echoes --> DXQ - quality product Latent Heat Nudging (M8) • Thermodynamic feedback and interactions of LHN • Adaptation of LHN to prognostic precipitation LMK 2.8 km & explicit convection (M9) • Numerical schemes • Physical parameterizations • Lateral and upper boundaries • Case studies and intercomparison • LMK Testsuites Verification (M10) • Traditional verifikation (Syn/Temp) • Use of synthetic satellite- and synthetic radar tools • New methods (pattern recognition, upscaling, ...)

  41. Examples (I) free pixels or pixel groups Rostock, 12.02.2006 23:00 UTC Anomalous propagation Berlin, 6.9.2005 12:55 UTC Negativ spokes Hannover, 17.01.2006 16:00 UTC  

  42. Examples (II) strong positive sectors reson: technical radar problems detection is essential ! detection by: - sharp edges - histograms  ‚Klops‘ reasons: warm bubbles? aerosols? detection by: - histograms 

  43. Spoke recognition Berlin 21.06.2005 22:10 UTC Examples (III) Original ‘Pos. 3’ of quality product after spoke recognition  LHN

  44. Quality product (DXQ) same data format as DX-data (planned: BUFR-format) • Header: • signature ('DXQ'), date/time, radar location, .... • coding of radar errors, concerning the whole radar picture: • hardware errors (Wartung, e.g technical problems) • ‘Klops’ • radome-Damping • binary part: pixelwise coding of 8 radar errors (1. bit: recognized, 2. bit: corrected) • exceed of threshold in signal processor (RVP) (LOG/SQI/WSP/CCOR/speckle) • cluster (def. by 9 neighbouring pixels) • spokes - positive- and negativespokes • vertical reflectivity profile • bright band • distance dependent damping • ...... • ....... April 2006: DXQ-composit (QY) and adequate DX-composit (RY) operational

  45. Project LMK: time table of the pre-operational test phase • July 2003 Start of project LMK • End 2003 First test suites with 2.8 km resolution • End 2004 First test suites with data assimilation • March 2006: Prototype-version of LMK-system with LHN for ‘summer 2005’ (test suites 3.x). • April 2006: DXQ-Composit operational • Mai 2006: Start of pre-operational test phase. Fine-Tuning and evaluation of all components • June 2006 ‘Introduction group’ (TI14) ‘Evaluation group’ (FE 15, WV, AG Evaluierung) • Dez. 2006: End of AP2003 • Spring 2007: Start of operational use after decision of the KG-NWV and board of directors

  46. LMK-forecast runs (18h-forecasts, started every 3h) -> LAF-ensembleavailable at: ..., 1, 4, 7, 10, ... , 22, ... UTC LMK-data assimilation cycle (rapid update cycle) • continuous assimilation -> nudging • short cut off ( ~ ½ h) • use all data, especially radar data • adiitionally: split into ‘main run analysis’ and a pure data assimilation cycle Lagged Average Forecast (LAF)-Ensemble (Prinzip) possible changes in this sequence: additional soil moisture analysis (SMA) GME, LME and LMK cannot run at the same time on the computer

  47. 2.8 km from: R. A. Houze, Jr.: Cloud Dynamics International Geophysics Series Vol. 53 Deep moist convection Schematic model from a Colorado storm case study (Raymer Hailstorm)

  48. Explicit Convection in LMK (case study '26.08.2004') LM Radar LMK, Testsuite 1.7

  49. Monatsmittel der Niederschläge ‚August 2004‘

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