1 / 59

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.

lois-pratt
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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‘

More Related