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Jason Milbrandt Recherche en Pr é vision Num é rique [RPN] PowerPoint Presentation
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Jason Milbrandt Recherche en Pr é vision Num é rique [RPN]

Jason Milbrandt Recherche en Pr é vision Num é rique [RPN]

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Jason Milbrandt Recherche en Pr é vision Num é rique [RPN]

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  1. A NewMulti-Moment Cloud Microphysics Packagefor the GEM-LAM Jason Milbrandt Recherche en Prévision Numérique [RPN] Meteorological Research Division, Environment Canada GEM Workshop, June 12, 2007

  2. Why develop a new cloud scheme for GEM? • Computer resources increasing • High-resolution NWP grids are becoming mainstream • Important to predict cloud processes as well as possible • GEM-LAM-2.5 has systematic problems with the precipitation forecasts

  3. OUTLINE • Background on bulk schemes • Description of the new microphysics package • Some advantages of the multi-moment approach

  4. One of the goals of NWP model: Predict the effects of the clouds

  5. MODEL GRID: (hypothetical NWP model) CPS CLOUDY (RH = 100%) EXPLICIT SCHEME CLOUD- FREE PARTLY CLOUDY (RH < 100%)

  6. Single cloudy grid element: CLOUDY (RH = 100%) EXPLICIT SCHEME

  7. Single cloudy grid element – interaction with NWP model: INPUT: w, T, p, qv

  8. MICROPHYSICAL PROCESSES in thecloudy grid element

  9. Single cloudy grid element – interaction with NWP model: MICROPHYSICAL PROCESSES • OUTPUT: • Latent heating • Hydrometeors • (cloud, rain, ice,…) •  qc, qr, qi, ... qc, qr, qi, ... Changes to w, T, p, qv and qc, qr, qi, ... Advection and Turbulent Mixing INPUT: w, T, p, qv

  10. Single cloudy grid element: Slight magnification = cloudy (saturated) air

  11. Single cloudy grid element: Extreme magnification

  12. Single cloudy grid element: Extreme magnification

  13. 1 m3 (unit volume) [e.g. Cloud droplets] (not to scale)

  14. 101 N(D) 100 [m-3 m-1] 10-1 10-2 0 40 20 80 60 100 D [ m] 1 m3 (unit volume) [e.g. Cloud droplets] (not to scale)

  15. 101 N(D) 100 [m-3 m-1] 10-1 10-2 0 40 20 80 60 100 D [ m] 1 m3 (unit volume) (Example of observed cloud droplet spectrum) [e.g. Cloud droplets] (not to scale)

  16. 101 N(D) 100 [m-3 m-1] 10-1 10-2 0 40 20 80 60 100 D [ m] Representing the size spectrum DISCRETE SIZE BINS 1 m3 (unit volume) SPECTRAL METHOD [e.g. Cloud droplets] (not to scale)

  17. 101 N(D) 100 [m-3m-1] 10-1 10-2 0 40 20 80 60 100 D [ m] Representing the size spectrum ANAYLTICAL FUNCTION 1 m3 (unit volume) BULK METHOD [e.g. Cloud droplets] (not to scale)

  18. Varying l: (N0 and a constant) Varying N0: (l and a constant) Varying a: (Q* and N0 constant) log N(D) log N(D) log N(D) D [mm] D [mm] D [mm] INCREASING VALUES (of l, N0 and a) Gamma Distribution Function: * Q = r q (mass content)

  19. BULK METHOD Total number concentration, NTx 101 N(D) 100 Hydrometeor Category x 10-1 10-2 Mass mixing ratio, qx 0 40 20 80 60 100 D Radar reflectivity factor, Zx Size Distribution Function: Example of Moments: pth moment:

  20. BULK METHOD Total number concentration, NTx Mass mixing ratio, qx Radar reflectivity factor, Zx Size Distribution Function: Example of Moments: Predict changes to specific moment(s) e.g. qx, NTx, ... Implies changes to values of parameters i.e. N0x, lx, ... pth moment:

  21. T < 0C * * (May contain traces of supercooled water)

  22. T < 0C = ICE CRYSTAL (May contain traces of supercooled water)

  23. T < 0C        = ICE CRYSTAL  = SNOW CRYSTAL / AGGRETATE (May contain traces of supercooled water)

  24. T < 0C            = ICE CRYSTAL  = SNOW CRYSTAL / AGGREGATE  = GRAUPEL (May contain traces of supercooled water)

  25.          T < 0C = ICE CRYSTAL  = SNOW CRYSTAL / AGGREGATE  = GRAUPEL = HAIL (May contain traces of supercooled water)

  26.          T < 0C = ICE CRYSTAL  = SNOW CRYSTAL / AGGREGATE  = GRAUPEL = HAIL = LIQUID WATER

  27.          PARTITIONING THE HYDROMETEOR SPECTRUM SNOW LIQUID WATER ICE HAIL GRAUPEL

  28.          PARTITIONING THE HYDROMETEOR SPECTRUM SNOW CLOUD ICE HAIL GRAUPEL RAIN

  29. BULK METHOD           PARTITIONING THE HYDROMETEOR SPECTRUM SNOW CLOUD ICE HAIL GRAUPEL RAIN

  30. Milbrandt-Yau CloudScheme* Full TRIPLE-MOMENT Version: • Six hydrometeor categories: • 2 liquid: cloud and rain • 4 frozen: ice, snow, graupel and hail • ~50 distinct microphysical processes • Warm-rain scheme based on Cohard and Pinty (2000a) • Ice-phase based on Murakami (1990), Ferrier (1994), Meyers et al. (1997), Reisner et al. (1998), etc. • Predictive equations forZx added for triple-moment* *Milbrandt and Yau (2005a,b) [J. Atmos. Sci.]

  31. Milbrandt-Yau CloudScheme* Diagnostic-Dispersion DOUBLE-MOMENT Version: Identical to full version except: • Diagnostic-ax relations added for double-moment* Recall: Size Distribution Function:

  32. Milbrandt-Yau CloudScheme CURRENT VERSIONS AVAILABLE FOR GEM: GEM_v3.2.2 / PHY_4.4 available upon request** GEM_v3.3.0 / PHY_4.5 part of official RPN/CMC library Single-moment version • Six hydrometeor categories • Single-moment (Qx) for each Double-moment version • Six hydrometeor categories • double-moment (Qx,, Nx) for each • fixed-ax **(also available for MC2_v4.9.8)

  33. Milbrandt-Yau CloudScheme UPCOMING VERSION AVAILABLE FOR GEM: Prototype cloud scheme for the 2010 Winter Olympics “Olympic” version * CLOUD double-moment (Qc, Nc) RAIN double-moment (Qr, Nr) [diagnostic-ar] ICE/SNOW double-moment (Qi, Ni) [hybrid category] GRAUPEL single-moment (Qg) HAIL double-moment (Qh, Nh) [diagnostic-ah] * To be implemented in GEM-LAM 2.5 km AUTUMN 2007

  34. Advantages of multi-moment approach: Prognostic Nc Double-Moment “CLOUD” Category: • Condensation rate based on saturation adjustment • Nc initialization is air-mass (CCN) dependent

  35. Advantages of multi-moment approach: CCN-dependent Nc nucleation: 103 CONTINENTAL 102 MARITIME NCCN (cm-3) 101 100 10-1 10.0 0.1 1.00 0.01 SUPERSATURATION (%)

  36. Qc (Cloud Mixing Ratio)

  37. Nc (Cloud Number Concentration)

  38. Dc (Cloud Mean-Mass Diameter)

  39. DRIZZLE Advantages of multi-moment approach: The warm-rain coalescence process RAIN CLOUD Mass Density [g m-3 (lnr)-1] Time [min] Radius [cm] Bin-resolving coalescence modelSOURCE: Berry and Reinhardt (1974)

  40. Advantages of multi-moment approach: DRIZZLE vs. RAIN Qr Mass Content [g m-3] RAIN 0.1–1 mm STRATIFORM RAIN Dr Mean Diameter [mm] DRIZZLE

  41. Advantages of multi-moment approach: SEDIMENTATION Analytic bin model calculation:(1D column) Mass Content Total Number Concentration Equivalent Reflectivity Mean-Mass Diameter INITIAL 5 min z [km] 10 min 15 min 20 min Dm[mm] Q[g m-3] NT[m-3] Ze[dBZ] Contours every 5 min

  42. SEDIMENTATION: Bulk scheme DM = mass-weighted fall velocity = number-weighted fall velocity TM = reflectivity-weighted fall velocity SM

  43. SINGLE-moment scheme (SM): z [km] Dm [mm] Q [g m-3] NT [m-3] Ze [dBZ] ANALYTIC BIN model (ANA): INITIAL 5 min z [km] 10 min 15 min 20 min Dm [mm] Q [g m-3] NT [m-3] Ze [dBZ]

  44. DOUBLE-moment scheme, FIXED DISPERSION (a = 0): z [km] Dm [mm] Q [g m-3] NT [m-3] Ze [dBZ] ANALYTIC BIN model (ANA): INITIAL 5 min z [km] 10 min 15 min 20 min Dm [mm] Q [g m-3] NT [m-3] Ze [dBZ]

  45. DOUBLE-moment scheme, DIAGNOSTIC DISPERSION, a = f (Dm): z [km] Dm [mm] Q [g m-3] NT [m-3] Ze [dBZ] ANALYTIC BIN model (ANA): INITIAL 5 min z [km] 10 min 15 min 20 min Dm [mm] Q [g m-3] NT [m-3] Ze [dBZ]

  46. TRIPLE-moment scheme: z [km] Dm [mm] Q [g m-3] NT [m-3] Ze [dBZ] ANALYTIC BIN model (ANA): INITIAL 5 min z [km] 10 min 15 min 20 min Dm [mm] Q [g m-3] NT [m-3] Ze [dBZ]

  47. INITIAL 5 min 10 min z [km] 15 min 20 min Q [g m-3] z [km] DOUBLE- MOMENT Fixed a = 0 DOUBLE- MOMENT Diagnosed a TRIPLE- MOMENT Prognosed a SINGLE- MOMENT Q [g m-3] Q [g m-3] Q [g m-3] Q [g m-3] Advantages of multi-moment approach: SEDIMENTATION Analytic model: Mass Content Bulk schemes:

  48. Advantages of multi-moment approach: MASS ≠ SIZE SNOW (large crystals / aggregates) Qs Mass Content [g m-3] 0.1 - 4 mm Ds Mean Diameter [mm] (equivalent sphere)

  49. SUMMARY • Efficient single-moment and double-moment versions of the Milbrandt-Yau scheme are available for GEM-LAM • Single-moment version will be proposed as the operational scheme for GEM-LAM_2.5 by fall 2007 • New version (“semi-double-moment”) will be developed and tested for implementation by spring 2007 • Large-scale version (diagostic cloud-fraction; fewer prognostic variables) to be developed soon • For code, support, bug reports, or question: • Jason.Milbrandt@ec.gc.ca

  50. MERCI