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A New Bulk Microphysical Parameterization in MM5 & WRF

A New Bulk Microphysical Parameterization in MM5 & WRF. and what’s so bad with the old scheme anyway?. Greg Thompson, Paul Field, Roy Rasmussen, Bill Hall. Microphysics species’ characteristics. Cloud water gamma distribution w/ shape factor dependent on droplet concentration

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A New Bulk Microphysical Parameterization in MM5 & WRF

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  1. A New Bulk Microphysical Parameterization in MM5 & WRF and what’s so bad with the old scheme anyway? Greg Thompson, Paul Field, Roy Rasmussen, Bill Hall Univ of Washington 03 Apr 2006

  2. Microphysics species’ characteristics • Cloud water • gamma distribution w/ shape factor dependent on droplet concentration • does not sediment • “autoconverts” to rain using Berry & Reinhardt with dependence on droplet concentration • Cloud ice • gamma distribution • “pristine” ice (D < 125 microns) • initiation T-dependent (Cooper) • prognosed Ni • slowly sediments at ~10-30 cm/s • Rain • gamma distribution • variable equiv y-intercept: • 2 x 109 m-4 (drizzle) • 8 x 106 (melted snow) • accurate fallspeed relation • Snow • sum of two gamma distributions (Field et al, 2005) • variable y-intercept depends on ice content and temperature • non-spherical geometry (m = aD2) • variable snow density (1/D) • Graupel • gamma distribution • variable equiv y-intercept depends on mixing ratio (simulate hail and snow-like graupel): • 2 x 106 m-4 (graupel) • 1 x 104 (hail) Univ of Washington 03 Apr 2006

  3. Microphysical processes (details) • Droplet condensation — removes all supersaturation in single timestep; now using Newton-Raphson iterative technique. • Ice nucleation — primary (>8% supersat w.r.t. ice; T-dependent/Cooper); secondary (rime-splinter); heterogeneous (literal interpretation of Bigg 1953); homogeneous below –40C (implicitly). • Ice depositional growth — T-dependent (Koenig, 1971). When ice grows larger than 125 mm, mass moved into snow category by explicit bin method. • Snow depositional growth — integrated from assumed spectra; capacitance=0.3-0.5 (aggregates). • Rain evaporation — integrated from assumed spectra (deposition neglected; grows CLW instead). • Autoconversion — Berry & Reinhardt, 1974 with properly computed characteristic diameters - compares well to Geresdi bin model and K&K scheme. • Rain, snow, and graupel collect cloud water — integrated from assumed spectra and uses Wisner approximation. • Snow collects cloud ice — low collision efficiency; use Wisner approximation. Ignore graupel/ice collisions. • Rain/ice collisions & rain/graupel collisions — pre-compute bin-model style; lookup table. Uses same assumption as bin modelers: when mass of water drop exceeds mass of ice, the drop freezes into graupel; otherwise the ice accretes the water to grow snow. • Sedimentation — rain uses Ferrier modified power law (match Gunn & Kinzer); snow uses power law and Ferrier constants; cloud ice and graupel use power laws. • Melting — cloud ice, instantly; snow and graupel use assumed spectra and thermodynamic equilibrium. Univ of Washington 03 Apr 2006

  4. Cloud water - details • Generalized gamma distribution: N(D) = N0 Dm e-lD • Shape parameter, m, depends on droplet concentration, Nt_c [from Martin et al. (1994)]: • m = min(15, 1000/Nt_c + 2) • Maritime: Nt_c <= 75 cm-3; m = 15; dispersion = 0.25 • Continental: Nt_c = 300 cm-3; m = 5; dispersion = 0.45 qc = 0.25 g m-3 Future: Will use variable Nt_c in space and time; attempt to initialize from MODIS or other sat data by end of CY2006 Univ of Washington 03 Apr 2006

  5. Rain - details d(qr) dt = • Generalized gamma distribution: N(D) = N0 Dm e-lD • Autoconversion from Berry & Reinhardt (1974; part 2). Previous implementation followed Walko et al. (1995), but oversimplified characteristic diameters. Current scheme properly implements B&R characteristic diameters: Univ of Washington 03 Apr 2006

  6. Rain - details (cont) • Y-intercept (equiv exponential distrib), N0, uniquely diagnosed; melted ice versus collision/coalescence. This was done to attempt to simulate freezing drizzle events while not ignoring classical rain (melting ice). • N0r_exp varies from 2 x 109 (drizzle-like) down to 2 x 106 m-4 (convective rain) producing median volume diameters from 50 microns to no larger than 3 mm. non-classical precip formation freezing drizzle T = 0oC melting ice mvd=50mm linear increase to melted equiv (~1-2 mm); then constant N0r_exp below N0r_exp computed as f(qr) but minimized below drizzle rain Future: Will add predictive number concentration for rain by end of CY2006 Univ of Washington 03 Apr 2006

  7. Terminal velocity • Power law for cloud ice, rain, snow, graupel: • Cloud ice: • Snow (Ferrier, 1994): • Graupel: • Rain (Ferrier, 1994): (exponential causes the flattening) Exponential distrib with N0=8x106 m-4 Snow varying N0 causes curvature; greatly reduced “step” from prior (2004) version Mitchell & Heymsfield Univ of Washington 03 Apr 2006

  8. Cloud ice (pristine) - details • Generalized gamma distribution: N(D) = N0 Dm e-lD • Threshold size for transfer to snow: D0s = 125 microns • Does not rime • Utilizes explicit size bins to determine mass transfer each timestep (look-up table) • Like Harrington et al. (1995), depositional growth of ice for diameters > D0s goes directly into snow category • Initiation: • primary nucleation (when T < -8oC or 8+% supersaturated w.r.t. ice) uses Cooper (1986) curve • freezing of water droplets using literal interpretation of Bigg (1953) temp/volume probabilities (look-up table) • secondary/rime-splinter (Hallet & Mossop 1974) Bigg, 1953 vapor deposition goes into snow mass here goes into snow Univ of Washington 03 Apr 2006

  9. Ice/snow size spectra (UK-C130 aircraft) Mid-latitude stratiform cloud around UK ~9000 10 second (~1.1 km) ice particle size distributions 2DC/2DP particle probes (100 mm to 6 mm) re-scaled N(D) [m-4] M33 N(D) ----- M24 D (mm) M2 D ----- M3 M2 D -----     M3 M2 D ----- M3 M2 D ----- M3 Univ of Washington 03 Apr 2006

  10. Moment relations contain lots of scatter until … stratified by temperature IWC =  m(D) N(D) dD IWC =  0.069 D2 N(D) dD M2 =  D2 N(D) dD IWC = 0.069 M2 Mn = a(n,T) M2b(a,T) Mn = a(n,T) [IWC/0.069]b(a,T) • Sum of exponential + gamma distributions • (Field et al. 2005): Univ of Washington 03 Apr 2006

  11. Graupel - details 75 % Prg_sacw to graupel 5 5 30 Riming:Deposition • Generalized gamma distribution: N(D) = N0 Dm e-lD • Y-intercept (equiv exponential distrib), N0, diagnosed as f(qg): • N0 = max(104, min(100*qg, 106)) • shifts from snow-like graupel towards hail category using single species • rimed snow converting to graupel remains ad-hoc and needs more research: Univ of Washington 03 Apr 2006

  12. Collisions between hydrometeors Hydrometeor y collides and collects species x governed by Stochastic Collection Equation (SCE): Nearly everyone assumes Exy is constant w.r.t. diameter (generally OK except cloud water self-collection). Next, some processes can apply Wisner (1972) approximation where v(D)x>>v(D)y & Dx>>Dy (example: rain, snow, and graupel collecting cloud water). When species have similar fallspeeds, Wisner approximation is unacceptable, so Mizuno (1990) pulled velocity difference outside the integral by using abs(vtx-vty) (for rain collecting hail/graupel). Old Reisner et al. (1998) code used this assumption for rain collecting either snow or graupel. The new scheme uses full SCE for rain collecting either snow or graupel and stores results in look-up table. It creates 100 log-spaced diameter bins of rain, snow, and graupel and sums the full double-integral. Nearly every researcher including Verlinde et al. (1990) and Gaudet & Schmidt (2005) assume the absolute value of the velocity difference (over diameters ranging from zero to infinity) whereas we “short-circuit” the integral once the diameter causes the velocity difference to become negative. The process is further complicated in a 3-way process like rain colliding with snow to form graupel. The old code only transferred the mass of snow into graupel, not the mass of rain involved in the collision. The new code converts both species properly to graupel (if mass of water drop exceeds mass of snowflake; alternatively snow accretes the water drop to increase snow mass - identical to how nearly all bin microphysics models are designed). Example, rain collecting snow… Univ of Washington 03 Apr 2006

  13. Rain collecting snow (and vice-versa) old Reisner scheme new scheme contour lines are production rate of graupel (g kg-1 s-1) Univ of Washington 03 Apr 2006

  14. Summary of physical process improvements • Generalized gamma replaces exponential distributions plus Field et al. (2005) snow distrib. • Rain evaporates only after cloud water evaporates. • Cloud ice, snow, and graupel sublimate and rain evaporates using more accurate Srivastava & Coen (1992) method. • Cloud ice converts to snow using explicit, not ad-hoc method. • Collisions between hydrometeors with similar fallspeed use explicit bin method in Stochastic Collection Eqn (SCE). • Rain collecting cloud ice or snow properly sums the rain amount into graupel, not just the ice amount. • Snow and graupel sublimate when above melting temperature (did not in old scheme). • Graupel y-intercept parameter (and terminal velocity) attempt to mimic hail when high mixing ratio (strong updrafts). • Rain y-intercept parameter mimics both precip formation mechanisms: classic melting ice & collision/coalescence. • Autoconversion uses correctly computed Berry & Reinhardt characteristic diameters. • Rimed snow conversion to graupel is no longer “all or nothing” but increases as riming:depositional increases. • Sedimentation uses non-zero terminal velocity for “new precipitation cores.” • Cloud ice has differential number/mass-weighted terminal velocities. Summary of code improvements • Generalized gamma replaces exponential distributions. • Look-up tables implemented for most costly calculations (SCE for rain and snow/graupel collisions). • Very simple parameters to change mass-diameter (and other) relations. • Rain evaporation only after cloud water evaporates. • When skipping timesteps, rain evaporates (and cloud water condenses/evaporates and hydrometeors sediment). • Cloud water condensation uses more accurate 1st order iterative Newton-Raphson technique. • “Clean slate” approach, no more legacy code, well documented, consistent variable naming, etc. Univ of Washington 03 Apr 2006

  15. Placeholder Univ of Washington 03 Apr 2006

  16. Tests in 2-d using MM5 & WRF MM5 results WRF results cloud water cloud ice & snow rain Thompson, G., R. M. Rasmussen, and K. Manning, 2004: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Wea. Rev.,132, 519-542. Univ of Washington 03 Apr 2006

  17. Tests in 3-d using MM5/WRF • 14 Feb 1990 (WISP) shallow, post-frontal, upslope cloud w/widespread FZDZ. • 30 Jan 1998 (NASA-SLDRP) shallow stratoCu, primarily CLW w/slight FZDZ. • 04 Feb 1998 (NASA-SLDRP) deep and dynamic snowstorm w/classic FZRA. • 01 Feb 2001 (IMPROVE-1) deep PacNW frontal system, abundant precip. • 28 Nov 2001 (IMPROVE-2) deep PacNW frontal system plus orographics. • 13 Dec 2001 (IMPROVE-2) deep PacNW frontal system plus orographics. • plus a series of recent (Feb/Mar 2006) cases and Jim Bresch’s realtime runs Thompson, et al., 2006: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Case studies. Mon. Wea. Rev.,in preparation. Univ of Washington 03 Apr 2006

  18. 30 Jan 1998 NASA-SLDRP case Canada Lake Erie Pennsylvania Ohio • Very broad and shallow stratus or stratocumulus cloud • High liquid water content but mostly cloud drops • Sporadic surface reports of light freezing drizzle • Numerous pilot reports, generally moderate intensity Univ of Washington 03 Apr 2006

  19. 30 Jan 1998: liquid water comparison dashed lines are MM5 data for same place/time KCLE ascent (1315z) KCAK descent (1330z) KYNG ascent (1400z) KCLE landing (1430z) solid lines are King probe LWC data from Twin Otter aircraft >95% of water mass in drops less than 30 microns Univ of Washington 03 Apr 2006

  20. 30 Jan 1998: Old snow/new snow comparison MM5 simulation using old snow scheme MM5 simulation using new snow scheme Cloud top temp = -11C Observed LWC = 0.7 g/m3 Higher (closer to observed) LWC Green shading = cloud water Blue contours = snow Shallow/mostly-water cloud simulation:   increased supercooled liquid cloud using new snow scheme versus old! Univ of Washington 03 Apr 2006

  21. 04 Feb 1998 NASA-SLDRP case • Widespread, deep glaciated cloud (nor’easter) • Classic “warm-nose” with freezing rain near OH/WV border (Parkersburg, WV) • Twin Otter experienced a ‘significant performance degradation’ Univ of Washington 03 Apr 2006

  22. 04 Feb 1998: Temp/Microphysics comparison Twin Otter 2d-gray probe indicated snow particles throughout entire depth to surface MM5 cloud water aircraft data MM5 model MM5 rain MM5 snow King LWC FSSP LWC >70% of water mass in drops greater than 700 microns Univ of Washington 03 Apr 2006

  23. 04 Feb 1998: Old snow/new snow comparison MM5 simulation using old snow scheme MM5 simulation using new snow scheme • Deep/mostly-glaciated cloud simulation: • decreased supercooled liquid cloud & decreased RHicealoft using new snow scheme versus old! Univ of Washington 03 Apr 2006

  24. 01 Feb 2001: old/new scheme comparison MM5 simulation using old bulk scheme MM5 simulation using entirely new bulk scheme Eerily similar results. Was the old scheme not that bad? Is something wrong with the new? Univ of Washington 03 Apr 2006

  25. Future work • verification and more testing (DTC in next 2 months - ARW&NMM) • upcoming additions/improvements: • 2nd moment for cloud water and rain • initial aerosol variable will vary in space/time (connect to Chem module?) • aerosol variable will influence cloud water condensation and ice nucleation • addition of “Asian dust outbreaks” (for NSF-proposed ICE-L field project) • needs: • testing un-tested aspects (gamma shape parameter) • better (and faster) sedimentation • improve handling of rimed snow conversion to graupel • addition of hail category for convective simulations? Univ of Washington 03 Apr 2006

  26. Thank you! gthompsn@ucar.edu Univ of Washington 03 Apr 2006

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