Jason Milbrandt Recherche en Prévision Numérique [RPN] Meteorological Research Division, Environment Canada GEM Workshop, June 12, 2007 Multi-Moment Cloud.

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Presentation transcript:

Jason Milbrandt Recherche en Prévision Numérique [RPN] Meteorological Research Division, Environment Canada GEM Workshop, June 12, 2007 Multi-Moment Cloud Microphysics Package A New Multi-Moment Cloud Microphysics Package for the GEM-LAM

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

1.Background on bulk schemes 2.Description of the new microphysics package 3.Some advantages of the multi-moment approach OUTLINE

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

MODEL GRID: (hypothetical NWP model) PARTLY CLOUDY (RH < 100%) CLOUDY (RH = 100%) CLOUD- FREE CPSEXPLICIT SCHEME

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

INPUT: w, T, p, q v Single cloudy grid element – interaction with NWP model:

MICROPHYSICAL PROCESSES in the cloudy grid element

Single cloudy grid element – interaction with NWP model: Changes to w, T, p, q v and q c, q r, q i,... Advection and Turbulent Mixing MICROPHYSICAL PROCESSES OUTPUT: Latent heating Hydrometeors (cloud, rain, ice,…)  q c, q r, q i,... INPUT: w, T, p, q v q c, q r, q i,...

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

Single cloudy grid element: Extreme magnification

1 m 3 (unit volume) [e.g. Cloud droplets] (not to scale)

N (D)N (D) D [  m] 100 [m -3  m -1 ] m 3 (unit volume) [e.g. Cloud droplets] (not to scale)

(Example of observed cloud droplet spectrum) N (D)N (D) D [  m] 100 [m -3  m -1 ] m 3 (unit volume) [e.g. Cloud droplets] (not to scale)

DISCRETE SIZE BINS SPECTRAL METHOD Representing the size spectrum N (D)N (D) D [  m] 100 [m -3  m -1 ] m 3 (unit volume) [e.g. Cloud droplets] (not to scale)

1 m 3 (unit volume) BULK METHOD N (D)N (D) D [  m] 100 [m -3  m -1 ] ANAYLTICAL FUNCTION [e.g. Cloud droplets] (not to scale) Representing the size spectrum

Gamma Distribution Function: * Q =  q (mass content) INCREASING VALUES (of, N 0 and  ) log N(D) D [mm] Varying : (N 0 and  constant) Varying  : (Q * and N 0 constant) Varying N 0 : ( and  constant)

BULK METHOD Size Distribution Function: p th moment: N (D)N (D) D Hydrometeor Category x Total number concentration, N Tx Radar reflectivity factor, Z x Mass mixing ratio, q x Example of Moments:

BULK METHOD Size Distribution Function: p th moment: Total number concentration, N Tx Radar reflectivity factor, Z x Mass mixing ratio, q x Example of Moments: Predict changes to specific moment(s) e.g. q x, N Tx,... Implies changes to values of parameters i.e. N 0x, x,...

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

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

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

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

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

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

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

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

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

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 for Z x added for triple-moment* * Milbrandt and Yau (2005a,b) [J. Atmos. Sci.] Milbrandt-Yau Cloud Scheme *

Diagnostic-Dispersion DOUBLE-MOMENT Version: Identical to full version except: Diagnostic-  x relations added for double-moment* Milbrandt-Yau Cloud Scheme * Recall: Size Distribution Function:

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 (Q x ) for each Double-moment version –Six hydrometeor categories –double-moment (Q x,, N x ) for each –fixed-  x Milbrandt-Yau Cloud Scheme **(also available for MC2_v4.9.8)

UPCOMING VERSION AVAILABLE FOR GEM: Prototype cloud scheme for the 2010 Winter Olympics “Olympic” version * CLOUDdouble-moment(Q c, N c ) RAINdouble-moment(Q r, N r ) [diagnostic-  r ] ICE/SNOWdouble-moment(Q i, N i ) [hybrid category] GRAUPELsingle-moment(Q g ) HAILdouble-moment(Q h, N h ) [diagnostic-  h ] Milbrandt-Yau Cloud Scheme * To be implemented in GEM-LAM 2.5 km AUTUMN 2007

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

CCN-dependent N c nucleation: MARITIME CONTINENTAL SUPERSATURATION (%) 10 1 N CCN (cm -3 ) 10 2 Advantages of multi-moment approach:

Q c (Cloud Mixing Ratio)

N c (Cloud Number Concentration)

D c (Cloud Mean-Mass Diameter)

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

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

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

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

SINGLE-moment scheme (SM): ANALYTIC BIN model (ANA): z [km] Q [g m -3 ] D m [mm] N T [m -3 ]Z e [dBZ] z [km] Q [g m -3 ] D m [mm] N T [m -3 ]Z e [dBZ] 5 min 10 min 15 min 20 min INITIAL

DOUBLE-moment scheme, FIXED DISPERSION (  = 0): ANALYTIC BIN model (ANA): z [km] Q [g m -3 ] D m [mm] N T [m -3 ]Z e [dBZ] z [km] Q [g m -3 ] D m [mm] N T [m -3 ]Z e [dBZ] 5 min 10 min 15 min 20 min INITIAL

ANALYTIC BIN model (ANA): DOUBLE-moment scheme, DIAGNOSTIC DISPERSION,  = f (D m ): z [km] Q [g m -3 ] D m [mm] N T [m -3 ]Z e [dBZ] z [km] Q [g m -3 ] D m [mm] N T [m -3 ]Z e [dBZ] 5 min 10 min 15 min 20 min INITIAL

TRIPLE-moment scheme: ANALYTIC BIN model (ANA): z [km] Q [g m -3 ] D m [mm] N T [m -3 ]Z e [dBZ] z [km] Q [g m -3 ] D m [mm] N T [m -3 ]Z e [dBZ] 5 min 10 min 15 min 20 min INITIAL

Mass Content Bulk schemes: Analytic model: z [km] Q [g m -3 ] 5 min 10 min 15 min 20 min INITIAL Q [g m -3 ] z [km] DOUBLE- MOMENT Fixed  SINGLE- MOMENT DOUBLE- MOMENT Diagnosed  Q [g m -3 ] TRIPLE- MOMENT Prognosed  Advantages of multi-moment approach:SEDIMENTATION

mm SNOW (large crystals / aggregates) Q s Mass Content [g m -3 ] D s Mean Diameter [mm] (equivalent sphere) Advantages of multi-moment approach:MASS ≠SIZE

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:

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