What we have learned about Orographic Precipitation Mechanisms from MAP and IMPROVE-2: MODELING

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What we have learned about Orographic Precipitation Mechanisms from MAP and IMPROVE-2: MODELING Socorro Medina, Robert Houze, Brad Smull University of Washington Matthias Steiner Princeton University Nicole Asencio Meteo-France

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What we have learned about Orographic Precipitation Mechanisms from MAP and IMPROVE-2: MODELING Socorro Medina, Robert Houze, Brad Smull University of Washington Matthias Steiner Princeton University Nicole Asencio Meteo-France. RADIAL VELOCITY. Height ( km ). Distance (km). - PowerPoint PPT Presentation

Transcript of What we have learned about Orographic Precipitation Mechanisms from MAP and IMPROVE-2: MODELING

Page 1: What we have learned about Orographic Precipitation Mechanisms  from MAP and IMPROVE-2: MODELING

What we have learned about Orographic Precipitation

Mechanisms from MAP and IMPROVE-2:

MODELING

Socorro Medina, Robert Houze, Brad Smull University of Washington

Matthias SteinerPrinceton University

Nicole AsencioMeteo-France

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Windward Shear Layer –Repeatable pattern in different

storms/mountain ranges

Medina, Smull, Houze, and Steiner (2005); JAS - IMPROVE Special Issue

RADIAL VELOCITY

Hei

gh

t (k

m)

Distance (km)

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Objective #1

Investigate how the shear layer

develops. Explore the role of:

–Pre-existing baroclinic shear

–Surface friction

– Stable flow retarded by steep terrain

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Approach – 2D Idealized simulations• Weather Research and Forecasting (WRF) model version

1.3 in Eulerian mass coordinates• Domain: 800 km x 30 km (120 vertical layers)• 2 km horizontal resolution; ~250 m vertical resolution• Lin et al. (1983) microphysical scheme• Land surface:

– Option 1: Frictionless “free-slip” surface– Option 2: Non-dimensional surface drag coefficient Cd =

0.01• 2D bell-shaped mountain (characterized by height h and

half-width a) placed in the center of horizontal domain• Alpine-like simulations: h=3.1 km; a=44 km• Cascade-like simulations: h=1.9 km; a=32 km• Results shown after 30 hours of initialization

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Initialized with vertically uniform wind speed (10 m/s) and stability; saturated atmosphere with Ts = 283 K

Stability

Friction

Color- Horizontal windContours-Wind shear

Nm2= 0.03x10-4 s-2

Nm2= 0.3x10-4 s-2

Nm2= 1.0x10-4 s-2

Free-slip Cd=0.01

Medina, Smull, Houze, and Steiner

(2005); JAS - IMPROVE Special

Issue

ALPS-like mountain

Distance (km)

Hei

gh

t (k

m)

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Friction

Color- Horizontal windContours-Wind shear

Nm2= 0.03x10-4 s-2

Nm2= 0.3x10-4 s-2

Nm2= 1.0x10-4 s-2

Stability

Medina, Smull, Houze, and Steiner

(2005); JAS - IMPROVE Special

Issue

Initialized with vertically uniform wind speed (10 m/s) and stability; saturated atmosphere with Ts = 283 K

CASCADE-like mountain

Free-slip Cd=0.01Distance (km)

Hei

gh

t (k

m)

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Idealized Simulation of Case 13-14 Dec 2001

Initial conditions: Solid lines

HORIZONTAL WIND

WIND SHEAR

Medina, Smull, Houze, and Steiner (2005); JAS -

IMPROVE Special Issue

RH (%) T (°C) Zonal Wind (m/s)H

eig

ht

(km

)

Distance (km)

Hei

gh

t (k

m)

Shear = 12.5 m s-1 km-1

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Conclusions # 1

Idealized simulations show that orographic effects alone are sufficient to produce a shear layer on the windward side of the terrain when the stability is high enough (e.g. Alpine cases)

Simulations based on IMPROVE-2 environmental and terrain condition indicate that surface friction and/or pre-existing shear were necessary to produce an enhanced layer of shear

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Objective #2

Investigate if mechanisms of orographic precipitation

enhancement deduced from observations are also present in mesoscale

models

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FLOW-OVER Precipitation enhancement by coalescence & riming

over first peak

TERRAIN

snow

rain

0ºC

cloud dropletsgraupel growingby riming

rain growingby coalescence

Slightly unstable

air

Medina and Houze (2003)

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Approach• Focus on MAP – IOP2b• Meso-NH: mesoscale non-hydrostatic model used by

French research community (Lafore et al. 1998)• 2.5-km horizontal resolution nested in a 10-km horizontal

resolution domain• Initial and lateral conditions:

– Given by linearly interpolating in time French Operational Analysis (ARPEGE) for 10-km resolution domain

– Given by 10-km resolution domain for 2.5 km resolution domain

• 2.5-km horizontal resolution domain: Microphysical bulk parameterization including cloud, rain, ice, snow, and graupel (Pinty and Jabouille 1998)

• Validation of simulation conducted by Asencio et al. 2003 (QJMRS)

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Comparison of IOP2b radar observations and simulation

20 SEP OBSERVED RAIN ACCUMULATION (mm)

20 SEP SIMULATED RAIN ACCUMULATION (mm)

20 SEP OBSERVED RADIAL VELOCITY (m/s)

20 SEP SIMULATED RADIAL VELOCITY (m/s)

(Provided by J. Vivekanandan)

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Observed and Simulated Mean Hydrometeors (over 7h)

FREQUENCY OF OCCURRENCE OF OBSERVED LIGHT RAIN (%)

FREQUENCY OF OCCURRENCE OF OBSERVED MODERATE RAIN (%)

FREQUENCY OF OCCURRENCE OF OBSERVED HEAVY RAIN (%)

MIXING RATIO OF SIMULATED RAIN (kg/kg)

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Observed and Simulated Mean Hydrometeors (over 7h)

FREQUENCY OF OCCURRENCE OF OBSERVED GRAUPEL (%)

MIXING RATIO OF SIMULATED GRAUPEL (kg/kg)

FREQUENCY OF OCCURRENCE OF OBSERVED DRY SNOW (%)

MIXING RATIO OF SIMULATED SNOW (kg/kg)

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MIXING RATIO OF CLOUD (kg/kg) RATE OF CLOUD GROWTH BYCONDENSATION (S-1)

Mean Microphysical Processes –CLOUD (over 7h)

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RATE OF GRAUPEL GROWTH BY COLLECTION OF CLOUD AND SNOW (S-1)

MIXING RATIO OF GRAUPEL(kg/kg)

Mean Microphysical Processes –GRAUPEL (over 7h)

RATE OF GRAUPEL GROWTH BY SNOW RIMING CLOUD (S-1)

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RATE OF RAIN GROWTH BYACCRETION OF CLOUD (S-1)

MIXING RATIO OF RAIN (kg/kg)

Mean Microphysical Processes –RAIN (over 7h)

RATE OF RAIN GROWTH BY GRAUPEL AND SNOW MELT (S-1)

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Conclusions # 2

A Meso-NH simulated cross-barrier flow of IOP2b had the correct structure but the speed was overestimated.

The Meso-NH simulation produced precipitation patterns comparable with the radar observations.

The location and occurrence of simulated microphysical processes of orographic precipitation enhancement are consistent with the S-Pol polarimetric radar data.

Graupel is created by riming of cloud and it grows by collection of snow and cloud.

Rain is produced via melting of graupel (& snow) followed by cloud accretion.

The model suggests that hydrometeor growth rates can be ~4-7 times larger over the mountains than over the low elevations.

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FIN

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Run Stability (Nm2; x10-4 s-2) Wind speed

perpendicular to terrain (u; m s-1)

h; x103m a; x103 m Lr

(x103 m)

Ro Fr

IOP2b 0.03 12.5d 3.1 44 54 2.84 2.33

IOP8 1.00 10 3.1 44 310 2.27 0.32

ALPS a-b

0.03 10 3.1 44 54 2.27 1.86

ALPS c-d

0.30 10 3.1 44 170 2.27 0.59

ALPS e-f

1.00 10 3.1 44 310 2.27 0.32

CASC. a-b

0.03 10 1.9 32 33 3.12 3.04

CASC. c-d

0.30 10 1.9 32 104 3.12 0.96

CASC. e-f

1.00 10 1.9 32 190 3.12 0.53

U=20m/s

0.30 20 1.9 32 104 6.25 1.92

U=30m/s

0.30 30 1.9 32 104 9.38 2.88

13 Dec 0.37d 20d 1.9 32 116 6.25 1.73

a Lr = (N h) f-1; f=Coriolis parameterb Ro = u (f a)-1

c Fr = u (N h)-1 d Vertically averaged over the lowest 3 km.

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Garvert et al. 2005

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IOP2b Wind profiler data

OBSERVATION

SIMULATION

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MIXING RATIO OF SNOW (kg/kg)MIXING RATIO OF GRAUPEL(kg/kg)

Mean Microphysical Processes –GRAUPEL (over 7h)

RATE OF GRAUPEL GROWTH BY COLLECTION OF CLOUD AND SNOW (S-1)

RATE OF GRAUPEL GROWTH BY SNOW RIMING CLOUD (S-1)

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RATE OF RAIN FALLOUT (S-1) RATE OF RAIN GROWTH BYACCRETION OF CLOUD (S-1)

MIXING RATIO OF RAIN (kg/kg)

Mean Microphysical Processes –RAIN (over 7h)

RATE OF RAIN GROWTH BY GRAUPEL MELTING (S-1)

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Observations Simulation

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2D Simulation with 100 m resolution of stable flow over a 2 km ridge conducted by

with Bryan and Fritsch (2002) model

Simulation conducted by D. Kirshbaum

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Precipitation

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Precipitation

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Precipitation

N_m^2=(g/T)(dT/dz + Gamma_m)(1+Lq_s/RT)

Gamma_m=Gamma_d(1+q_w)(11+Lq_s/RT)*f(T,q_s,q_L)