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

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)

Objective #1

Investigate how the shear layer

develops. Explore the role of:

–Pre-existing baroclinic shear

–Surface friction

– Stable flow retarded by steep terrain

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

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)

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)

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

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

Objective #2

Investigate if mechanisms of orographic precipitation

enhancement deduced from observations are also present in mesoscale

models

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)

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)

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)

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)

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)

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

Mean Microphysical Processes –CLOUD (over 7h)

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)

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)

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.

FIN

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.

Garvert et al. 2005

IOP2b Wind profiler data

OBSERVATION

SIMULATION

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)

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)

Observations Simulation

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

Precipitation

Precipitation

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)