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