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

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slide1
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

windward shear layer repeatable pattern in different storms mountain ranges

RADIAL VELOCITY

Height (km)

Distance (km)

Windward Shear Layer –Repeatable pattern in different storms/mountain ranges

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

objective 1
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
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
slide5

ALPS-like mountain

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

Color- Horizontal wind

Contours-Wind shear

Nm2= 0.03x10-4 s-2

Stability

Nm2= 0.3x10-4 s-2

Height (km)

Nm2= 1.0x10-4 s-2

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

Free-slip

Cd=0.01

Distance (km)

Friction 

slide6

CASCADE-like mountain

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

Color- Horizontal wind

Contours-Wind shear

Nm2= 0.03x10-4 s-2

Stability

Nm2= 0.3x10-4 s-2

Height (km)

Nm2= 1.0x10-4 s-2

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

Free-slip

Cd=0.01

Distance (km)

Friction 

idealized simulation of case 13 14 dec 2001
Idealized Simulation of Case 13-14 Dec 2001

HORIZONTAL WIND

Height (km)

Height (km)

Shear = 12.5

m s-1 km-1

WIND SHEAR

Zonal Wind (m/s)

RH (%)

T (°C)

Initial conditions: Solid lines

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

Distance (km)

slide8

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
Objective #2

Investigate if mechanisms of orographic precipitation enhancement deduced from observations are also present in mesoscale models

slide10

cloud droplets

graupel growing

by riming

rain growing

by coalescence

snow

0ºC

Slightly unstable air

rain

TERRAIN

FLOW-OVER Precipitation enhancement by coalescence & riming over first peak

Medina and Houze (2003)

approach
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
Comparison of IOP2b radar observations and simulation

20 SEP OBSERVED RAIN

ACCUMULATION (mm)

20 SEP OBSERVED RADIAL

VELOCITY (m/s)

(Provided by J. Vivekanandan)

20 SEP SIMULATED RAIN

ACCUMULATION (mm)

20 SEP SIMULATED RADIAL

VELOCITY (m/s)

observed and simulated mean hydrometeors over 7h

FREQUENCY OF OCCURRENCE

OF OBSERVED LIGHT RAIN (%)

FREQUENCY OF OCCURRENCE

OF OBSERVED MODERATE RAIN (%)

Observed and Simulated Mean Hydrometeors (over 7h)

FREQUENCY OF OCCURRENCE

OF OBSERVED HEAVY RAIN (%)

MIXING RATIO OF

SIMULATED RAIN (kg/kg)

observed and simulated mean hydrometeors over 7h1

FREQUENCY OF OCCURRENCE

OF OBSERVED GRAUPEL (%)

MIXING RATIO OF

SIMULATED GRAUPEL (kg/kg)

Observed and Simulated Mean Hydrometeors (over 7h)

FREQUENCY OF OCCURRENCE

OF OBSERVED DRY SNOW (%)

MIXING RATIO OF

SIMULATED SNOW (kg/kg)

slide15

MIXING RATIO OF CLOUD (kg/kg)

RATE OF CLOUD GROWTH BY

CONDENSATION (S-1)

Mean Microphysical Processes –CLOUD (over 7h)

mean microphysical processes graupel over 7h

MIXING RATIO OF GRAUPEL(kg/kg)

RATE OF GRAUPEL GROWTH

BY COLLECTION OF CLOUD

AND SNOW (S-1)

RATE OF GRAUPEL GROWTH

BY SNOW RIMING CLOUD (S-1)

Mean Microphysical Processes –GRAUPEL (over 7h)
mean microphysical processes rain over 7h

MIXING RATIO OF RAIN (kg/kg)

RATE OF RAIN GROWTH BY

ACCRETION OF CLOUD (S-1)

Mean Microphysical Processes –RAIN (over 7h)

RATE OF RAIN GROWTH BY

GRAUPEL AND SNOW MELT (S-1)

slide20

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.
slide22

aLr = (N h) f-1; f=Coriolis parameter

b Ro = u (f a)-1

c Fr = u (N h)-1

d Vertically averaged over the lowest 3 km.

iop2b wind profiler data
IOP2b Wind profiler data

OBSERVATION

SIMULATION

mean microphysical processes graupel over 7h1

MIXING RATIO OF SNOW (kg/kg)

MIXING RATIO OF GRAUPEL(kg/kg)

RATE OF GRAUPEL GROWTH

BY COLLECTION OF CLOUD

AND SNOW (S-1)

Mean Microphysical Processes –GRAUPEL (over 7h)

RATE OF GRAUPEL GROWTH

BY SNOW RIMING CLOUD (S-1)

mean microphysical processes rain over 7h1

RATE OF RAIN FALLOUT (S-1)

RATE OF RAIN GROWTH BY

GRAUPEL MELTING (S-1)

MIXING RATIO OF RAIN (kg/kg)

RATE OF RAIN GROWTH BY

ACCRETION OF CLOUD (S-1)

Mean Microphysical Processes –RAIN (over 7h)
slide42
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

precipitation2
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)