1. 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

2. Windward Shear Layer – Repeatable pattern in different storms/mountain ranges Medina, Smull, Houze, and Steiner (2005); JAS - IMPROVE Special Issue RADIAL VELOCITY Height (km) Distance (km) 

3. Objective #1 Investigate how the shear layer develops. Explore the role of: –Pre-existing baroclinic shear –Surface friction – Stable flow retarded by steep terrain

4. 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 C d = 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

5. Initialized with vertically uniform wind speed (10 m/s) and stability; saturated atmosphere with Ts = 283 K Stability Friction  Color- Horizontal wind Contours-Wind shear N m 2 = 0.03x10 -4 s -2 N m 2 = 0.3x10 -4 s -2 N m 2 = 1.0x10 -4 s -2 Free-slipC d =0.01 Medina, Smull, Houze, and Steiner (2005); JAS - IMPROVE Special Issue ALPS-like mountain Distance (km) Height (km)  

6. Friction  Color- Horizontal wind Contours-Wind shear N m 2 = 0.03x10 -4 s -2 N m 2 = 0.3x10 -4 s -2 N m 2 = 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-slipC d =0.01 Distance (km) Height (km)  

7. 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) Height (km) Distance (km) Height (km) Shear = 12.5 m s -1 km -1

8. 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

9. Objective #2 Investigate if mechanisms of orographic precipitation enhancement deduced from observations are also present in mesoscale models

10. FLOW-OVER Precipitation enhancement by coalescence & riming over first peak Medina and Houze (2003)

11. 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)

12. 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)

13. 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)

14. 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)

15. MIXING RATIO OF CLOUD (kg/kg) RATE OF CLOUD GROWTH BY CONDENSATION (S -1 ) Mean Microphysical Processes – CLOUD (over 7h)

16. 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 )

17. RATE OF RAIN GROWTH BY ACCRETION 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 )

20. 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.

21. FIN

22. RunStability (N m 2 ; x10 -4 s -2 )Wind speed perpendicular to terrain (u; m s -1 ) h; x10 3 ma; x10 3 mL r (x10 3 m) RoFr IOP2b0.0312.5 d 3.144542.842.33 IOP81.00103.1443102.270.32 ALPS a-b 0.03103.144542.271.86 ALPS c-d 0.30103.1441702.270.59 ALPS e-f 1.00103.1443102.270.32 CASC. a-b 0.03101.932333.123.04 CASC. c-d 0.30101.9321043.120.96 CASC. e-f 1.00101.9321903.120.53 U=20 m/s 0.30201.9321046.251.92 U=30 m/s 0.30301.9321049.382.88 13 Dec0.37 d 20 d 1.9321166.251.73 a Lr = (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.

29. Garvert et al. 2005

30. IOP2b Wind profiler data OBSERVATION SIMULATION

33. 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 )

34. RATE OF RAIN FALLOUT (S -1 ) RATE OF RAIN GROWTH BY ACCRETION 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 )

35. Observations Simulation

42. 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

43. Precipitation

45. 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)