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Prof. Alexander Khain:. Modeling of different atmospheric phenomena: Tropical cyclones; Boundary layer convection; Clouds, cloud microphysics; precipitation formation Research group: Dr. Mark Pinsky; Dr. Barry Lynn, Dr. Andrei Pokrovsky; postdoc Dr. Yaron Segal,
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Prof. Alexander Khain: Modeling of different atmospheric phenomena: Tropical cyclones; Boundary layer convection; Clouds, cloud microphysics; precipitation formation Research group: Dr. Mark Pinsky; Dr. Barry Lynn, Dr. Andrei Pokrovsky; postdoc Dr. Yaron Segal, PhD student: Boris Grits, Master student Nir Benmoshe
Method: Solution of equation system: Motion equation (equations of Navier-Stokes) Conservation of mass; Conservation of heat; Conservation of moisture; Conservation of liquid water mass; Conservation of ice content, etc. Complicated and interesting hydrodynamics (with sources of heat, phase transitions, particle collisions, etc)
לפני אחרי הוריקן Camille, ספטמבר 1969
ציקלונים טרופיים באוקיאנוס האטלנטי (חודש ספטמבר) רובם נוצרים בקרבת יבשת אפריקה הנקודות הלבנות - היכן שהציקלונים גדלו להוריקנים
מבנה ההוריקן אוויר מתכנס אל מרכז הציקלון, מגיע עד רדיוס מסוים (רדיוס הרוחות המקסימליות) , עולה, יוצר עננים (קיר העין) ומתפשט לצדדים ברום
Tracks of the model storms in coupled with ocean and uncoupled simulations vs tracks of TCs Iris and Humberto (1995). Symbols represent storm position each 6 hours.
Questions: What are mechanisms leading to attraction of repulsion of tropical cyclones? What is the role of the ocean coupling on TC motion?
Wind-pressure relationship in the observations (top) and the GFDL hurricane model (bottom) during 1999 hurricane season.
RADARSAT SAR and TRMM images from Hurricane Mitch on 27 October 1998. The SAR image at 1133 UTC covers 184 H 322 km. The TRMM image at 0837 UTC has the same orientation as the SAR image, and is 1060 H 1100 km.
Profiles of vertical velocities in the absence and in the presence of convection in the boundary layer of TC Convection in the BL increases the wind speed near the surface
Convection in the BL increases heat and moisture surface fluxes Time dependence of the sensible (a) and latent (b) heat fluxes at the sea surface after the convective equations are turned on for the moderate (dashed line) and strong (solid line) winds.
Future plans: TC mesh Convective meshes Questions: a) What is the role of the BL convection on TC intensity? b) What is the role of spray flux from the sea surface on humidity fluxes and on cloud development? c) What is the role of Saharan dust on TC development?
CLOUD PHYSICS LOCAL SCALES OUR INVESTIGATIONS 1. Numerical modeling of clouds and cloud ensembles 2. Theoretical cloud physics
Models with spectral (bin) microphysics Scientific group of the HUJI is developing a numerical basis consisting of a complex of novel one- , two- , and three-dimensional cloud microphysical modelsfor simulation of effects of AEROSOLSon PRECIPITATION and effects of CLOUDS ON AEROSOLS
Updraft, supersaturation CCN budget NUCLEATION OF DROPLETS AND ICE CRYSTALS supersaturation DIFFUSIONAL GROWTH/ EVAPORATION OF DROPLETS supersaturation DEPOSITION / SUBLIMATION OF ICE FREEZING OF WATER DROPS DROP-DROP, DROP-ICE and ICE-ICE COLLISIONS SEDIMENTATION OF PARTICLES MELTING OF ICE ICE MULTIPLICATION BREAKUP OF DROPLETS RAIN ICE PRECIPITATION MICROPHYSICAL SCHEME OF THE HEBREW UNIVERSITY CLOUD MODEL (HUCM)
Simulation of “GREEN OCEAN”, “SMOKY” and PYRO-CLOUDS, 4 Oct., 19UT, SMOCC, 2002 LOCAL SCALES
“Green” ocean (over jungles) The drop mass distribution is very wide like over the ocean!
The drop mass distribution is narrow! Small number of large droplets Rain drop formation is inefficient
The drop mass distribution is very narrow! No large droplets Over forest fire No rain drop formation
Comparison of calculated DSD with measurements, t=1800s (Khain et al 2004-2005) CWC max: 1.1 gm-3 CWC max: 3.0 gm-3 X=71.75 km (Green ocean) X=71.75 km (Smoky) Z1 = 2.25 km Z2 = 3.0 km Z3 = 4.25 km Z1 = 2.375 km Z2 = 3.625 km Z3 = 4.375 km 2338 m, 5 OCT 02, 20 UT 2150 m, 4 OCT 02, 15 UT 3640 m, 5 OCT 02, 20 UT 3069 m, 4 OCT 02, 15 UT 4403 m, 5 OCT 02, 20 UT 4265 m, 4 OCT 02, 15 UT Cloud drop mass Cloud drop mass Green ocean cloud Smoky cloud Concentration max: 130 cm-3 Concentration max: 2400 cm-3 Height (km) Andreae et al. 2004: 2200 cm-3 Distance (km) Distance (km) Aerosols decrease droplet size and increase CWC
12 mm 11 mm Drop diameter, mm Drop diameter, mm 4165 m, 4 OCT, 19 UT Droplet size spectra in PYRO Cb as simulated by HUCM Cloud water content Cloud water content t=60 min Max: 3.5 gm-3 Max: 3.5 gm-3 t=30 min 2700-3000 cm-3 2700-3000 cm-3 Height (km) Andreae et al, 2004: mean volume diameter = 12 mm, Droplet concentration 2700cm-3 Aerosols decrease droplet size and increase CWC
Mesoscale spectral microphysics model HUCM-MM5Barry Lynn, Alexander Khain, Daniel Rosenfeld, Andrei PokrovskyThe Hebrew University of Jerusalem, IsraelThe non-hydrostatic Mesoscale Modeling System, Generation 5, MM5 ( the Pennsylvania State University and the National Center for Atmospheric Research) has been coupled with the spectral microphysics package developed in HUCM.For the first time a spectral (bin ) microphysics (SBM) mesoscale model has been developed.
Simulated radar reflecivities vs. observations Observations Spectral bin model Reisner 2 TIME = 21.00 h TIME = 21.00 h TIME = 21.00 h TIME = 22.00 h TIME = 22.00 h TIME = 22.00 h
Simulation of effects of anthropogenic aerosols on precipitation
Questions:a) What are the mechanisms by means of which aerosols influence precipitation amount and spatial distribution of precipitation? b) Is the effect of aerosols on precipitation is local or global?
THEORETICAL CLOUD PHYSICS Problems: How do droplets grow in clouds? Why do clouds precipitate?
Classic evaluation of time of raindrop development: 60 min+60 min=120 min. In real clouds: 15 min PROBLEM OF TIME 300 mk collisions 200 mk condensation 100 mk 25 mk 20 min 40 min 60 min
S1 S2 Collisions between drops Stochastic equation for collisions: f(m,t) – The mass distribution function for drops (of mass m at time t) Relative drop velocity Geometrical cross section S1 Collision efficiency Collision kernel Probability of collision of droplets m1 and m2 in unit of time E=S2/S1<< 1
EXAMPLE: 10 mm and 20 mm-radii droplets Droplets starting with green surface collide the target in case of no hydrodynamic interaction Droplets starting from red surface collide the target with hydrodynamic interaction taken into account target
Mean collision efficiency Turbulent casee=100 cm2s-3, Rel=104 (weak cumulus clouds) Gravitational case Collision efficiency in a turbulent cloud is several times higher than in pure gravity case! This fact is very important for correct description of cloud evolution and precipitation formation. The HUCM is the only model that takes turbulent effects into account
TOPICS: • To study effects of turbulence on rain formation in turbulent clouds • To study effects of porosity on collisions of cloud particles • To study effects of aerosol scavenging by droplets and porous ice particles • Why droplet tracks tend to collect in a turbulent flow?
Cloud rainwater content Model simulation
Questions Tropical cyclones: What are mechanisms leading to attraction of repulsion of tropical cyclones? What is the role of the ocean coupling on TC motion? Boundary layer What is the role of the BL convection on TC intensity? What is the role of spray flux from the sea surface on humidity fluxes and on cloud development? What is the role of Saharan dust on TC development? Cloud-aerosol interaction How do aerosols influence amount and spatial distribution of precipitation? Is the effect of aerosols on precipitation is local or global? Turbulent effects on collisions: What are effects of turbulence on rain formation in turbulent clouds? What are the effects of porosity on collisions of cloud particles and aerosol scavenging? Why droplet tracks tend to collect in a turbulent flow?