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Microphysics in mesoscale snowbands

Microphysics in mesoscale snowbands. A modest proposal. Mesoscale snowbands. Snow is concentrated in snowbands Snowband : linear radar reflectivity structure 20–100 km in width, >250 km in length, with an intensity >30 dB Z , which is maintained for at least 2 h (Novak et al., 2004)

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Microphysics in mesoscale snowbands

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  1. Microphysics in mesoscalesnowbands A modest proposal

  2. Mesoscalesnowbands • Snow is concentrated in snowbands • Snowband: linear radar reflectivity structure 20–100 km in width, >250 km in length, with an intensity >30 dBZ, which is maintained for at least 2 h (Novak et al., 2004) • Very high snowfall rates and snow totals may occur • Importance in forecasting is obvious

  3. From Novak et al. 2004

  4. Mesoscalesnowbands • Often develop in north or northwest quadrants of extratropical cyclones • Vertical motions forced by frontogenesis • Moist symmetric instability is present (although assessment is difficult)

  5. From Novak et al. 2004

  6. Motivation • Little is known of ice particle microphysics in snowbands over the Midwest • ice habits, degree of riming, fallspeeds • Relative importance of growth processes uncertain • Deposition (growth from vapor) • Accretion or riming(collection of supercooled droplets) • Aggregation (collision and “sticking” of two crystals, dendrites and thin plates most commonly)

  7. Motivation • How does microphysics change as thermodynamics and dynamics evolve? • Better model parameterizations are needed • There is large variability in model microphysics, thus a need for verification with observations.

  8. To assess ice particle growth, knowledge of vertical motions needed • Vertical motions are maximized in snowbands • Increased flux of water vapor (mixing ratio not different, VV is) • This benefits deposition, but is there more to story? • More and larger cloud droplets: increased LWC enhances riming! • Residence time is increased too

  9. Vertical velocities are uncertain • Theory: Emmanuel (1983) predicts ~ 1 m s-1 in CSI • Modeling: ~ 10 cm s-1 (e.g. Zhang & Cho, 1995) • Measurements: Sanders and Bosart (1985) ~ 1 m s-1 max in N.Eng. Houze (1981) ~ 0.8 m s-1 max in Pacific Northwest Cronce et al. (2007) 35% > 1 m s-1, 9% > 2 m s-1 in central & southern U.S. Doppler 915 MHz wind profiler

  10. From Novak et al. 2004

  11. KOKX 0.5 deg reflectivity at 1000Z 20 Dec 2009 3-km RUC at 0500Z 20 Dec 2009 Omega (μbar/s) KOKX WSR-88D and 1from Colle et al. (2012)

  12. Focus on riming (accretion) • Riming growth is much faster than deposition or aggregation

  13. Deposition • =ffor spherical symmetry • ∫dm=dt • m

  14. Riming • =for spherical symmetry • ∫dm=dt • t • m

  15. Aggregation • =for spherical symmetry • ∫dm=dt • t • m

  16. How important is riming? Possible methodology • Observe snow crystals every 15-30 minutes under a microscope with camera • Classify crystals (81 types given by Magono and Lee (1966)). • Estimate percentage of each crystal type for each observation time • Obtain snow density by measuring the snow volume and melted volume

  17. Assessment of riming • Adopt scale of Mosimann et al. (1994) • degree of riming based on visual observation under magnification • 0-5 scale (no riming to heavy riming)

  18. light riming (0-1)

  19. moderate riming (2-3)

  20. heavy riming (4-5)

  21. What % of mass in snow is due to accreted cloud droplets? • Compare snowfall rates for unrimed vs. rimed crystals of varying degrees of riming • Feng & Grant (1982) and Mitchell et al (1990) show as much as a doubling of the snowfall rate due to riming • More field studies are needed! • Snow depth will not be increased proportionally if rimed crystals have a higher density • Matt Taraldsen’s SLR study (next talk) can help

  22. Importance of -15°C • Dendritic mode in operation here • Highest growth rate by deposition here • Growth of dendrites by deposition and aggregation produces greatest snowfall rates and accumulations (Passarelli, 1978)

  23. Dendrites are also good rimers • Air passes around crystal and through it, too • This enhances the collection efficiency • Irregular rotating and tumbling fall behavior likewise helps collect droplets • Can occur simultaneously with aggregation (Fujiyoshi and Wakahama, 1985), as one might expect

  24. Other data streams • SCSU disdrometer • Size and fallspeed information • Newly available ZDR data • It’s all about shape! • Local soundings? • Numerical modeling?

  25. AHS 452 projects, anyone? • Opportunities for senior research projects • See me if interested! Senior Research Paper Investigation of Critical Thicknesses for Snowman Melting AHS 452 St Cloud State University Spring 2013 Jane Q. Public

  26. Questions and/or comments?

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