Experiments in Fluids

, 55:1737 | Cite as

Measurement of atmospheric boundary layer based on super-large-scale particle image velocimetry using natural snowfall

  • M. Toloui
  • S. Riley
  • J. Hong
  • K. Howard
  • L. P. Chamorro
  • M. Guala
  • J. Tucker
Research Article


We present an implementation of super-large-scale particle image velocimetry (SLPIV) to characterize spatially the turbulent atmospheric boundary layer using natural snowfall as flow tracers. The SLPIV technique achieves a measurement area of ~22 m × 52 m, up to 56 m above the ground, with a spatial resolution of ~0.34 m. The traceability of snow particles is estimated based on their settling velocity obtained from the wall-normal component of SLPIV velocity measurements. The results are validated using coincident measurements from sonic anemometers on a meteorological tower situated in close proximity to the SLPIV sampling area. A contrast of the mean velocity and the streamwise Reynolds stress component obtained from the two techniques shows less than 3 and 12 % difference, respectively. Additionally, the turbulent energy spectra measured by SLPIV show a similar inertial subrange and trends when compared to those measured by the sonic anemometers.


  1. Adrian RJ (1991) Particle-imaging techniques for experimental fluid mechanics. Ann Rev Fluid Mech 23:261–304CrossRefGoogle Scholar
  2. Adrian RJ (2005) Twenty years of particle image velocimetry. Exp Fluids 39:159–169CrossRefGoogle Scholar
  3. Adrian RJ, Westerweel J (2011) Particle image velocimetry. Cambridge Univ. Press, CambridgeGoogle Scholar
  4. Adrian RJ, Meinhart CD, Tomkins CD (2000) Vortex organization in the outer region of the turbulent boundary layer. J Fluid Mech 422:1–54CrossRefMATHMathSciNetGoogle Scholar
  5. Aitken ML, Rhodes ME, Lundquist JK (2012) Performance of a wind-profiling lidar in the region of wind turbine rotor disks. J Atmos Ocean Technol 29:347–355CrossRefGoogle Scholar
  6. Aydin K, Singh J (2004) Cloud ice crystal classification using a 95-GHz polarimetric radar. J Atmos Ocean Technol 21:1679–1688CrossRefGoogle Scholar
  7. Barthelmie RJ, Larsen GC, Frandsen ST et al (2006) Comparison of wake model simulations with offshore wind turbine wake profiles measured by sodar. J Atmos Ocean Technol 23:888–901CrossRefGoogle Scholar
  8. Barthelmie RJ, Hansen K, Frandsen ST et al (2009) Modelling and measuring flow and wind turbine wakes in large wind farms offshore. Wind Energy 12:431–444CrossRefGoogle Scholar
  9. Bosbach J, Kühn M, Wagner C (2009) Large scale particle image velocimetry with helium filled soap bubbles. Exp Fluids 46:539–547CrossRefGoogle Scholar
  10. Brandes E, Ikeda K, Zhang G et al (2007) A statistical and physical description of hydrometeor distributions in Colorado snowstorms using a video disdrometer. J Appl Meteorol Climatol 46:634–650CrossRefGoogle Scholar
  11. Clifton A, Manes C, Rüedi J et al (2008) On shear-driven ventilation of snow. Bound Layer Meteorol 126:249–261CrossRefGoogle Scholar
  12. Crowe CT, Schwarzkopf JD, Sommerfeld M et al (1998) Multiphase flows with droplets and particles. CRC Press, Boca RatonGoogle Scholar
  13. Genthon C, Town MS, Six D et al (2010) Meteorological atmospheric boundary layer measurements and ECMWF analyses during summer at Dome C, Antarctica. J Geophys Res: Atmos 115:D05104Google Scholar
  14. Handorf D, Foken T, Kottmeier C (1999) The stable atmospheric boundary layer over an Antarctic ice sheet. Bound Layer Meteor 91:165–189CrossRefGoogle Scholar
  15. Hanesch M (1999) Fall velocity and shape of snowflakes. Dissertation, Swiss Federal Institute of Technology, ZürichGoogle Scholar
  16. Hickson P, Carlberg R, Gagne R et al (2010) Boundary layer seeing measurements in the Canadian High Arctic. Proc SPIE 77331R:1–11Google Scholar
  17. Hobbs P, Chang S, Locatelli J (1974) The dimensions and aggregation of ice crystals in natural clouds. J Geophys Res 79:2199–2206CrossRefGoogle Scholar
  18. Hong J, Katz J, Schultz MP (2011) Near-wall turbulence statistics and flow structures over three-dimensional roughness in a turbulent channel flow. J Fluid Mech 667:1–37CrossRefMATHGoogle Scholar
  19. Hosler C, Jensen D, Goldshlak L (1957) On the aggregation of ice crystals to form snow. J Atmos Sci 14:415–420Google Scholar
  20. Huang H, Dabiri D, Gharib M (1997) On errors of digital particle image velocimetry. Meas Sci Technol 8:1427CrossRefGoogle Scholar
  21. Hutchins N, Chauhan K, Marusic I et al (2012) Towards reconciling the large-scale structure of turbulent boundary layers in the atmosphere and laboratory. Bound Layer Meteorol 145:273–306CrossRefGoogle Scholar
  22. Jiménez J (2004) Turbulent flows over rough walls. Annu Rev Fluid Mech 36:173–196CrossRefGoogle Scholar
  23. Jiusto JE, Bosworth GE (1971) Fall velocity of snowflakes. J Appl Meteorol 10:1352–1354CrossRefGoogle Scholar
  24. Kaempfer T, Schneebeli M (2007) Observation of isothermal metamorphism of new snow and interpretation as a sintering process. J Geophys Res: Atmos 112:D24101CrossRefGoogle Scholar
  25. Keane RD, Adrian RJ (1993) Theory of cross-correlation analysis of PIV images. In: Nieuwstadt FTM (ed) Flow visualization and image analysis. Kluwer Academic Publishers, Dordrecht, pp 1–25CrossRefGoogle Scholar
  26. Langleben MP (1954) The terminal velocity of snowflakes. Q J R Meteorol Soc 80:174–181CrossRefGoogle Scholar
  27. Lavoie P, Avallone G, De Gregorio F et al (2007) Spatial resolution of PIV for the measurement of turbulence. Exp Fluids 43:39–51CrossRefGoogle Scholar
  28. Libbrecht KG (2005) The physics of snow crystals. Rep Prog Phys 68:855CrossRefGoogle Scholar
  29. Maahn M, Kollias P (2012) Improved micro rain radar snow measurements using Doppler spectra post-processing. Atmos Meas Tech 5:2661–2673CrossRefGoogle Scholar
  30. Manes C, Guala M, Egli L et al (2008) Statistical property of fresh snow roughness. Water Resour Res 44:W11407CrossRefGoogle Scholar
  31. Matrosov SY (1993) Possibilities of cirrus particle sizing from dual-frequency radar measurements. J Geophys Res: Atmos 98:20675–20683CrossRefGoogle Scholar
  32. Matrosov SY, Heymsfield AJ, Wang Z (2005) Dual-frequency radar ratio of nonspherical atmospheric hydrometeors. Geophys Res Lett 32:L13816CrossRefGoogle Scholar
  33. Melling A (1997) Tracer particles and seeding for particle image velocimetry. Meas Sci Technol 8:1406–1416CrossRefGoogle Scholar
  34. Metzger M, McKeon B, Holmes H (2007) The near-neutral atmospheric surface layer: turbulence and non-stationarity. Philos Trans R Soc Lond Ser A 365:859–876CrossRefMATHGoogle Scholar
  35. Mitchell DL, Heymsfield AJ (2005) Refinements in the treatment of ice particle terminal velocities, highlighting aggregates. J Atmos Sci 62:1637–1644CrossRefGoogle Scholar
  36. Morris SC, Stolpa SR, Slaboch PE et al (2007) Near-surface particle image velocimetry measurements in a transitionally rough-wall atmospheric boundary layer. J Fluid Mech 580:319–338CrossRefMATHGoogle Scholar
  37. Musial W, Butterfield S, McNiff B (2007) Improving wind turbine gearbox reliability. In: Proceedings of the European wind energy conference NREL CP-50041548Google Scholar
  38. Nakiboğlu G, Gorlé C, Horváth I et al (2009) Stack gas dispersion measurements with large scale-PIV, aspiration probes and light scattering techniques and comparison with CFD. Atmos Environ 43:3396–3406CrossRefGoogle Scholar
  39. Raffel M, Willert CE, Wereley ST et al (2007) Particle image velocimetry: a practical guide, 2nd edn. Springer, BerlinGoogle Scholar
  40. Raupach M, Antonia R, Rajagopalan S (1991) Rough-wall turbulent boundary layers. Appl Mech Rev 44:1–25CrossRefGoogle Scholar
  41. Stanislas M, Okamoto K, Kähler CJ, Westerweel J, Scarano F (2008) Main results of the third international PIV challenge. Exp Fluids 45:27–71CrossRefGoogle Scholar
  42. Van Hout R, Zhu W, Luznik L et al (2007) PIV measurements in the atmospheric boundary layer within and above a mature corn canopy. Part I: statistics and energy flux. J Atmos Sci 64:2805–2824CrossRefGoogle Scholar
  43. Vanderwende BJ, Lundquist JK (2012) The modification of wind turbine performance by statistically distinct atmospheric regimes. Environ Res Lett 7:034035CrossRefGoogle Scholar
  44. Whale J, Anderson CG, Bareiss R et al (2000) An experimental and numerical study of the vortex structure in the wake of a wind turbine. J Wind Eng Ind Aerodyn 84:1–21CrossRefGoogle Scholar
  45. White AB, Gottas DJ, Strem ET et al (2002) An automated brightband height detection algorithm for use with Doppler radar spectral moments. J Atmos Ocean Technol 19:687–697CrossRefGoogle Scholar
  46. Wilson BM, Smith BL (2013) Uncertainty on PIV mean and fluctuating velocity due to bias and random errors. Meas Sci Technol 24:035302CrossRefGoogle Scholar
  47. Zhang W, Wang Y, Lee SJ (2008) Simultaneous PIV and PTV measurements of wind and sand particle velocities. Exp Fluids 45:241–256CrossRefGoogle Scholar
  48. Zilitinkevich S, Baklanov A, Rost J et al (2002) Diagnostic and prognostic equations for the depth of the stably stratified Ekman boundary layer. Q J R Meteorol Soc 128:25–46CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • M. Toloui
    • 1
    • 2
  • S. Riley
    • 1
    • 2
  • J. Hong
    • 1
    • 2
  • K. Howard
    • 1
    • 3
  • L. P. Chamorro
    • 1
    • 4
  • M. Guala
    • 1
    • 3
  • J. Tucker
    • 1
  1. 1.Saint Anthony Falls LaboratoryMinneapolisUSA
  2. 2.Department of Mechanical EngineeringUniversity of MinnesotaMinneapolisUSA
  3. 3.Department of Civil EngineeringUniversity of MinnesotaMinneapolisUSA
  4. 4.Department of Mechanical Science and EngineeringUniversity of Illinois at Urbana-ChampaignUrbanaUSA

Personalised recommendations