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Snow grain size measurements in Dronning Maud Land, Antarctica

Snow grain size measurements in Dronning Maud Land, Antarctica. Roberta Pirazzini, Petri Räisänen, Timo Vihma, Milla Johansson and Esa-Matti Tastula Finnish Meteorological Institute, Helsinki, Finland.

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Snow grain size measurements in Dronning Maud Land, Antarctica

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  1. Snow grain size measurements in Dronning Maud Land, Antarctica Roberta Pirazzini, Petri Räisänen, Timo Vihma, Milla Johansson and Esa-Matti Tastula Finnish Meteorological Institute, Helsinki, Finland

  2. Overall objective: verify/improve physically based snow albedo parameterizations on the basis of detailed observations • Specific objectives: • - To determine which grain metric best correlates to the snowpack reflective properties. • - To study the link between snow micro- and macro-physical properties and the surface albedo. • ABOA Data from the summer campaign 2009-2010 near Aboa, Antarctica: - Global and diffuse broadband shortwave radiation, - Broadband and spectral albedo, - Vertical profile of snow density and temperature (twice a day), - Vertical profile of snow grain size distribution obtained from macro photography (twice a day), - Other meteorological observations (wind, temperature, humidity, clouds).

  3. Problem in the sampling of snow crystals • From manual sampling, it is difficult and laborious to extract a representative distribution of the snow crystals from the snowpack.

  4. Cave for snow grain photos Snow grain photos with macro objective were taken twice a day using snow samples from the same area, from the depths of 0, 5, 10, and 20 cm

  5. Data processing (with Matlab Image processing toolbox) • Image enhancement Original resolution: 2848x4272 pixels for each of the three color planes. • Image segmentation (to convert the image into a binary mask) Edge detection function . Final image resolution Between 0.008 mm and 0.014 mm To perform the most truthful and “objective” grain detection, for each imageit is necessary to manually set and optimize each phase of the image processing!!

  6. 10:45 UTC: 44 grains 20:00 UTC: 368 grains • Example from 29.12.2009 :

  7. Problems in the visual quantification of snow grain size • From image processing, it is not clear what is the most relevant dimension that can be directly related to the grain scattering properties (or to the optically equivalent grain radius): • Different studies (some of which hypotize a single, uniform snowpack layer) suggest that different metrics have the best agreement with optically equivalent grain radius: • - Half of the snow dendrite width (Aoki et al, 2000, 2007) • - Radius of the circle with equiv. projected area (Tedesco&Kokhanowsky, 2007) • - Radius of the sphere with same volume-to-surface-ratio (Warren& Wiscombe, 1981; Grenfell&Warren, 1999) or specific surface area (Dominé et al.,2006) II. The shape of grains affect the relationship between measured and optically equivalent grain radius (Sergent et al., 1998) • III.A single measured parameter might be not enough to characterize the grain geometry, and the standard deviation of the grain size distribution is equally or, in some cases, even more important than the mean or mode of the distribution (Shi et al., 1993; Nolin et al., 1993; Lagagneux&Dominé, 2005; Flanner&Zendler, 2006).

  8. Calculated snow grain metrics Radii calculated from the image processing: Shortest skeleton branch Longest skeleton branch Equivalent radius MAJOR AXIS of the ellipse with same second central moments as the projected area. MINOR AXIS of the ellipse with same second central moments as the projected area. Radii calculated from the above quantities: R equiv. Volume-to-Area-ratio as an HEXAGONAL COLUMN (IV and V) R equiv. Volume-to-Area-ratio as a CYLINDER (IV and V) VIII. R equiv. Volume-to-Area-ratio as a PROLATE ELLIPSOID (IV and V) IX. R equiv. Volume-to-Area-ratio as a TRI-AXIAL ELLIPSOID (I, IV and V) R equiv. Volume-to-Area-ratio as a ELLIPTIC PLATE (IV and V) R equiv. Volume-to-Area-ratio as a ELLIPTIC PLATE (I, IV and V)

  9. 10:45 UTC 20:20 UTC Example from 29.12.2009

  10. Example from 29.12.2009: 10:45 UTC: 77 grains 20:00 UTC: 563 grains

  11. Time series of daily mean broadband albedo during the summer campaign 2009-2010

  12. Modelled grains I. Spherical particle models have been found to fit the measured spectral albedo (Grenfell and Warren, 1999; Aoki et al., 2000) II. Fractal particle model has been proposed by Kokhanovsky and Zege (2004): it gives ~40% larger values of the optical grain radius compared to the spherical model • Spheres Single scattering properties from: Mie solution (using observed grain sizes and density profiles) Henyey-Greenstein phase function • Droxtals • Single scattering properties from: • Yang et al (J. Atmos. Sci., 2013) Ice refractive index from: Warren and Brandt (JGR 2008)

  13. Disort calculations (Stamnes et al., 2000) with 32 streams: results for spheres

  14. Results for spheres and droxtals:

  15. Summary and highlights • Snow grain sampling for macro-photography requires a lot of care and special environmental conditions (shelter from wind and Sun, below freezing temperatures) • An objective, repeatable snow grain detection from image processing, which preserves the 2D shape and size of the grains population, requires careful manual setting in each step of the image processing. • The grain size obtained with 5 independent metrics and 6 derived dimensions can be divided in three size groups: largest size: long axis of the ellipse with equivalent area; medium size: dimensions based on the axes of the equivalent ellipse, longest skeleton branch, and equivalent radius; shortest size: shortest skeleton branch and dimensions based on it.

  16. The snow grain at the surface showed a significant noon-evening variability at the surface during the analyzed week, although surface temperature did not reach the melting point. • The increase in snow grain size, expected from the observed decrease in snow albedo, was indeed visible from the noon values. • Disort calculations of albedo based on the calculated metrics and assuming spherical and droxtal shapes reached the best agreement with NIR albedo observations in the case of the shortest skeleton branch (and the sizes derived from it) .

  17. Do do next: • We extend the analysis to a second week, when the snow surface is melting and the surface density is decoupled from the density at deeper layers. • We identify the quantities that best represent the observed snow grain size distribution when modeling the surface albedo (mean/mode + std, ratio between mean volume and mean surface area, ecc). • We apply the SNICAR code (Flanner&Zender, 2006) to simulate the diurnal and seasonal albedo evolution. Further applications: • Validation of remote sensing retrieval of snow grain size and albedo (in collaboration with Dr. Kokhanovsky, University of Bremen, and Dr. Dumont, Meteo France - CNRS) • Snow modeling with a detailed and operational snow scheme (CROCUS code, in collaboration with Dr. Morin, Meteo France - CNRS)

  18. Thank you!

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