Snow particles extracted from X-ray computed microtomography imagery and their single-scattering properties

Ishimoto, Hiroshi; Adachi, Sadao; Yamaguchi, Satoru; Tanikawa, Tomonori; Aoki, Teruo; Masuda, Kazuhiko

doi:10.1016/j.jqsrt.2018.01.021

Cited by 29 publications

(31 citation statements)

References 56 publications

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“…where r col VA and r agg VA are the snow grain radii for the Voronoi column and aggregate model, respectively. The mixing ratio f agg varies linearly as a function of logarithm of r VA between a and b, similar in concept to those given by Ishimoto et al (2018) and Saito et al (2019): Note.…”

Section: 1029/2019jd031858supporting

confidence: 58%

“…Consequently, the feature of this model is to provide a more realistic shape model and a smooth phase function without any microscopic crystal surface roughness like a regulation parameter. The spatial Poisson-Voronoi tessellation was successfully applied to the shape modeling of mineral dust particles, irregular ice particles, and snow particles for light-scattering calculations (Ishimoto et al, 2010(Ishimoto et al, , 2018(Ishimoto et al, , 2019. The Voronoi column model consisted of number of Voronoi cells stretched to 6:1 aspect ratio to model the fresh snow grains.…”

Section: Snow Grain Shape Modelmentioning

confidence: 99%

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Effects of Snow Grain Shape and Mixing State of Snow Impurity on Retrieval of Snow Physical Parameters From Ground‐Based Optical Instrument

et al. 2020

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We proposed new snow grain model and snow impurity mixture models for the purpose of accurate retrievals of snow grain size and concentration of light‐absorbing particles (LAP) in snow from the optical remote sensing data. Two kinds of ice crystal models, irregularly shaped Voronoi columns, and Voronoi aggregates were employed. LAP can be captured by the snow through two processes: dry and wet deposition. Two different snow impurity mixture models were proposed. One is an external mixture model. We employed a coated sphere model in which soot were hydrophilic particles to represent a hygroscopic property of aerosol and assumed the hydrophilic soot particles to be externally mixed with snow particles. The other is an internal mixture model. We employed a dynamic effective medium approximation method in which soot particles were randomly located within snow particle with any size distribution and number concentration. Validation of these models is conducted using a ground‐based spectral radiometer system with in situ measurement data. For snow grain size retrievals, a Voronoi mixture model seamlessly representing a geometrical shape and an optical properties of various snow types can provide accurate retrievals. For the retrieval of LAP concentration in snow, employing different mixing state models depending on season and measurement site gives accurate results. We also discussed the uncertainty of retrieved snow parameters on the surface slope involved in the retrieval accuracy. These models are expected to be useful for advanced airborne/satellite remote sensing and climate studies via radiative transfer modeling in the atmosphere‐snow system.

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Section: 1029/2019jd031858supporting

confidence: 58%

Section: Snow Grain Shape Modelmentioning

confidence: 99%

Effects of Snow Grain Shape and Mixing State of Snow Impurity on Retrieval of Snow Physical Parameters From Ground‐Based Optical Instrument

et al. 2020

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“…The SPMP, affected strongly by temperature, is a dominant factor for the variabilities of SGS, SPS, and SSA (LaChapelle,1969;Sokratov and Kazakov, 2012;Saito et al, 2019). Snow particle size increases dramatically and the ice crystal particles are compacted in the strong SPMP (Aoki et al, 1999;Nakamura et al 2001;Ishimoto et al 2018). Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Saito et al (2019) pointed out that SPSs of fresh snow in the polar regions are typically a mixture of irregular shapes such as column and platelike shape. Ishimoto et al (2018) found that aged snow can have an aggregate structure. The optical properties of small ice crystal particles in aged snow may be well-characterized by granular/roundish shapes, while SPS tends to be irregular or severely roughened shapes during the SPMP (Ishimoto et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

“…Ishimoto et al (2018) found that aged snow can have an aggregate structure. The optical properties of small ice crystal particles in aged snow may be well-characterized by granular/roundish shapes, while SPS tends to be irregular or severely roughened shapes during the SPMP (Ishimoto et al, 2018). Pirazzini et al (2015) investigated the impact of ice crystal sphericity on the estimation of snow albedo and found droxtal is a reasonable assumption to take ice particle non-sphericity into account.…”

Section: Resultsmentioning

confidence: 99%

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The retreival of snow properties from SLSTR/Sentinel-3 – part 2: results and validation

Mei¹,

Розанов²,

Jäkel³

et al. 2020

Preprint

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Abstract. To evaluate the performance of eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm, presented in part 1 of the companion paper, this manuscript applies the XBAER algorithm on the Sea and Land Surface Temperature Radiometer (SLSTR) and Ocean and Land Colour Instrument (OLCI) instruments onboard Sentinel-3. Snow properties: Snow Grain Size (SGS), Snow Particle Shape (SPS), and Specific Surface Area (SSA) are derived under cloud-free conditions. XBAER derived snow properties are compared to other existing satellite products and validated by ground-based/aircraft measurements. Cloud screening is performed by standard XBAER algorithm synergistically using OLCI and SLSTR instruments both onboard Sentinel-3. The atmospheric correction is performed on SLSTR for cloud-free scenarios using Modern-Era Retrospective Analysis for Research and Applications (MERRA) Aerosol Optical Thickness (AOT) and aerosol typing strategy according to the standard XBAER algorithm. The optimal SGS and SPS are estimated iteratively utilizing a Look-Up-Table (LUT) approach, minimizing the difference between SLSTR-observed and SCIATRAN simulated surface directional reflectances at 0.55 and 1.6 μm. The SSA is derived for a given SGS and SPS pair. XBAER derived SGS, SPS and SSA have been validated using in-situ measurements from the recent campaign SnowEx17 during February 2017. The comparison of the retrieved SGS with the in-situ data shows a relative difference between XBAER-derived SGS and SnowEx17 measured SGS of less than 4 %. The difference between XBAER-derived SSA and SnowEx17 measured SSA is 2.7 m2/kg. XBAER-derived SPS can be reasonable-explained by the SnowEx17 observed snow particle shapes. The comparison with aircraft measurements, during the Polar Airborne Measurements and Arctic Regional Climate Model Simulation Project (PAMARCMiP) campaign held in March 2018, also shows good agreement (with R = 0.82 and R = 0.81 for SGS and SSA, respectively). XBAER-derived SGS and SSA reveal the variability of the aircraft track of PAMARCMiP campaign. The comparison between XBAER-derived SGS results and MODIS Snow-Covered Area and Grain size (MODSCAG) product over Greenland shows similar spatial distributions. The geographic distribution of XBAER-derived SPS over Greenland and the whole Arctic can be reasonable-explained by campaign-based and laboratory investigations, indicating reasonable retrieval accuracy of the retrieved SPS. The geographic variabilities of XBAER-derived SGS and SSA over both Greenland and Arctic-wide agree with the snow metamorphism process.

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Solar Heating of the Cryosphere: Snow and Ice Sheets

Dombrovsky

Kokhanovsky²

2021

Springer Series in Light Scattering

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Snow particles extracted from X-ray computed microtomography imagery and their single-scattering properties

Cited by 29 publications

References 56 publications

Effects of Snow Grain Shape and Mixing State of Snow Impurity on Retrieval of Snow Physical Parameters From Ground‐Based Optical Instrument

Effects of Snow Grain Shape and Mixing State of Snow Impurity on Retrieval of Snow Physical Parameters From Ground‐Based Optical Instrument

The retreival of snow properties from SLSTR/Sentinel-3 – part 2: results and validation

Solar Heating of the Cryosphere: Snow and Ice Sheets

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