New shortwave infrared albedo measurements for snow specific surface area retrieval

Montpetit, Benoît; Royer, A.; Langlois, Alexandre; Cliche, P.; Roy, Alexandre; Champollion, Nicolas; Picard, Ghislain; Dominé, Florent; Obbard, Rachel W.

doi:10.3189/2012jog11j248

Cited by 60 publications

(63 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The specific surface area (SSA), which represents the ratio of the surface area per unit of mass, is a well-defined parameter representing the geometric characteristics of a porous medium, such as snow (Dominé et al, 2001). Methods based on snow reflectance in the shortwave infrared (SWIR) can now provide rapid and reproducible field measurements of SSA (Gallet et al, 2009;Arnaud et al, 2011;Montpetit et al, 2012), which can be related theoretically to grain size. SSA can be related to the radius of a monodisperse collection of ice spheres, each having the same surface area to volume ratio, called the optical radius (R opt ):…”

mentioning

confidence: 99%

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

et al. 2013

Self Cite

View full text Add to dashboard Cite

Abstract. Snow grain size is a key parameter for modeling microwave snow emission properties and the surface energy balance because of its influence on the snow albedo, thermal conductivity and diffusivity. A model of the specific surface area (SSA) of snow was implemented in the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS) version 3.4. This offline multilayer model (CLASS-SSA) simulates the decrease of SSA based on snow age, snow temperature and the temperature gradient under dry snow conditions, while it considers the liquid water content of the snowpack for wet snow metamorphism. We compare the model with ground-based measurements from several sites (alpine, arctic and subarctic) with different types of snow. The model provides simulated SSA in good agreement with measurements with an overall point-to-point comparison RMSE of 8.0 m 2 kg −1 , and a root mean square error (RMSE) of 5.1 m 2 kg −1 for the snowpack average SSA. The model, however, is limited under wet conditions due to the single-layer nature of the CLASS model, leading to a single liquid water content value for the whole snowpack. The SSA simulations are of great interest for satellite passive microwave brightness temperature assimilations, snow mass balance retrievals and surface energy balance calculations with associated climate feedbacks.

show abstract

mentioning

confidence: 99%

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

et al. 2013

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sandells et al: Microstructure in snow models SSA per unit mass of ice (m 2 kg −1 ) can then be converted to grain diameter with D = 6/ (ρ i SSA) (Mätzler, 2002;Montpetit et al, 2012).…”

Section: Microstructure Evolutionmentioning

confidence: 99%

Microstructure representation of snow in coupled snowpack and microwave emission models

Sandells¹,

Essery

Rutter

et al. 2017

The Cryosphere

View full text Add to dashboard Cite

Abstract. This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions, and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland, were used to drive the model over the 2011-2012 and 2012-2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error −0.16 to −0.24 mm for MOSES and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS, or DMRT-ML) provided a consistently good fit across all frequencies and polarisations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarisation ranged from 0.7 to 6.9 K.Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERMempirical MEMLS model combination (2011)(2012) and 3.3 for DMRT-ML in conjunction with MOSES microstructure (2012)(2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack-microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K at 36.5 GHz H-pol and 18 K at V-pol when the Jules Investigation Model ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011-2012 season. The impact of snowpack parameterisation increases as the microwave scattering increases. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

show abstract

“…Numerous studies have quantified this relationship with calibrated measurements of snow samples (e.g. Domine and others, 2006;Matzl and Schneebeli, 2006;Gallet and others, 2009;Langlois and others, 2010;Arnaud and others, 2011;Montpetit and others, 2012) and using radiative transfer theory (e.g. Wiscombe and Warren, 1980;Kokhanovsky and Zege, 2004;Grenfell and others, 2005;Picard and others, 2009;Negi and Kokhanovsky, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Bühler and others (2015) used high-resolution airborne imagery to identify regions of fresh snow, windblown snow and wet snow in alpine terrain by calculating normalized difference indices with reflectance measurements from visible and NIR wavelengths. At smaller scales, NIR photography can be used to retrieve the vertical layering of SSA in exposed snow pit walls (Matzl and Schneebeli, 2006;Langlois and others, 2010;Montpetit and others, 2012).…”

Section: Introductionmentioning

confidence: 99%

Spectral measurements of surface hoar crystals

Horton

Jamieson

2017

J. Glaciol.

View full text Add to dashboard Cite

ABSTRACT. Surface hoar crystals are common on the surface of mountain snow covers. Once buried, layers of large plate-shaped surface hoar crystals are prone to releasing dangerous snow-slab avalanches. Since snow microstructure influences the optical properties of snow, remote sensors could potentially detect the formation of surface hoar and other snow types associated with avalanche release. The spectral reflectance of 377 snow samples was measured with a field spectrometer between 750 and 2500 nm, including 161 samples of surface hoar. Morphological snow shapes associated with critical avalanche layers (surface hoar, near-surface faceted particles and depth hoar) had lower average reflectance factors than non-critical snow shapes at infrared wavelengths. Needle-shaped surface hoar was more reflective than plate-shaped surface hoar, but there were no significant differences between different sizes of surface hoar. Normalized difference indices calculated with reflectance from two wavelength bands is presented as a potential method to classify critical snow surfaces remotely, although melt-freeze crusts near the surface complicated the classification. Accordingly, further studying on the effect of melt-freeze crusts and quantification of the bidirectional reflective properties of critical snow types is needed.

show abstract

New shortwave infrared albedo measurements for snow specific surface area retrieval

Cited by 60 publications

References 56 publications

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

Microstructure representation of snow in coupled snowpack and microwave emission models

Spectral measurements of surface hoar crystals

Contact Info

Product

Resources

About