Influence of light-absorbing  particles on snow  spectral irradiance profiles

Tuzet, François; Dumont, Marie; Arnaud, Laurent; Voisin, Didier; Lamare, Maxim; Larue, Fanny; Revuelto, Jesús; Picard, Ghislain

doi:10.5194/tc-13-2169-2019

Cited by 37 publications

(48 citation statements)

References 60 publications

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“…ber of LAPs led to a high decrease in the albedo in the visible domain (Tuzet et al, 2019(Tuzet et al, , 2020. This sensitivity is well described in Dumont et al (2017).…”

Section: Snow Surface Propertiesmentioning

confidence: 69%

Snow albedo sensitivity to macroscopic surface roughness using a new ray-tracing model

et al. 2020

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Abstract. Most models simulating snow albedo assume a flat and smooth surface, neglecting surface roughness. However, the presence of macroscopic roughness leads to a systematic decrease in albedo due to two effects: (1) photons are trapped in concavities (multiple reflection effect) and (2) when the sun is low, the roughness sides facing the sun experience an overall decrease in the local incidence angle relative to a smooth surface, promoting higher absorption, whilst the other sides have weak contributions because of the increased incidence angle or because they are shadowed (called the effective-angle effect here). This paper aims to quantify the impact of surface roughness on albedo and to assess the respective role of these two effects, with (1) observations over varying amounts of surface roughness and (2) simulations using the new rough surface ray-tracing (RSRT) model, based on a Monte Carlo method for photon transport calculation. The observations include spectral albedo (400–1050 nm) over manually created roughness surfaces with multiple geometrical characteristics. Measurements highlight that even a low fraction of surface roughness features (7 % of the surface) causes an albedo decrease of 0.02 at 1000 nm when the solar zenith angle (θs) is larger than 50∘. For higher fractions (13 %, 27 % and 63 %), and when the roughness orientation is perpendicular to the sun, the decrease is of 0.03–0.04 at 700 nm and of 0.06–0.10 at 1000 nm. The impact is 20 % lower when roughness orientation is parallel to the sun. The observations are subsequently compared to RSRT simulations. Accounting for surface roughness improves the model observation agreement by a factor of 2 at 700 and 1000 nm (errors of 0.03 and 0.04, respectively) compared to simulations considering a flat smooth surface. The model is used to explore the albedo sensitivity to surface roughness with varying snow properties and illumination conditions. Both multiple reflections and the effective-angle effect have a greater impact with low specific surface area (SSA; <10 m2 kg−1). The effective-angle effect also increases rapidly with θs at large θs. This latter effect is larger when the overall slope of the surface is facing away from the sun and has a roughness orientation perpendicular to the sun. For a snowpack where artificial surface roughness features were created, we showed that a broadband albedo decrease of 0.05 may cause an increase in the net shortwave radiation of 80 % (from 15 to 27 W m−2). This paper highlights the necessity of considering surface roughness in the estimation of the surface energy budget and opens the way for considering natural rough surfaces in snow modelling.

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“…ber of LAPs led to a high decrease in the albedo in the visible domain (Tuzet et al, 2019(Tuzet et al, , 2020. This sensitivity is well described in Dumont et al (2017).…”

Section: Snow Surface Propertiesmentioning

confidence: 69%

Snow albedo sensitivity to macroscopic surface roughness using a new ray-tracing model

et al. 2020

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“…This specific massif was chosen because it encompasses the Col du Lautaret (2058 m a.s.l. ), where field campaigns have been carried out since winter 2016-2017 close to an automatic weather station (Tuzet et al, 2019).…”

Section: Case Studymentioning

confidence: 99%

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

Cluzet

Revuelto

Lafaysse

et al. 2020

Cold Regions Science and Technology

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Uncertainties of snowpack models and of their meteorological forcings limit their use by avalanche hazard forecasters, or for glaciological and hydrological studies. The spatialized simulations currently available for avalanche hazard forecasting are only assimilating sparse meteorological observations. As suggested by recent studies, their forecasting skills could be significantly improved by assimilating satellite data such as snow reflectances from satellites in the visible and the near-infrared spectra. Indeed, these data can help constrain the microstructural properties of surface snow and light absorbing impurities content, which in turn affect the surface energy and mass budgets. This paper investigates the prerequisites of satellite data assimilation into a detailed snowpack model. An ensemble version ofMétéo-France operational snowpack forecasting system (named S2M) was built for this study. This operational system runs on topographic classes instead of grid points, so-called 'semi-distributed' approach. Each class corresponds to one of the 23 mountain massifs of the French Alps (about 1000km 2 each), an altitudinal range (by step of 300m) and aspect (by step of 45 o ). We assess the feasability of satellite data assimilation in such a semi-distributed geometry. Ensemble simulations are compared with satellite observations from MODIS and Sentinel-2, and with in-situ reflectance observations. The study focuses on the 2013-2014 and 2016-2017 winters in the Grandes-Rousses massif. Substantial Pearson R 2 correlations (0.75-0.90) of MODIS observations with simulations are found over the domain. This suggests that assimilating it could have an impact on the spatialized snowpack forecasting system. However, observations contain significant biases (0.1-0.2 in reflectance) which prevent their direct assimilation. MODIS spectral band ratios seem to be much less biased. This may open the way to an operational assimilation of MODIS reflectances into the Météo-France snowpack modelling system. Highlights -Ensemble simulations of the snowpack are compared with satellite reflectances -Spatial aggregation into the semi-distributed geometry filters the observation noises -Satellite reflectances carry useful information worth to assimilate -MODIS reflectances can not be directly assimilated because they are biased -Ratios of MODIS reflectances show no evidence of bias and could be assimilated

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“…in the eastern part of the pass (Figure 1). This site is completely covered by low alpine grassland, and because of its accessibility and high elevation has been involved in other snow studies (Charrois et al, 2016; Tuzet et al, 2019). The experimental area was set up in October 2017 for observations of snowpack dynamics using TLS data acquisition during the 2017/18 snow season.…”

Section: Study Areasmentioning

confidence: 99%

Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas

Revuelto

Billecocq

Tuzet

et al. 2020

Hydrological Processes

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The small scale distribution of the snowpack in mountain areas is highly heterogeneous, and is mainly controlled by the interactions between the atmosphere and local topography. However, the influence of different terrain features in controlling variations in the snow distribution depends on the characteristics of the study area. As this leads to uncertainties in high spatial resolution snowpack simulations, a deeper understanding of the role of terrain features on the small scale distribution of snow depth is required. This study applied random forest algorithms to investigate the temporal evolution of snow depth in complex alpine terrain using as predictors various topographical variables and in situ snow depth observations at a single location. The high spatial resolution (1 m x 1 m) snow depth distribution database used in training and evaluating the random forests was derived from terrestrial laser scanner (TLS) devices at three study sites, in the French Alps (2 sites) and the Spanish Pyrenees (1 site). The results show the major importance of two topographic variables, the topographic position index and the maximum upwind slope parameter. For these variables the search distances and directions depended on the characteristics of each site and the TLS acquisition date, but are consistent across sites and are tightly related to main wind directions. The weight of the different topographic variables on explaining snow distribution evolves while major snow accumulation events still take place and minor changes are observed after reaching the annual snow accumulation peak. Random forests have demonstrated good performance when predicting snow distribution for the sites included in the training set with R 2 values ranging from 0.82 to 0.94 and mean absolute errors always below 0.4 m. Oppositely, this algorithm failed when used to predict snow distribution for sites not included in the training set, with mean absolute errors above 0.8 m.

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Influence of light-absorbing particles on snow spectral irradiance profiles

Cited by 37 publications

References 60 publications

Snow albedo sensitivity to macroscopic surface roughness using a new ray-tracing model

Snow albedo sensitivity to macroscopic surface roughness using a new ray-tracing model

Towards the assimilation of satellite reflectance into semi-distributed ensemble snowpack simulations

Random forests as a tool to understand the snow depth distribution and its evolution in mountain areas

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