The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankylä Finnish Lapland in 2009–2010

Anttila, Kati; Manninen, Terhikki; Karjalainen, Tuure; Lahtinen, Panu; Riihelä, Aku; Siljamo, Niilo

doi:10.1002/2014jd021597

Cited by 11 publications

(23 citation statements)

References 61 publications

(135 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Roughness increased during snowmelt as compared to immediately after snowfall, as previously observed (Anttila et al, 2014;Fassnacht et al, 2009). The cavities developed during the melting trap a fraction of the reflected light into their walls, particularly at the shortest wavelengths due to multiple reflections between the walls.…”

Section: Model Discrepanciessupporting

confidence: 72%

“…This implies that surface roughness of amplitude 10 cm (such as sastrugi) reduces the visible albedo, but much smaller irregularities can affect the near-infrared albedo. Nevertheless, only few measurements of millimetre-scale snow surface roughness have been carried out so far (Anttila et al, 2014;Frassnacht et al, 2009;Manninen, 1997), and they have not yet been applied to interpret the surface albedo.…”

Section: Model Discrepanciesmentioning

confidence: 99%

See 1 more Smart Citation

Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet

et al. 2015

View full text Add to dashboard Cite

Abstract. The albedo of a snowpack depends on the singlescattering properties of individual snow crystals, which have a variety of shapes and sizes, and are often bounded in clusters. From the point of view of optical modelling, it is essential to identify the geometric dimensions of the population of snow particles that synthesize the scattering properties of the snowpack surface. This involves challenges related to the complexity of modelling the radiative transfer in such an irregular medium, and to the difficulty of measuring microphysical snow properties. In this paper, we illustrate a method to measure the size distribution of a snow particle parameter, which roughly corresponds to the smallest snow particle dimension, from two-dimensional macro photos of snow particles taken in Antarctica at the surface layer of a melting ice sheet. We demonstrate that this snow particle metric corresponds well to the optically equivalent effective radius utilized in radiative transfer modelling, in particular when snow particles are modelled with the droxtal shape. The surface albedo modelled on the basis of the measured snow particle metric showed an excellent match with the observed albedo when there was fresh or drifted snow at the surface. In the other cases, a good match was present only for wavelengths longer than 1.4 µm. For shorter wavelengths, our modelled albedo generally overestimated the observations, in particular when surface hoar and faceted polycrystals were present at the surface and surface roughness was increased by millimetre-scale cavities generated during melting. Our results indicate that more than just one particle metric distribution is needed to characterize the snow scattering properties at all optical wavelengths, and suggest an impact of millimetre-scale surface roughness on the shortwave infrared albedo.

show abstract

Section: Model Discrepanciessupporting

confidence: 72%

Section: Model Discrepanciesmentioning

confidence: 99%

Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Cameras mounted on manned or unmanned aerial vehicles allow the mapping of HS [ 120 , 121 , 122 ] and snow BRDF [ 123 ], the latter requiring calibrated white targets at the surface as a reference. Snow surface roughness is obtained from the processing of photos with reference scales, such as a graduated blackboard vertically inserted into the snow surface [ 124 ] and graduated survey strings aligned over the glacier surface [ 125 ]. Macro-photography is applied to measure snow grain shape and size metrics (as the optical equivalent snow grain size) of snow particles extracted from the snow pit wall (e.g., [ 30 , 31 ]) and of drifting snow particles attached to sticking slides (e.g., [ 126 ]).…”

Section: Table A1mentioning

confidence: 99%

European In-Situ Snow Measurements: Practices and Purposes

Pirazzini

Leppänen

Picard

et al. 2018

Sensors

View full text Add to dashboard Cite

In-situ snow measurements conducted by European institutions for operational, research, and energy business applications were surveyed in the framework of the European Cooperation in Science and Technology (COST) Action ES1404, called “A European network for a harmonised monitoring of snow for the benefit of climate change scenarios, hydrology, and numerical weather prediction”. Here we present the results of this survey, which was answered by 125 participants from 99 operational and research institutions, belonging to 38 European countries. The typologies of environments where the snow measurements are performed range from mountain to low elevated plains, including forests, bogs, tundra, urban areas, glaciers, lake ice, and sea ice. Of the respondents, 93% measure snow macrophysical parameters, such as snow presence, snow depth (HS), snow water equivalent (SWE), and snow density. These describe the bulk characteristics of the whole snowpack or of a snow layer, and they are the primary snow properties that are needed for most operational applications (such as hydrological monitoring, avalanche forecast, and weather forecast). In most cases, these measurements are done with manual methods, although for snow presence, HS, and SWE, automatized methods are also applied by some respondents. Parameters characterizing precipitating and suspended snow (such as the height of new snow, precipitation intensity, flux of drifting/blowing snow, and particle size distribution), some of which are crucial for the operational services, are measured by 74% of the respondents. Parameters characterizing the snow microstructural properties (such as the snow grain size and shape, and specific surface area), the snow electromagnetic properties (such as albedo, brightness temperature, and backscatter), and the snow composition (such as impurities and isotopes) are measured by 41%, 26%, and 13% of the respondents, respectively, mostly for research applications. The results of this survey are discussed from the perspective of the need of enhancing the efficiency and coverage of the in-situ observational network applying automatic and cheap measurement methods. Moreover, recommendations for the enhancement and harmonization of the observational network and measurement practices are provided.

show abstract

“…Citation: Anttila, K., T. Manninen, T. Karjalainen, P. Lahtinen, A. Riihelä, and N. Siljamo (2014), The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankylä Finnish Lapland in 2009-2010, J. Geophys. Res.…”

Section: Acknowledgementsmentioning

confidence: 99%

“…The bidirectional reflectance distribution function (BRDF) of snow is also significantly affected by the roughness of the snow surface [Peltoniemi et al, 2010b;Warren et al, 1998]. In radiative models the albedo of a snow layer is reduced when surface roughness is taken into account [Zhuravleva and Kokhanovsky, 2011]. Understanding how roughness affects the signal received by satellite instruments and knowing the connections between surface roughness and the geophysical properties of the snow pack (for example crystal size and shape, density, specific surface area, and state of crystal metamorphosis) could support the use of surface roughness information in interpreting the state of snow cover from remote sensing data, e.g., the level of melting.…”

Section: Introductionmentioning

confidence: 99%

Empirical approach to satellite snow detection

Siljamo¹

Self Cite

View full text Add to dashboard Cite

Snow cover plays a significant role in the weather and climate system, ecosystems and many human activities, such as traffic. Weather station snow observations (snow depth and state of the ground) do not provide highresolution continental or global snow coverage data. The satellite observations complement in situ observations from weather stations. Geostationary weather satellites provide observations at high temporal resolution, but the spatial resolution is low, especially in polar regions. Polarorbiting weather satellites provide better spatial resolution in polar regions with limited temporal resolution. The best detection resolution is provided by optical and infra-red radiometers onboard weather satellites. Snow cover in itself is highly variable. Also, the variability of the surface properties (such as vegetation, water bodies, topography) and changing light conditions make satellite snow detection challenging. Much of this variability is in subpixel scales, and this uncertainty creates additional challenges for the development of snow detection methods. Thus, an empirical approach may be the most practical option when developing algorithms for automatic snow detection. In this work, which is a part of the EUMETSAT-funded H SAF project, two new empirically developed snow extent products for the EUMETSAT weather satellites are presented. The geostationary MSG/SEVIRI H32 snow product has been in operational production since 2008. The polar product Metop/AVHRR H32 is available since 2015. In addition, validation results based on weather station snow observations between 2015 and 2019 are presented. The results show that both products achieve the requirements set by the H SAF.

show abstract

The temporal and spatial variability in submeter scale surface roughness of seasonal snow in Sodankylä Finnish Lapland in 2009–2010

Cited by 11 publications

References 61 publications

Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet

Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet

European In-Situ Snow Measurements: Practices and Purposes

Empirical approach to satellite snow detection

Contact Info

Product

Resources

About