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2022 ◽  
Vol 14 (2) ◽  
pp. 959
Author(s):  
Yanjiao Zheng ◽  
Lijuan Zhang ◽  
Wenliang Li ◽  
Fan Zhang ◽  
Xinyue Zhong

The amount of black carbon (BC) on snow surface can significantly reduce snow surface albedo in the visible-light range and change the surface radiative forcing effect. Therefore, it is key to study regional and global climate changes to understand the BC concentration on snow. In this study, we simulated the BC concentration on the surface snow of northeast China using an asymptotic radiative transfer model. From 2001 to 2016, the BC concentration showed no significant increase, with an average increase of 82.104 ng/g compared with that in the early 21st century. The concentration of BC in December was the largest (1344.588 ng/g) and decreased in January and February (1248.619 ng/g and 983.635 ng/g, respectively). The high black carbon content centers were concentrated in the eastern and central regions with dense populations and concentrated industries, with a concentration above 1200 ng/g, while the BC concentration in the southwest region with less human activities was the lowest (below 850 ng/g), which indicates that human activities played an important role in snow BC pollution. Notably, Heilongjiang province has the highest concentration, which may be related to its atmospheric stability in winter. These findings suggest that the BC pollution in northeast China has been aggravated from 2001 to 2016. It is estimated that the snow surface albedo will decrease by 16.448% due to the BC pollution of snow in northeast China. The problem of radiative forcing caused by black carbon to snow reflectivity cannot be ignored.


2021 ◽  
Author(s):  
Taylor Hodgdon ◽  
Anthony Fuentes ◽  
Brian Quinn ◽  
Bruce Elder ◽  
Sally Shoop

With changing conditions in northern climates it is crucial for the United States to have assured mobility in these high-latitude regions. Winter terrain conditions adversely affect vehicle mobility and, as such, they must be accurately characterized to ensure mission success. Previous studies have attempted to remotely characterize snow properties using varied sensors. However, these studies have primarily used satellite-based products that provide coarse spatial and temporal resolution, which is unsuitable for autonomous mobility. Our work employs the use of an Unmanned Aeriel Vehicle (UAV) mounted hyperspectral camera in tandem with machine learning frameworks to predict snow surface properties at finer scales. Several machine learning models were trained using hyperspectral imagery in tandem with in-situ snow measurements. The results indicate that random forest and k-nearest neighbors models had the lowest Mean Absolute Error for all surface snow properties. A pearson correlation matrix showed that density, grain size, and moisture content all had a significant positive correlation to one another. Mechanically, density and grain size had a slightly positive correlation to compressive strength, while moisture had a much weaker negative correlation. This work provides preliminary insight into the efficacy of using hyperspectral imagery for characterizing snow properties for autonomous vehicle mobility.


2021 ◽  
Vol 7 ◽  
Author(s):  
Hasler M ◽  
Jud W ◽  
Nachbauer W

For many years, a frictional meltwater film has been assumed to be the reason for the low friction between skis and snow, but experimental studies have been inconclusive. Therefore, the aim of our study was to find indications or evidence for the presence of frictional meltwater. The friction between snow at −4°C and an XC ski as well as a flat ski was measured on a large-scale linear snow tribometer at realistic skiing speeds from 5 to 25 m/s. We used an infrared camera to analyze the snow temperature behind the skis. From the maximum snow surface temperature, we estimated the temperature at the spots where ski and snow contacted. Assuming that the contact spot temperature does not notably exceed 0°C, we calculated the relative contact area between ski and snow. Maximum snow surface temperatures were very close to 0°C. Given that not the entire snow surface is in contact with the ski, this finding is a strong indication for snow melting. Heat flow considerations led to the conclusion that there must be energy dissipation beyond the heat flow into ski and snow. The most obvious mechanism for the additional energy dissipation is snow melting. Presuming that the contact spot temperatures are at most slightly above 0°C, we calculated relative contact areas of 21–98%. Previous research has reported much lower values; however, most studies were conducted under conditions that are not realistic for skiing.


2021 ◽  
Vol 15 (7) ◽  
pp. 3293-3315
Author(s):  
Jürg Schweizer ◽  
Christoph Mitterer ◽  
Benjamin Reuter ◽  
Frank Techel

Abstract. Avalanche danger levels are described in qualitative terms that mostly are not amenable to measurements or observations. However, estimating and improving forecast consistency and accuracy require descriptors that can be observed or measured. Therefore, we aim to characterize the avalanche danger levels based on expert field observations of snow instability. We analyzed 589 field observations by experienced researchers and forecasters recorded mostly in the region of Davos (Switzerland) during 18 winter seasons (2001–2002 to 2018–2019). The data include a snow profile with a stability test (rutschblock, RB) and observations on snow surface quality, drifting snow, signs of instability and avalanche activity. In addition, observers provided their estimate of the local avalanche danger level. A snow stability class (very poor, poor, fair, good, very good) was assigned to each profile based on RB score, RB release type and snowpack characteristics. First, we describe some of the key snowpack characteristics of the data set. In most cases, the failure layer included persistent grain types even after a recent snowfall. We then related snow instability data to the local avalanche danger level. For the danger levels 1–Low to 4–High, we derived typical stability distributions. The proportions of profiles rated poor and very poor clearly increased with increasing danger level. For our data set, the proportions were 5 %, 13 %, 49 % and 63 % for the danger levels 1–Low to 4–High, respectively. Furthermore, we related the local avalanche danger level to the occurrence of signs of instability such as whumpfs, shooting cracks and recent avalanches. The absence of signs of instability was most closely related to 1–Low and the presence of them to 3–Considerable. Adding the snow stability class and the 3 d sum of new snow depth improved the discrimination between the lower three danger levels. Still, 2–Moderate was not well described. Nevertheless, we propose some typical situations that approximately characterize each of the danger levels. Obviously, there is no single easily observable set of parameters that would allow us to fully characterize the avalanche danger levels. One reason for this shortcoming is the fact that the snow instability data we analyzed usually lack information on spatial frequency, which is needed to reliably assess the danger level.


Author(s):  
Barella Riccardo ◽  
Carlo Marin ◽  
Callegari Mattia ◽  
Marco Gianinetto ◽  
Thomas Moranduzzo ◽  
...  

Author(s):  
Jing Guo ◽  
Ziti Jiao ◽  
Xiaoning Zhang ◽  
Lei Cui ◽  
Siyang Yin ◽  
...  

2021 ◽  
Author(s):  
Tomás R Bolaño-Ortiz ◽  
Maria Ruggeri ◽  
Lucas Luciano Berná Peña ◽  
S. Enrique Puliafito ◽  
Francisco Cereceda-Balic

2021 ◽  
Vol 13 (6) ◽  
pp. 1164
Author(s):  
Elisa Pinat ◽  
Pascale Defraigne ◽  
Nicolas Bergeot ◽  
Jean-Marie Chevalier ◽  
Bruno Bertrand

Acquiring reliable estimates of the Antarctic Ice Sheet surface mass balance is essential for trustworthy predictions of its evolution and future contribution to sea level rise. Snow height variations, i.e., the net change of the surface elevation resulting from a combination of surface processes such as snowfall, ablation, and wind redistribution, can provide a unique tool to constrain the uncertainty on mass budget estimations. In this study, GNSS Interferometric Reflectometry (GNSS-IR) is exploited to assess the long-term variations of snow accumulation and ablation processes. Eight antennas belonging to the Polar Earth Observing Network (POLENET) network are considered, together with the ROB1 antenna, deployed in the east part of Antarctica by the Royal Observatory of Belgium. For ROB1, which is located on an ice rise, we highlight an annual variation of snow accumulation in April–May (~30–50 cm) and ablation during spring/summer period. A snow surface elevation velocity of +0.08 ± 0.01 ma−1 is observed in the 2013–2016 period, statistically rejecting the “no trend” null hypothesis. As the POLENET stations are all located on moving glaciers, their associated downhill motion must be corrected for using an elevation model. This induces an increased uncertainty on the snow surface elevation change determined from GNSS-IR. Among the eight stations analyzed, only three of them show a long-term snow height variation larger than the uncertainties. One is located on the Flask Galcier in the Antarctic Peninsula, with a decrease of more than 4 m between 2012 and 2014, with an uncertainty of 2.5 m. The second one is located on the Lower Thwaites Glacier where we observe, between 2010 and 2020, a snow surface drop of 10 m, with a conservative uncertainty of 1 m. The third station, located on the West Antarctic Ice Sheet (WAIS) divide, shows on the opposite an upward motion from 2005 to 2019, of 1.2 m with an uncertainty of 0.4 m. The snow surface change of the other POLENET stations analyzed is smaller than the uncertainty associated with the glacier slope.


2021 ◽  
Author(s):  
Nick Rutter ◽  
Richard Essery

<p>Above canopy air temperatures, as simulated prognostic variables in earth system models or as driving data in snow-physics models, are used as a basis to calculate energy transfers through forest canopies and down to the snow surface. Consequently, simulations of absorption of solar radiation, emission of longwave radiation and coupling between canopy and air temperatures become critical. Parts of the forest canopy, especially the shaded downward-facing elements, are often in equilibrium with sub-canopy air temperatures.</p><p>Measurements of sub-canopy incoming longwave radiation, air temperatures, and forest canopy structure were made in a snow-covered boreal forest, March through April 2012 in Sodankylä, Finland. Accurate simulations of longwave radiation to the snow surface were enabled by using measured sub-canopy air temperatures as a proxy for downward-facing forest canopy temperatures. However, there was a notable decoupling of measured above and below forest canopy air temperatures in stable conditions (air temperatures warmer above the canopy than below), which was enhanced during night-time. Hence, here we present results of an experiment using a multi-physics snow model including a forest canopy (FSM2.1.1) to investigate the impact of above and below canopy air temperature decoupling on simulations of sub-canopy longwave radiation. Simulations compare the use of 1- and 2-layer canopy models, and application of Monin–Obukhov similarity theory across a wide range of forest densities.</p>


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