An Improved Backscattering Theoretical Model for Snow Area

Remote sensing has been studied for a long time to monitor the earth terrain. Remote sensing technology has been used globally in many different fields and one of the most popular area of study that uses remote sensing technology is snow monitoring. In previous researches, remote sensing has been modelled on snow area to study the scattering mechanisms of various scattering processes. In this paper, surface volume second order term that was dropped in previous study is derived, included and studied to observe the improvement in the surface volume backscattering coefficient. This new model is applied on snow layer above ground and the snow layer is modelled as a volume of ice particles as the Mie scatterers that are closely packed and bounded by irregular boundaries. Various parameters are used to investigate the improvement of adding the new term. Results show improvement in cross-polarized return, for all the range of parameters studied. Comparison is made with the field measurement result from U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) in 1990. Close agreement is shown between developed model and data field backscattering coefficient result.

SNICAR-ADv4: A physically based radiative transfer model to represent the spectral albedo of glacier ice

Accurate modeling of cryospheric surface albedo is essential for our understanding of climate change as snow and ice surfaces regulate the global radiative budget and sea-level through their albedo and mass balance. Although significant progress has been made using physical principles to represent the dynamic albedo of snow, models of glacier ice albedo tend to be heavily parameterized and not explicitly connected with physical properties that govern albedo, such as the number and size of air bubbles, specific surface area (SSA), presence of abiotic and biotic light absorbing constituents (LAC), and characteristics of any overlying snow. Here, we introduce SNICAR-ADv4, an extension of the multi-layer two-stream delta-Eddington radiative transfer model with the adding-doubling solver that has been previously applied to represent snow and sea-ice spectral albedo. SNICAR-ADv4 treats spectrally resolved Fresnel reflectance and transmittance between overlying snow and higher-density glacier ice, scattering by air bubbles of varying sizes, and numerous types of LAC. SNICAR-ADv4 simulates a wide range of clean snow and ice broadband albedos (BBA), ranging from 0.88 for (30 μm) fine-grain snow to 0.03 for bare and bubble free ice under direct light. Our results indicate that representing ice with a density of 650 kg m−3 as snow with no refractive Fresnel layer, as done previously, generally overestimates the BBA by an average of 0.058. However, because most naturally occurring ice surfaces are roughened "white ice", we recommend modeling a thin snow layer over bare ice simulations. We find optimal agreement with measurements by representing cryospheric media with densities less than 650 kg m−3 as snow, and larger density media as bubbly ice with a Fresnel layer. SNICAR-ADv4 also simulates the non-linear albedo impacts from LACs with changing ice SSA, with peak impact per unit mass of LAC near SSAs of 0.1–0.01 m2 kg−1. For bare, bubble-free ice, LAC actually increase the albedo. SNICAR-ADv4 represents smooth transitions between snow, firn, and ice surfaces and accurately reproduces measured spectral albedos of a variety of glacier surfaces. This work paves the way for adapting SNICAR-ADv4 to be used in land ice model components of Earth System Models.

Tracking changes in the area, thickness, and volume of the Thwaites tabular iceberg “B30” using satellite altimetry and imagery

Icebergs account for half of all ice loss from Antarctica and, once released, present a hazard to maritime operations. Their melting leads to a redistribution of cold fresh water around the Southern Ocean which, in turn, influences water circulation, promotes sea ice formation, and fosters primary production. In this study, we combine CryoSat-2 satellite altimetry with MODIS and Sentinel-1 satellite imagery and meteorological data to track changes in the area, freeboard, thickness, and volume of the B30 tabular iceberg between 2012 and 2018. We track the iceberg elevation when it was attached to Thwaites Glacier and on a further 106 occasions after it calved using Level 1b CryoSat data, which ensures that measurements recorded in different acquisition modes and within different geographical zones are consistently processed. From these data, we map the iceberg's freeboard and estimate its thickness taking snowfall and changes in snow and ice density into account. We compute changes in freeboard and thickness relative to the initial average for each overpass and compare these to estimates from precisely located tracks using the satellite imagery. This comparison shows good agreement (correlation coefficient 0.87) and suggests that colocation reduces the freeboard uncertainty by 1.6 m. We also demonstrate that the snow layer has a significant impact on iceberg thickness change. Changes in the iceberg area are measured by tracing its perimeter, and we show that alternative estimates based on arc lengths recorded in satellite altimetry profiles and on measurements of the semi-major and semi-minor axes also capture the trend, though with a 48 % overestimate and a 15 % underestimate, respectively. Since it calved, the area of B30 has decreased from 1500±60 to 426±27 km2, its mean freeboard has fallen from 49.0±4.6 to 38.8±2.2 m, and its mean thickness has reduced from 315±36 to 198±14 m. The combined loss amounts to an 80 %±16 % reduction in volume, two thirds (69 %±14 %) of which is due to fragmentation and the remainder (31 %±11 %) of which is due to basal melting.

Variability in black carbon mass concentration in surface snow at Svalbard

Black carbon (BC) is a significant forcing agent in the Arctic, but substantial uncertainty remains to quantify its climate effects due to the complexity of the different mechanisms involved, in particular related to processes in the snowpack after deposition. In this study, we provide detailed and unique information on the evolution and variability in BC content in the upper surface snow layer during the spring period in Svalbard (Ny-Ålesund). A total of two different snow-sampling strategies were adopted during spring 2014 (from 1 April to 24 June) and during a specific period in 2015 (28 April to 1 May), providing the refractory BC (rBC) mass concentration variability on a seasonal variability with a daily resolution (hereafter seasonal/daily) and daily variability with an hourly sampling resolution (hereafter daily/hourly) timescales. The present work aims to identify which atmospheric variables could interact with and modify the mass concentration of BC in the upper snowpack, which is the snow layer where BC particles affects the snow albedo. Atmospheric, meteorological and snow-related physico-chemical parameters were considered in a multiple linear regression model to identify the factors that could explain the variations in BC mass concentrations during the observation period. Precipitation events were the main drivers of the BC variability during the seasonal experiment; however, in the high-resolution sampling, a negative association has been found. Snow metamorphism and the activation of local sources (Ny-Ålesund was a coal mine settlement) during the snowmelt periods appeared to play a non-negligible role. The statistical analysis suggests that the BC content in the snow is not directly associated to the atmospheric BC load.

Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet

Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of the surface mass balance of ice sheets. While in situ snow accumulation measurements are temporally and spatially limited due to their high cost, airborne radar sounders can achieve ice sheet wide coverage by capturing and tracking annual snow layers in the radar images or echograms. In this paper, we use deep learning to uniquely identify the position of each annual snow layer in the Snow Radar echograms taken across different regions over the Greenland ice sheet. We train with more than 15,000 images generated from radar echograms and estimate the thickness of each snow layer within a mean absolute error of 0.54 to 7.28 pixels, depending on dataset. A highly precise snow layer thickness can help improve weather models and, thus, support glaciological studies. Such a well-trained deep learning model can be used with ever-growing datasets to aid in the accurate assessment of snow accumulation on the dynamically changing ice sheets.

Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (TB) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow TB from the snow’s surface was compared with the bulk TB of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk TB error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk TB of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm.

Changes in the Thickness of Ice Cover on Water Bodies Subject to Human Pressure (Silesian Upland, Southern Poland)

The paper discusses the reasons behind the variation in the thickness of ice on 39 anthropogenic water bodies located in the Silesian Upland (southern Poland). The studies were conducted over the course of three consecutive winter seasons. The measurements and observations were scheduled every 2 days during the freezing and ablation of the ice, and every 4 days when ice cover was present. Each time the thickness of the ice cover and the snow layer covering it were measured. The results show that the 35 water bodies studied are characterized by a similar—quasi-natural—ice regime, in which ice thickness variation depends mostly on the air temperature and the thickness of the snow layer covering the ice. The ice thickness on those water bodies does not significantly differ from that observed on lakes located in northern Poland, measuring on average from circa 4 to 21 cm, and with maximum thicknesses ranging from circa 14 to 40 cm, depending on the season. Four water bodies are characterized by different ice conditions; in their case the average and maximum ice thickness was significantly lower. In the Niezdara N water body this was caused by the inflow of warmer potamic water (quasi-natural regime), whereas in Pod Borem, Sośnicka, and Somerek it was caused by discharges of warm mine water (anthropogenic regime).

Variability of Black Carbon mass concentration in surface snow at Svalbard

Black Carbon (BC) is a significant forcing agent in the Arctic, but substantial uncertainty remains to quantify its climate effects due to the complexity of the different mechanisms involved, in particular related to processes in the snow-pack after deposition. In this study, we provide detailed and unique information on the evolution and variability of BC content in the upper surface snow layer during the spring period in Svalbard (Ny-Ålesund). Two different snow-sampling strategies were adopted during spring 2014 and 2015, providing the refractory BC (rBC) mass concentration variability on a seasonal/daily and daily/hourly time scales. The present work aims to identify which atmospheric variables could interact and modify the mass concentration of BC in the upper snowpack, the snow layer which BC particles affects the snow albedo. Despite the low BC mass concentrations, a relatively high daily variability was observed. Atmospheric, meteorological, and snow-related physico-chemical parameters were considered in a multiple statistical model to separate the factors determining observations. Precipitation events were the main drivers of the BC variability. Snow metamorphism and activation of local sources during the snow melting periods appeared to play a non-negligible role (wind resuspension in specific Arctic areas where coal mines were present). The BC content in the snow resulted in being statistically decoupled from the atmospheric BC load.