Continuous monitoring of the temporal evolution of the snowpack using upward-looking ground penetrating radar technology

Snow stratigraphy and water percolation are key parameters in avalanche forecasting. It is, however, difficult to model or measure stratigraphy and water flow in a sloping snowpack. Numerical modeling results depend highly on the type and availability of input data and the parameterization of the physical processes. Furthermore, the sensors themselves may influence the snowpack or be destroyed due to snow gliding and avalanches. Radar technology allows non-destructive scanning of the snowpack and deducing internal snow properties. If the radar system is buried in the ground, it cannot be destroyed by avalanche impacts or snow creep. During the winter seasons 2010-2011 and 2011-2012 we recorded continuous data with upward-looking pulsed radar systems (upGPR) at two test sites. We demonstrate that it is possible to determine the snow height with an accuracy comparable to conventional snow depth measuring devices. We determined the bulk volumetric liquid water content and tracked the position of the first stable wetting front. Wet-snow avalanche activity increased, when melt water penetrated deeper into the snowpack

Increased risk of severe clinical course of COVID-19 in carriers of HLA-C*04:01

Background: Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, there has been increasing urgency to identify pathophysiological characteristics leading to severe clinical course in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Human leukocyte antigen alleles (HLA) have been suggested as potential genetic host factors that affect individual immune response to SARS-CoV-2. We sought to evaluate this hypothesis by conducting a multicenter study using HLA sequencing. Methods: We analyzed the association between COVID-19 severity and HLAs in 435 individuals from Germany (n = 135), Spain (n = 133), Switzerland (n = 20) and the United States (n = 147), who had been enrolled from March 2020 to August 2020. This study included patients older than 18 years, diagnosed with COVID19 and representing the full spectrum of the disease. Finally, we tested our results by meta-analysing data from prior genome-wide association studies (GWAS). Findings: We describe a potential association of HLA-C*04:01 with severe clinical course of COVID-19. Carriers of HLA-C*04:01 had twice the risk of intubation when infected with SARS-CoV-2 (risk ratio 1.5 [95% CI 1.1-2.1], odds ratio 3.5 [95% CI 1.9-6.6], adjusted p-value = 0.0074). These findings are based on data from four countries and corroborated by independent results from GWAS. Our findings are biologically plausible, as HLA-C*04:01 has fewer predicted bindings sites for relevant SARS-CoV-2 peptides compared to other HLA alleles. Interpretation: HLA-C*04:01 carrier state is associated with severe clinical course in SARS-CoV-2. Our findings suggest that HLA class I alleles have a relevant role in immune defense against SARS-CoV-2. Funding: Funded by Roche Sequencing Solutions, Inc

Simulation of snow stratigraphy using full-waveform inversion applied to data from an upward-looking radar system

Snow stratigraphy is a key contributing factor for assessing avalanche danger, but so far only destructive methods can provide this kind of information. Furthermore, continuous monitoring of the temporal evolution of the snowpack is not possible with destructive methods. Radar technology provides information on the snowpack nondestructively and allows deriving internal snow properties from its signal response. In our previous work, we demonstrated that it is feasible to quantitatively derive snowpack properties relevant for avalanche formation and monitor their evolution in time using an upward-looking ground penetrating radar system (upGPR) that was buried in a wooden box underneath the snow. Reliable results could only be obtained for the time when the snow cover was dry. In addition, to determine some properties, we still needed additional information such as independently measured snow height or modeled snow density. Hence, the system was not yet able to provide information from avalanche starting zones, since this type of information is generally not available in avalanche-prone terrain. To fully exploit the information content of upGPR data, and thus to at least partially compensate for the lack of information, we applied full-waveform inversion (FWI) techniques. We refined the model of the snowpack by repeated forward modeling the waveforms and updating the model parameters to match it with recorded data. The forward model took into account both the effect of the snow density on the velocity of the electromagnetic wave, as well as the influence of snow wetness on the attenuation. This allowed the density and the liquid water content for each layer in the snowpack to be determined. As we conducted a measurement every 3 hours (every 30 minutes as soon as the snowpack became wet), we could also simulate the temporal evolution of the density and the liquid water profiles. The method worked without assumptions or external measurements, even when the snow cover was wet

Determining the ice volume distribution of glaciers using helicopter-borneGPR and glaciological modeling

ISSN:1029-7006ISSN:1607-796

Inversion von Daten eines aufwärtsschauenden GPR zur Bestimmung von Schneeeigenschaften

Wir präsentieren Ansätze zur Inversion von GPR-Daten zur Bestimmung der Schneeeigenschaften, insbesondere des Flüssigwasseranteils. Die Datensätze umfassen Daten der letzten vier Jahre des MUSI-Projekts, aufgenommen mit kommerziellen GPR- sowie neue entwickelten FMCW-Systemen