Francesca Carletti

We monitored the formation and seasonal evolution of suncup roughness over three snow ablation seasons at Weissfluhjoch in the Swiss Alps using a terrestrial LiDAR scanner. From high-temporal-resolution digital surface models, we find that suncup onset required three concurrent conditions: sustained daytime surface melting, above-freezing wet-bulb temperatures, and reduced wind speeds. Suncups formed in all three years, but their planar arrangement and geometry varied substantially, controlled by whether radiative or turbulent conditions dominated ablation. Comparing measured broadband albedo to flat-surface TARTES simulations forced by SNOWPACK-modelled snow properties, we find that suncup roughness and surface impurity loading together reduce albedo by 0.03–0.1, depending on illumination geometry and impurity load, consistent with previous work. Isolating the two contributions is complicated by their co-evolution: the same melt processes that deepen suncups also drive surface enrichment and the spatial redistribution of impurities, which are uniformly distributed during early suncup formation but concentrate into the hollows as melt progresses. As a practical alternative, we identify a robust logarithmic relationship between broadband albedo and the aerodynamic roughness length z ₀ that captures the combined radiative effect of roughness and impurities regardless of their relative contribution. Because broadband albedo decays fastest at the onset of suncup formation, we infer that a uniform early impurity distribution is particularly effective at accelerating albedo loss. Because z ₀ evolves by one order of magnitude through ablation, yet is kept constant in most snow models, our results call for its explicit, time-varying representation in physics-based snow models, which would improve both the turbulent fluxes z ₀ governs directly and, through its link to albedo, the roughness–impurity darkening.

We monitored the formation and seasonal evolution of suncup roughness over three snow ablation seasons at Weissfluhjoch, Swiss Alps, using a terrestrial LiDAR scanner. Suncup onset required two concurrent conditions, identified from high-temporal-resolution digital surface models of surface roughness: sustained surface melting through most of the day and reduced wind speeds. Suncups formed in all three years, but their planar arrangement and geometric properties varied substantially across seasons, controlled by whether radiative or turbulent heat exchange dominated ablation. Comparing measured broadband albedo to flat-surface simulations from TARTES forced by SNOWPACK-modelled snow properties, we find that the combined effect of suncup roughness and surface impurity loading reduces albedo by 0.02–0.15, depending on illumination geometry and impurity load, consistent with previous literature. Isolating the two contributions is complicated by their co-evolution during the ablation season: the same melt processes that progressively deepen suncups also drive surface enrichment and spatial redistribution of impurities. Resolving the two effects independently would in principle require equally fine-scale measurements of both roughness and impurity distribution. We identify a robust logarithmic correlation between broadband albedo and aerodynamic roughness length that simultaneously captures the radiative effects of roughness and impurities, regardless of their relative contributions. During early suncup formation, impurities remained uniformly distributed. At later stages, meltwater scavenging concentrated impurities to the suncup hollows. As the rate of broadband albedo decay is strongest at the beginning of suncup formation and relaxes thereafter, we infer that the interaction between the multiple-reflection mechanism and the more uniform distribution of impurities is particularly effective in accelerating albedo decay, beyond the effect of either factor alone. Given that C-band SAR backscatter is sensitive to the early development of surface roughness on wet snow, these findings encourage future work on the assimilation of surface roughness into snow energy balance models.

We monitored the formation and seasonal evolution of suncup roughness over three snow ablation seasons at Weissfluhjoch, Swiss Alps, using a terrestrial LiDAR scanner. Suncup onset required two concurrent conditions, identified from high-temporal-resolution digital surface models of surface roughness: sustained surface melting through most of the day and reduced wind speeds. Suncups formed in all three years, but their planar arrangement and geometric properties varied substantially across seasons, controlled by whether radiative or turbulent heat exchange dominated ablation. Comparing measured broadband albedo to flat-surface simulations from TARTES forced by SNOWPACK-modelled snow properties, we find that the combined effect of suncup roughness and surface impurity loading reduces albedo by 0.02–0.15, depending on illumination geometry and impurity load, consistent with previous literature. Isolating the two contributions is complicated by their co-evolution during the ablation season: the same melt processes that progressively deepen suncups also drive surface enrichment and spatial redistribution of impurities. Resolving the two effects independently would in principle require equally fine-scale measurements of both roughness and impurity distribution. We identify a robust logarithmic correlation between broadband albedo and aerodynamic roughness length that simultaneously captures the radiative effects of roughness and impurities, regardless of their relative contributions. During early suncup formation, impurities remained uniformly distributed. At later stages, meltwater scavenging concentrated impurities to the suncup hollows. As the rate of broadband albedo decay is strongest at the beginning of suncup formation and relaxes thereafter, we infer that the interaction between the multiple-reflection mechanism and the more uniform distribution of impurities is particularly effective in accelerating albedo decay, beyond the effect of either factor alone. Given that C-band SAR backscatter is sensitive to the early development of surface roughness on wet snow, these findings encourage future work on the assimilation of surface roughness into snow energy balance models.

We monitored the formation and seasonal evolution of suncup roughness over three snow ablation seasons at Weissfluhjoch, Swiss Alps, using a terrestrial LiDAR scanner. Suncup onset required two concurrent conditions, identified from high-temporal-resolution digital surface models of surface roughness: sustained surface melting through most of the day and reduced wind speeds. Suncups formed in all three years, but their planar arrangement and geometric properties varied substantially across seasons, controlled by whether radiative or turbulent heat exchange dominated ablation. Comparing measured broadband albedo to flat-surface simulations from TARTES forced by SNOWPACK-modelled snow properties, we find that the combined effect of suncup roughness and surface impurity loading reduces albedo by 0.02–0.15, depending on illumination geometry and impurity load, consistent with previous literature. Isolating the two contributions is complicated by their co-evolution during the ablation season: the same melt processes that progressively deepen suncups also drive surface enrichment and spatial redistribution of impurities. Resolving the two effects independently would in principle require equally fine-scale measurements of both roughness and impurity distribution. We identify a robust logarithmic correlation between broadband albedo and aerodynamic roughness length that simultaneously captures the radiative effects of roughness and impurities, regardless of their relative contributions. During early suncup formation, impurities remained uniformly distributed. At later stages, meltwater scavenging concentrated impurities to the suncup hollows. As the rate of broadband albedo decay is strongest at the beginning of suncup formation and relaxes thereafter, we infer that the interaction between the multiple-reflection mechanism and the more uniform distribution of impurities is particularly effective in accelerating albedo decay, beyond the effect of either factor alone. Given that C-band SAR backscatter is sensitive to the early development of surface roughness on wet snow, these findings encourage future work on the assimilation of surface roughness into snow energy balance models.

We monitored the formation and seasonal evolution of suncup roughness over three snow ablation seasons at Weissfluhjoch, Swiss Alps, using a terrestrial LiDAR scanner. Suncup onset required two concurrent conditions, identified from high-temporal-resolution digital surface models of surface roughness: sustained surface melting through most of the day and reduced wind speeds. Suncups formed in all three years, but their planar arrangement and geometric properties varied substantially across seasons, controlled by whether radiative or turbulent heat exchange dominated ablation. Comparing measured broadband albedo to flat-surface simulations from TARTES forced by SNOWPACK-modelled snow properties, we find that the combined effect of suncup roughness and surface impurity loading reduces albedo by 0.02–0.15, depending on illumination geometry and impurity load, consistent with previous literature. Isolating the two contributions is complicated by their co-evolution during the ablation season: the same melt processes that progressively deepen suncups also drive surface enrichment and spatial redistribution of impurities. Resolving the two effects independently would in principle require equally fine-scale measurements of both roughness and impurity distribution. We identify a robust logarithmic correlation between broadband albedo and aerodynamic roughness length that simultaneously captures the radiative effects of roughness and impurities, regardless of their relative contributions. During early suncup formation, impurities remained uniformly distributed. At later stages, meltwater scavenging concentrated impurities to the suncup hollows. As the rate of broadband albedo decay is strongest at the beginning of suncup formation and relaxes thereafter, we infer that the interaction between the multiple-reflection mechanism and the more uniform distribution of impurities is particularly effective in accelerating albedo decay, beyond the effect of either factor alone. Given that C-band SAR backscatter is sensitive to the early development of surface roughness on wet snow, these findings encourage future work on the assimilation of surface roughness into snow energy balance models.

The spatiotemporal evolution of snowmelt is fundamental for water resources management and risk mitigation in mountain catchments. Synthetic aperture radar (SAR) images acquired by satellite systems such as Sentinel-1 (S1) are promising for monitoring wet snow due to their high sensitivity to liquid water content (LWC) and ability to provide spatially distributed data at a high temporal resolution. While recent studies have linked multitemporal S1 backscatter to snowmelt phases, a correlation with detailed snowpack properties is still missing. To address this, we collected the first dataset of comprehensive wet-snow properties tailored for SAR applications over two consecutive snow seasons at the Weissfluhjoch field site near Davos, Switzerland. First, we tested previous methods which use multitemporal S1 backscatter to characterize melting phases and demonstrated that the observed monotonous increase in backscatter following the local minimum is due to the development of surface roughness. Then, we used the measured snow properties as input to the Snow Microwave Radiative Transfer (SMRT) model to reproduce S1 backscatter signals. Our simulations showed that rather than melting phases, time series of backscatter identify regimes dominated by either LWC, early in the season, or surface roughness, later on. The results also highlight several key challenges for reconciling S1 signals with radiative transfer simulations of wet snow: (i) the discrepancy in spatiotemporal variability of LWC as seen by the satellite and validation measurements, (ii) the lack of fully validated permittivity, microstructure and roughness models for wet snow in the C-band, and (iii) the difficulty of capturing wet-snow features potentially generating stronger scattering effects on a large scale – such as internal snowpack structures, soil features in case of low LWC and surface roughness – which are not necessarily captured by pointwise measurements.

The spatiotemporal evolution of snow melt is fundamental for water resources management and risk mitigation in mountain catchments. Synthetic Aperture Radar (SAR) images acquired by satellite systems such as Sentinel-1 (S1) are promising for monitoring wet snow due to their high sensitivity to liquid water content (LWC) and ability to provide spatially distributed data at a high temporal resolutions. While recent studies have successfully linked S1 backscattering to various phases of snowpack melting, a correlation with detailed snowpack properties is still missing. To address this, we collected the first dataset of detailed wet snow properties tailored for SAR applications over two consecutive snow seasons at the Weissfluhjoch field site in Switzerland. First, our dataset enabled the validation of previous methods relying on multitemporal SAR backscattering to characterize melting snowpacks and physically linked the increase in backscattering following the local minimum to the evolution of surface roughness. Then, the dataset was used as input to the Snow Microwave Radiative Transfer (SMRT) model to reproduce the S1 backscattering signal. Our simulations showed a general negative bias compared to the satellite data, with the most significant drivers being LWC early in the melt season and the surface roughness later on. The results also highlight several key challenges for reconciling S1 signals with radiative transfer simulations of wet snow: (i) the discrepancy in spatiotemporal variability of LWC as seen by the satellite and validation measurements, (ii) the lack of fully validated permittivity, microstructure and roughness models for wet snow in the C-band, (iii) the difficulty of capturing wet snow features potentially generating stronger scattering effects on a large scale, such as internal snowpack structures, soil features in case of low LWC, and surface roughness, which are not necessarily captured by point-wise measurements.

The spatiotemporal evolution of snow melt is fundamental for water resources management and risk mitigation in mountain catchments. Synthetic Aperture Radar (SAR) images acquired by satellite systems such as Sentinel-1 (S1) are promising for monitoring wet snow due to their high sensitivity to liquid water content (LWC) and ability to provide spatially distributed data at a high temporal resolutions. While recent studies have successfully linked S1 backscattering to various phases of snowpack melting, a correlation with detailed snowpack properties is still missing. To address this, we collected the first dataset of detailed wet snow properties tailored for SAR applications over two consecutive snow seasons at the Weissfluhjoch field site in Switzerland. First, our dataset enabled the validation of previous methods relying on multitemporal SAR backscattering to characterize melting snowpacks and physically linked the increase in backscattering following the local minimum to the evolution of surface roughness. Then, the dataset was used as input to the Snow Microwave Radiative Transfer (SMRT) model to reproduce the S1 backscattering signal. Our simulations showed a general negative bias compared to the satellite data, with the most significant drivers being LWC early in the melt season and the surface roughness later on. The results also highlight several key challenges for reconciling S1 signals with radiative transfer simulations of wet snow: (i) the discrepancy in spatiotemporal variability of LWC as seen by the satellite and validation measurements, (ii) the lack of fully validated permittivity, microstructure and roughness models for wet snow in the C-band, (iii) the difficulty of capturing wet snow features potentially generating stronger scattering effects on a large scale, such as internal snowpack structures, soil features in case of low LWC, and surface roughness, which are not necessarily captured by point-wise measurements.

The spatiotemporal evolution of snow melt is fundamental for water resources management and risk mitigation in mountain catchments. Synthetic Aperture Radar (SAR) images acquired by satellite systems such as Sentinel-1 (S1) are promising for monitoring wet snow due to their high sensitivity to liquid water content (LWC) and ability to provide spatially distributed data at a high temporal resolutions. While recent studies have successfully linked S1 backscattering to various phases of snowpack melting, a correlation with detailed snowpack properties is still missing. To address this, we collected the first dataset of detailed wet snow properties tailored for SAR applications over two consecutive snow seasons at the Weissfluhjoch field site in Switzerland. First, our dataset enabled the validation of previous methods relying on multitemporal SAR backscattering to characterize melting snowpacks and physically linked the increase in backscattering following the local minimum to the evolution of surface roughness. Then, the dataset was used as input to the Snow Microwave Radiative Transfer (SMRT) model to reproduce the S1 backscattering signal. Our simulations showed a general negative bias compared to the satellite data, with the most significant drivers being LWC early in the melt season and the surface roughness later on. The results also highlight several key challenges for reconciling S1 signals with radiative transfer simulations of wet snow: (i) the discrepancy in spatiotemporal variability of LWC as seen by the satellite and validation measurements, (ii) the lack of fully validated permittivity, microstructure and roughness models for wet snow in the C-band, (iii) the difficulty of capturing wet snow features potentially generating stronger scattering effects on a large scale, such as internal snowpack structures, soil features in case of low LWC, and surface roughness, which are not necessarily captured by point-wise measurements.

The spatiotemporal evolution of snow melt is fundamental for water resources management and risk mitigation in mountain catchments. Synthetic Aperture Radar (SAR) images acquired by satellite systems such as Sentinel-1 (S1) are promising for monitoring wet snow due to their high sensitivity to liquid water content (LWC) and ability to provide spatially distributed data at a high temporal resolutions. While recent studies have successfully linked S1 backscattering to various phases of snowpack melting, a correlation with detailed snowpack properties is still missing. To address this, we collected the first dataset of detailed wet snow properties tailored for SAR applications over two consecutive snow seasons at the Weissfluhjoch field site in Switzerland. First, our dataset enabled the validation of previous methods relying on multitemporal SAR backscattering to characterize melting snowpacks and physically linked the increase in backscattering following the local minimum to the evolution of surface roughness. Then, the dataset was used as input to the Snow Microwave Radiative Transfer (SMRT) model to reproduce the S1 backscattering signal. Our simulations showed a general negative bias compared to the satellite data, with the most significant drivers being LWC early in the melt season and the surface roughness later on. The results also highlight several key challenges for reconciling S1 signals with radiative transfer simulations of wet snow: (i) the discrepancy in spatiotemporal variability of LWC as seen by the satellite and validation measurements, (ii) the lack of fully validated permittivity, microstructure and roughness models for wet snow in the C-band, (iii) the difficulty of capturing wet snow features potentially generating stronger scattering effects on a large scale, such as internal snowpack structures, soil features in case of low LWC, and surface roughness, which are not necessarily captured by point-wise measurements.