Spatial extent of rain-on-snow (ROS) events in the Arctic (Svalbard) : combining wet snow maps from TerraSAR-X and Radarsat Constellation Mission with ERA5 reanalysis, glaciological measurements, and optical Planet images
Bibliographic record
Abstract
In the Arctic, extreme weather conditions such as rain-on-snow events (ROS) make the monitoring of the snowpack with remote sensing techniques increasingly relevant and necessary. In recent years, remote sensing methods based on active radar images (SAR) are well described for mapping the spatial extent of ROS events in the terrestrial Arctic (Vickers, 2022; Bartsch, 2023). However, few methods are proposed to validate the relationship between ROS and elevation for such events over glaciers likely due to the lack of in-situ measurement networks in these high latitude areas. Svalbard provides several meteorological and snow monitoring sites, which is a great value for detecting the occurrence of these ROS events and validation of the remote sensing methods.The purpose of this study is to investigate the spatial and temporal effects of recent ROS events over the Brøgger peninsula (210 km2) in Svalbard (N 78°55’ / E 11° 55’), using remote sensing methods, local meteorological measurements and reanalyses. For each ROS event of the 2017-2023 time period, remote sensing SAR maps of wet snow (Nagler and Rott, 2000) are produced from images obtained with the TerraSAR-X (DLR) and RCM (CSA) high resolution sensors (5-m), respectively at X- and C-band frequency.The validation of the affected areas is based (i) on ERA5 reanalysis data used to estimate the altitude of the 0°C isoline and (ii) on a network of temperature sensors installed on the Austre Lovén glacier. SAR maps, ERA5 isoline, and in-situ data are in good agreement, resulting in altitude differences between 10 and 25 m for the transition of wet and dry snow, depending on the event.Although optical images availability is limited due to polar night and cloud cover during precipitation, it was further possible to use optical Planet images at high temporal and spatial resolution (3-m) to determine the ROS impact after the events on the properties of the snow cover. The decreasing signal of the red-edge and near-infrared bands indicate higher snow densities and a stronger wetness of the snowpack, which closely aligns with in-situ observations through snow stratigraphy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".