Impact of recent climate change in the Arctic on snow physical parameters retrieval using SAR data (Svalbard)
Bibliographic record
Abstract
Arctic snow cover dynamics exhibit strong changes in terms of extent and duration due to recent climate changeconditions (Mudryk et al., 2018; Lemke & Jacobi, 2011). In this context, innovative observation methods arehelpful for a better comprehension of the role of the snow for climate research and hydrology. The spatialvariability of snow properties is here addressed for the Ny-Alesund area, Svalbard using satellite radar images in the X-band. This remote sensing method removes the limitations and ambiguities ofoptical imaging limited by the polar night and cloud cover.This study contributes to the Precip-A2 project (OSUG 2020, Grenoble, France), focusing on snow and itsinteraction with the atmosphere: chemistry, radiative processes, and precipitation. One sub-task of the projectis dedicated to X-band active radar measurements (SAR) to retrieve physical properties of arctic snow (spatialvariability, depth estimation), involving consistent ground network including a large international partnership(France, Germany, Norway, Italy).1. Climatology context: for Ny-Alesund area, a change in the occurrence frequency of source region of air masseshas been identified. Consequently, an increase in temperature and water vapour content was detected (Dahlkeand Maturilli, 2016). Temperature time series since 1969 were analyzed and an increase in annual temperature of 0.07 C per year was found. This increase is mainly driven by a positive seasonal trend in winter (DJF); thus,influencing the fraction of annual precipitation falling as snow / rain. 2. Remote sensing application: a set of ten SAR images was provided by the DLR during winter 2017 from theTerraSAR-X sensor (3.1 cm, 9.6 GHz) in dual co-pol HH, VV (2.5 m resolution). Descending and ascending orbitswere combined at 35-38 incidence angles to avoid topographic constraints. The data were processed with the ESA SNAP toolbox. Output products: non-polarimetric analysis providing regular snow mapping from Marchto June 2017 and polarimetric analysis related to the physical properties of the snow pack. The non-polarimetricmode (single polarization HH or VV) processed with adaptive thresholding (Nagler et al., 2000) allows retrievingsnow cover areas (SCA) and their temporal evolution, which are afterwards compared to optical Sentinel-2simultaneous acquisition for dates without clouds. SCA results are well correlated (0.95) assessing the interest ofSAR images in regard of optical mode suffering from polar night and cloud coverage. The polarimetric analysisis based on a co-polar phase difference (CPD) set between HH and VV polarization (Leinss, 2015). Resultsindicate that CPD values are linked to the snow metamorphism: positive values for dry snow, negative valuesafter recrystallization processes. The best R2 correlation performances between estimated and measured snowheight are ranging from 0.51 to 0.75. However, the X-band signal is strongly influenced by the snow stratigraphy:internal ice layers reduce or block the penetration of the signal into the snow pack. Due to warming during winterseason coupled with increasing soil temperatures, this snow metamorphism evolution more relevant of temperateregion seems unfortunately to occur now in this Arctic area (Boike et al., 2018)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".