Application of L-band radiometry in snow characteristics analysis - L-band snow measurements in the Canadian Arctic
Bibliographic record
Abstract
The Arctic is warming up to four times faster than the global average, yet data availability over this area is notoriously scarce. L-band radiometry is a promising cryosphere surface state variable monitoring method, due to its ability to penetrate through snow and pass through clouds. For proper application in the cryosphere, it is necessary to understand the effects that snow has on L-band emissions. \n \nThe main objective of this thesis was to investigate the potential of L-band in informing on snow state variables utilizing a new L-band radiometer. To achieve this, I conducted ground reference measurements in Cambridge Bay, Nunavut, between the 1st and 14th of April 2023. Snowpack macrostructure and microstructure properties, as well as snow interface interactions, were analyzed in the context of ground L-band emissions. \n \nThe impact of snow on ground L-band emissions was found to be highly spatially variable, with effects reaching up to ±7%. The effects varied by polarization and measurement angle, with horizontally polarized emissions experiencing strengthening at low measurement angles from nadir, while the impact on vertical polarization was mostly arbitrary. Snowpack microstructure had a noticeable impact on the emissions; increasing prevalence of depth hoar was found to strongly correlate with decreasing polarization ratio. Ground surface roughness also showed negative correlation with the emissions. A surface ice layer exhibited a strong but varying impact on the emissions. \n \nOverall, the Canadian Arctic snowpack was found to exhibit unique responsiveness especially to snowpack microstructural properties, which underscores the need for further understanding between the Arctic snowpack and L-band. The findings also emphasize snow's relevance in L-band applications, as properly characterizing these interactions will enhance accurate ground and snow data retrieval in the Arctic.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".