Influence of stratigraphy and heterogeneity on simulated \nmicrowave brightness temperatures of shallow snowpacks
Bibliographic record
Abstract
Snow accumulation has potential climatological, hydrological and ecological impacts at a global scale. Satellite passive microwave radiometers have the potential to provide snow accumulation data with a historical record of over 30 years, however, current data products contain unknown uncertainty and error. \nSnowpack stratigraphy is the spatial variation in snowpack properties caused by the layered nature of the snowpack. Snowpack stratigraphy influences the accuracy and increases uncertainty in simulations of microwave emission from snow which in turn increases uncertainty in satellite derived estimates of snow water equivalent using microwave radiometers. \n \nTwo methods were developed to help better quantify snowpack stratigraphy. An improved technique for characterising snowpack stratigraphy within a snow trench was developed. Secondly a new method was developed to quantify the density of ice layers that form in snowpacks with known error and uncertainty. \n \nSnowpack stratigraphy was characterised using the improved technique across the Trail Valley Creek watershed in the Canadian Northwest Territories. Two 50 m trenches and eleven 5 m trenches were dug across the range of landcover types found in the watershed. This dataset allowed layer boundary roughness to be characterised and the properties of snow layers to be mapped with an unprecedented level of accuracy. \n \nIce lens density was measured 60 times at three locations in the Arctic and midlatitudes at locations with coincident ground based radiometer measurements. \nThe impact that accurate parameterisation of density has on modelled estimates of brightness temperature was quantified. \n \nSimulations of microwave brightness temperatures were conducted using snow emission models at all locations. The output of these simulations, and comparison \nto ground based observations where available, allowed for the characterisation of variability in brightness temperature simulations caused by stratigraphic heterogeneity. The findings presented in this thesis will inform research aiming to better characterise the satellite error budget. Improvements in this area helps improve global snow mass and snow accumulation estimates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".