Towards improving radiometry-derived snow depths with SNOWPACK and SMRT
Bibliographic record
Abstract
The depth of overlying snow on sea ice exerts a strong control on atmosphere-ocean heat and light flux and introduces major uncertainties in the remote sensing of sea ice thickness. Satellite-mounted microwave radiometers have enabled retrieval of snow depths over first year ice, but such retrievals are subject to a wide margin of error due to spatial variation in snow stratigraphy and roughness. Here we model the microwave signature of snow on sea ice using a recently released sea ice variant of the snowpack evolution model, SNOWPACK (Wever et al., 2020). By advecting parcels of sea ice using ice motion vectors and exposing them to the relevant atmospheric forcing using ERA5 reanalysis, we model the accumulation of snow and the development of snowpack stratigraphy. We then pass these modelled snowpacks to the Snow Microwave Radiative Transfer model (Picard et al., 2018) to estimate their microwave emission characteristics. By using relationships from the literature relating the ratios of the 37GHz and 19GHz channels, we calculate whether the traditional “gradient ratio” method (Markus and Cavalieri, 1998) over- or underestimates the depth of snow at a particular point based on our modelling. We then adjust the observed gradient ratio based on the model results in an attempt to better characterise snow depths. References Wever, Nander, et al. "Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model." Geoscientific Model Development 13.1 (2020): 99-119. Picard, Ghislain, Melody Sandells, and Henning Löwe. "SMRT: An active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1. 0)." Geoscientific Model Development 11.7 (2018): 2763-2788. Markus, Thorsten, and Donald J. Cavalieri. "Snow depth distribution over sea ice in the Southern Ocean from satellite passive microwave data." Antarctic sea ice: physical processes, interactions and variability 74 (1998): 19-39.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".