Bibliographic record
Abstract
Abstract. This study presents a detailed theoretical assessment of the information content of polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses parameter covariance across the entire parameter space. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing impurities (LAIs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR) and short-wave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAIs in the VIS-NIR, and to grain size at different depths in the snowpack in the NIR-SWIR. When present in the snow or in the atmosphere, LAIs introduce correlated information on grain size in the VIS. Such sensitivity can be disentangled by including multi-spectral and multi-angle polarimetric measurements, leading to a better estimate of grain shape and ice crystal roughness, and in turn of the asymmetry parameter which is critical for the determination of albedo. Polarimetry in the SWIR also contains information on aerosol optical thickness while remaining essentially unaffected when the same impurities are embedded in the snow, so that it can effectively partition LAIs between the atmosphere and the surface (a notorious challenge for snow remote sensing based on measurements of total reflectance only) as prospected in a precursor study. The GSA results were used to select state parameters in retrievals performed on data simulated for plausible polar conditions and for multiple instrument configurations. Mono-angle measurements of total reflectance in the VIS-SWIR (akin to MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS-NIR provides information on grain shape and microscale roughness, significantly decreasing the uncertainty in the derived impurity concentration and aerosol optical depth. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors, like those onboard the upcoming PACE and 3MI spaceborne missions. The better characterization of surface and atmospheric parameters in snow-covered regions of the cryosphere ultimately benefits albedo estimates in climate models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.425 | 0.335 |
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".