Validation of Snow Backscattering Models Using Ground-Based Measurements
Bibliographic record
Abstract
Microwave remote sensing signals are commonly used to retrieve key snowpack parameters, such as snow water equivalent and snow depth, and their accuracy is influenced by various factors. Snow radiative transfer models are crucial for understanding the interactions between microwave signals and snowpacks. In this context, this study aims to perform the validation and intercomparison of two commonly used active microwave snow forward models at four-frequency, C- (5.4 GHz), X- (10 GHz), Ku-band (13.3 and 17.7 GHz) and tri-polarization (VV, HH and VH) channels using field campaign measurements collected from Finland, Canada, and China. Here the Snow Microwave Radiative Transfer (SMRT) and the Bicontinuous Dense Media Radiative Transfer (Bic-DMRT) models are selected. Our preliminary results indicate that both SMRT and Bic-DMRT models perform similar performances at 10 and 14 GHz, with the correlation values of 0.84-0.95, ubRMSE values of 0.64-1.57 dB. Meanwhile, the simulated values at HH polarization exhibit high bias (up to -1.38 dB) and larger ubRMSE (1.52 dB) than VV polarization. We also find both models present poor performances at 17.75 GHz, especially for HH polarization. For example, the ubRMSE (bias) values are 2.85 (-2.55) dB and 1.93 (-1.21) dB respectively for SMRT and Bic-DMRT models. Our subsequent validation and intercomparison results of SMRT and Bic-DMRT in Finland and Canada are in progress.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".