AMSR-E Snow: Can Snowfall Help Improve SWE Estimates?
Bibliographic record
Abstract
Abstract Snowfall and snowpack are tightly coupled within the snow water cycle and careful monitoring is crucial to better understand snow’s role in Earth’s water and energy cycles. Current and future estimates of the total amount of seasonal snow on the ground are limited by the variability in the initial snowfall and uncertainties in in situ and remote sensing observations. In this study, passive microwave remote sensing estimates of snowfall and snow water equivalent (SWE) from the Advanced Microwave Scanning Radiometer (AMSR-E) instrument are used to assess the consistency in the snow products. A snow evolution model, SnowModel, is employed to simulate snow processes that occur between the initial snowfall and subsequent SWE. AMSR-E is found to have significant discrepancies in both snowfall and SWE compared to MERRA-2 reanalysis and the Canadian Meteorological Centre (CMC) snow product. It is shown that AMSR-E snowfall is currently not a useful metric to estimate SWE without applying large corrections throughout the winter season. Regions of consistency in the AMSR-E snow products occur for reasons that pertain to underestimation in both snowfall and SWE. In addition to snow consistency, microwave brightness temperatures (TBs) are analyzed in response to the snowpack and snowfall physical properties. These experiments indicate significant sensitivity to regime-dependent scattering characteristics that must be accounted for to accurately estimate global snow properties and provide better physical consistency in the snow products from remote sensing platforms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".