Bibliographic record
Abstract
Abstract. Snow water equivalent (SWE) is a valuable characteristic of snow cover, and it can be estimated using passive spaceborne radiometer measurements. The radiometer-based GlobSnow SWE retrieval methodology, which assimilates weather station snow depth observations with passive microwave brightness temperatures, has improved the reliability and accuracy of SWE retrieval when compared to stand-alone radiometer PMW methods. However, even this assimilation-based method fails to estimate large (> 150 mm) SWE values as snow changes from a scatterer to an emitter. Correcting for these systematic biases can improve PMW-based SWE estimates, especially for high SWE magnitudes. Previously, a monthly bias correction using snow course observations was applied to the GlobSnow v3 product for February – May. This method reduced the spread in March SWE estimated from four gridded products (GlobSnow v3.0, MERRA2, Crocus and Brown snow models forced by ERA-Interim). In this research, we use newly available snow course data to update this bias correction and expand it to cover the months of December through May; we also extend the monthly bias correction to a daily bias correction. The new monthly and daily bias corrections are applied to an updated version of the GlobSnow product – Snow CCI v3.1 product. The Northern Hemisphere climatological snow mass from the Snow CCI v3.1 bias corrected products (daily and monthly) is consistent with that from a suite of reanalysis products. This represents a significant improvement for the months of April and May compared to the original GSv3.0 bias corrected product, as is the provision of daily bias corrected SWE 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.431 | 0.352 |
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".