Updated monthly and new daily bias correction for assimilation-based passive microwave SWE retrieval
Bibliographic record
Abstract
Snow water equivalent (SWE) is a valuable characteristic of global snowpack, 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 passive microwave (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. 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 GlobSnow v3.0 bias corrected product, as is the provision of daily bias corrected SWE estimates.
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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".