Northern Hemisphere in situ snow water equivalent dataset (NorSWE, 1979–2021)
Bibliographic record
Abstract
Abstract. In situ observations of snow water equivalent (SWE) are critical for climate applications and resource management, yet there is no global database of in situ SWE observations. Here, we present the Northern Hemisphere in situ snow water equivalent dataset (NorSWE), consisting of over 11.5 million SWE observations from more than 10 000 different locations across the Northern Hemisphere spanning the modern satellite era (1979–2021). NorSWE builds on an existing framework applied to Canadian data (CanSWE; Vionnet et al., 2021). It includes SWE observations from manual snow courses covering Canada, the United States, Norway, Finland, and Russia and from automated sensors (snow pillows, snow scales, and automated passive gamma radiation sensors) in Canada, the United States, Norway, and Nepal. Airborne passive gamma SWE estimates provide additional coverage over North America. Exceptionally, to expand coverage over Europe, we also include single-point manual SWE observations from 11 sites in Switzerland. In addition to SWE, snow depth (SD) and derived bulk snow density are included when available. A consistent quality control is applied to all records, and the final dataset is delivered as a single NetCDF file that is publicly available at https://doi.org/10.5281/zenodo.15263370 (Mortimer and Vionnet, 2025).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".