New recommendations for snow water equivalent products: results from the international snow products intercomparison and evaluation exercise (SnowPEx+)
Bibliographic record
Abstract
<!--!introduction!--> The SnowPEx+ initiative is a continuation of the original International Snow Products Intercomparison and Evaluation Exercise (SnowPEx), an international effort carried out under the umbrella of Global Cryosphere Watch / WMO and funded by the European Space Agency. We present the latest results concerning historical snow water equivalent products (SWE), which characterize climatological snow mass and snow mass trends. We assess a wide variety of products ranging from those based strictly on remote sensing information, blended Earth-observation products that include assimilation of ground measurements, reanalysis products of varying complexity, and snow models forced by reanalysis meteorology. We first provide a statistical assessment of product performance using two fully independent reference data sets. We demonstrate that we can robustly validate SWE product estimates in non-mountain regions where the evaluation statistics are comparable for both reference datasets. In mountainous regions, validation is complicated by systematic elevation-related biases between reference data and the assessed products, but we demonstrate that we can partially account for these biases in order to provide consistent product rankings. Secondly, we perform an intercomparison of product snow mass trends for various Northern Hemisphere subregions over the 1981-2020 period. We demonstrate good agreement among selected products. For blended Earth-observation products in particular, their agreement depends strongly on the period analyzed and is suggestive of discontinuities (related to both satellite and surface measurements) occurring during 1987 and 2008. Based on the combined validation and intercomparison results, we make recommendations for appropriate use for individual products on a case by case basis.
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".