Evaluation of the Snow Climate Change Initiative (Snow CCI) snow-covered area product within a mountain snow water equivalent reanalysis
Bibliographic record
Abstract
Abstract. An accurate characterization of global snow water equivalent (SWE) is essential in the study of climate and water resources. The current global SWE dataset from the European Space Agency Snow Climate Change Initiative (Snow CCI) is derived from the assimilation of passive microwave satellite data and in situ snow depth measurements. However, gaps exist in the current Snow CCI SWE dataset in complex terrain due to difficulties in characterizing mountain SWE via the passive microwave sensing approach and limitations of the in situ snow depth measurements. This study applies a Bayesian snow reanalysis approach with the existing Snow CCI snow cover fraction (SCF) dataset (1 km resolution) to develop a SWE dataset over four mountainous domains in western North America for water years (WYs) 2001–2019. The reanalysis SWE estimates are evaluated through comparisons with independent SWE datasets and a parallel SWE reanalysis generated using snow extent retrieved from Landsat imagery (30 m resolution). Biases in Snow CCI reanalysis SWE were diagnosed by comparing Snow CCI snow cover with the Landsat reference. Both the number of SCF images and their characteristics (such as zenith angle) significantly affect the accuracy of SWE estimation. Overall, the Snow CCI SCF inputs produce reanalysis SWE of sufficient quality to fill the mountain SWE gap in the current Snow CCI SWE climate data record. A better characterization of the SCF uncertainty and a bias correction could further improve the accuracy of the reanalysis SWE estimates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".