Insights into the North Hemisphere daily snowpack at high resolution from the new Crocus–ERA5 product
Bibliographic record
Abstract
Abstract. This article provides an overview of the daily Crocus–ERA5 snow product covering the Northern Hemisphere from 1950 to 2022. It assesses the product's performance in terms of snow depth and cover compared to in situ observations and satellite data. Compared to its predecessor, Crocus-ERA-Interim, Crocus–ERA5 benefits from improved spatial resolution and better atmospheric data assimilation, resulting in more accurate snowpack estimates, especially during spring in Eurasia. The findings show a good match with observations, though biases remain, particularly in some Arctic regions, where the model tends to overestimate spring melt. In low-vegetation areas such as tundra, Crocus–ERA5 may introduce biases due to its limited consideration of interannual vegetation changes, leading to inaccuracies in the simulation of snowmelt. The production of this snow dataset responds to the request of the continental cryosphere community. In particular the French and Canadian government institutions CNRM (National Center for Meteorological Research) and ECCC (Environment and Climate Change Canada) have been involved in monitoring Arctic snow cover as part of the ”Terrestrial Snow” section of the Arctic Report Card since 2017. The Crocus–ERA5 product is freely available on a daily basis and at 0.25° resolution over the 1 July 1950 to 30 June 2023 period (Decharme et al., 2024, https://doi.org/10.5281/zenodo.14513248).
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".