Evaluation of Gridded Snow Water Equivalent Products Using Cloudsat-Cpr Snowfall Estimates
Bibliographic record
Abstract
Arctic snow is a critical contributor to the global water and energy budget with important connections to cold region flooding and water resource management practices. Snow water equivalent (SWE) is the amount of liquid water contained within a snowpack. Measuring Arctic SWE through traditional ground-based techniques is challenging due to the vast size and remote nature of the region. Remote sensing applications offer new perspectives towards SWE accumulation estimates across the Arctic. The cloud profiling radar (CPR) instrument installed on the NASA CloudSat satellite observes snapshots of falling snow in clouds and has been effectively used in previous work for estimating monthly surface SWE accumulation across high latitude regions. By comparing consecutive month-pair estimates of accumulated SWE from CloudS at$(\overline{SWE}_{C})$with the positive difference in SWE on ground over the same period in gridded SWE products$(\Delta SWE_{B})$, we gain new insights into areas and locations of statistically inconsistent accumulation estimates. Applying this technique to the Blended-4 gridded SWE product from 60° to 82° N allows us to generate a quality flag which automatically highlights areas of inconsistent accumulation. This technique has flagged 4885 outliers over 107 month-pairs spanning 2007–2015 across the Northern Hemisphere when applied to the Blended-4 dataset. CPR snowfall data acts as an independent observational constraint for accumulated SWE in a region which is both difficult and expensive to traditionally observe. This comparison process can be used to further enhance the accuracy and robustness of future gridded SWE products across high latitude regions.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".