Estimation of snow depth over open prairie environments using GOES imager observations
Bibliographic record
Abstract
Abstract We assess the potential for estimating snow depth using observations in the visible and infrared spectral bands from the imager instrument onboard the Geostationary Operational Environmental Satellites (GOES). The approach makes use of a correlation between depth of the snowpack and satellite‐derived subpixel fractional snow cover over non‐forested and sparsely forested areas. To retrieve the snow depth we propose a simple analytical formula approximating the statistical relationship between the snow depth and the snow fraction. The primary focus of this study was the US Great Plains and Canadian prairies area. Daily maps of snow depth at a spatial resolution of 4 km have been produced for this region for four winter seasons from late 1999 to the beginning of 2003. Validation of the algorithm developed was performed through comparison of the satellite‐based product with snow depth measurements made at first‐order synoptic stations, US Cooperative Network stations and Canadian climate stations. The accuracy of snow depth retrievals was found to be about 30% of the observed snow depth for snow depths below 30 cm. Copyright © 2004 John Wiley & Sons, Ltd.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".