Utilizing satellite snow cover data for climatological analysis: a comparison of passive microwave and optically derived time series, 1978 - 1995
Bibliographic record
Abstract
When Special Sensor Microwave/Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) data are combined, the time series of spaceborne passive microwave brightness temperatures extends from 1978 to the present. The Meteorological Service of Canada (MSC) has developed a series of operational snow water equivalent (SWE) retrieval algorithms for western Canada that can be applied to both SMMR and SSM/I data. Before research issues can be addressed with a cross-platform time series, however, attention must be given to the impact of spatial, temporal, and radiometric differences between the SMMR and SSM/I data on time series continuity and consistency. In this study, we illustrate that passive microwave SWE retrievals with the MSC algorithms during the nine SMMR winter seasons (1978 - 1987) are consistently lower than the SWE estimates produced for the following eight SSM/I winter seasons (1987 - 1995). A snow extent anomaly time series produced by the National Oceanic and Atmospheric Administration (NOAA) from optical satellite data shows that the SMMR seasons of 1978/79 through 1986/87 are not characterized by deficit snow cover when compared to the SSM/I seasons of 1987/88 through 1994/95, indicating a consistency problem in the cross-platform passive microwave time series. Previously derived empirical brightness temperature corrections are examined, but appear to be unsuitable for use with the MSC algorithms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 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".