Synthetic Aperture Radar Observations of the Surface Signatures of Cold-Season Bands over the Great Lakes
Bibliographic record
Abstract
An important aspect of operational meteorology in and around the Great Lakes region of the United States and Canada in the winter months is the forecasting of lake-effect precipitation. While the synoptic- and mesoscale processes that govern the development of lake-effect precipitation have been well understood for many years, problems observing these bands remain because of the limited boundary layer coverage provided by the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. While traditional visible and infrared satellite imagery helps alleviate these coverage limitations, overcast conditions often negate this advantage. Here, a new method for observing lake-effect bands by using synthetic aperture radar (SAR) to identify and characterize their surface signatures is presented. SAR is a remote sensing tool that images surface roughness. Over water, this roughness is related to the surface wind stress and, hence, surface wind field. Here, three cases are documented where the SAR aboard the Canadian Radar Satellite-1 imaged the footprints of precipitating bands over the Great Lakes: one case with multiple snowbands west of one main band over Lake Superior, and two cases with shore-parallel bands over each of Lakes Ontario and Michigan. These cases are first documented using traditional observing methods: infrared satellite imagery, WSR-88D, and surface observations. Then, each SAR image is interpreted based upon the traditional observations. The ultimate goal is to demonstrate that SAR is capable of detecting the surface signatures associated with Great Lakes precipitation bands that could be of value to forecasters when data from traditional observation platforms are unavailable.
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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 | 0.000 |
| 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) | 0.000 | 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".