Solid snowfall rate estimation using a C‐band radar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Snow water‐equivalent (SWE) estimation is important for meteorologists and hydrologists, but solid snowfall estimation (snow depth) is essential for on‐duty meteorologists, the snow removal authorities and airports. Such an estimation can help meteorologists better quantify solid snowfall amounts and allow them to issue more accurate alerts to designated agencies, cities, municipalities, airports or the public. These agencies and the public are usually more interested in snow depth. Data from the dual‐polarimetric C‐band King City radar (CWKR) near Toronto in Ontario, Canada, and solid snowfall observations from nearby Oakville were used to establish radar‐based solid‐snowfall algorithms. A nonlinear regression analysis method was used to develop two power‐law algorithms to estimate solid snowfall rates (cm/hr): one used reflectivity and the other both reflectivity and differential reflectivity. These algorithms directly determine snowfall rates (which can be translated to snow accumulation on the ground or snow depth), in contrast to the conventional radar‐based technique of estimating the melted snowfall rate (mm/hr), before applying a constant snow–liquid ratio (SLR) to obtain the solid snowfall rate. Both new algorithms were similar at estimating solid snowfall rates and showed far superior results when compared with the one currently used by Environment Canada which uses a constant SLR of 10:1. Although there is no unique SLR for any geographical area, the solid and SWE ground measurements from Oakville suggests a higher SLR (14:1) as a better representative value than that currently assumed. Further validation of the algorithm requires frequent accurate solid snowfall data.
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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) | 0.003 | 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 it