What millimeter-wavelength radar reflectivity reveals about snowfall: an information-centric analysis
Bibliographic record
Abstract
Abstract. The ability of single-frequency, millimeter-wavelength radar reflectivity observations to provide useful constraints for retrieval of snow particle size distribution (PSD) parameters, snowfall rates, and snowfall accumulations is examined. An optimal estimation snowfall retrieval that allows analyses of retrieval uncertainties and information content is applied to observations of near-surface W-band reflectivities from multiple snowfall events during the 2006–2007 winter season in southern Ontario. Retrieved instantaneous snowfall rates generally have uncertainties greater than 100 %, but single-event and seasonal snow accumulations from the retrieval results match well with collocated measurements of accumulations. Absolute fractional differences are mainly below 30 % for individual events that have more substantial accumulations and, for the season, 12.6 %. Uncertainties in retrieved snowfall rates are driven mainly by uncertainties in the retrieved PSD parameters, followed by uncertainties in particle model parameters and, to a lesser extent, the uncertainties in the fall-speed model. Uncertainties attributable to assuming an exponential distribution are negligible. The results indicate that improvements to PSD and particle model a priori constraints provide the most impactful path forward for reducing uncertainties in retrieved snowfall rates. Information content analyses reveal that PSD slope is well-constrained by the retrieval. Given the sensitivity of PSD slope to microphysical transformations, the results show that such retrievals, when applied to radar reflectivity profiles, could provide information about microphysical transformations in the snowing column. The PSD intercept is less well-constrained by the retrieval. While applied to near-surface radar observations in this study, the retrieval is applicable as well to radar observations aloft, such as those provided by profiling ground-based, airborne, and satellite-borne radars under lighter snowfall conditions when attenuation and multiple scattering can be neglected.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".