Snow Studies. Part IV: Ensemble Retrieval of Snow Microphysics from Dual-Wavelength Vertically Pointing Radars
Bibliographic record
Abstract
Abstract Based on the theory developed in Part III, this paper introduces a new method to retrieve snow microphysics from ground-based collocated X- and W-band vertically pointing Doppler radars. To take into account the variety of microphysical relations observed in natural precipitating snow and to quantify the uncertainty in the retrieval results caused by this variety, the retrieval is formulated using the ensemble-based method. The ensemble is determined by the spread of uncertainties in the microphysical descriptions applied to map the same radar observables to the retrieved quantities. The model descriptors use diverse assumptions concerning functional forms of particle size distribution and mass–velocity relations, all taken from previous observational studies. The mean of each ensemble is assumed to be the best estimate of the retrieval while its spread is defined by the standard deviation that characterizes its uncertainty. The main retrieved products are the characteristic size, the snow mass content, and the density parameter, as well as the vertical air motion. Four observables used in the retrieval are the difference in reflectivities and in Doppler velocities at two wavelengths, together with the equivalent reflectivity factor and Doppler velocity at X band. The solutions that are not consistent with all four observables after taking into account their estimated measurement errors are eliminated from the ensembles. The application of the retrieval algorithm to the real data yields a snow microphysical description that agrees with the snow characteristics seen in the vertical profile of the observed Doppler spectrum.
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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 | 0.001 |
| 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 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".