Snow Studies. Part III: Theoretical Derivations for the Ensemble Retrieval of Snow Microphysics from Dual-Wavelength Vertically Pointing Radars
Bibliographic record
Abstract
Abstract As a first step toward retrieval of snow microphysics from two vertically pointing radars operating at X band and W band, a theoretical model of snow microphysics is formulated in which the number of unknown parameters is reduced to snow particle density and to two bulk quantities controlling the particle size distribution. This reduction of parameters is achieved by normalizing not only the size distribution but also the snow particle mass in the mass–size relationship as well as by using a relationship between snow density and snow terminal fall velocity. However, no single snow microphysical model could describe the observed variability in the radar measurements. The uncertainty in the developed deterministic relations that map the microphysical parameters to the observables is shown to be mainly associated with the assumed dependence of particle velocity on its mass and on the particle size distribution (PSD) representation. Hence, various mass–velocity relationships together with different generic functional forms of the PSD reported in literature are described in this paper and then used in the retrieval. The derived relations provide a reasonable range of uncertainty associated with the microphysics when used for the actual retrieval of snow properties from observations in Part IV. The uncertainty in the backscattering computations of an individual particle, performed using Mie theory assuming spherical form with nonuniform density, is not taken into account in this study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".