Observed relations between snowfall microphysics and triple‐frequency radar measurements
Bibliographic record
Abstract
Abstract Recently published studies of triple‐frequency radar observations of snowfall have demonstrated that naturally occurring snowflakes exhibit scattering signatures that are in some cases consistent with spheroidal particle models and in others can only be explained by complex aggregates. Until recently, no in situ observations have been available to investigate links between microphysical snowfall properties and their scattering properties. In this study, we investigate for the first time relations between collocated ground‐based triple‐frequency observations with in situ measurements of snowfall at the ground. The three analyzed snowfall cases obtained during a recent field campaign in Finland cover light to moderate snowfall rates with transitions from heavily rimed snow to open‐structured, low‐density snowflakes. The observed triple‐frequency signatures agree well with the previously published findings from airborne radar observations. A rich spatiotemporal structure of triple‐frequency observations throughout the cloud is observed during the three cases, which often seems to be related to riming and aggregation zones within the cloud. The comparison of triple‐frequency signatures from the lowest altitudes with the ground‐based in situ measurements reveals that in the presence of large (>5 mm) snow aggregates, a bending away in the triple‐frequency space from the curve of classical spheroid scattering models is always observed. Rimed particles appear along an almost horizontal line in the triple‐frequency space, which was not observed before. Overall, the three case studies indicate a close connection of triple‐frequency signatures and snow particle structure, bulk snowfall density, and characteristic size of the particle size distribution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".