Automated Retrievals of Precipitation Parameters Using Non-Rayleigh Scattering at 95 GHz
Bibliographic record
Abstract
Abstract Automated retrievals of vertical air motion and the drop size distribution (DSD) slope parameter from the surface to the base of the melting layer are presented using a technique for W-band (95 GHz) profiling radars. The technique capitalizes on non-Rayleigh resonance signatures found in the observed Doppler spectra to estimate the mean vertical air motion. The slope parameter of the DSD for an assumed exponential form is retrieved through an inversion of the Doppler spectra. Extended testing is performed in central Oklahoma for a monthlong period of observation that includes several midlatitude convective line trailing stratiform events featuring low to moderate rainfall rates (<1 to 30 mm h−1). Low-level DSD slope parameter retrievals are shown in agreement (bias of −1.48 cm−1 and rms error of 4.38 cm−1) with collocated surface disdrometer DSD observations. Velocity retrievals indicate a net downward motion in stratiform rain of 0.05 m s−1 with a standard deviation of 0.24–0.3 m s−1. Time–height examples drawn from the available dataset illustrate finescale structures, as well as evidence of drop sorting due to differential terminal velocity and wind shear.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".