Unshielded precipitation gauge collection efficiency with wind speed and hydrometeor fall velocity. Part I: modelling results
Bibliographic record
Abstract
Abstract. A new method for assessing collection efficiency using wind speed and hydrometeor fall velocity is presented for the unshielded Geonor T-200B3 precipitation gauge based on computational fluid dynamics results. Time-averaged Navier–Stokes simulations with a k–e turbulence model were used to determine the airflow around the gauge for 0 to 10 m s−1 wind speeds. Hydrometeor trajectories and collection efficiencies were determined using Lagrangian analysis for spherical 10 hydrometeor fall velocities between 0.25 to 10 m s−1 for rain (0.01–3.9 mm diameter), wet snow (0.2–21 mm diameter), dry snow (0.2–7.1 mm diameter), and ice pellets (1.5–4.3 mm diameter). The model results demonstrate that gauge collection efficiency strongly depends on both wind speed and hydrometeor fall velocity. Collection efficiency differences for identical hydrometeor fall velocities are within 0.05 for wind speeds less than 4 m s−1, despite differences in hydrometeor type, diameter, density, and mass. An empirical expression for collection efficiency with dependence on wind speed and fall velocity is 15 presented based on the numerical results, giving a RMSE of 0.03 for dry snow, wet snow, and rain, for wind speeds between 0 and 10 m s−1. The use of fall velocity captures differences in collection efficiency due to different hydrometeor types and sizes, and can be broadly applied even where the precipitation type may be unknown or uncertain. Results are compared to previous models and good model agreement with experimental results is demonstrated in Part II.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".