Influence of the DSD variability at the radar subgrid scale on radar power laws
Bibliographic record
Abstract
Power laws relating the different radar observables (from conventional or polarimetric radar systems) are commonly used in the processing of radar measurements (e.g., attenuation correction) and in their conversion into rain rate values (e.g., Z-R relationship). These power laws are usually established from measured raindrop size distributions (DSD) collected by disdrometers (at the ground level), that correspond to point-scale measurements. These power laws are then applied at the scale of a radar sampling volume (in the order of 1 km3). The DSD can be variable within a radar pixel, and this variability hence questions the representativity of power laws derived from point measurements and applied to radar observations. To investigate the small-scale variability of the DSD, a network of 16 disdrometers (Parsivel, 1st generation) has been deployed over EPFL campus in Lausanne, Switzerland, during 16 months. 36 rain events have been selected and classified in 3 groups of rain types: convective, transitional and stratiform. This set of events can reasonably be seen as representative of the local climatology. The spatially distributed DSD measurements from the disdrometer network are used to quantify the spatial variability and the spatial structure of the DSD within a typical radar pixel. The coefficients of the Z-R and the RKdp power laws are estimated at the point and the pixel scales. The influence of their variability within a radar pixel on the quality of the rain rate estimate is quantified and is shown to be potentially significant.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".