Precipitation measurements using VHF wind profiler radars: Measuring rainfall and vertical air velocities using only observations with a VHF radar
Why this work is in the frame
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Bibliographic record
Abstract
In addition to a proper radar calibration, quantitative estimation of precipitation from radars also requires the extraction of the precipitation signal out of the Doppler spectra. It also requires the proper conversion of this precipitation signal into reflectivity factor. This study shows how the measurement of rainfall and vertical air velocities can be performed using only observations from a radar operating at the VHF band (i.e., meter wavelengths). We verify the assumption that the dielectric factor ∣K∣ 2 = 0.93 is valid for rain observations in the VHF band. We then derive, analytically and numerically, a more general version of the radar equation valid for vertically pointing radars with targets within a few kilometers range but still within the antenna far‐field region. Following this, we describe a new algorithm for extraction of rain signal out of VHF Doppler spectra. To validate our methods, we made colocated measurements of VHF Doppler spectra aloft and raindrop sizes at the ground. The analytical version of our radar equation compares well with similar equations available in current literature, and this validates the particular case of our numerically derived radar equation. We combine our numerical version of the radar equation and our algorithm for extracting precipitation signal. This combination allows us to obtain reflectivity factors (from rain signals) and vertical velocities (from air signals), these being simultaneous observations within the same sampling volume. From the data set collected, we found good agreement (linear correlation coefficient around 0.8) between the rain signals derived from VHF observations aloft and from drop sizes at ground level. Hence we are able to measure rainfall amounts and vertical air velocities in a simpler and more efficient way, using only observations from a VHF wind profiler. This represents a promising step toward the analysis of precipitation from large sets of radar data.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it