Analyses of precipitation signal using VHF vertically-pointing radar
Bibliographic record
Abstract
In addition to a proper radar calibration, quantitative estimation of precipitation from VHF radars requires the extraction of the precipitation signal out of the Doppler spectra. It also requires the proper conversion of this precipitation signal into a reflectivity factor. This research develops a multi-faceted approach for the calibration of VHF vertically-pointing radars, by combining a first calibration method that compares the recorded VHF signal to power coming from a noise generator and a second calibration method that compares recorded VHF signal to cosmic radiation. This approach allows the retrieval of antenna and receiver parameters (such as noise levels, efficiency, and gain), and four other equations for the corresponding errors. In addition, we develop an equation for calibrating Doppler spectra. The analysis is focused on rain observations with VHF radar. We verify the hypothesis that |K|2 = 0.93 for most of the rain observations at VHF band. A signal-processing algorithm for extracting the rain signal out of the VHF power spectra is then presented. This work also derives a general version of the radar equation valid for vertically pointing radars, as well as a particular version of this equation valid for the McGill VHF radar. The study then makes numerical simulations of several profiles of precipitation signal at VHF band, by combining high-resolution profiles of precipitation signal (from a calibrated X-band radar) and the VHF antenna pattern in our general version of the radar equation. The analyses indicate that VHF reflectivity at gates above the melting layer is artificially enhanced by the precipitation signal collected from the side lobes. This work also studies the effect of precipitation in the scattering properties of clear air. We analyze several cases of stratiform and convective rain, occurring in a continental mid-latitude environment (Montreal, Lat.45.41°N, Long.73.94°W). For these cases, Doppler spectra taken by a VHF vertically-pointing radar were used to retrieve simultaneous co-located values of precipitation intensity (rainrates) and degrees of refractive index fluctuation (structure-function parameter for refractivity turbulence, Cn2). We validated these retrievals using co-located, calibrated measurements of precipitation signal at X-band. The comparison between equivalent reflectivity factors at X and VHF bands agrees within 1 dB. The study includes rainrates between 0.3 and 78 mm/h, and Cn2 values between 10-16 and 10-12 m-2/3 , retrieved from the VHF spectra at 2.5 km height. The study finds that the occurrence of rain is associated with distinctive changes in the structure of air refractive index fluctuations, and that these changes are of a turbulent nature for the most intense rainrates.
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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 | 0.000 |
| 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) | 0.005 | 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".