Precipitation measurement using VHF wind‐profiler radars: A multifaceted approach to calibrate radar antenna and receiver chain
Bibliographic record
Abstract
Many quantitative analyses of radar signal require a radar calibration. Established calibration methods for VHF radar provide only partial information about antenna or receiver parameters. We propose that a more complete approach to calibrate VHF radar can be obtained by combining multiple calibration methods. To test this, we developed a calibration technique 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. We derive four equations that allow us to retrieve antenna and receiver‐chain parameters (such as noises, efficiency, and gain), and four other equations for the corresponding errors. In addition, we develop an equation for calibrating Doppler spectra. To test our calibration technique, we collected an extensive data set from the McGill VHF radar. For validation, we performed a third calibration using measurements of voltage and impedance to compute power losses in the antenna transmission lines. On the basis of our equations, we have found the values for the antenna and receiver‐chain parameters in the McGill VHF radar, and their corresponding uncertainties, and we have compared these to the energy losses obtained by the third calibration method. The antenna efficiencies derived by our technique and by the third calibration method agreed within 0.5 dB. Furthermore, analyses of our calibrated Doppler spectra in rain demonstrate the potential of this calibration technique for absolute measurement of precipitation by wind‐profiler radar.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".