Precipitation measurement using VHF wind‐profiler radars: A multifaceted approach to calibrate radar antenna and receiver chain
Why this work is in the frame
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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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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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