Calibration of weather radars with a target simulator
Bibliographic record
Abstract
We present findings from radar calibration experiments involving three radars operated by the Colorado State University (CSU) in the US and by the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. The experiments were based on the comparison between measured radar variables and the known properties of artificial point targets electronically generated with a polarimetric radar target simulator (RTS) from Palindrome Remote Sensing. Radars under test included the two magnetron-based radars CHILL and SPLASH (its mobile version) from CSU and EPFL's new solid-state radar StXPol. For the CHILL and SPLASH calibration measurements in Colorado, a mobile lifting platform was employed that elevated the target simulator instrument to approximately 15 m above ground. The creation of virtual targets with polarimetric signatures allowed for a direct calibration of polarimetric variables. While the SPLASH radar exhibited good Zdr and sufficient Zh accuracy, remarkable precision and stability were found in CHILL's reflectivity data time series, where the reflectivity bias compared to the virtual target was less than 0.2 dB over a 1 h time series. Calibration issues that arise with solid-state radar systems were investigated with experiments conducted with the EPFL StXPol radar. This pulse compression system transmits a linear frequency-modulated long pulse as well as a non-modulated short pulse for observations at close ranges. The two pulses are separated in frequency by 50 MHz, and consequently calibration targets were generated independently for the two channels. Excellent stability and accuracy were found for Zdr in both channels. While Zh stability was also very high, a large reflectivity bias in both the long and the short pulse channel was detected. For the first time, the article introduces and analyzes a weather radar calibration procedure that is based on electronically generated radar targets. Experimental data suggest that precise absolute and differential calibrations can be achieved if data are obtained in an environment free from multipaths and if the generated targets are precisely located in the center of the radar's range gate. Experimental shortcomings associated with limited sampling resolution of the radar scan over the targets are also investigated.
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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) | 0.000 | 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) | 0.000 | 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".