ERRORS IN THE RADAR CALIBRATION BY GAGE, DISDROMETER, AND POLARIMETRY: THEORETICAL LIMIT AND APPLICATION TO OPERATIONAL RADAR.
Bibliographic record
Abstract
Abstract A long time record of drop size distributions (DSDs) is used to evaluate the effect of the DSD variability on the accuracy of radar calibratin by a) comparison with a rain gage on a daily basis and b) with polarimetric information. A calibration of reflectivity can be done if a disdrometer is available. Good correlations between radar and disdrometric reflectivities indicate that this could be an excellent way of calibrating radar on a daily basis. The information from operational S-band polarimetric radar is also used for calibration. The sensitivity of a polarimetric calibration with respect to the drop deformation is tested. Furthermore, the consistency in the disdrometric and polarimetric calibration suggests that the use of both calibrations allows to estimate the mean drop deformation. Key Words: Radar calibration, rain gage, disdrometer, operational polarimetric radar, drop size distributions (DSD), drop deformation Introduction When considering radar calibration there are several areas of interest. The stability of the electronic equipment is a concern in its own right. Possible effects of radome on the measurements are a problem that could depend on the state of the radome, whether dry, wet, or with rain streaks. In hydrological applications calibration of the radar may imply some adjustment with ground truth. The differences between the radar measurements and precipitation intensity at the ground may be caused by the height of the radar measurement coupled with the vertical profile of reflectivity, contamination by non-meteorological target, etc (Wilson and Brandes 1979; Zawadzki 1984). The transformation of radar reflectivity into rain rate is another source of discrepancy. Instead of adjusting radar information with ground truth, the radar calibration with radar data themselves is another possibility that can provide an independent monitoring of the performance of system. Due to its inherent characteristics, the specific differential phase shift
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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) | 0.000 | 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 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".