An attempt to calibrate the UHF strato-tropospheric radar at Arecibo using NexRad radar and disdrometer data
Bibliographic record
Abstract
Abstract. The goal of this paper is to present a methodology to calibrate the reflectivity of the UHF Strato-Tropospheric (ST) radar located at NAIC in Puerto Rico. The UHF lower relevant altitude is at 5.9km, the melting layer being at around 4.8km. The data used for the calibration came from the observations of clouds, carried out with Strato-Tropospheric dual-wavelength (UHF and VHF) radars and a disdrometer; those instruments being located on the NAIC site in Arecibo, Puerto Rico. The National Weather Service operates other instruments like the radiosondes and the NexRad Radar in other sites. The proposed method proceeds in two steps. The first consists of the comparison between the NexRad reflectivity and the reflectivity computed from the drop size distributions measured by the disdrometer for one day with a noticeable rainfall rate. In spite of the distance of both instruments, the agreement between the reflectivities of both instruments is enough good to be used as a reference for the UHF ST radar. The errors relative at each data set is found to be 2.75dB for the disdrometer and 4dB for the NexRad radar, following the approach of Hocking et al. (2001). The inadequacy between the two sampled volume is an important contribution in the errors. The second step consists of the comparison between the NexRad radar reflectivity and the UHF non-calibrated reflectivity at the 4 altitudes of common observations during one event on 15 October 1998. Similar features are observed and a coefficient is deduced. An offset around 4.7dB is observed and the correlation factor lies between 0.628 and 0.730. According to the errors of the data sets, the precision on the calibration is of the order of 2dB. This method works only when there are precipitation hydrometeors above the NAIC site. However, the result of the calibration could be applied to other data obtained during the campaign, the only constraint being the same value of the transmitter power. Key words. Meteorology and atmospheric dynamics (tropical meteorology; remote sensing; instruments and techniques)
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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 | 0.001 |
| Open science | 0.001 | 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".