Design and Performance Characteristics of the New 8.5-m Dual-Offset Gregorian Antenna for the CSU–CHILL Radar
Bibliographic record
Abstract
Abstract The Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) national weather radar facility has been operated by the Colorado State University under a cooperative agreement with the U.S. National Science Foundation from 1990 to the present. The radar is configured to measure the elements of the 3 × 3 polarimetric covariance matrix based on using a two-transmitter and two-receiver system in the horizontal–vertical polarization basis. This S-band Doppler, dual-polarized radar facility is used for observations of precipitation with the highest possible data quality. To achieve this, a new dual-offset 8.5-m Gregorian antenna was custom designed and built by VertexRSI (now General Dynamics SATCOM) in Kilgore, Texas, to replace the circa 1994 center-fed parabolic reflector antenna. Here, the design features used to achieve the stringent specifications in terms of the sidelobe envelope and off-axis cross-polar levels are described, and the way in which they were validated at the manufacturer’s long- and short-range pattern measurement facility. Measurements in several different storm types, including stratiform rain and an intense hailstorm, and ground clutter (from mountains) are used to illustrate the new antenna performance. The linear depolarization ratio (LDR) system limit is shown to be −40 dB or better, which should lead to more insights into the microphysics of convective precipitation at subfreezing temperatures (e.g., hail formation, improved hydrometeor-type classification), and in winter precipitation in general (e.g., aggregation processes, rimed versus unrimed particles). In the case of the intense hailstorm, it is shown that measurement artifacts resulting from strong cross-beam gradients of reflectivity, up to 40 dB km−1 at 40-km range, have been greatly reduced or eliminated. Previously noted measurement artifacts with the 1994 antenna at storm tops in intense convection have been eliminated with the dual-offset antenna. The ground (mountain) clutter example shows greatly reduced returns (in terms of near-zero mean Doppler velocity areas) because of rapid falloff in the sidelobe levels with increasing elevation angle. The greatly improved antenna performance as compared with the 1994 antenna are expected to result in corresponding data quality improvements leading to more accurate measurement of rain rate and hydrometeor classification.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".