Improving Terminal Area Supercooled Large Drop Detection with One-Minute Ceilometer Profiles Obtained During the In-Cloud Icing and Large Drop Experiment (ICICLE)
Bibliographic record
Abstract
The Federal Aviation Administration (FAA) Terminal Area Icing Weather Information for NexGen (TAIWIN) project seeks to develop and improve icing condition nowcasting and forecasting in airport terminal areas, with an emphasis on identifying and discriminating between small-drop (Appendix C) and large-drop (Appendix O) icing conditions. Remote sensing of clouds and precipitation via radar and satellite are invaluable in this pursuit, but one widely available remote sensing asset remains underutilized: the ceilometer. Installed at all 900+ Automated Surface Observing Stations (ASOSs) at airports across the conterminous United States, the Vaisala CL31 ceilometer currently provides cloud base height and sky coverage information for up to three cloud decks in hourly METAR observations. To produce such observations, the CL31 generates a vertical backscatter profile every minute to which it applies an algorithm to calculate cloud base heights and sky coverages. Once sky conditions have been calculated, the raw backscatter profiles are discarded. These discarded profiles may provide valuable insight into sub-cloud-base precipitation extent and structure due to the CL31’s 10-meter vertical resolution which is up to two orders of magnitude greater than that provided by a WSR-88D volume scan. To investigate such a possibility, the National Center for Atmospheric Research (NCAR) installed, operated, and archived data from a CL31 at Kalamazoo International Airport (KAZO) as part of a surface observing instrument suite during the FAA In-Cloud Icing and Large Drop Experiment (ICICLE) from January to March, 2019. Raw backscatter profiles are analyzed against other available meteorological datasets, including those from GOES-16, the WSR-88D located 74km away at Grand Rapids (KGRR), and the ICICLE dataset, to assess its applicability to terminal area icing diagnosis.
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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".