Misreporting of Freezing Fog during Snowfall Conditions in U.S. METAR Observations
Bibliographic record
Abstract
Abstract Misreported weather conditions at airports can cause significant and unnecessary flight delays and cancellations, while increasing costs to the airlines. In 2022, updates to the Federal Aviation Administration (FAA) holdover time tables for aircraft ground deicing operations included guidance for snow (SN) mixed with freezing fog (FZFG). Holdover time tables provide information on the length of time (i.e., holdover time) anti-icing fluids will protect the aircraft prior to takeoff under various winter weather conditions. The new holdover times for SN mixed with FZFG are significantly shorter than the holdover times for SN or FZFG reported individually. Prior to the introduction of this guidance, pilots would often assess the SN and FZFG conditions individually and use the most conservative holdover time between the two weather conditions. The new guidance has led pilots and ground deicing crews to express concern that FZFG conditions are often reported with SN when FZFG is not present. To assess this, 1-min-observation data from selected Automated Surface Observing System (ASOS) locations prone to SN and FZFG conditions were analyzed to determine if an FZFG signal could be detected using measurements other than visibility during SN conditions. Additionally, Meteorological Aerodrome Reports (METARs) from two nearly collocated airports (one in the United States and one in Canada) were analyzed since Canada relies on human observers to report obscurations, including FZFG. The outcome of both methods indicates a significant number (∼85%) of misreported FZFG reports during SN conditions and provides a basis for improving the automated weather-reporting algorithms. Significance Statement The purpose of this work is to demonstrate a problem with the method for automatically reporting freezing fog with snow conditions using the current automated weather observing systems in the United States. The key findings show that freezing fog is misreported with snow conditions at least 85% of the time. Incorrect reporting of freezing fog conditions with snow can have significant impacts on aircraft ground deicing operations, resulting in unnecessary flight delays and cancellations, and may require the aircraft to be deiced multiple times unnecessarily, leading to increased costs and decreased efficiency during winter conditions for airline operators.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".