A Fuzzy Logic–Based Analog Forecasting System for Ceiling and Visibility
Bibliographic record
Abstract
Abstract WIND-3 is an application for aviation weather forecasting that uses the analog method to produce deterministic predictions of cloud ceiling height and horizontal visibility at airports. For data, it uses historical and current airport observations [routine aviation weather reports (METARs)], and model-based guidance. It uses the perfect prognosis assumption as it is designed to use any model-based predictions of wind direction and speed, temperature and humidity, and precipitation occurrence and type to specify conditions in the 1–24-h projection period. To identify and rank analogs, according to their degree of similarity with the present situation, it uses a fuzzy logic–based algorithm to measure similarity between past situations, which are complete series of METARs, and current situations, which are a composite of recent METARs and model-based guidance. It uses the retrieved analog ensemble, the set of most similar analogs, to make predictions of ceiling and visibility in the 1–24-h projection period. WIND-3 has been tested by being run continuously in real time for 1 yr, producing forecasts for 190 major Canadian airports. It produces accurate forecasts, based on summaries of Heidke skill score (HSS) statistics, and compared to two benchmarks, persistence and official aerodrome forecasts [terminal aerodrome forecasts (TAFs)]. WIND-3 predictions of instrument flight regulation (IFR) conditions in the 0–6-h period have an HSS of 0.56, and in the 7–24-h period have an HSS of about 0.40, compared to respective HSS scores for persistence forecasts of 0.53 and less than 0.20.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".