A Fuzzy Logic–Based Analog Forecasting System for Ceiling and Visibility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 it