Evaluating western North Pacific tropical cyclone forecast in the subseasonal to seasonal prediction project database
Bibliographic record
Abstract
The Daily Tropical Cyclone Probability (DTCP), defined as the probability of tropical cyclone occurrence within 500 km of a location in 1 day, is proposed and used in evaluating subseasonal to seasonal (S2S) predictions from the subseasonal to seasonal Prediction Project Database, from May 1 to October 31, 1999 to 2010. The ensemble forecasts are collected from eleven operational centers, the Bureau of Meteorology (BoM), China Meteorological Administration (CMA), Environment and Climate Change Canada (ECCC), European Centre for Medium-Range Weather Forecasts (ECMWF), Hydrometeorological Centre of Russia (HMCR), the Institute of Atmospheric Sciences and Climate of the National Research Council of Italy (ISAC), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), Météo-France/Centre National de Recherche Meteorologiques (METFR), the United States National Centers for Environmental Prediction (NCEP), and the United Kingdom Met Office (UKMO). In both observation and these eleven forecast models, the daily tropical cyclone probability is modulated by the Boreal Summer Intraseasonal Oscillation (BSISO), depicted by the two indices, boreal summer intraseasonal oscillation 1 and boreal summer intraseasonal oscillation 2. During boreal summer intraseasonal oscillation 1 phases 1, 5, 6, 7, and 8, the daily tropical cyclone probability in the western North Pacific region is ∼3.5 times higher. Similarly, during phases 1, 2, 3, 4, and 8 of boreal summer intraseasonal oscillation 2, the daily tropical cyclone probability is ∼2.5 times higher. Among the eleven models, the European Centre for Medium-Range Weather Forecasts model best reproduces the climatological daily tropical cyclone probability and its modulation by the boreal summer intraseasonal oscillation in the western North Pacific region, followed by the United States National Centers for Environmental Prediction, the Korea Meteorological Administration, the Japan Meteorological Agency models. Using the daily tropical cyclone probability metric, the highest debiased Brier Skill Score of the eleven models is from European Centre for Medium-Range Weather Forecasts, which has a slightly less skillful prediction than the reference climatological forecast with lead time 11–30 days. The skill of the eleven models is higher during the non-active phases of tropical cyclone activity than their skill during the active phases.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".