Introduction to the special issue on “25 years of ensemble forecasting”
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
Twenty‐five years ago the first operational, ensemble forecasts were issued by the European Centre for Medium‐Range Weather Forecasts and the National Centers for Environmental Prediction. These centres were followed in 1996 by the Meteorological Service of Canada, and in the subsequent years by many others. Operational ensemble‐based, probabilistic forecasts signed a paradigm shift in weather prediction: for the first time, forecasters and users could have reliable and accurate estimates of the range of possible future scenarios, and not just a single realization of the future. Today, ensembles are used not only to provide reliable and accurate forecasts for the short and medium range, the monthly and seasonal time‐scale, but also to provide estimates of the initial state of the atmosphere, and to generate future climate projections. This article provides an overview on how we developed the early ensembles, illustrates the key characteristics of the seven operational, global, medium‐range ensembles, and discusses ongoing trends to further improve ensemble performance.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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