A Review on the developments of NCEP, ECMWF and CMC global ensemble forecast system
Bibliographic record
Abstract
The paper summarizes the developments of the National Centers for Environmental Prediction(NCEP),the European Centre for Medium-Range Weather Forecasts(ECMWF),and the Canadian Meteorological Centre(CMC),which are the most representative of global ensemble forecast system(GEFS).Due to the enlarging of computational resources,the model resolution and ensemble size of their GEFS subsequently increase.At the same time,for promoting the improvement of the forecast skill,they all devote to develop the initial and model perturbation methods used to simulate the effect of initial and model uncertainties.The initial perturbation methods are updated from the singular vector(SV) method(ECMWF),the breeding method(NCEP) and the perturbed observation(PO) method(CMC) to the ensemble of data assimilation and singular vector(EDA-SV) method(ECMWF),the ensemble transform with rescaling(ETR) method(NCEP) and the ensemble Kalman filter(EnKF) method(CMC).Several attempts are also made to account for model-related uncertainty.ECMWF and CMC have revised their stochastic physics parameterization tendencies(ECMWF) and multi-parameterization(CMC) schemes,and NCEP also develops stochastic total tendency perturbation to estimate the model-related uncertainty.To accelerate improvements on the accuracy of global high-impact weather forecasts,TIGGE(the THORPEX interactive grand global ensemble) was initiated to enhance international collaboration on multi-center and multi-model ensemble forecast,and NAEFS(North American ensemble forecast system) can provide an operational framework for global multi-model ensemble forecast system.They are all helpful for developing the global interactive forecast system(GIFS).
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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) | 0.001 | 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) | 0.001 | 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 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".