Aspects of ECMWF model performance in polar areas
Bibliographic record
Abstract
Global numerical weather prediction skill over polar areas is assessed, mostly based on the European Centre for Medium‐Range Weather Forecasts (ECMWF) system but also the Met Office, Japan Meteorological Agency (JMA), Environment Canada and National Centers for Environmental Prediction (NCEP) analysis data. Polar forecast verification against analyses shows a similar trend of forecast improvement over the past 12 years compared with improvements at lower latitudes. These improvements are presumably due to increased model resolution and model sophistication, improved data assimilation methods and increased observational data coverage and better data quality. By comparing ECMWF's real‐time forecast skill changes against those from reforecasts initialized from reanalyses, it is possible to quantify how much of the improvement is from system improvements and how much is attributable to weather variability. Ensemble skill also improved over time and, again, consistently across latitudes. The quality of analyses serving for forecast verification and initialization has been investigated further. An intercomparison of The Observing system Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) analyses and forecasts revealed substantial differences for surface parameters, but also at lower levels in the troposphere, where most of the physical processes relevant to weather in the short‐to‐medium range take place over the poles. The differences between the TIGGE analyses were generally much larger than differences between members of the ECMWF 4D‐Var ensemble of analyses generated internally at ECMWF. This suggests that neither the multi‐analysis approach nor ensemble data assimilation may represent polar analysis uncertainty properly. This is particularly visible at the surface and lower levels in the atmosphere. Forecast spread and error match much better north of 65∘N where less atmospheric variability prevails along the entire forecast range, while in areas of significant synoptic activity the spread also appears too low.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".