An atmospheric dynamics perspective on the amplification and propagation of forecast error in numerical weather prediction models: A case study
Bibliographic record
Abstract
Despite huge progress made, state‐of‐the‐art numerical weather prediction systems occasionally experience severe forecast busts for the large‐scale extratropical circulation. This study investigates one of the most severe forecast busts for Europe in the European Centre for Medium‐Range Weather Forecasts integrated forecasting system (IFS) in recent years. The forecast bust occurred in March 2016 and was associated with a misforecast of the onset of a blocking regime. We investigate the evolution of the forecast error in the IFS ensemble by employing a potential vorticity perspective combined with Lagrangian diagnostics. We show that the error grows rapidly from an initially small perturbation in the detailed structure of an upper‐level trough near Newfoundland. This trough triggers strong diabatic warm conveyor belt activity in the North Atlantic region. The misrepresentation of this warm conveyor belt activity in the ensemble forecast amplifies the initial condition error and communicates it downstream into Europe. Specifically, the ensemble underestimates poleward warm conveyor belt ascent and associated warm conveyor belt outflow into high latitudes. Instead, all ensemble members forecast too strong warm conveyor belt outflow further to the south, which ultimately results in a wrong forecast of the upper‐level Rossby wave pattern over Europe. This case study shows that warm conveyor belts and the associated latent heat release in slantwise ascending air can trigger a nonlinear feedback mechanism that amplifies forecast error strongly and communicates it into regions far downstream. It corroborates the fact that multiscale interactions and moist‐and dry‐dynamical processes ranging from microphysical to synoptic scales need to be represented accurately in numerical weather prediction, in order to predict the extratropical large‐scale circulation correctly.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".