Bibliographic record
Abstract
This thesis investigates the characteristics of the corrections to the initial state of the atmosphere. The technique employed is the key analysis error algorithm, recently developed to estimate the initial state errors responsible for poor short-range to medium-range numerical weather prediction (NWP) forecasts. The main goal of this work is to determine to which extent the initial corrections obtained with this method can be associated with analysis errors. A secondary goal is to understand their dynamics in improving the forecast. In the first part of the thesis, we examine the realism of the initial corrections obtained from the key analysis error algorithm in terms of dynamical balance and closeness to the observations. The result showed that the initial corrections are strongly out of balance and systematically increase the departure between the control analysis and the observations suggesting that the key analysis error algorithm produced initial corrections that represent more than analysis errors. Significant artificial correction to the initial state seems to be present. The second part of this work examines a few approaches to isolate the balanced component of the initial corrections from the key analysis error method. The best results were obtained with the nonlinear balance potential vorticity (PV) inversion technique. The removal of the imbalance part of the initial corrections makes the corrected analysis slightly closer to the observations, but remains systematically further away as compared to the control analysis. Thus the balanced part of the key analysis errors cannot justifiably be associated with analysis errors. In light of the results presented, some recommendations to improve the key analysis error algorithm were proposed. In the third and last part of the thesis, a diagnosis of the evolution of the initial corrections from the key analysis error method is presented using a PV approach. The initial corrections tend to grow rapidly in time and can thus modify significantly the trajectory of a forecast over a relatively short period of time. The results shed light on different mechanisms about the evolution of small and fast growing initial perturbations.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".