Impact of hydrometeor control variables in a convective‐scale 3DEnVar data assimilation scheme
Bibliographic record
Abstract
Abstract Initialization of condensate water variables for convective‐scale weather forecasting has been a topic of active research for the last few years. In this article, we present the addition of hydrometeor fields as control variables of a three‐dimensional ensemble variational scheme (3DEnVar) for the cloud‐resolving model AROME‐France. Even without any direct assimilation of hydrometeor observations, analysis increments of hydrometeors can be produced via covariances with observed variables in the ensemble‐derived background‐error covariance matrix. Cycled forecast–analysis experiments in near‐operational conditions have been performed over a three‐month summer period. Three configurations are compared: (i) a control experiment without hydrometeor control variables, (ii) a test experiment with hydrometeor control variables but without cycling the resulting forecasts, and (iii) an experiment with hydrometeor variables and cycled forecasts. Compared with the control experiment, both hydrometeor experiments show a positive impact up to 9 hr of forecasts in terms of cloud cover, and in the first hour for precipitation. The spin‐up period, as evidenced by a reduction of precipitation forecast skill at the beginning of the forecasts, is reduced in length and intensity in the hydrometeor experiments.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".