Heavy precipitation events in the Mediterranean: sensitivity to cloud physics parameterisation uncertainties
Bibliographic record
Abstract
Abstract. In autumn, southeastern France is often affected by heavy precipitation events which may result in damaging flash-floods. The 20 October and 1 November 2008 are two archetypes of the meteorological situations under which these events occur: an upper-level trough directing a warm and moist flow from the Mediterranean towards the Cévennes ridge or a quasi stationary meso-scale convective complex developing over the Rhone valley. These two types of events exhibit a contrasting level of predictability; the former being usually better forecast than the latter. Control experiments performed with the Meso-NH model run with a 2.5 km resolution confirm these predictability issues. The deterministic forecast of the November case (Cévennes ridge) is found to be much more skilful than the one for the October case (Rhone valley). These two contrasting situations are used to investigate the sensitivity of the model for cloud physics parameterisation uncertainties. Three 9-member ensembles are constructed. In the first one, the rain distribution intercept parameter is varied within its range of allowed values. In the second one, random perturbations are applied to the rain evaporation rate, whereas in the third one, random perturbations are simultaneously applied to the cloud autoconversion, rain accretion, and rain evaporation rates. Results are assessed by comparing the time and space distribution of the observed and forecasted precipitation. For the Rhone valley case, it is shown that not one of the ensembles is able to drastically improve the skill of the forecast. Taylor diagrams indicate that the microphysical perturbations are more efficient in modulating the rainfall intensities than in altering their localization. Among the three ensembles, the multi-process perturbation ensemble is found to yield the largest spread for most parameters. In contrast, the results of the Cévennes case exhibit almost no sensitivity to the microphysical perturbations. These results clearly show that the usefulness of an ensemble prediction system based upon microphysical perturbations is case dependent. Additional experiments indicate a greater potential for the multi-process ensemble when the model resolution is increased to 500 m.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".