Comparison of a deterministic model, a stochastic multifractal model and radar rainfall data
Bibliographic record
Abstract
Two primary approaches to model rainfall are the stochastic approaches, which aim at mimicking the rain phenomenology, and the deterministic approaches. The latter mainly rely on geophysical fluid dynamics equations, in particular the Navier-Stokes equations. Solving these equations require simplifying assumptions such as parameterization and scale truncations. In the present case, we used Meso-NH model, which is a meteorological non-hydrostatic mesoscale model developed by Meteo-France/CNRM and Laboratoire d'Aérologie (Toulouse, France). Multiplicative cascade models, which are physically based models involving huge ratios of scales and intensities, allow to bridge the gap between the two previously mentioned methods. In this study, we considered a multifractal space-time cascade model, whose space-time anisotropy is defined with the help of a unique scaling exponent. We worked in the framework of universal multifractals in which the scaling variability and the extremes of the rain are quantified with the help of three parameters. The rainfall outputs of the Meso-NH model and the radar data were analyzed in the framework of the stochastic model. We selected a case study corresponding to a heavy rainfall event in the south of France. Both data sets exhibit a similar qualitative multifractal behavior in accordance with the framework of a unified space-time scaling model. Quantitatively, the estimates of the multifractal parameters suggest that the deterministic model under-represent the natural variability of the rainfall field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".