A Study on the Scale Dependence of the Predictability of Precipitation Patterns
Bibliographic record
Abstract
Abstract A methodology is proposed to investigate the scale dependence of the predictability of precipitation patterns at the mesoscale. By applying it to two or more precipitation fields, either modeled or observed, a decorrelation scale can be defined such that all scales smaller than are fully decorrelated. For precipitation forecasts from a radar data–assimilating storm-scale ensemble forecasting (SSEF) system, is found to increase with lead time, reaching 300 km after 30 h. That is, for , the ensemble members are fully decorrelated. Hence, there is no predictability of the model state for these scales. For , the ensemble members are correlated, indicating some predictability by the ensemble. When applied to characterize the ability to predict precipitation as compared to radar observations by numerical weather prediction (NWP) as well as by Lagrangian persistence and Eulerian persistence, increases with lead time for most forecasting methods, while it is constant (300 km) for non–radar data–assimilating NWP. Comparing the different forecasting models, it is found that they are similar in the 0–6-h range and that none of them exhibit any predictive ability at meso-γ and meso-β scales after the first 2 h. On the other hand, the radar data–assimilating ensemble exhibits predictability of the model state at these scales, thus causing a systematic difference between corresponding to the ensemble and corresponding to model and radar. This suggests that either the ensemble does not have sufficient spread at these scales or that the forecasts suffer from biases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".