Predictability of Precipitation from Continental Radar Images. Part V: Growth and Decay
Bibliographic record
Abstract
Abstract In a Lagrangian frame of reference, the accuracy of rainfall systems predicted by nowcasting algorithms can be improved by incorporating the growth and decay of the rainfall. The scale dependence of predictability of growth and decay of continental-scale precipitating systems is studied with the help of the U.S. national radar composites. The growth and decay of precipitating systems is estimated in a time interval τ by correcting the precipitation image for advection and rotation at time t + τ with respect to the precipitation image at time t and then subtracting the former from the latter. Results show that the two-dimensional correlation of growth and decay has an elliptical structure, indicating that growth and decay is nonisotropic. The probability density function of precipitation intensities and of growth and decay follows a Gaussian distribution. The scale-dependence analysis of growth and decay patterns indicates that the growth and decay of rainfall may be predictable up to about 2 h for scales larger than 250 km.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".