Scale Dependence of the Predictability of Precipitation from Continental Radar Images. Part II: Probability Forecasts
Bibliographic record
Abstract
Eulerian and Lagrangian persistence of precipitation patterns derived from continental-scale radar composite images are used as a measure of predictability and for nowcasting [the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE)]. A previous paper introduced the method and focused on the lifetime of patterns of rainfall rates and the scale dependence of predictability. This paper shows how the method of persistence of radar precipitation patterns can be extended to produce probabilistic forecasts. For many applications, probabilistic information is at least as important as the expected point value. Four techniques are presented and compared. One is entirely new and makes use of the intrinsic relationship between scale and predictability. The results with this technique suggest potential use for downscaling of numerical model output. For the 143 h of precipitation analyzed so far, roughly a factor of 2 was obtained between lead times of Eulerian and Lagrangian techniques. Three of the four techniques involve a scale parameter. The slope of the relationship between optimum scale and lead time is about 1 and 2 km min−1 for Lagrangian and Eulerian techniques, respectively. The skill scores obtained for the four techniques can be used as a measure of predictability in terms of probabilistic rainfall rates. The progress of other probabilistic forecasting methods, such as expert systems or numerical models, can be evaluated against the standard set by simple persistence.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| 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".