Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The lifetime of precipitation patterns in Eulerian and Lagrangian space derived from continental-scale radar images is used as a measure of predictability. A three-step procedure is proposed. First, the motion field of precipitation is determined by variational radar echo tracking. Second, radar reflectivity is advected by means of a modified semi-Lagrangian advection scheme assuming stationary motion. Third, the Eulerian and Lagrangian persistence forecasts are compared to observations to calculate the lifetime and other measures of predictability. The procedure is repeated with images that have been decomposed according to scales to describe the scale-dependence of predictability. The analysis has a threefold application: (i) determine the scale-dependence of predictability, (ii) set a standard against which the skill for quantitative precipitation forecasting by numerical modeling can be evaluated, and (iii) extend nowcasting by optimal extrapolation of radar precipitation patterns. The methodology can be applied to other field variables such as brightness temperatures of weather satellites imagery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it