Climatology of Size, Shape, and Intensity of Precipitation Features over Great Britain and Ireland
Bibliographic record
Abstract
Abstract A climatology of precipitation features (or objects) from the Great Britain and Ireland radar-derived precipitation mosaic from 2006 to 2015 is constructed, with features defined as contiguous areas of nonzero precipitation rates. Over the 10 years, there are 54 811 747 nonunique precipitating features over 100 km2 in area, with a median precipitation feature area of 249 km2, median major axis length of 29.2 km, median aspect ratio of 2.0:1, median feature mean precipitation rate of 0.49 mm h−1, and median feature maximum precipitation rate of 2.4 mm h−1. Small-scale precipitating systems are most common, but larger systems exceeding 10 000 km2 contribute close to 70% of the annual precipitation across the study region. Precipitation feature characteristics are sensitive to changes in annual and diurnal environment, with feature intensities peaking during the afternoon in summer and the largest precipitation features occurring during winter. Precipitation intensities less than 5 mm h−1 comprise 97.3% of all precipitation occurrences and contribute 83.6% of the total precipitation over land. Banded precipitation features (defined as precipitation features with aspect ratio at least 3:1 and major axis length at least 100 km) comprise 3% of all precipitation features by occurrence, but contribute 23.7% of the total precipitation. Mesoscale banded features (defined as banded precipitation features with major axis length at least 100 km and total area not exceeding 10 000 km2) and mesoscale convective banded features (defined as banded precipitation features with at least 100 km2 of precipitation rates exceeding 10 mm h−1) are most prevalent in southwestern England, with mesoscale convective banded features contributing up to 2% of precipitation.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".