On the Climatological Use of Radar Data Mosaics: Possibilities and Challenges
Bibliographic record
Abstract
Abstract Continental mosaics of radar data have now been generated for more than 20 years. They offer information on precipitation climatology that is simply not available or archived elsewhere: How often does it rain at any particular location? At what time? And with what intensity distribution? What are the geographical and temporal patterns of precipitation occurrence, formation, and decay? What is the climatology of severe weather? Answers to these questions have value on their own and invariably trigger more questions about the processes causing these patterns but also suggest some answers. They also have considerable pedagogical value in illustrating in the classroom the impacts of different processes—such as sea–land breezes, topography, and seasons—on precipitation. In this work, U.S. mosaics of radar data from 1996 to 2015 are used to demonstrate the possibilities offered by such a dataset. Three topics are discussed: (i) climatologies and daily cycles of precipitation and convection, and what they can teach us about precipitation mechanisms; (ii) the spatial and temporal distribution of the appearance and occurrence of convection, and what it reveals about the importance of surface terrain properties for these events; and (iii) the power and challenges of looking for a small signal in such a large dataset using the influence of weekly activity cycles and cities on precipitation as an illustration.
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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 | 0.031 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".