The Informational Value of Pressure-Based Single-Station Proxies for Storm Activity
Bibliographic record
Abstract
Abstract Air pressure readings and their variations are commonly used to make inferences about storm activity. More precisely, it is assumed that the variation of annual and seasonal statistics of several pressure-based proxies describes changes in the past storm climate qualitatively, an assumption that has yet to be proven. A systematic evaluation of the informational content of five pressure-based proxies for storm activity based on single-station observations of air pressure is presented. The number of deep lows, lower percentiles of pressure, the frequency of absolute pressure tendencies above certain thresholds, as well as mean values and high percentiles of absolute pressure tendencies is examined. Such an evaluation needs long and homogeneous records of wind speed, something that is not available from observations. Consequently, the proxies are examined by using datasets of ground-level wind speeds and air pressure from the NCEP-driven and spectrally nudged regional model, REMO. The proxies are gauged against the 95th and 99th percentile time series of ground-level wind speeds to quantify the relation between pressure-based proxies and storminess. These analyses rely on bootstrap and binomial hypothesis testing. The analyses of single-station-based proxies indicate that the proxies are generally linearly linked to storm activity, and that absolute pressure tendencies have the highest informational content. Further, it is investigated as to whether the proxies have the potential for describing storminess over larger areas, also with regard to surface conditions. It is found that absolute pressure tendencies have improved informational value when describing storm activity over larger areas, while low pressure readings do not show improved informational value.
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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 | 0.000 | 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) | 0.000 | 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".