Improved time constant of a newly released air temperature sensor and its implications
Bibliographic record
Abstract
Abstract The World Meteorological Organization (WMO), National Oceanic and Atmospheric Administration, and other organizations provide guidance on expected response time for sensors used to measure air temperatures intended for meteorological applications. Quantified as the sensor time constant (the time it takes for a sensor to reflect some percentage of a step change), recommendations differ somewhat depending on the organization. For instance, the WMO specifies the 63% time constant should be ≤20 s, although, crucially, the organization does not state the air flow velocity at which this time constant should be achieved. Recent independent tests at two laboratory facilities (initially the University of Reading, United Kingdom, and subsequently at Campbell Scientific, Logan, Utah, United States) were undertaken to determine time constants of a range of commercially available platinum resistance thermometer sensors. Results showed that many sensors fell far short of the WMO specification at airflow rates typical of naturally ventilated thermometer screens or radiation shields (1 m·s −1 or lower). In contrast, a recently released platinum resistance thermometer sensor from Campbell Scientific was shown to meet both specifications, even at airflow rates within a laboratory wind tunnel as low as 0.2 m·s −1 , which is more typical of naturally ventilated thermometer screens or radiation shields. Across multiple sensors and repeated test runs, the new sensor's 63% response time averaged 10.7 s (standard deviation 0.5 s) at an airflow of 1 m·s −1 and 17.1 s (standard deviation 0.9 s) at 0.2 m·s −1 . To our knowledge, this is the first commercially available sensor to attain this WMO specification. However, using or switching to faster‐response sensors has important implications for long‐term data records, the measurement of extreme temperatures (specifically daily maximum and minimum data), and intersite comparisons. This is compounded by seemingly conflicting recommendations from the WMO regarding sensor time constant versus data processing methods.
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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".