Analysis and modelling of water vapour and temperature changes in Hong Kong using a 40‐year radiosonde record: 1973–2012
Bibliographic record
Abstract
ABSTRACT Radiosonde observations provide a good data source for examining the long‐term trend of atmospheric water vapour, temperature and cold point tropopause (CPT). In this article, precipitable water vapour (PWV) and surface temperature from the Hong Kong radiosonde station over the period of 1973–2012 are analysed. We find that the atmospheric water vapour in Hong Kong in the layers from the surface to approximately 1181 m, from ∼1181 to ∼2509 m, from ∼2509 to ∼5126 m and from ∼5126 to ∼8093 m accounts for 50, 25, 20 and 5% of the PWV, respectively. The atmosphere is almost completely dry above approximately 8000 m. Surface temperature has increased at a rate of 0.16° decade−1 over the past 40 years. On a seasonal timescale, the largest rate of increase is 0.23° decade−1 in winter and the smallest rate is 0.09° decade−1 in spring. The CPT height is located at approximately 17.5 km above mean sea level over the period of this study. This CPT height is estimated to have risen at a rate of 87.3 m decade−1 over 1983–2012, which is 1.36 times the global average. The CPT temperature is observed to decrease at a rate of 0.84° decade−1, which is 2.05 times the global average. Taking advantage of the periodicity of PWV and surface temperature over the past 40 years, Fourier series analysis models have been developed. The models are evaluated using 1 year (2012) of radiosonde data. It was found that the modelled PWV data can achieve root mean square error accuracy of 9.23 mm and the modelled surface temperature can achieve a standard deviation of 2.34° with a bias of −0.19°.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".