Comparative analysis of three methods detecting inhomogeneity of radiosonde temperature data in China
Bibliographic record
Abstract
Combining reanalysis data as a reference series and the detailed metadata of each radiosonde station in China,homogeneity test and corrections were carried out for monthly temperature data at mandatory levels at 123 radiosonde stations from 1951 to 2008 in China using a Pairwise method developed by America National Climatic Data Center,PM FT and PM T methods developed by the Environment Center of Canada. The results showthat adjusted values of radiosonde temperature data in China and its trends before and after correction have some differences for three different methods. One of the main reasons is that design of the Pairwise method leads to the weak capability for break points detection if majority regional stations of China change their radiation correction method and update the system simultaneously. Another reason is that the PM FT method cannot remove the climate temperature trend because of no reference series,so some break points cannot be detected. The PM T method combines with reanalysis data,which is more suitable for homogeneity test and correction of radiosonde temperature data in China. The adjusted results showthat the statistical characteristics of radiosonde temperature are greater than those of the global scale at the lowlevel,and both are consistent at the high level. Two break points and adjusted values between- 0. 2 ℃ to 0. 0 ℃ have a larger proportion in datasets. After adjustment,the troposphere temperature average over China is warming trend. This warming trend weakens gradually with increase of heights and becomes a weak cooling trend till 100 h Pa.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".