Monitoring urban heat island intensity based on GNSS tomography technique
Bibliographic record
Abstract
Abstract Monitoring urban heat island (UHI) effect is critical because it causes health problems and excessive energy consumption more energy when cooling buildings. In this study, we propose an approach for UHI monitoring by fusing data from ground-based global navigation satellite system (GNSS), space-based GNSS radio occultation (RO), and radiosonde. The idea of the approach is as follows: First, the first and second grid tops are defined based on historical RO and radiosonde observations. Next, the wet refractivities between the first and second grid tops are fitted to higher-order spherical harmonics and they are used as the inputs of GNSS tomography. Then, the temperature and water vapor partial pressure are estimated by using best search method based on the tomography-derived wet refractivity. In the end, the UHI intensity is evaluated by calculating the temperature difference between the urban regions and nearby rural regions. Feasibility of the UHI intensity monitoring approach was evaluated with GNSS RO and radiosonde data in 2010–2019, as well as ground-based GNSS data in 2020 in Hong Kong, China, by taking synoptic temperature data as reference. The result shows that the proposed approach achieved an accuracy of 1.2 K at a 95% confidence level.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".