Monitoring Urban Heat Island Intensity with Ground-based GNSS Observations and Space-based Radio Occultation and Radiosonde Historical Data
Bibliographic record
Abstract
Abstract. Since Urban Heat Islands (UHI) not only negatively impact human health but consume more energy when cooling buildings, accurate monitoring of its impact is critical. In this study, we propose a ground based GNSS technique to fuse GNSS Radio Occultation (RO) and radiosonde products to monitor the UHI intensity, which described as follows: First, the first and second grid tops are defined using the historical RO and radiosonde products. Then, the wet refractivity between the first and second grid tops is fitted to the higher-order spherical harmonic function based on the RO and radiosonde products, and they are used as the inputs of GNSS tomography, which can reduce the number of unknowns voxels of tomography while increasing the effective number of satellite rays, and improving the accuracy of tomography results. Next, according to the relationships among wet refractivity, temperature, and water vapor partial, as well as the function relationships among temperature, wet pressure, and height in adjacent vertical layers, the temperature and water vapor partial pressure can be obtained using the best search method according to the tomography-derived wet refractivity. Finally, the UHI intensity is monitored by the temperature difference between the urban regions and the surrounding rural regions. The radio occultation and radiosonde products of the Hong Kong region from 2010 to 2019, and the observed GNSS network data of the Hong Kong region for the year of 2020 are employed to evaluate the UHI intensity algorithm. The validation of the algorithm is done by comparing the UHI intensity estimated from the algorithm with the temperature data obtained from weather stations. The result shows that the proposed algorithm can achieve 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 | 0.001 |
| 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".