Positive or Negative? Urbanization‐Induced Variations in Diurnal Skin‐Surface Temperature Range Detected Using Satellite Data
Bibliographic record
Abstract
Abstract Diurnal temperature range (DTR) is an important indicator for assessing the local climate change due to urbanization. Studies that focused on surface air temperature (SAT) have reported decreased DTR SAT in urban areas. However, this urbanization‐induced effect becomes more complex with regard to land skin‐surface temperature (LST), which is highly localized and extremely sensitive to land surface properties. We thus investigated the urban‐rural DTR LST difference (ΔDTR LST ) over 354 cities across China using satellite‐retrieved LSTs within 2008−2013. Our major findings include the following: (1) urban areas experience increased (decreased) DTR LST compared with rural areas on the annual average for the majority of cities located in southern (northern) China; (2) the ΔDTR LST is mostly positive in warm months but negative in cold months. It generally becomes more positive from January to August and becomes more negative afterward; and (3) the ΔDTR LST is positively related to the daytime surface urban heat island intensity; it is yet negatively correlated with the urban‐rural difference in vegetation abundance. We consider these insights valuable for in‐depth understanding urban thermal environment and will likely help improve urban planning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".