Spatial bias in placement of citizen and conventional weather stations and their impact on urban climate research: A case study of the Urban Heat Island effect in Canada
Bibliographic record
Abstract
Citizen Weather Stations (CWS) are a source of Crowdsourced Geographic Information for urban climate research, which can provide extensive datasets in areas where data are scarce or unavailable. In this article, we explore the efficacy of using meteorological data from CWS in studying the Urban Heat Island (UHI) effect across Canada during late spring and summer of 2022. In particular, we evaluate the distribution of CWS before relying on them for UHI intensity estimates, since potential spatial biases in placement of these sensors can greatly affect canopy-level measurements. We compared the spatial distribution of Netatmo CWS with conventional weather stations from Environment and Climate Change Canada (ECCC), and found that ECCC sensors are more numerous in rural areas, while Netatmo sensors are present in greater numbers in urban areas. We then computed UHI intensity using urban temperature from Netatmo sensors and peri-urban temperature from ECCC sensors. The resulting intensity values were higher than those estimated using either the Netatmo or the ECCC sensors individually, thus highlighting the influence of sensor distribution in estimating UHI magnitude. Overall, our research explores the distribution of both ECCC and CWS sensors, and highlights their potential complementarity in urban climate research. • Citizen weather stations (CWS) are located largely in Census Metropolitan Areas (CMAs), surrounded mostly by built-up areas • Conventional weather stations are more distributed in rural areas, and are mostly surrounded by vegetation and water • Overall quantity of CWS increases with population • Spatial distribution of CWS and conventional sensors is complementary
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".