The Observed Effects of Utility-Scale Photovoltaics on Near-Surface Air Temperature and Energy Balance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Utility-scale solar power plants are a rapidly growing component of the renewable energy sector. While most agree that solar power can decrease greenhouse gas emissions, the effects of photovoltaic (PV) systems on surface energy exchanges and near-surface meteorology are not well understood. This study presents data from two eddy covariance observational towers, placed within and adjacent to a utility-scale PV array in southern Arizona. The observational period (October 2017–July 2018) includes the full range of annual temperature variation. Average daily maximum 1.5-m air temperature at the PV array was 1.3°C warmer than the reference (i.e., non-PV) site, whereas no significant difference in 1.5-m nocturnal air temperature was observed. PV modules captured the majority of solar radiation and were the primary energetically active surface during the day. Despite the removal of energy by electricity production, the modules increased daytime net radiation Q * available for partitioning by reducing surface albedo. The PV modules shift surface energy balance partitioning away from upward longwave radiation and heat storage and toward sensible heat flux Q H because of their low emissivity, low heat capacity, and increased surface area and roughness, which facilitates more efficient Q H from the surface. The PV modules significantly reduce ground heat flux Q G storage and nocturnal release, as the soil beneath the modules is well shaded. Our work demonstrates the importance of targeted observational campaigns to inform process-based understanding associated with PV systems. It further establishes a basis for observationally based PV energy balance models that may be used to examine climatic effects due to large-scale deployment.
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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 | 0.000 |
| 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) | 0.000 | 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 it