The usage of fog and dew in solar power plants of the Atacama Desert
Bibliographic record
Abstract
The Atacama desert is one of the most promising places on Earth for developing solar power energy due to its aridity, irradiation, and market conditions. However, the high levels of dust attenuate solar power production. This problem is solved by frequent cleaning of the solar panels, which requires a significant amount of water in one of the driest places in the world. Despite the drought condition, the fog and dew formed at the coastal zone of the desert arise as a complementary water source that can potentially be tapped. In this study, we assess the potential of atmospheric water for usage in four solar power plants. We conduct this assessment by combining a satellite-spatial analysis of fog and low cloud frequency, a thermodynamic vertical characterization of the marine boundary layer, and an observational analysis of fog and dew collection using different instruments. Our results reveal that fog and dew are a regular phenomenon in the solar power plants analyzed, being present between 3% and 20% of the year. Oceanic conditions control such phenomena through the inland advection of the marine boundary layer. This layer interacts with a complex topography characterized by natural corridors that allow fog and low clouds to penetrate farther inland. Our observations show that fog and dew are collected mainly during the night, with average rates between 0.1 and 0.2 L m-2 day-1. Our research confirms that atmospheric water potential vastly exceeds the solar power plant water demand, demonstrating that atmospheric water is a reliable source for the industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".