Probing Cloud Dynamics Using Metrics of Irradiance Variability
Bibliographic record
Abstract
Understanding solar irradiance variability is necessary for its prediction as part of photovoltaic system performance forecasting, especially in grid-connected real time markets. Deeper understanding on sub-hour time scales is critical for probabilistic treatments of energy balance and grid stability consideration. In particular, irradiance variation dominated by cloud dynamics is still poorly characterized in the broad temporal window that extends down to the sub-second ramping regime. We report preliminary analyses of our now-multiyear database of narrow and broadband global horizontal irradiance (GHI) measurements, comparing results for 2022 with those of 2023. These local spectral irradiance measurements were acquired in Ottawa, Canda beginning in 2022 using a custom Spectrafy SolarSIM G multi-filter radiometer augmented for 4 Hz sampling. We removed the orbital and diurnal deterministic components of the GHI measurements through clear-sky normalization with respect to a standard atmosphere, creating timeseries that are then dominated by the cloud dynamics. We focused on their forward- differenced values as a metric sensitive to variations in these cloud dynamics. For such clear-sky index increments, their probability density functions (PDF) were found by kernel density estimation for time shifts ranging from 1 to 2000 s. Power law scaling over two to three orders of magnitude was observed with interesting similarities and differences between the two years in both the broadband and spectral GHI results. The two years had significantly different cloud type distributions that may have been due to the presence of substantial wildfire smoke in Ottawa in 2023. A preliminary assessment for the smokiest month suggests that there is a correlation between atmospheric fine particulate matter and the dominant daily cloud type. We suggest that this may be the origin of the differences in the power law scaling behavior between years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".