Design Principles and Field Performance of Software-Augmented Solar Sensors for Resolving Spectral Irradiance and Atmospheric Parameters
Bibliographic record
Abstract
The present work details the design principles and field performance of software-augmented, multi-functional sensors capable of resolving solar spectral irradiance and atmospheric parameters, such as aerosol optical depth, total column ozone and precipitable water vapour. The primary motivation behind this research and development work is the solar industry's increasing need for spectral and atmospheric data, the acquisition of which, prior to this work, was not commercially practical or cost-effective. This thesis presents the direct solar spectral irradiance meter (SolarSIM-D2), which was designed for photovoltaic and atmospheric science applications. The instrument measures the direct normal irradiance (DNI) in six narrow wavelength bands. These measurements are combined with radiative transfer models to determine the spectral transmittance profiles of key atmospheric components, such as spectral aerosols, total column ozone and precipitable water vapour, and subsequently resolve the spectral DNI over the complete 280-4000 nm range. Multiple SolarSIM-D2s were calibrated and validated at the National Renewable Energy Laboratory (NREL) in the United States and at the World Radiation Center in Switzerland against reference instrumentation. In addition, a comprehensive uncertainty analysis was performed for all of the SolarSIM-D2's measurands. This work also describes the global solar spectral irradiance meter (SolarSIM-G), which was designed to resolve the spectral and broadband global irradiances over the complete 280-4000 nm spectral range, primarily for use in the photovoltaic industry. The all-sky parameterized transmittance model was developed, capable of deriving the spectral and broadband global irradiances from the SolarSIM-G's nine optical measurements under clear and cloudy conditions. This model uses radiative transfer algorithms to resolve in real-time the combined contribution of the direct and diffuse irradiance components, including the effects of atmospheric and cloud scattering. Multiple SolarSIM-Gs were calibrated and validated at NREL against reference instrumentation. The SolarSIM sensors represent a significant advancement in the field of solar measurements by providing a wealth of detailed spectral and atmospheric data in a compact, affordable system as compared to traditional alternatives. A notable impact of this work is the commissioning of the Canadian Solar Spectral Irradiance Meter (CanSIM) network, consisting of seven stations across Canada, each containing a SolarSIM-D2 and a SolarSIM-G. CanSIM data was used to develop a novel global irradiance decomposition algorithm to derive the direct and diffuse irradiance components from the SolarSIM-G's measurements. This method has a factor of two to three improvement in the root-mean-square error for the DNI retrieval as compared to the existing state-of-the-art decomposition algorithms.
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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 | 0.000 |
| 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 | 0.001 | 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".