Evaluation of the solarcalc model for simulating hourly and daily incoming solar radiation in the Northern great plains of Canada
Bibliographic record
Abstract
Kahimba, F.C., P.R. Bullock, R. Sri Ranjan and H.W. Cutforth. 2009. Evaluation of the SolarCalc model for simulating hourly and daily incoming solar radiation in the Northern Great Plains of Canada. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 51: 1.11 1.21. Solar radiation models have become essential tools for estimating incident solar energy reaching the earth’s surface. Extensive evaluation of these models is a problem due to limited availability of measured solar radiation data. The performance of the SolarCalc model, which estimates hourly incoming solar radiation from limited daily meteorological data, was evaluated in the Northern Great Plains of Canada. The simulated hourly solar radiation was compared with observed hourly radiation from five meteorological stations operating through the growing season over a 4-yr period from 2003 to 2006. In addition, long-term year-round hourly solar radiation from one station over the period 1997 to 2007 was used to test the seasonal performance of the model. The model was also compared against the existing 1996-Liu and the FAO-56 models. The model simulated the year-round hourly solar radiation well (R 0.85; d 0.96; E 0.82;MAE 50.84 W/m; and RMSE 96.92 W/m). The model performance was also comparable to the existing models. However, the model over-predicted the hourly peak solar radiation during the summer and under-predicted during the winter. In addition, the model had none to a very small time-lag during all the seasons. Better model simulations were observed during the summer (R 0.88, E 0.85) compared to the winter (R 0.77, E 0.69). Improvements need to be done on simulation of peak radiation during winter and summer seasons. The SolarCalc model could be used as a potential tool for obtaining year-round hourly and daily solar radiation in areas with limited meteorological data.
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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 | 0.002 | 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".