Evaluation of Solar Radiation Prediction Models in North America
Bibliographic record
Abstract
Solar radiation data at the earth's surface (Rs, MJ m−2 d−1) are not typically recorded at weather stations, but they may be predicted from other meteorological measurements. For one location, Keiser, AR, we developed an empirical equation for predicting Rs. The mechanistic models of Hargreaves–Samani (HS) and two forms of the Bristow–Campbell model, described by Thornton and Running (TR) and Weiss et al. (WS), were also evaluated for predicting Rs at 13 sites, covering a 23° range in latitude and a 42° range in longitude. For the HS, TR, and WS models, we used coefficients as they were originally published, and for the HS model, a site‐specific coefficient (HS‐SS) was derived and evaluated for each site. Regression of predicted vs. observed Rs values using the empirical equation for Keiser gave r2 values (0.77) similar to the best of the mechanistic models. The HS‐SS model had the lowest root mean square error of 3.50 MJ m−2 d−1, followed by the TR (3.56), the HS (3.86), and the WS (4.33) models. Predicted vs. observed values gave r2 values ranging from 0.72 (TR model) to 0.56 (WS model). There was a slight superiority of the TR model over the HS‐SS and HS models. Similar fits (r2 > 0.87) and errors were found among the TR, HS‐SS, and HS models when Rs values were averaged over a 7‐d period, and it was concluded that these three models provided accurate and precise Rs estimations for our sites without further model modification.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".