Improved empirical models for estimating surface direct and diffuse solar radiation at monthly and daily level: A case study in North China
Bibliographic record
Abstract
Precise knowledge of direct and diffuse solar radiation is important for energy utilization and agricultural activities. However, field measurements in most areas of the world are only for total solar radiation. The satellite-retrieved direct and diffuse solar radiation show poor performance under overcast skies. Therefore, better empirical models are needed to estimate direct and diffuse solar radiation by considering the impact of aerosols over polluted regions. A case study is conducted in North China with the ground-measured solar radiation and satellite-retrieved aerosol optical depth to improve new empirical models at monthly (from 2000 to 2016) and daily (from 2006 to 2009) level. The improved empirical models are validated using the field measurements and compared with the existing models. Results suggest that these models perform well in estimating direct solar radiation at monthly ( R 2 = 0.86–0.91, RMSE = 0.76–0.83 MJ/m 2 ) and daily ( R 2 = 0.91–0.94, RMSE = 1.51–1.64 MJ/m 2 ) level. The accuracy of estimated monthly ( R 2 = 0.95–0.96, RMSE = 0.57–0.65 MJ/m 2 ) and daily ( R 2 = 0.91–0.93, RMSE = 1.09–1.15 MJ/m 2 ) diffuse solar radiation, particularly the maximum diffuse solar radiation value, has been improved compared to the existing models. The models presented in this study can be useful in the improvement and evaluation of solar radiation dataset over polluted regions similar to North China.
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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 | 0.000 | 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".