Are re-analyses from ERA or MERRA suitable to assess surface solar irradiance in solar energy applications?
Bibliographic record
Abstract
Meteorological re-analyses such as the ERA-Interim and the MERRA ones provide surface solar irradiance (SSI) for long periods of time. This capability is appealing in solar energy as it may help in determining the potential of a given site in any part of the world. The present study presents a comparison made between ground measurements of daily means of the SSI with the same quantity extracted from the ERA-Interim and the MERRA respectively for the period 1985 to 2009. 40 stations with no marked orographic features were retained located in Europe and Africa. It was found that the SSI from re-analyses exhibit a strong bias, most often an over-estimation of the measured SSI. The correlation coefficient is low compared to what is usually observed when comparing satellite-derived assessments and ground measurements. Further analyses demonstrate that the cloud cover of the ERA-Interim and the MERRA re-analyses is not reliable in case of cloudy skies. The ERA-Interim and MERRA re-analyses often underestimate the cloud cover and therefore predict clear skies while the sky is actually overcast. It is concluded that the SSI derived from the ERA-Interim and MERRA re-analyses should not be recommended for use in solar energy applications.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".