Evaluation of regional cloud climate simulations over Scandinavia using a 10‐year NOAA Advanced Very High Resolution Radiometer cloud climatology
Bibliographic record
Abstract
A satellite‐derived (NOAA Advanced Very High Resolution Radiometer) cloud climatology over the Scandinavian region covering the period 1991–2001 has been used to evaluate the performance of cloud simulations of the Swedish Meteorological and Hydrological Institute Rossby Centre regional climate model (RCA3). Several methods of adapting the satellite and model data sets to allow a meaningful comparison were applied. RCA3‐simulated total cloud cover was shown to agree within a few percent of the satellite‐retrieved cloud amounts on seasonal and annual timescales. However, a substantial imbalance between the respective RCA3 contributions from low‐, medium‐ and high‐level clouds was seen. The differences from satellite‐derived contributions were +2.4% for high‐level clouds, −5.2% for medium‐level clouds and +4.0% for low‐level clouds. In addition, an overrepresentation of cloud categories with high optical thicknesses was seen for all vertical cloud groups, particularly during the summer season. Some specific features of the geographical distribution of cloudiness were also noticed. Most pronounced were the excess of cloud amounts over the Scandinavian mountain range and a deficit leeward of the mountains. The overall results imply problems with the RCA3‐modeled surface radiation budget components by causing reduced incoming solar radiation and increased downwelling longwave radiation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".