The ultimate meteorological question from observational astronomers: how good is the cloud cover forecast?
Bibliographic record
Abstract
To evaluate the capability of numerical cloud forecasting as a meteorological reference for astronomical observations, we compare the cloud forecast from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model for total, layer and convective cloud with normalized satellite observations from the International Satellite Cloud Climatology Project (ISCCP) for the period of 2005 July–2008 June. In general, the model forecast is consistent with the ISCCP observations. For total cloud cover, our result shows the goodness of the GFS model forecast, with a mean error within ±15 per cent in most areas. The global mean probability of <30 per cent forecast error (polar regions excluded) declines from 73 per cent to 58 per cent throughout the 180-h forecast period and is more skilled than the ISCCP-based climatology forecast up to τ ∼ 120 h. Comparison using layer clouds reveals a distinct negative regional tendency for low cloud forecast and a questionable positive global tendency for high cloud forecast. Fractional and binary comparisons are performed on the convective cloud forecast and it is revealed that the GFS model can identify less than half of such cloud. In short, our result suggests that the GFS model can provide satisfactory worldwide total cloud forecasts up to a week ahead for observation-scheduling purposes, but layer and convective cloud forecasts are less reliable than the total cloud forecast.
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 | 0.005 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".