How Skilful Are Cloud Cover Products in Representing Observed Cloudiness in Québec?
Bibliographic record
Abstract
In this study, we performed, for the first time, a detailed analysis of cloudiness in Québec using station based observations and evaluated the trustworthiness of various satellite and reanalysis based cloud cover products. We found only 12 stations with observational time series long enough for providing robust analysis due to various issues related to observing methods and recording of information during the 1990s. Our results showed that Québec can be fairly cloudy throughout the year with annual mean cloud cover fraction ranging between 0.5 and 0.7 with autumn being the cloudiest season at most station locations. We also found a significantly increasing trend in cloudiness in the winter season over Québec during 1982–2012 (∼2–6% per decade depending on the location). Among the cloudiness products evaluated in this study, a satellite based cloudiness product (i.e. based on AVHRR) performed best in representing the observed cloudiness over Québec including the wintertime increasing trend. Two reanalysis based cloudiness products (i.e. ERA5 and NARR) also performed well in representing the observed cloudiness indicated by high correlations, relatively lower mean biases and smaller root mean square errors. However, the reanalysis products did not capture well the observed seasonal trends. All products showed relatively larger biases in the winter season except JRA-25 reanalysis product. During the warmer months, however, JRA-25 showed largest biases followed by MERRA-2 reanalysis product. JRA-25 and MERRA-2 also had lower correlation for annual and winter means and highest root mean square errors for all seasons. Furthermore, based on two skill scores, we found that the ERA5, AVHRR and NARR performed relatively better than JRA-25 and MERRA-2. Based on these results we conclude that the satellite product and the two reanalysis products can be useful resources (as observational proxies) along with station based observations for understanding long term changes in cloudiness of this region and also potentially evaluating regional climate model simulations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".