Effect of data coverage on the estimation of mean and variability of precipitation at global and regional scales
Bibliographic record
Abstract
We use monthly precipitation simulated by a high‐resolution global climate model (the MIROC4h) to examine the effects of spatial and temporal coverage on the estimation of mean, trend, and variability of precipitation for large land regions and the global land area. We consider spatial and temporal coverage typical of publicly available precipitation data sets of in situ station observations. We find that the spatial coverage of these data sets is not sufficient for the estimation of total precipitation for the global and hemispheric land areas and for some large regions considered. Estimates of global and hemispheric total land precipitation tend to be biased to higher values due to undersampling in low precipitation regions. The existing station coverage may nevertheless provide reasonable estimates for the magnitude of trend and variability in global to regional land area mean precipitation. However, the incomplete spatial coverage of the observational records results in larger sampling errors in trend estimates, making it harder to detect statistically significant trends. Publicly available gridded precipitation data that are based on larger collection of stations (all of which are not publicly available) may provide a better alternative for the time being.
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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 | 0.032 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".