Internal variability of fine‐scale components of meteorological fields in extended‐range limited‐area model simulations with atmospheric and surface nudging
Bibliographic record
Abstract
Abstract Internal variability (IV) in dynamical downscaling with limited‐area models (LAMs) represents a source of error inherent to the downscaled fields, which originates from the sensitive dependence of the models to arbitrarily small modifications. If IV is large it may impose the need for probabilistic verification of the downscaled information. Atmospheric spectral nudging (ASN) can reduce IV in LAMs as it constrains the large‐scale components of LAM fields in the interior of the computational domain and thus prevents any considerable penetration of sensitively dependent deviations into the range of large scales. Using initial condition ensembles, the present study quantifies the impact of ASN on IV in LAM simulations in the range of fine scales that are not controlled by spectral nudging. Four simulation configurations that all include strong ASN but differ in the nudging settings are considered. In the fifth configuration, grid nudging of land surface variables toward high‐resolution surface analyses is applied. The results show that the IV at scales larger than 300 km can be suppressed by selecting an appropriate ASN setup. At scales between 300 and 30 km, however, in all configurations, the hourly near‐surface temperature, humidity, and winds are only partly reproducible. Nudging the land surface variables is found to have the potential to significantly reduce IV, particularly for fine‐scale temperature and humidity. On the other hand, hourly precipitation accumulations at these scales are generally irreproducible in all configurations, and probabilistic approach to downscaling is therefore recommended.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".