Impact of data assimilation filtering methods on the mesosphere
Bibliographic record
Abstract
Three‐dimensional data assimilation schemes typically produce analyses that are not in balance. This is evidenced by the generation of spurious high‐frequency waves during the first 2 d of forecasts which start from analyses. To remove these spurious waves, assimilation systems frequently filter analyses before using them in models. This work examines the behavior of various spurious wave filtering methods in the context of a model with a mesosphere. Since gravity waves comprise a significant portion of the mesospheric energy spectrum, it is necessary to retain naturally occurring high‐frequency waves while filtering spurious waves. The results show that filtering the full analysis state can remove many important high‐frequency oscillations from the mesosphere. On the other hand, filtering analysis increments preserves much more of the natural variability of the model. The incremental analysis updating scheme and the incremental digital filter, which are equivalent for linear models and identical coefficients, are shown to give very similar results in the context of a realistic nonlinear model. Results also show a nonlocal response to the insertion of analysis increments in the troposphere and stratosphere. The global mean temperature in the vicinity of the model lid and the diurnal tidal amplitudes are sensitive to the choice of filtering schemes because the filters reduce the amount of resolved waves available to propagate upward into mesosphere. This sensitivity of the mesosphere to the filtering of the lower atmosphere is exploited to choose an optimal filter for our system using measurements of the mesosphere.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".