Application of Deep Learning to Estimate Atmospheric Gravity Wave Parameters in Reanalysis Datasets
Bibliographic record
Abstract
Gravity waves play an essential role in driving and maintaining global circulation. To understand their contribution in the atmosphere, the accurate reproduction of their distribution is important. Thus, a deep learning approach for the estimation of gravity wave momentum fluxes was proposed, and its performance at 100 hPa was tested using data from low resolution zonal and meridional winds, temperature, and specific humidity at 300, 700, and 850 hPa in the Hokkaido region (Japan). To this end, a deep convolutional neural network was trained on 29-year reanalysis datasets (JRA-55 and DSJRA-55), and the final 5-year data were reserved for evaluation. The results showed that compared to ground truth data, the fine-scale momentum flux distribution of the gravity waves could be estimated at a low computational cost. Particularly, in winter, when gravity waves are stronger, the median RMSE of the maximum momentum flux in the target area was 0.06–0.13 mPa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".