A hybrid deep learning and data assimilation method for model error estimation
Bibliographic record
Abstract
Forecast errors of numerical weather prediction consist of model errors and the growth of initial condition errors, while the initial condition is often optimized based on short-term forecasts. Thus it is difficult to untangle the initial condition error and model error, but it is essential to infer model errors not just for prediction but also for data assimilation (DA). A hybrid deep learning (DL) and DA method is proposed here, aiming to correct model errors. It uses a convolutional neural network (CNN) to extract characteristics of initial conditions and forecast errors, and then provides estimations for model errors. The CNN-based model error estimation method can consider the model error resulted from inaccurate model parameters, or simultaneously consider the model error and initial condition error. Based on the Lorenz05 model, offline and online experiments demonstrate that the CNN-based model error estimation method can effectively correct model errors resulted from inaccurate model parameters, including the forcing F, coupling coefficient c, and relative scale b. For both online and offline model error estimations, simultaneously considering model errors and initial condition errors are beneficial to infer the model errors, compared to considering model errors only. Moreover, using the observations to verify the forecasts has advantages over using the analyses, to estimate the model errors. Using observations can also achieve a faster convergence of model error estimation with online DA than using analyses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".