Knowledge-Enhanced Deep Learning for Simulation of Extratropical Cyclone Wind Risk
Bibliographic record
Abstract
Boundary-layer wind associated with extratropical cyclones (ETCs) is an essential element for posing serious threats to the urban centers of eastern North America. Using a similar methodology for tropical cyclone (TC) wind risk (i.e., hurricane tracking approach), the ETC wind risk can be accordingly simulated. However, accurate and efficient assessment of the wind field inside the ETC is currently not available. To this end, a knowledge-enhanced deep learning (KEDL) is developed in this study to estimate the ETC boundary-layer winds over eastern North America. Both physics-based equations and semi-empirical formulas are integrated as part of the system loss function to regularize the neural network. More specifically, the scale-analysis-based reduced-order Navier–Stokes equations that govern the ETC wind field and the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA) ERA-interim data-based two-dimensional (2D) parametric formula (with respect to radial and azimuthal coordinates) that prescribes an asymmetric ETC pressure field are respectively employed as rationalism-based and empiricism-based knowledge to enhance the deep neural network. The developed KEDL, using the standard storm parameters (i.e., spatial coordinates, central pressure difference, translational speed, approach angle, latitude of ETC center, and surface roughness) as the network inputs, can provide the three-dimensional (3D) boundary-layer wind field of an arbitrary ETC with high computational efficiency and accuracy. Finally, the KEDL-based wind model is coupled with a large ETC synthetic track database (SynthETC), where 6-hourly ETC center location and pressure deficit are included to effectively assess the wind risk along the US northeast coast in terms of annual exceedance probability.
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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 | 0.000 | 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) | 0.002 | 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".