The all-source Green’s function (ASGF) and its applications to storm surge modeling, part I: from the governing equations to the ASGF convolution
Bibliographic record
Abstract
In this study, a new method of storm surge modeling is proposed. This method is orders of magnitude faster than the traditional method within the linear dynamics framework. The tremendous enhancement of the computational efficiency results from the use of a pre-calculated all-source Green’s function (ASGF), which connects a point of interest (POI) to the rest of the world ocean. Once the ASGF has been pre-calculated, it can be repeatedly used to quickly produce a time series of a storm surge at the POI. Using the ASGF, storm surge modeling can be simplified as its convolution with an atmospheric forcing field. If the ASGF is prepared with the global ocean as the model domain, the output of the convolution is free of the effects of artificial open-water boundary conditions. Being the first part of this study, this paper presents mathematical derivations from the linearized and depth-averaged shallow-water equations to the ASGF convolution, establishes various auxiliary concepts that will be useful throughout the study, and interprets the meaning of the ASGF from different perspectives. This paves the way for the ASGF convolution to be further developed as a data-assimilative regression model in part II. Five Appendixes provide additional details about the algorithm and the MATLAB functions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".