Acceleration of the Weather Research & Forecasting (WRF) Model using OpenACC and Case Study of the August 2012 Great Arctic Cyclone
Bibliographic record
Abstract
This work presents two research projects: the first is a project to boost the performance of a weather model by extending the Weather Research and Forecasting (WRF) Model programming code with new technology, OpenACC, a directive-based command language.Combined with a compatible compiler, this allows WRF to run select subroutines on accelerators such as the popular NVIDIA Tesla video cards.Preliminary results show a 1.2× speed-up of the overall model by simply adding around 20 lines of easy-to-understand compiler directives to just one of several WRF physics schemes.The latest hardware from NVIDIA and Intel may allow further speed-up in future work due to hardware design improvements and better memory management.This modified model is then used in a second project, a Case Study of the August 2012 "Great Arctic Cyclone" (3-13 August).This Case Study provides a simulation of the atmospheric processes that helped to intensify the storm and to assess whether the storm had a major effect on the dramatic decline of sea ice extent in August.Results show that interaction with an upper-level vortex and warm air advection near the surface assisted in the persistence of the cyclone.Strong surface winds assisted in a break-up of the already-melting sea ice.Existing literature points to upwelling of warm ocean water, and the combination of these conditions rapidly melted sea ice from above and below.The unprecedented loss of sea ice starting on 6 August 2012 and in the
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".