Evaluation of WRF-SFIRE performance with field observations from the FireFlux experiment
Bibliographic record
Abstract
Abstract. This study uses in situ measurements collected during the FireFlux field experiment to evaluate and improve the performance of the coupled atmosphere–fire model WRF-SFIRE. The simulation by WRF-SFIRE of the experimental burn shows that WRF-SFIRE is capable of providing realistic head-fire rate of spread and vertical temperature structure of the fire plume, and fire-induced surface flow and vertical velocities within the plume up to 10 m above ground level. The simulation captured the changes in wind speed and direction before, during, and after fire front passage, along with the arrival times of wind speed, temperature, and updraft maxima, at the two instrumented flux towers used in FireFlux. The model overestimated vertical wind speeds and underestimated horizontal wind speeds measured at tower heights above 10 m. It is hypothesized that the limited model spatial resolution led to overestimates of the fire front depth, heat release rate, and updraft speed. However, on the whole, WRF-SFIRE simulated fire plume behavior that is consistent with FireFlux observations. The study suggests optimal experimental pre-planning, design, and execution strategies for future field campaigns that are intended to evaluate and develop further coupled atmosphere–fire models.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".