Evaluation of WRF parameterizations for extreme wind events over the eastern coast of Spain
Bibliographic record
Abstract
Extreme wind speed events cause more than half of the economic losses associated with natural disasters in Europe, and they are becoming more frequent and severe due to climate change. A better understanding of these extreme events is essential to reduce their associated risks. The overall aim of this research is to study the performance of different parametrizations (micro physics and planetary boundary layer) of the WRF model for the simulation of extreme wind speed and gusts events on the eastern coast of the Iberian Peninsula (IP), Spain. Two typical extreme wind events are simulated: (i) a cold front passage in winter (January 4th, 2022), and (ii) a downburst in summer (August 15th, 2021). Three 2-way nested domains are defined, the two largest ones (horizontal resolutions of 9- and 3-km) centered on the IP and the third one (1-km) centered on the Valencian region. The simulations are initialized with data from the ERA5 reanalysis and the boundary conditions in the coarser domain are updated hourly. The evaluation of the simulations is assessed by means of observed data provided by the Spanish State Meteorological Agency (AEMET) including automatic station network data, radar and satellite images and atmospheric soundings. The model accurately simulates the passage of the cold front, matching the time and magnitude of the observed wind speed and gusts. However, the WRF model does not reproduce the downburst, at least at the observed location. In general, the simulations are more sensitive to the choice of the boundary layer parameterization than to the microphysics parameterization.
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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 | 0.002 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".