Validation of ERA5 fire weather conditions in Greece between 2007 and 2019: A preliminary analysis
Bibliographic record
Abstract
Accurate simulations of fire weather conditions for both the past and the future are of great importance for fire management and preparedness. With the advancement of numerical weather prediction models and data assimilation techniques, more accurate reanalysis products have been developed the recent years. Here we validate fire weather conditions in Greece which are computed based on ERA5 reanalysis data using surface observations from the automatic weather station network of the National Observatory of Athens (NOA). We assess the fire weather conditions in an application of the Canadian Forest Fire Weather Index (FWI) System in both datasets. Although, ERA5 FWI archive is available since 1979 here we limit our analysis during the period of 2007 to 2019, due to the limited data availability from the NOA network. The validation of FWI in ERA5 data shows good agreement with the NOA observations with a mean correlation of 0.87. Furthermore, FWI in ERA5 data is overall slightly underestimated compared to NOA observations, which is driven by an underestimation of the three moisture components of FWI, namely the Fine Fuel Moisture Code (FFMC), the Drought Code (DC) and the Duff Moisture Code (DMC). Preliminary results also indicate that the largest errors are found over the eastern and southern parts of Greece, which is the are that experiences the highest FWI values during the summer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".