Evaluation of the reliability of the high-resolution WRF fire danger forecasts in Poland
Bibliographic record
Abstract
Fires negatively affect the composition and structure of fauna and flora, as well as the quality of air, soils and water. They cause economic losses and pose a risk to human life. Poland is at the forefront of European countries in terms of forest fires. Therefore, Institute of Meteorology and Water Management - National Research Institute (IMWM-NIR) implemented fire danger forecast system based on high-resolution (2.5 km) Weather Research and Forecast (WRF) model. Forecasted meteorological data are used to calculate parameters of Canadian Forest Fire Weather Index (FWI) System: Fire Weather Index (FWI), Initial Spread Index (ISI), Buildup Index (BUI), Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), and Drought Code (DC). Each parameter is presented in one of the classes corresponding to the fire danger – from low to extreme. In this way, a daily 24- and 48-hour fire danger forecasts are generated for the whole area of Poland and presented on IMWM-NIR meteorological website (meteo.imgw.pl). In this presentation we show analyses of reliability of implemented FWI system. For this purpose, data reprocessing from March to September 2019 were made. Also data on fires occurrence on forest lands: time of occurrence, characteristics and location, from the resources of the State Fire Service were collected. Finally, for the selected period, we obtained a dataset of about 8 thousand events for which we assigned values of FWI parameters. Generally, based on our analysis, correlation between number of fires and averaged value of FWI amounted over 0.8. We found out, the correlation coefficient calculated for regions differ. The correlation is higher in central and northern Poland compared to the eastern part of the country, which also correspond to the number of fires. This may be related to the different forest structure - there is a higher proportion of broadleaf forests in the east. The comparison of 24- and 48-hour forecasts showed that they have similar reliability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
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".