Reanalysis of ECMWF data for updating the Fire Weather Index for the Indonesia Fire Danger Rating System (InaFDRS)
Bibliographic record
Abstract
Fire Weather Index (FWI) as one of the parameters of the Fire Danger Rating System (FDRS) was developed about 80 years ago. The most widely adopted index is the Canadian FWI. The FWI requires daily temperature, humidity, and wind speed measured at local noon, and accumulation of rainfall over the last 24 hours. Since 2018, FWI has been measured for the Regency of Ogan Komering Ilir (OKI), South Sumatra District. To supplement the sparse weather stations, we used the daily ECMWF (European Centre for Medium-Range Weather Forecasts ) reanalysis from 1979 to 2018 to calculate the FWI values. Data reanalysis using power spectral density function show intra-seasonal cycles (5.9 months) and annual cycles (11.9 months) of monsoons, and 3- year cycles detected from Indian Ocean Dipole (IOD) phenomena and cycles of El Niño Southern Oscillation (ENSO) phenomena with repeat signals every 1.5, 3.5, 5.5, and 9.75-years cycle. The pattern of changes in the FWI time series values follows the local weather system, the regional climate system (monsoon), the climate system in the Indian Ocean with its IOD phenomenon, and the tele-connected climate system in the Pacific Ocean, ENSO phenomenon. Based on this pattern, an updated FWI classes was determined to better fit the climate condition of Indonesia, specifically OKI Regency that yield a better rating for the FDRS
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".