Application of operational seasonal prediction systems for seasonal prediction of fire danger in tropical ecosystems
Bibliographic record
Abstract
The extreme wildfire events in California, Portugal, Spain, Australia, Sweden and Greece in 2017 and 2018 caused considerable economic, environmental and human life losses and gathered much media attention. These events highlighted the need for both short and medium-term forecasts of wildfire danger, the latter useful for raising awareness and preparing for wildfire prevention and suppression strategies. In tropical areas such as the Amazon basin and Indonesia, wildfires are greatly affected by inter-annual fluctuations in tropical Sea Surface Temperatures (SSTs). The 1997-1998 and 2015-2016 El Niño events were associated with above-average wildfires in Indonesia and Amazonia. While countries such as the United States, Canada and Australia have developed extensive and reliable short-term and seasonal wildfire forecasting systems, similar systems are less well established for predicting wildfire in tropical regions. Our approach to seasonal prediction of fire risk is to use real-time climate forecasts, such as those from ECMWF's System 5 forecast system, to issue predictions of the Canadian Fire Weather Index (FWI) and McArthur Forest Fire Danger Index (FFDI), using the methodology of ECMWF's short-term Global ECMWF Fire Forecast (GEFF). These indices are computed from daily values of temperature, precipitation, relative humidity and wind speed, accounting for factors that are important for fire severity and spread. As operational forecasts produce ensemble predictions of these variables, we are able to formulate probabilistic predictions from ensemble daily predictions of fire danger indices at a global scale. Global observations of burned area from the MCD64 global burned area product and the Global Fire Emissions Database version 4 (GFED4) are used to evaluate the skill of the predictions. We will show the skill of the predictions for the Amazon basin and cerrado region, with a focus on the extreme wildfire seasons associated with El Niño events. Â
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".