Subseasonal fire forecast in the Amazon using week-2 precipitation forecast combined with a vegetation health indicator

<p>Numerical predictions for a lead time of 2 to 4 weeks, a timescale known as subseasonal, has only in recent years begun transitioning from research to operational settings. One experiment dedicated to that effort is the Subseasonal Experiment (SubX). In here, SubX multi-model ensemble (MME) mean precipitation forecast (2017-2021) for days 8 to 14 (week-2 forecast) is used as a covariate in logistic regression models to predict fire risk in the Amazon. The hybrid (dynamical and statistical) modelling approach describes the NextGen methodology aimed at improving forecast outcomes at the seasonal and subseasonal time scales. In a complementary experiment, a vegetation health index (VHI) is added to SubX precipitation forecasts as a predictor to fires. The findings show that fire risk can be skillfully assessed in most of the Amazon where fires occur regularly. In some sectors, SubX week-2 precipitation alone is a reliable predictor of fire risk, but the addition of VHI results both in (i) a larger portion of the Amazon domain with skillful forecasts and; (ii) higher skill in some sectors. The added information provided by VHI as a predictor is most relevant where the mosaic of land covers includes savannas and grassland, whereas SubX precipitation can be used as the sole predictor for week-2 fire risk forecast where the mosaic of land cover is dominated by forests. The operationalization of the methods presented in this study could allow for better preparedness and fire risk reduction in the Amazon with a lead time greater than a week.</p>