The method of prediction

Overview

The method of annual FSS prediction is based on Chen et al. (2011) with some modifications. We developed our empirical model of FSS using fire counts detected by MODIS onboard NASA's Terra satellite along with Oceanic Nino Index (ONI) and Atlantic Multidecadal Oscillation index (AMO) SST anomaly time series. Sea surface temperatures prior to the onset of the fire season have the strongest relationship with the number of satellite observed fires during the fire season in many areas of South America. The lead times enable us to make a prediction for the upcoming fire season.

Data

Model

We defined our empirical predictive model as a linear combination of the two climate indices sampled during the months of maximum correlation:

FSSpredicted(x,t,τc)=a(x,τc)×ONI[t,m(x)-τONI(x,τc)]+b(x,τc)×AMO[t,m(x)-τAMO(x,τc)]+c(x,τc).

FSSpredicted is the predicted FSS in region x and year t. The parameter τc indicates the lead time (number of months before the peak fire month) when the prediction was made. a and b are spatial varying coefficients that represent the sensitivities of FSS in each region to ONI and AMO, individually, and c is a constant. ONI and AMO were sampled each year during months with lead times τONI and τAMO relative to the peak fire month (m) in each region. Given a target τc, the optimal τONI and τAMO values were derived from a series of linear regressions using ONI and AMO values at different months (with a cutoff(minimum) lead time of τc).

Prediction

Based on the data (ONI and AMO) availability and the peak fire month, we derived the τc for each region. We then applied the predictive model with corresponding coefficients (a, b, and c) and optimal lead times (τONI and τAMO) to derive the FSS in the target fire year. The range of the prediction was calculated using the 1-sigma uncertainty estimates for the parameters of the predictive model. Therefore, we have a set of predictions derived from different months (though with different confidence).

Versions

Version 1.0 (method used in Chen et al., 2011)

Version 2.0 (used for 2012 fire season forecast)

Version 2.1 (used for 2013 fire season forecast)

Disclaimer

The model predictions contained on this website are highly experimental. They cannot be used to predict the occurrence of individual fires. Use of this information for planning purposes should also draw upon other independent and reliable climate information sources. The Regents of The University of California will not be liable for any consequences that may occur if you rely on this information.


Contact: Yang.Chen@uci; Updated in April 2012