we used 2001–2009 fire counts detected by the Moderate Resolution Imaging Spectroradiometer (MODIS)
The success of this model with only small amounts of training data is encouraging for other areas of ecology and environmental science where the available time-series may be short.
Fire season severity, here defined as the sum of satellite-based active fire counts in a 9-month period centered at the peak fire month, depends on multiple parameters that influence fuel moisture levels and fire activity in addition to precipitation, including vapor pressure deficits, wind speeds, ignition sources, land use decisions, and the duration of the dry season. As a result, the relationship between FSS and SSTs may be more complex than the relationships between precipitation and SSTs described above.
This recognition of additional factors that could influence fire, and the fact it more complex models using the same data may be able to indirectly use some of these influences is really valuable. It is, in effect, positing that latent variables associated with some of these causes may be associated with measurable aspects of SST.
This is a nice example of chaining together separate pieces of knowledge to understand what form of forecasting model might be successful. Large scale climate phenomena -> variation in precipitation -> variation in fire season severity.