This article has been peer reviewed and accepted for publication. It is in production and has not been edited, so may differ from the final published form.
Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications
Local wind fields that account for topographic interaction are a key element for any wildfire spread simulator. Currently available tools to generate near-surface winds with acceptable accuracy do not meet the tight time constraints required for data driven applications. This article presents the specific problem of data-driven wildfire spread simulation (strategy based on using observed data to improve results), for which wind diagnostic models must be run iteratively during an optimisation loop. An interpolation framework is proposed as a feasible alternative to keep a positive lead time while minimizing the loss of accuracy. The proposed methodology was compared to the WindNinja solver in eight different topographic scenarios with multiple resolutions and reference –pre-run– wind maps sets. Results showed a major reduction in computation time (about 100 times once the reference fields are available) with average deviations of 3% in wind speed and 3 deg in direction. This indicates that high-resolution wind fields can be approximated from a finite set of reference maps previously computed. Finally, wildfire spread simulations using original and interpolated maps were compared showing minimal deviations in the fire shape evolution. This methodology may have an important impact on data assimilation frameworks and probabilistic risk assessment where high-resolution wind fields must be computed for multiple weather scenarios.
WF17027 Accepted 13 February 2018
© CSIRO 2018