Jason graduated with a Bachelor of Science (Mathematics/Physics) and Bachelor of Mathematics in 1995 and an Honours degree in Mathematics in 1996, all from the University of Newcastle. He then completed his PhD in pure mathematics and mathematical physics at the University of Canberra.
In 2001, Jason was appointed as a Postdoctoral Fellow at the Australian National University where he worked on the spatiotemporal analysis of climatic variables such as rainfall and evaporation. In 2006 Jason moved to the School of PEMS where he worked on the Bushfire CRC's HighFire Risk Project.
Between 2008 and 2011 Jason worked in the School of PEMS as a Research Associate on an ARC funded project addressing the existence and stability of combustion waves arising in simplified reaction schemes.
In 2011 Jason was appointed as Lecturer in Mathematics in the School of PEMS, where he works as part of the Applied and Industrial Mathematics Research Group. Jason has been a Discovery Indigenous Award recipient, working on an ARC funded project concerned with better understanding the dynamics of extreme bushfire development. In 2014 he was appointed as an Associate Professor at the University of NSW Canberra.
Spotting can be the dominant fire propagation mechanism during times of extreme fire weather. Spot fires can merge and collapse on one another creating regions of deep flaming, which produce violent pyroconvection. Understanding and modelling the intrinsic dynamics of spot fire coalescence is an important step in providing ways of mitigating the effects of extreme fires.
Understanding the variability of wind speed and direction across complex terrain is a vital part of understanding volatile fire behaviour. A probabilistic characterisation of wind fields can complement the current deterministic approaches to enhance discussion of uncertainties in the modelling process.
Modelling wind direction can be recast in probabilistic terms using wind response distributions. The impacts of vegetation regrowth on wind direction response can then be investigated using statistical comparison techniques.
A key problem in wildfire modelling is how to capture dynamic fire behaviour in models suitable for operational use.
|Presentation-Audio-Video||27 Oct 2014||Environmental thresholds for dynamic fire propagation||fire, propagation|
|Presentation-Slideshow||04 Dec 2014||Fire coalescence and mass spotfire dynamics||Save (704.99 KB)||fire, fire severity, modelling|
|Presentation-Slideshow||11 Sep 2015||Linking local wildfire dynamics to PyroCB development||Save (8.53 MB)||fire, modelling|
|Presentation-Slideshow||30 Aug 2016||Wind speed reduction induced by post-fire vegetation regrowth - Rachael Quill||Save (4.44 MB)||fire, fire impacts, fire weather|
|Presentation-Audio-Video||20 Oct 2016||Fire coalescence and mass spotfire dynamics - project overview||Save (0 bytes)||fire impacts, fire severity, fire weather|
|Presentation-Slideshow||24 Oct 2016||Fire coalescence and mass spot fire dynamics||Save (4.18 MB)||fire, fire impacts, fire weather|
|HazardNoteEdition||25 Oct 2016||Next generation fire modelling||Save (1.35 MB)||fire impacts, fire severity, fire weather|