@article {bnh-5376, title = {Modelling wind direction distributions using a diagnostic model in the context of probabilistic fire spread prediction}, journal = {Frontiers in Mechanical Engineering: Thermal and Mass Transport}, year = {2019}, month = {02/2019}, abstract = {

With emerging research into the dynamics of extreme fire behaviour, it is increasingly important for wind models used in operational fire prediction to accurately capture areas of complex flow across rugged terrain. Additionally, the emergence of ensemble and stochastic modelling frameworks has led to the discussion of uncertainty in fire prediction. To capture the uncertainty of modelled fire outputs, it is necessary to recast uncertain inputs in probabilistic terms.\ 

WindNinja is the diagnostic wind model currently applied within a number of operational fire prediction frameworks across the world. For computational efficiency, allowing for real-time or faster than real-time prediction, the physical equations governing wind flow across complex terrain are often simplified. The model has a number of well documented limitations, for instance, it is known to perform least well on leeward slopes. This study first aims to understand these limitations in a probabilistic context by comparing individual deterministic predictions to observed distributions of wind direction. Secondly, a novel application of the deterministic WindNinja model is presented and shown to enable prediction of wind direction distributions that capture some of the variability of complex wind flow.\ 

Recasting wind fields in terms of probability distributions enables better understanding of variability across the landscape, and provides the probabilistic information required to capture uncertainty through ensemble or stochastic fire modelling. The comparisons detailed in this study indicate the potential for WindNinja to predict multimodal wind direction distributions that represent complex wind behaviours, including recirculation regions on leeward slopes. However, the limitations of using deterministic models within probabilistic frameworks are also highlighted. To enhance fire prediction and better understand uncertainty, it is recommended that statistical approaches also be developed to complement existing physics-based deterministic wind models.

}, keywords = {complex terrain, deterministic, ensemble modelling, Probability distributions, uncertainty, von Mises, wind modelling, WindNinja}, doi = {10.3389/fmech.2019.00005}, url = {https://www.frontiersin.org/articles/10.3389/fmech.2019.00005/abstract}, author = {Rachael Quill and Jason J. Sharples and Natalie Wagenbrenner and Leesa Sidhu and Jason Forthofer} } @article {bnh-5771, title = {A Statistical Approach to Understanding Canopy Winds over Complex Terrain}, journal = {Environmental Modeling \& Assessment}, year = {2019}, month = {08/2019}, pages = {1-20}, abstract = {

Winds that flow within a forest canopy in complex terrain exhibit considerable variability that can have significant consequences for environmental processes such as the spread of bushfires, seed dispersal and transport of pollutants. This variability is still poorly understood, due in part to limited observations of canopy winds over complex terrain, and as such is often unaccounted for in deterministic modelling approaches. Probabilistic representation of wind fields can better characterise variability and build understanding of uncertainty in applications such as bushfire prediction. This study introduces two new, publicly available, datasets that support analyses of canopy winds over complex terrain using novel statistical approaches. Wind fields are characterised using bivariate distributions, representing the response of synoptic winds to changing landscapes, including post-bushfire forest regrowth and varying topography across uniform vegetation. For statistical comparison, non-parametric Kolmogorov-Smirnov style tests are considered. A new test is proposed for the comparison of bivariate circular distributions arising from analyses of wind direction. The study broadly reaffirms established theory while highlighting the variability of canopy winds over complex terrain and the importance of developing statistical techniques that result in a better quantitative understanding of these wind fields and the processes driving them.

}, keywords = {Circular statistics, Flow separation, forest canopy, uncertainty, Wind field observations, wind modelling}, doi = {https://doi.org/10.1007/s10666-019-09674-w}, url = {https://link.springer.com/article/10.1007/s10666-019-09674-w}, author = {Rachael Quill and Jason J. Sharples and Leesa Sidhu} }