Severe convective wind storms (i.e., thunderstorms and tornadoes) cause billions of dollars in damage to global infrastructure each year. This suggests there are systematic deficiencies in the way structures are designed to withstand these events. If progress is to be made towards mitigating the devastating impacts of future events, there is an urgent need to better understand, quantify, and design for wind hazards. Currently, no spatially complete assessment of wind hazards from thunderstorms and tornadoes exists for Australia. Where weather station data is available it is often short in duration, so there is great uncertainty in its extrapolation to the long return periods needed for engineering design. In addition, no useable information currently exists on how potential changes to large-scale climate over Australia will impact severe convective wind storms. This information is of vital importance to engineering design as structures with design lives between 50 – 100 years will be required to withstand any changes to extreme weather events that may occur.
The objective of this research project is to develop a stochastic event-based modelling approach that simulates an extended period of convective wind storm activity over Australia. The framework for the stochastic model development is divided into four steps. The first step is to create a spatially complete national convective wind storm climatology. In areas where reliable information is available, storm frequency will be estimated based on a combination of observational data (Bureau of Meteorology's Severe Storm Archive) aggregation and inference methods. Spatial probability information will be used, along with reanalysis data sets (ERA-Interim), to infer wind storms occurrence frequency in areas where observational data is limited. The second step is to generate and track convective events. Using climatological frequency information, a Poisson sampling method will be utilised to generate a national synthetic event set of convective wind storms occurring over an extended period of time. The third step is to model the wind field. High-resolution spatial wind data measured during convective storms will be used to characterize the three-dimensional wind field and be ingested into the stochastic model. Finally, wind hazard maps will be developed for a range of return periods for the entire country using exceedance probability curves.
To understand potential changes to convective wind storm hazard under a range of Intergovernmental Panel on Climate Change (IPCC) climate change scenarios, large-scale global climate model environmental parameters (i.e. CAPE, Wind Shear) used in the stochastic model to estimate convective wind storm frequency will be studied. Running the stochastic model with these “changed” environments will allow variations in estimated storm frequency to be determined as well as subsequent changes to convective wind storm hazard. Resulting data can then be used to generate hazard maps and stochastic event sets to inform wind-resistant design standards and facilitate risk-based decision making by government and industry.
Friday, 24 February 2017
Room 420, General Purpose South (Building 78), St Lucia Campus