20
BAMOS
Sept 2017
Figure 6. Wind speed (m s -1 ) for January 4, 2013 at 0400hrs. A red square in the left panel highlights the Giblin River fire
location. The right panel shows wind speed for the region within the red rectangle based on data from the Initial Analysis
(at 1.5 km resolution) for Tasmania.
Cobbler Road fire New South Wales
The Cobbler Road grassfire during January 2013 in New South
Wales (Figure 7) is one of the fires recently examined to assess
fire spread simulators used in Australia. The New South Wales
Rural Fire Service (2013) describes the details around the
Cobbler Road fire under the title “Speed and fury”:
“The Cobbler Road fire, which started under Extreme conditions,
burnt quickly, travelling 35 kms and covering 14,000 hectares
within six hours. It caused significant damage to farming
country including extensive livestock losses. Much of the activity
took place overnight on 8–9 January 2013. The strong westerly
wind did not ease overnight and nearly 150 firefighters worked
intensively to protect properties in the path of this remarkably
fast moving grass fire.”
Fire spread simulators are used to predict the surface spread
of a fire, and can be valuable tools in operational decision
making and planning. These simulators have been assessed
by comparison with the observed burnt areas (Faggian et al
2017). Preliminary studies incorporating the reanalysis data
(Chris Bridge, Bureau of Meteorology, pers. comm.), compared
with data from the Bureau of Meteorology Australian Digital
Forecast Database, show that for the small set of case studies
examined, incorporating the reanalysis data generally led to
better overall results using the same evaluation metrics as used
in the fire project. This can vary somewhat depending on the
type of simulator used and the circumstances of the fire events.
This is an exciting potential area of study for the reanalysis data.
Making use of the reanalysis data
The reanalysis data set will be especially valuable in providing
climatologies of weather extremes across the nation. Due to
the high spatial and temporal resolution of the reanalysis data,
mapping of the extremes of variables, such as the 95th percentile
of daily maximum wind speed (Figure 8), is made possible. This
is information required for example in infrastructure design
and to assess the intermittency and covariability of resources in
renewable power generation.
Understanding of short-lived or fast-developing atmospheric
phenomena such as thunderstorms will be improved with the
detailed temporal and spatial information on atmospheric
conditions. Processing of a reanalysis dataset can provide
an indication of thunderstorm environments, and assist in
studies of those environments conducive to massive lightning
occurrence. Severe thunderstorm environments are reasonably
well-understood, so a comprehensive climatology of these
environments can be generated from a reanalysis dataset.
The vast amount of data currently being generated will permit
studies leading to an unprecedented understanding of local
weather, particularly in areas that are currently poorly served by
weather observations such as the Tasmanian Wilderness World
Heritage Area that suffered significant bushfires in January
2016.
Bushfires are one of the most costly natural disasters in
Australia in terms of loss of life and damage to property and
the environment (Crompton, 2011). BARRA will enable studies
of the passage of cold fronts over southern Australia during
summertime, identifying characteristics of such fronts that
often lead to extreme fire or fire weather activity, deriving
variables such as the Forest Fire Danger Index (FFDI, Figure 9)
and permitting the development of “climatologies” of fuel load
for grass fire danger studies. This will allow for greater resilience
in the future, especially under future climate projections that
predict an increase in the frequency of severe fire weather (FFDI
over 50, CSIRO & Australian Bureau of Meteorology 2015).