Climate Change Implications and Use of Early Warning Systems for Global Dust Storms - Reducing Disaster: Early Warning Systems for Climate Change - page 154

countries have been monitoring dust events for 50 years or more (Wang et al. 2008 ;
Shao et al. 2011 ), others have not. Models can help to close the data gap that exists in
long-term meteorological data collections. Forecasts, derived from models, help deci-
sion-makers and scientists gather information about where and how fast a storm is
moving. Information can be used by offi cials to deliver early warning information and
by scientists to study patterns, as well as by medical offi cials and policymakers who
can use forecast information to aid in determining the role of dust storms in origin and
transport patterns of pathogens and disease (Stefanski and Sivakumar 2009 ).
Both regional and global models are essential for creating accurate forecasts due
to variations in dust events across the globe. For example, March to May 2012 was
an extremely active year for dust storms over the northern tropical Atlantic Ocean,
the Arabian Peninsula, the northern Indian Ocean and the United States (Benedetti
et al. 2013 ). However, during the same period, China experienced fewer dust storms
than normal (10 instead of 17) and northern China had the least number of dust days
since 1961 (Zhang et al. 2013 ). Furthermore, on the western edge of China,
Mongolia experienced frequent dust storms, even a few particular days of extensive
damage across the country in April.
An example of a regional model is the CUACE/Dust (Chinese Unifi ed
Atmospheric Chemistry Environment for Dust) model, which is used to produce
forecasts for up to 3 days and is considered a quality model for dust events in East
Asia (Wang et al. 2008 ). The model also has the ability to provide scientists and
forecasters with information to make more educated impact projections because the
model can provide insight about the distribution of desert and semi-desert land
cover types, soil grain size, soil moisture content, snow cover and land use (Zhou
et al. 2008 ). Alternatively, the Dust Regional Atmospheric Model (DREAM), uti-
lised by the Barcelona Supercomputing Centre (BSC) and the United States National
Weather Service (NWS), applies a global model to different regions and most exten-
sively in the Mediterranean, North Africa and Middle East regions (NASA 2013a ).
With open online access, the BSC offers animated cycles of forecasts, using the 8b
version of the DREAM model, in 6-h intervals from real time to 72 h in the future,
for four substantial dust-producing regions in the world (BSC 2012 ). Forecasts
using models such as the DREAM help to not only identify the immediate location
of a dust particle, but also to determine point (or points of origin) and the anticipated
path of the storm.
Other methods of creating forecasts and early warning information include using
Light Detection and Ranging (LiDAR) data which allows for real-time monitoring.
For example, the Ministry of Environment in Japan uses LiDAR to measure the
presence of yellow sands and distinguish dust particles from other pollutants. The
information is then incorporated into regional dust and sand storm monitoring net-
works to warn citizens of incoming dust storms as opposed to haze caused by pol-
lutants. Ground-based global networks such as the Global Atmosphere Watch
Aerosol LiDAR Observation Network (GALION) also use LiDAR data from sev-
eral different regions and help to create a more complete picture of dust movement
and the global dust cycle (Shao et al. 2011 ).