Operational Dust Prediction - Mineral Dust: A Key Player in the Earth System - page 222

10.1 Introduction

10.1.1 Motivation for Dust Forecasting

While the importance of airborne dust for visibility, air quality and climate has
been recognised for a long time, it was only in the past decade that development
of operational forecasting capabilities for atmospheric aerosols in general and dust
in particular has intensified. Several reasons motivated the development of dust
monitoring and forecasting capabilities:

1. Decision-makers have long desired the ability to forecast severe dust events in
order to mitigate their impacts on transportation, military operations, energy
and health. In some regions of the world, people's livelihoods are threatened
by severe dust storms that can force the closing of roads and airports due
to poor visibility. Health advisories to susceptible populations require dust
information as input (Chap. 15 ) . Commercial solar energy production systems
require forecasts of solar insolation to help predict their contribution to the power
grid, especially those that rely on direct solar radiation (Schroedter-Homscheidt
et al. 2013 ). Dust also affects the semiconductor industry, which requires a clean
atmosphere to manufacture electronic chips.
2. Dust interacts with atmospheric radiation and can significantly modify the
Earth's radiative budget (Chap. 11 ) . While the importance of dust-climate
interactions has long been recognised (Chap. 13 ) , it is only recently that the
importance of dust for weather forecasting itself has been appreciated (Pérez
et al. 2006a ). Haywood et al. ( 2005 ) showed that the UK Met Office numerical
weather prediction (NWP) model had a bias of

35 Wm 2 in its top-of-the-
atmosphere radiative budget over the Saharan region because it neglected the
effects of dust on radiation. Such systematic biases in NWP models can be
addressed by prescribing better aerosol climatologies (e.g. Rodwell and Jung
2008 ), but interactive aerosols in NWP models are increasingly being exploited
to improve the skill of weather forecasts.
3. Dust's infrared (IR) signature causes interference in satellite retrievals and
subsequent assimilation of temperature, humidity and sea surface temperature
(SST). For example, Weaver et al. ( 2003 ) show how TOVS (see Table 10.1 for a
list of acronyms) temperature profiles can be contaminated by dust. Ruescas et al.
( 2011 ) demonstrated the impact on SST retrievals, which are used operationally
as a boundary condition in models. Maddy et al. ( 2012 ) demonstrated significant
dust impacts of up to 4 K on AIRS retrievals of the atmospheric temperature
profile. Given the extreme loadings of some dust events from Africa and Asia,
dust must be accounted for in models that utilise data assimilation based on IR
wavelengths.
4. There is a pressing need to monitor the Earth's environment to better understand
changes and adapt to them, especially in the context of climate. Since the dust
cycle is closely related to meteorological conditions, the benefit of combining