Operational Dust Prediction - Mineral Dust: A Key Player in the Earth System - page 249

related to the complex emission sources and the heterogeneous characteristics of the
emitting surfaces, including texture, composition, vegetation type and topography.
Dust prediction is also limited by the paucity of observations available for data
assimilation, model initialisation and verification. A significant current limitation
is that satellite instruments do not precisely distinguish the presence of dust from
that of other aerosol species. As more products from satellite- and ground-based
stations become available, it is foreseeable that dust prediction will improve. In
order to provide the best dust forecasts possible, along with improving the dust
models, there are currently international efforts to bring together several operational
and quasi-operational models to form multi-model ensembles. The merit of these
ensembles is to bring together the strengths of the various state-of-the-art models
while offering the possibility to approach the dust prediction from a probabilistic
perspective, thus enhancing the range of applications. The development of these
multi-model ensembles is at an early stage, and exploitation of their potential is
still limited, also because of the relatively small number of participating models.
However, it is anticipated that the probabilistic approach to dust prediction both at
level of the individual centres and within the context of the multi-model ensembles
will become more important in the future.

Appendix A: Technical Aspects of Data Assimilation
for Dust Prediction

A10.1 Assimilation Techniques

Variational Methods (CMA, ECMWF, FNMOC/NRL, Met Office, NASA
GMAO)

The variational method is a well-established approach that combines model back-
ground information with observations to obtain the “best” initial conditions pos-
sible. In the 2D- and 3D-Var versions, the fields are adjusted at the analysis time
whereas in 4D-Var, a short-term forecast is run over the selected time window
(usually 12 h) to provide a so-called first guess. In 4D-Var, the dynamical model
is then used as a strong constraint to minimise the difference between the model
background and the observations. This approach is widely used in many NWP
centres. The fundamental idea of the variational methods is based on minimisation
of a cost function which measures the distance between observations and their
model equivalent, subject to a background constraint usually provided by the model
itself. Optimisation of this cost function is performed with respect to selected control
variables (e.g. the initial conditions). Adjustments to these control variables allow
for the updated model trajectory to match the observations more closely. Assuming
the update to the initial condition is small, an incremental formulation can be
adopted to ensure a good compromise between operational feasibility and physical