Operational Dust Prediction - Mineral Dust: A Key Player in the Earth System - page 237

Regional Mineral Dust Forecast Model in Taiwan

Taiwan's Environment Protection Administration (TEPA) has conducted East Asian
dust storm forecasts since 2002 in collaboration with the Department of Atmo-
spheric Sciences, National Taiwan University (NTU). They incorporated the dust
deflation module of Wang et al. ( 2000 ) into the Taiwan Air Quality Model (TAQM)
in 2002 and into the CMAQ model in 2010. Some of the model details can be found
in Chen et al. ( 2004 ) and Table 10.3 . The dust-coupled TAQM (or TAQM-KOSA)
is run twice a day for 57 and 81 km horizontal grid spacing, each providing a 5-day
forecast. TAQM-KOSA has also been used as a research tool to study dust effects
on cloud microphysics and marine phytoplankton bloom by the NTU group. Studies
of dust produced from dry riverbeds and agricultural lands using 3 km grid spacing
showed that these local sources may raise regional PM concentration more than dust
from long-range transport. Local daily dust forecasts have been included in routine
operation since 2010. The dust scheme is being improved and incorporated into
WRF and WRF-CHEM models and coupled with the cloud microphysical scheme
to provide better calculation of in-cloud and below-cloud scavenging of dust as well
as dust-radiation feedback. These versions will be gradually incorporated into daily
operation after extensive tests.

10.3 Multi-model Ensembles

Ensemble prediction aims to describe the future state of the atmosphere from
a probabilistic point of view. Multiple simulations are run to account for the
uncertainty of the initial state and/or for the inaccuracy of the model and the
mathematical methods used to solve its equations. Multi-model forecasting intends
to alleviate the shortcomings of individual models while offering an insight on the
uncertainties associated with a single-model forecast. Use of ensemble forecast is
especially encouraged in situation associated to unstable weather patterns or in
extreme conditions. Ensemble approaches are also known to have more skills at
longer ranges (>6 days) where the probabilistic approach provides more reliable
information than a single model run due to the model error increasing over time.
Moreover, an exhaustive comparison of different models with each other and against
multi-model products as well as observations can reveal weaknesses of individual
models and provide an assessment of model uncertainties in simulating the dust
cycle. Multi-model ensembles also represent a paradigm shift in which offering
the best product to the users as a collective scientific community becomes more
important than competing for achieving the best forecast as individual centres.
This new paradigm fosters collaboration and interaction and ultimately results in
improvements in the individual models and in better final products. Two examples
of multi-model ensembles for dust prediction are shown below.