Operational Dust Prediction - Mineral Dust: A Key Player in the Earth System - page 252

Data Quality Aspects and Bias Correction
Perhaps the most pressing issue for satellite data assimilation is the development of
appropriate satellite error models. Indeed, a key assumption in data assimilation is
that the observation errors are uncorrelated spatially. For satellite aerosol products,
and dust products in particular, there is considerable spatially correlated bias.
Such bias is formed from a number of factors, including biases in the algorithm's
lower boundary condition/surface reflectance, microphysical bias in the assumed
optical model of the aerosol particles and the cloud mask. These biases can lead to
unphysical analysis fields, which in turn can lead to positive or negative perturbation
“plumes” in forecast fields. Currently, satellite data providers do not generate
prognostic error models, and it has fallen on the data assimilation community to
modify the products for their own purposes. Debiasing data products and developing
reliable point-by-point uncertainties are time-consuming. Further, aerosol product
algorithms update frequently, leaving previous error analyses obsolete.
Each centre's development team has approached satellite data quality and
bias correction differently. Development for FNMOC systems at NRL and the
University of North Dakota has favoured extensive error analysis at the expense
of sophistication in the data assimilation technology. MODIS over ocean, land
and Deep Blue products have had extensive debiasing based on comparison with
AERONET observations and error modelling applied (Zhang and Reid 2006 ;Shi
et al. 2011a , 2012 ;Hyeretal. 2011 ). In addition, the spatial covariance of the
MODIS and MISR products has also been undertaken (Shi et al. 2011b ). Internal
studies at NRL have shown that, overall, the assimilation of raw satellite aerosol
products boosts model verification scores. After a set of quality assurance steps were
taken with the satellite products, NAAPS root-mean-square error (RMSE) improved
by more than 40 %. Lidar assimilation has taken a similar method, with considerable
quality assurance (QA) checks (Campbell et al. 2010 ).
At ECMWF, a variational bias correction is implemented based on the oper-
ational set-up for assimilated radiances following the developments by Dee and
Uppala ( 2009 ). The bias model for the MODIS data consists of a global constant
that is adjusted variationally in the minimisation based on the first-guess departures.
Although simple, this bias correction works well in the sense that the MACC
analysis is not biased with respect to MODIS observations. Moreover, this approach
has the advantage of being tied to the optimisation of the cost function, and as such
it is estimated online, not requiring previous preprocessing of the observations. The
bias error model allows more complex treatment with the addition of other bias
predictors that are relevant for AOD, for example, instrument geometry, viewing
angle, cloud cover, wind speed, etc. Improvements to the bias model are currently
being undertaken.