An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation

To better understand and characterize current uncertainties
in the important observational constraint of climate
models of aerosol optical depth (AOD), we evaluate
and intercompare 14 satellite products, representing
nine different retrieval algorithm families using observations
from five different sensors on six different platforms. The
satellite products (super-observations consisting of 1 1
daily aggregated retrievals drawn from the years 2006, 2008
and 2010) are evaluated with AErosol RObotic NETwork
(AERONET) and Maritime Aerosol Network (MAN) data.
Results show that different products exhibit different regionally
varying biases (both under- and overestimates) that may reach 50 %, although a typical bias would be 15 %–25%
(depending on the product). In addition to these biases, the
products exhibit random errors that can be 1.6 to 3 times
as large. Most products show similar performance, although
there are a few exceptions with either larger biases or larger
random errors. The intercomparison of satellite products extends
this analysis and provides spatial context to it. In particular,
we show that aggregated satellite AOD agrees much
better than the spatial coverage (often driven by cloud masks)
within the 1 1 grid cells. Up to  50% of the difference
between satellite AOD is attributed to cloud contamination.
The diversity in AOD products shows clear spatial patterns and varies from 10% (parts of the ocean) to 100%
(central Asia and Australia). More importantly, we show that
the diversity may be used as an indication of AOD uncertainty,
at least for the better performing products. This provides
modellers with a global map of expected AOD uncertainty
in satellite products, allows assessment of products
away from AERONET sites, can provide guidance for future
AERONET locations and offers suggestions for product improvements.
We account for statistical and sampling noise in
our analyses. Sampling noise, variations due to the evaluation
of different subsets of the data, causes important changes in
error metrics. The consequences of this noise term for product
evaluation are discussed