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@ARTICLE{Kloss:862204,
author = {Kloss, Corinna and von Hobe, Marc and Höpfner, M. and
Kaley, A. and Walker, K. A. and Riese, Martin and Ungermann,
Jörn and Hassler, B. and Kremser, St. and Bodeker, G. E.},
title = {{S}ampling bias adjustment for sparsely sampled satellite
measurements applied to {ACE}-{FTS} carbonyl sulfide
observations},
journal = {Atmospheric measurement techniques},
volume = {12},
number = {4},
issn = {1867-1381},
address = {Katlenburg-Lindau},
publisher = {Copernicus},
reportid = {FZJ-2019-02552},
pages = {2129-2138},
year = {2019},
abstract = {When computing climatological averages of atmospheric
trace-gas mixing ratios obtained from satellite-based
measurements, sampling biases arise if data coverage is not
uniform in space and time. Homogeneous spatiotemporal
coverage is essentially impossible to achieve. Solar
occultation measurements, by virtue of satellite orbit and
the requirement of direct observation of the sun through the
atmosphere, result in particularly sparse spatial coverage.
In this proof-of-concept study, a method is presented to
adjust for such sampling biases when calculating
climatological means. The method is demonstrated using
carbonyl sulfide (OCS) measurements at 16 km altitude from
the ACE-FTS (Atmospheric Chemistry Experiment Fourier
Transform Spectrometer). At this altitude, OCS mixing ratios
show a steep gradient between the poles and Equator. ACE-FTS
measurements, which are provided as vertically resolved
profiles, and integrated stratospheric OCS columns are used
in this study. The bias adjustment procedure requires no
additional information other than the satellite data product
itself. In particular, the method does not rely on
atmospheric models with potentially unreliable transport or
chemistry parameterizations, and the results can be used
uncompromised to test and validate such models. It is
expected to be generally applicable when constructing
climatologies of long-lived tracers from sparsely and
heterogeneously sampled satellite measurements. In the first
step of the adjustment procedure, a regression model is used
to fit a 2-D surface to all available ACE-FTS OCS
measurements as a function of day-of-year and latitude. The
regression model fit is used to calculate an adjustment
factor that is then used to adjust each measurement
individually. The mean of the adjusted measurement points of
a chosen latitude range and season is then used as the
bias-free climatological value. When applying the adjustment
factor to seasonal averages in 30∘ zones, the maximum
spatiotemporal sampling bias adjustment was $11 \%$ for
OCS mixing ratios at 16 km and $5 \%$ for the
stratospheric OCS column. The adjustments were validated
against the much denser and more homogeneous OCS data
product from the limb-sounding MIPAS (Michelson
Interferometer for Passive Atmospheric Sounding) instrument,
and both the direction and magnitude of the adjustments were
in agreement with the adjustment of the ACE-FTS data.},
cin = {IEK-7},
ddc = {550},
cid = {I:(DE-Juel1)IEK-7-20101013},
pnm = {244 - Composition and dynamics of the upper troposphere and
middle atmosphere (POF3-244)},
pid = {G:(DE-HGF)POF3-244},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000463862300001},
doi = {10.5194/amt-12-2129-2019},
url = {https://juser.fz-juelich.de/record/862204},
}