Abstract
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.
Highlights
Creating climatologies of atmospheric trace-gas concentrations from satellite-based measurements is usually done by collecting available observations into latitudinal and monthly or seasonal bins and calculating the respective averages
We present a method to adjust the spatiotemporal sampling bias in climatologies calculated from sparsely sampled satellite observations without requiring additional observational evidence beyond the data set used
The fact that this method is exclusively based on observations and is independent of parameterization of atmospheric models makes it accessible for potential sampling-bias-corrected climatologies used to test and improve such atmospheric models
Summary
Kloss et al.: ACE-FTS sampling bias adjustment spheric Chemistry Experiment Fourier Transform Spectrometer, ACE-FTS, observations) For such methods, an evenly distributed coverage with no significant measurement gaps is desirable to avoid introducing sampling biases when calculating climatological means. Satellite-based instruments, perform measurements only on distinct orbits, leaving spatiotemporal measurement gaps. This inhomogeneous sampling in space and time can introduce significant biases when calculating climatological averages (Aghedo et al, 2011; Toohey et al, 2013) if they are calculated in the traditional way. The bias can become large when analyzing data from solar occultation instruments that typically provide two measurements per orbit, leading to sparse and spatially structured data coverage.
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