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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/acp-2017-691
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
06 Sep 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).
Characterizing Sampling and Quality Screening Biases in Infrared and Microwave Limb Sounding
Luis F. Millán1, Nathaniel J. Livesey1, Michelle L. Santee1, and Thomas von Clarmann2 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
2Institut für Meteorologie und Klimaforschung, Karlsruhe Institute of Technology, Karlsruhe, Germany
Abstract. This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS). MIPAS acts as a proxy for typical infrared limb emission sounders, while MLS acts as a proxy for microwave limb sounders. These biases were calculated for temperature and several trace gases by interpolating model fields to real sampling patterns and, additionally, screening those locations as directed by their corresponding quality criteria. Both instruments have dense uniform sampling patterns typical of limb emission sounders, producing almost identical sampling biases. However, there is a substantial difference between the number of locations discarded. MIPAS, as a mid-infrared instrument, is very sensitive to clouds, and measurements affected by them are thus rejected from the analysis. For example, in the tropics, the MIPAS yield is strongly affected by clouds, while MLS is mostly unaffected.

The results show that upper tropospheric sampling biases in zonally averaged data, for both instruments, can be up to 10 % to 30 %, depending on the species, and up to 3 K for temperature. For MIPAS, the sampling reduction due to quality screening worsens the biases, leading to values as large as 30 % to 100 % for the trace gases and expanding the 3 K bias region for temperature. This type of sampling bias is largely induced by the geophysical origins of the screening (e.g. clouds). Further, analysis of long-term time series reveals that these additional quality screening biases may affect the ability to accurately detect upper tropospheric long-term changes using such data. In contrast, MLS data quality screening removes sufficiently few points that no additional bias is introduced, although the vertical range of reliable measurements is slightly reduced. We emphasize that the results of this study refer only to the representativeness of the respective data, not to their intrinsic quality.


Citation: Millán, L. F., Livesey, N. J., Santee, M. L., and von Clarmann, T.: Characterizing Sampling and Quality Screening Biases in Infrared and Microwave Limb Sounding, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-691, in review, 2017.
Luis F. Millán et al.
Luis F. Millán et al.
Luis F. Millán et al.

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This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS).
This study investigates orbital sampling biases and evaluates the additional impact caused by...
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