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. This study investigates orbital sampling biases and evaluates the additionalimpact caused by data quality screening for the Michelson Interferometer forPassive 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 werecalculated for temperature and several trace gases by interpolating modelfields to real sampling patterns and, additionally, screening those locationsas directed by their corresponding quality criteria. Both instruments havedense uniform sampling patterns typical of limb emission sounders, producingalmost identical sampling biases. However, there is a substantial differencebetween the number of locations discarded. MIPAS, as a mid-infraredinstrument, is very sensitive to clouds, and measurements affected by themare thus rejected from the analysis. For example, in the tropics, the MIPASyield is strongly affected by clouds, while MLS is mostly unaffected. The results show that upper-tropospheric sampling biases in zonally averageddata, for both instruments, can be up to 10 to 30 %, depending on thespecies, and up to 3 K for temperature. For MIPAS, the sampling reductiondue to quality screening worsens the biases, leading to values as large as30 to 100 % for the trace gases and expanding the 3 K bias region fortemperature. This type of sampling bias is largely induced by the geophysicalorigins of the screening (e.g. clouds). Further, analysis of long-term timeseries reveals that these additional quality screening biases may affect theability to accurately detect upper-tropospheric long-term changes using suchdata. In contrast, MLS data quality screening removessufficiently few points that no additional bias is introduced, although itspenetration is limited to the upper troposphere, while MIPAS may cover wellinto the mid-troposphere in cloud-free scenarios. We emphasize that theresults of this study refer only to the representativeness of the respectivedata, not to their intrinsic quality.

Characterizing sampling and quality screening biases in infrared and microwave limb sounding