The application of mean averaging kernels to mean trace gas distributions

To avoid unnecessary data traffic it is sometimes desirable to apply mean averaging kernels to mean profiles of atmospheric state variables. Unfortunately, application of averaging kernels and averaging are not commutative in cases when averaging kernels and state variables are correlated. That is to say, the application of individual averaging kernels to individual profiles and subsequent averaging will, in general, lead to different results than averaging of the original profiles prior to the application of the mean averaging kernels unless profiles and averaging kernels are fully independent. The resulting error, however, can be corrected by subtraction of the covariance between the averaging kernel and the vertical profile. Thus it is recommended to calculate the covariance profile along with the mean profile and the mean averaging kernel

Satellite data validation: a parametrization of the natural variability of atmospheric mixing ratios

High-resolution model data are used to estimate the statistically typical mixing ratio variabilities of trace species as a function of distance and time separation. These estimates can be used to explain the fact that some of the differences between observations made with different observing systems are due to the less-than-perfect co-location of the measurements. The variability function is approximated by a two-parameter regression function, and lookup tables of the natural variability values as a function of distance separation and time separation are provided. In addition, a reparametrization of the variability values as a function of latitudinal gradients is proposed, and the seasonal independence of the linear approximation of such a function is demonstrated

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

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 its penetration is limited to the upper troposphere, while MIPAS may cover well into the mid-troposphere in cloud-free scenarios. We emphasize that the results of this study refer only to the representativeness of the respective data, not to their intrinsic quality

The MIPAS/Envisat climatology (2002–2012) of polar stratospheric cloud volume density profiles

A global data set of vertical profiles of polar stratospheric cloud (PSC) volume density has been derived from Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) space-borne infrared limb measurements between 2002 and 2012. To develop a well characterized and efficient retrieval scheme, systematic tests based on limbradiance simulations for PSCs from in situ balloon observations have been performed. The finally selected wavenumber range was 831–832.5 cm$^{-1}$. Optical constants of nitric acid trihydrate (NAT) have been used to derive maximum and minimum profiles of volume density which are compatible with MIPAS observations under the assumption of small, non-scattering and larger, scattering PSC particles. These max/min profiles deviate from their mean value at each altitude by about 40 %–45 %, which is attributed as the maximum systematic error of the retrieval. Further, the retrieved volume density profiles are characterized by a random error due to instrumental noise of 0.02–0.05 μm$^{3}$ cm$^{-3}$, a detection limit of about 0.1–0.2 μm$^{3}$ cm$^{-3}$ and a vertical resolution of around 3 km. Comparisons with coincident observations by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite showed good agreement regarding the vertical profile shape. Quantitatively, in the case of supercooled ternary solution (STS) PSCs, the CALIOP dataset fits to the MIPAS retrievals obtained under the assumptions of small particles. Unlike for STS and NAT, in the case of ice PSCs the MIPAS retrievals are limited by the clouds becoming optically thick in the limb-direction. In these cases, the MIPAS volume densities represent lower limits. Among other interesting features, this climatology helps to study quantitatively the on-set of PSC formation very near to the South Pole and the large variability of the PSC volume densities between different Arctic stratospheric winters