[1] The Mars Climate Sounder (MCS) onboard the Mars Reconnaissance Orbiter is the latest of a series of investigations devoted to improving the understanding of current Martian climate. MCS is a nine-channel passive midinfrared and far-infrared filter radiometer designed to measure thermal emission in limb and on-planet geometries from which vertical profiles of atmospheric temperature, water vapor, dust, and condensates can be retrieved. Here we describe the algorithm that is used to retrieve atmospheric profiles from MCS limb measurements for delivery to the Planetary Data System. The algorithm is based on a modified Chahine method and uses a fast radiative transfer scheme based on the Curtis-Godson approximation. It retrieves pressure and vertical profiles of atmospheric temperature, dust opacity, and water ice opacity. Water vapor retrievals involve a different approach and will be reported separately. Pressure can be retrieved to a precision of 1-2% and is used to establish the vertical coordinate. Temperature profiles are retrieved over a range from 5-10 to 80-90 km altitude with a typical altitude resolution of 4-6 km and a precision between 0.5 and 2 K over most of this altitude range. Dust and water ice opacity profiles also achieve vertical resolutions of about 5 km and typically have precisions of 10 À4 -10 À5 km À1 at 463 cm À1 and 843 cm À1 , respectively. Examples of temperature profiles as well as dust and water ice opacity profiles from the first year of the MCS mission are presented, and atmospheric features observed during periods employing different MCS operational modes are described. An intercomparison with historical temperature measurements from the Mars Global Surveyor mission shows good agreement. Citation: Kleinböhl, A., et al. (2009), Mars Climate Sounder limb profile retrieval of atmospheric temperature, pressure, and dust and water ice opacity,

The first systematic observations of the middle atmosphere of Mars (35km–80km) with the Mars Climate Sounder (MCS) show dramatic patterns of diurnal thermal variation, evident in retrievals of temperature and water ice opacity. At the time of writing, the dataset of MCS limb retrievals is sufficient for spectral analysis within a limited range of latitudes and seasons. This analysis shows that these thermal variations are almost exclusively associated with a diurnal thermal tide. Using a Martian General Circulation Model to extend our analysis we show that the diurnal thermal tide dominates these patterns for all latitudes and all seasons.

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The concept of alternative error models is suggested as a means to redefine estimation problems with non-Gaussian additive errors so that familiar and near-optimal Gaussian-based methods may still be applied successfully. The specific example of a mixed error model including both alignment errors and additive errors is examined. Using the specific form of a soliton, an analytical solution to the Korteweg–de Vries equation, the total (additive) errors of states following the mixed error model are demonstrably non-Gaussian for large enough alignment errors, and an ensemble of such states is handled poorly by a traditional ensemble Kalman filter, even if position observations are included. Consideration of the mixed error model itself naturally suggests a two-step approach to state estimation where the alignment errors are corrected first, followed by application of an estimation scheme to the remaining additive errors, the first step aimed at removing most of the non-Gaussianity so the second step can proceed successfully. Taking an ensemble approach for the soliton states in a perfect-model scenario, this two-step approach shows a great improvement over traditional methods in a wide range of observational densities, observing frequencies, and observational accuracies. In cases where the two-step approach is not successful, it is often attributable to the first step not having sufficiently removed the non-Gaussianity, indicating the problem strictly requires an estimation scheme that does not make Gaussian assumptions. However, in these cases a convenient approximation to the two-step approach is available, which trades obtaining a minimum variance estimate ensemble mean for more physically sound updates of the individual ensemble members.