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assessment of mars atmospheric temperature retrievals from the thermal emission spectrometer radiancesMotivated by the needs of Mars data assimilation. particularly quantification of measurement errors and generation of averaging kernels. we have evaluated atmospheric temperature retrievals from Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) radiances. Multiple sets of retrievals have been considered in this study; (1) retrievals available from the Planetary Data System (PDS), (2) retrievals based on variants of the retrieval algorithm used to generate the PDS retrievals, and (3) retrievals produced using the Mars 1-Dimensional Retrieval (M1R) algorithm based on the Optimal Spectral Sampling (OSS ) forward model. The retrieved temperature profiles are compared to the MGS Radio Science (RS) temperature profiles. For the samples tested, the M1R temperature profiles can be made to agree within 2 K with the RS temperature profiles, but only after tuning the prior and error statistics. Use of a global prior that does not take into account the seasonal dependence leads errors of up 6 K. In polar samples. errors relative to the RS temperature profiles are even larger. In these samples, the PDS temperature profiles also exhibit a poor fit with RS temperatures. This fit is worse than reported in previous studies, indicating that the lack of fit is due to a bias correction to TES radiances implemented after 2004. To explain the differences between the PDS and Ml R temperatures, the algorithms are compared directly, with the OSS forward model inserted into the PDS algorithm. Factors such as the filtering parameter, the use of linear versus nonlinear constrained inversion, and the choice of the forward model, are found to contribute heavily to the differences in the temperature profiles retrieved in the polar regions, resulting in uncertainties of up to 6 K. Even outside the poles, changes in the a priori statistics result in different profile shapes which all fit the radiances within the specified error. The importance of the a priori statistics prevents reliable global retrievals based a single a priori and strongly implies that a robust science analysis must instead rely on retrievals employing localized a priori information, for example from an ensemble based data assimilation system such as the Local Ensemble Transform Kalman Filter (LETKF).
Document ID
20120013308
Document Type
Reprint (Version printed in journal)
Authors
Hoffman, Matthew J.
(Rochester Inst. of Tech. NY, United States)
Eluszkiewicz, Janusz
(Atmospheric and Environmental Research, Inc. Lexington, MA, United States)
Weisenstein, Deborah
(Atmospheric and Environmental Research, Inc. Lexington, MA, United States)
Uymin, Gennady
(Atmospheric and Environmental Research, Inc. Lexington, MA, United States)
Moncet, Jean-Luc
(Atmospheric and Environmental Research, Inc. Lexington, MA, United States)
Date Acquired
August 26, 2013
Publication Date
July 1, 2012
Publication Information
Publication: Icarus
Volume: 220
Issue: 2
ISSN: 0019-1035
Subject Category
Lunar and Planetary Science and Exploration
Funding Number(s)
CONTRACT_GRANT: NNH09CF94C
Distribution Limits
Public
Copyright
Other