Assimilation of both column‐ and layer‐integrated dust opacity observations in the Martian atmosphere

Ruan, T.; Young, R. M. B.; Lewis, S. R.; Montabone, L.; Valeanu, A. and Read, P. L. (2021). Assimilation of both column‐ and layer‐integrated dust opacity observations in the Martian atmosphere. Earth and Space Science, 8(12), article no. e2021EA001869.



A new dust data assimilation scheme has been developed for the UK version of the Laboratoire de Météorologie Dynamique (LMD) Martian General Circulation Model. The Analysis Correction scheme (adapted from the UK Met Office) is applied with active dust lifting and transport to analyze measurements of temperature, and both column-integrated dust optical depth (CIDO), τref (rescaled to a reference level), and layer-integrated dust opacity (LIDO). The results are shown to converge to the assimilated observations, but assimilating either of the dust observation types separately does not produce the best analysis. The most effective dust assimilation is found to require both CIDO (from Mars Odyssey/THEMIS) and LIDO observations, especially for Mars Climate Sounder data that does not access levels close to the surface. The resulting full reanalysis improves the agreement with both in-sample assimilated CIDO and LIDO data and independent observations from outside the assimilated dataset. It is thus able to capture previously elusive details of the dust vertical distribution, including elevated detached dust layers that have not been captured in previous reanalyses. Verification of this reanalysis has been carried out under both clear and dusty atmospheric conditions during Mars Years 28 and 29, using both in-sample and out of sample observations from orbital remote sensing and contemporaneous surface measurements of dust opacity from the Spirit and Opportunity landers. The reanalysis was also compared with a recent version of the Mars Climate Database (MCD v5), demonstrating generally good agreement though with some systematic differences in both time mean fields and day-to-day variability.

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