Data assimilation for other planets

Lewis, Stephen R. (2010). Data assimilation for other planets. In: Lahoz, William; Khattatov, Boris and Menard, Richard eds. Data Assimilation: Making Sense of Observations. Springer, pp. 681–699.

DOI: https://doi.org/10.1007/978-3-540-74703-1_25

URL: http://www.springer.com/earth+sciences+and+geograp...

Abstract

The application of data assimilation methodology to terrestrial problems in meteorology, atmospheric physics and physical oceanography has already been described extensively within this book. Data assimilation, the combination of observations and numerical models which provide physical constraints, organize and propagate the observational information which is introduced, also offers significant potential advantages for the analysis of atmospheric data from other planets. The Solar System provides seven examples of thick neutral atmospheres in addition to that of the Earth: Mars, Venus and Saturn's moon Titan, which all have relatively large rocky cores surrounded by thinner atmospheres, like the Earth, and four largely gaseous Giant Planets, Jupiter, Saturn, Uranus and Neptune. In recent years satellites have been placed in orbit about Mars in particular, but also Venus, Jupiter and Saturn, in contrast to the relatively rapid fly-by missions in the initial stages of the exploration of the Solar System. These spacecraft provide the potential for long sequences of atmospheric observations. Together with the necessary advances in numerical modelling of planetary atmospheres, these new missions have provided an opportunity for the application of data assimilation techniques for the analysis of planetary observations. As described in this chapter, data assimilation has now been employed with some success in the context of the atmosphere of Mars and more ambitious studies are planned for the future. Assimilation in these unfamiliar and, compared to Earth, data-poor environments also provides valuable lessons for the development of terrestrial assimilation, especially in situations where it is vital to extract the maximum information from a limited observational record.

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