DPS Pasadena Meeting 2000, 23-27 October 2000
Session 51. Mars Atmosphere Posters
Displayed, 1:00pm, Monday - 1:00pm, Friday, Highlighted Tuesday and Thursday, 3:30-6:30pm, C101-C105, C211

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[51.17] The Mars Global Surveyor as a Platform for Operational Meteorology

H. Houben (Space Physics Research Institute), MGS Thermal Emission Spectrometer Team

In the mapping phase of the Mars Global Surveyor (MGS) mission, the Thermal Emission Spectrometer (TES) has been taking approximately 150,000 infrared spectra per day. Atmospheric temperature retrievals of some of these spectra for a 100 sol period beginning in late northern summer (Ls=141) have been assimilated into a general circulation model (GCM) using a four-dimensional variational technique with a rms \Delta T of 2 K. One-day forecasts based on the assimilated fields (which include winds, geopotential heights, and surface pressure) yield a rms \Delta T of 3 K. Assimilated zonal winds are in near gradient wind balance and show a sharp transition to hemispherically symmetric equinoctial jets at Ls=180. As expected, the meridional circulation is dominated by a Hadley cell rising at the subsolar point. Assimilated global average surface pressure reaches a minimum of 5.44 mb at Ls=157. Current efforts are focussed on algorithmic development that will allow the direct assimilation of infrared radiances (for faster turnaround times) and will incorporate data from the Horizon Sensor (which observes other times of day), TES limb scans (which observe higher altitudes), and Radio Science occultations (which observe surface pressure). Thus, the MGS is an excellent platform for observing Martian meteorology and, in particular, for specifying the mass distribution of the Martian atmosphere which is crucial to such operations as aerobraking and aerocapture. It also provides fundamental data for the testing and improvement of GCM's. Further developments in the data assimilation process (such as the use of Kalman filtering techniques that better determine the sensitivity to various observations and the error covariance of predictions) should lead to optimized mission design and observing strategies in the future.

Supported by the Mars Data Analysis Program under RTOP 896--50--21--04.

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