Data Assimilation With the Mars Climate Sounder

Details Data Assimilation With the Mars Climate Sounder Data Assimilation With the Mars Climate Sounder

Dec 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006agufm.p23b0061l&link_type=abstract

American Geophysical Union, Fall Meeting 2006, abstract #P23B-0061

Computer Science

Sound

3315 Data Assimilation, 3346 Planetary Meteorology (5445, 5739), 5445 Meteorology (3346)

Scientific paper

Data assimilation is a suite of techniques that has proven useful to studies in terrestrial atmospheric science. Data assimilation elicits the information content in observations in order to improve one's estimate of the atmospheric state, typically the state of a numerical model. The improved estimate is the ideal data product to be queried and diagnosed for scientific study of dynamics, circulation, and transport. Martian atmospheric modeling efforts have been improving, and several models can now reasonably reproduce the climate implied by historical observations (e.g., MGS / TES). The global planetary version of WRF (NCAR's mesoscale Weather Research and Forecasting model) is one such model that can reasonably simulate the martian atmosphere and its variability. Previous data assimilation efforts for the martian atmosphere have used techniques that assume stationary error statistics. Our intent is to combine the information content in observations from the Mars Climate Sounder (MCS) with the global planetary version of WRF configured for Mars using a modern data assimilation technique, proven in terrestrial applications, which approximates the evolution of atmospheric error statistics. Terrestrial research experience has shown that ensemble-based data assimilation approaches (e.g., the ensemble Kalman filter) are effective when observations are relatively sparse because their flow-dependent error estimates can maximally spread observational information content. Having a data stream from a single orbiting atmospheric sounder should provide a good test bed for such approaches in a martian context. We present our initial tests and thoughts for how ensemble-based data assimilation can provide improved martian atmospheric state estimates using MCS data. We also discuss unique challenges for performing data assimilation in the martian atmosphere. We initially focus on assimilating retrieved profiles, but we intend to address directly assimilating radiances.

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