HR: 0800h
AN: P11A-0251    [Abstracts]
TI: Ensemble-Based Data Assimilation With a Martian GCM
AU: * Lawson, W
EM: wglawson@gps.caltech.edu
AF: Caltech, 1200 E. California Blvd., Pasadena, CA 91125, United States
AU: Richardson, M I
EM: mir@gps.caltech.edu
AF: Caltech, 1200 E. California Blvd., Pasadena, CA 91125, United States
AU: McCleese, D J
EM: daniel.j.mccleese@jpl.nasa.gov
AF: JPL, 4800 Oak Grove Dr., Pasadena, CA 91109, United States
AU: Anderson, J L
EM: jla@ucar.edu
AF: NCAR, P.O. Box 3000, Boulder, CO 80307-3000, United States
AU: Chen, Y
EM: yochen@ucar.edu
AF: NCAR, P.O. Box 3000, Boulder, CO 80307-3000, United States
AU: Snyder, C
EM: chriss@ucar.edu
AF: NCAR, P.O. Box 3000, Boulder, CO 80307-3000, United States
AB: Quantitative study of Mars weather and climate will ultimately stem from analysis of its dynamic and thermodynamic fields. Of all the observations of Mars available to date, such fields are most easily derived from mapping data (radiances) of the martian atmosphere as measured by orbiting infrared spectrometers and radiometers (e.g., MGS / TES and MRO / MCS). Such data-derived products are the solutions to inverse problems, and while individual profile retrievals have been the popular data-derived products in the planetary sciences, the terrestrial meteorological community has gained much ground over the last decade by employing techniques of data assimilation (DA) to analyze radiances. Ancillary information is required to close an inverse problem (i.e., to disambiguate the family of possibilities that are consistent with the observations), and DA practitioners inevitably rely on numerical models for this information (e.g., general circulation models (GCMs)). Data assimilation elicits maximal information content from available observations, and, by way of the physics encoded in the numerical model, spreads this information spatially, temporally, and across variables, thus allowing global extrapolation of limited and non-simultaneous observations. If the model is skillful, then a given, specific model integration can be corrected by the information spreading abilities of DA, and the resulting time sequence of "analysis" states are brought into agreement with the observations. These analysis states are complete, gridded estimates of all the fields one might wish to diagnose for scientific study of the martian atmosphere. Though a numerical model has been used to obtain these estimates, their fidelity rests in their simultaneous consistency with both the observations (to within their stated uncertainties) and the physics contained in the model. In this fashion, radiance observations can, say, be used to deduce the wind field. A new class of DA approaches based on Monte Carlo approximations, "ensemble-based methods," has matured enough to be both appropriate for use in planetary problems and exploitably within the reach of planetary scientists. Capitalizing on this new class of methods, the National Center for Atmospheric Research (NCAR) has developed a framework for ensemble-based DA that is flexible and modular in its use of various forecast models and data sets. The framework is called DART, the Data Assimilation Research Testbed, and it is freely available on-line. We have begun to take advantage of this rich software infrastructure, and are on our way toward performing state of the art DA in the martian atmosphere using Caltech's martian general circulation model, PlanetWRF. We have begun by testing and validating the model within DART under idealized scenarios, and we hope to address actual, available infrared remote sensing datasets from Mars orbiters in the coming year. We shall present the details of this approach and our progress to date.
DE: 3315 Data assimilation
DE: 3346 Planetary meteorology (5445, 5739)
DE: 5704 Atmospheres (0343, 1060)
DE: 6225 Mars
SC: Planetary Sciences [P]
MN: 2007 Fall Meeting