HR: 1340h
AN: SF53A-0733    [Abstracts]
TI: Inverting observations of GPS refractivies to reveal dynamical structures for climate model testing
AU: * Dykema, J A
EM: dykema@fas.harvard.edu
AF: Harvard University, Division of Engineering and Applied Science, 12 Oxford Street, Cambridge, MA 02138
AU: Leroy, S S
AF: Harvard University, Division of Engineering and Applied Science, 12 Oxford Street, Cambridge, MA 02138
AU: Farrell, B F
AF: Harvard University, Division of Engineering and Applied Science, 12 Oxford Street, Cambridge, MA 02138
AU: Anderson, J G
AF: Harvard University, Division of Engineering and Applied Science, 12 Oxford Street, Cambridge, MA 02138
AB: Refractivities derived from measurements of GPS radio occultation, with an optimal choice of orbits, can provide a globally homogeneous record of the state of the climate. These refractivities contribute information in both the troposphere and stratosphere, sensitive to temperature, water vapor, and pressure in all weather conditions. This submission describes a method to extract information directly from these space observations to diagnose climate model dynamics. Although traditionally remotely sensed variables (such as refractivities) have been inverted to produce profiles of more familiar atmospheric state variables such as temperature, pressure, or water vapor, the refractivities themselves provide an ideal state vector for analysis by linear inverse modeling (LIM). The success of LIM for ENSO and seasonal climate forecasting (Penland and Magorian 1993; Winkler et al. 2001) reveals that inverting selected observations for dynamics is a powerful methodology compared with approximating dynamics of complex processes from first principles. Development of this approach based on observation state space reconstruction is motivated in part by the realization that identifying model error and improving model parameterizations is a very difficult task to accomplish by appeal to physical argument and first principle reasoning alone, as the variety of cloud parameterizations testifies. Continued progress in model refinement requires developing methods to systematize parameterization improvement. A benchmark for model improvement, therefore, is that the model reproduce the LIM dynamics in appropriate variables.
DE: 1694 Instruments and techniques
DE: 1610 Atmosphere (0315, 0325)
DE: 1620 Climate dynamics (3309)
DE: 1640 Remote sensing
SC: Special Focus: Advances in Data Acquisition, Management, Analysis and Display [SF]
MN: 2004 AGU Fall Meeting