HR: 11:55h
AN: SM52A-04    [Abstracts]
TI: Data assimilation and parameter estimation using extended Kalman filtering.
AU: * Shprits, Y Y
EM: yshprits@atmos.ucla.edu
AF: UCLA, 405 Hilgard Ave, 7127 Math Sci Bldg, Los Angeles, CA 90095, United States
AU: Kondrashov, D A
EM: dkondras@atmos.ucla.edu
AF: UCLA, 405 Hilgard Ave, 7127 Math Sci Bldg, Los Angeles, CA 90095, United States
AU: Chen, Y
EM: cheny@lanl.gov
AF: LANL, LANL, Mail Stop D436, Los Alamos, NM 87545, United States
AU: Ghil, M
EM: ghil@atmos.ucla.edu
AF: UCLA, 405 Hilgard Ave, 7127 Math Sci Bldg, Los Angeles, CA 90095, United States
AU: Thorne, R
EM: rmt@atmos.ucla.edu
AF: UCLA, 405 Hilgard Ave, 7127 Math Sci Bldg, Los Angeles, CA 90095, United States
AB: Data assimilation models combine measurements and first principles models to provide the most realistic possible picture of the present condition or updates and corrections to the propagation of conditions forward in time. The Kalman filter incorporates measurements and physics based model according to underlined error structure of the model and data. It provides a powerful framework to estimate the state of the system in a way that minimizes mean of the squared errors. In particular for applications in the radiation belts, data at different L- shells can be combined with the model and will affect the forecasted fluxes at all radial locations. We present analysis of the phase space density measured on CRRES using Kalman filter and the radial diffusion model. The results indicate the presence of the local acceleration source at L~5.5. We also present results of the parameter estimation using extended Kalman filtering.
DE: 2716 Energetic particles: precipitating
DE: 2720 Energetic particles: trapped
DE: 2753 Numerical modeling
DE: 3315 Data assimilation
DE: 4260 Ocean data assimilation and reanalysis (3225)
SC: SPA-Magnetospheric Physics [SM]
MN: 2007 Joint Assembly