1. VII Driver-Response Relationships Tomoko Matsuo (CU) Low dimensional modeling of neutral density Gary Bust (ASTRA) Inference of thermospheric parameters from ionospheric assimilative maps

2. PRESENTED BY: Tomoko Matsuo (CU) (a) EOF-based reduced-state modeling using CTIPe and CHAMP Suzzane Smith (REU student), Mariangel Fedrizzi (CU), Tim Fuller-Rowell (CU), Mihail Codrescu (NOAA), Jeff Forbes (CU), & Jiuhou Lei (CU) (b) Ensemble Kalman filtering with TIE-GCM Jeff Anderson (NCAR) & DART developers HAO TIEGCM developers Low and High Dimensional Modeling of Neutral Density

3. CTIPe and CHAMP By Courtesy of Mariangel Fedrizzi

4. Diagonalize a sample covariance by SVD CTIPe EOFs: 2005 Singular value decomposition

5. 3-deg averaged CHAMP data normalized to 400 km using NRLMSISe00 8 years (2001-2008) precession through local time once every 133 days [Sutton et al., 2007] For p th EOF, minimizeWith orthonormal constraint for Mean at 400km (2001-2008) 2 Sequential non-linear regression analysis of CHAMP data

6. [Matsuo and Forbes, 2010] CHAMP EOFs: 2001-2008 Sequential non-linear regression

7. By Courtesy of Jiuhou Lei

9. EOF-based reconstruction of CTIPe

10. Density Modeling with CTIPe EOFs (1/3)

11. Density Modeling with CTIPe EOFs (2/3) EOF-based regression model CHAMPEOF

12. Density Modeling with CTIPe EOFs (3/3)

13. Driver-response relationship in terms of EOF [Matsuo and Forbes, 2010] EOF Modes for CIR, CME, northward IMF? From CHAMP 2001-2008

14. http://www.image.ucar.edu/DARes/DA RT Data Assimilation Research Testbed Observations : forward model DART *GCM Ensemble Kalman filtering (1/3) TIEGCM 1.93 http://www.hao.ucar.edu/modeling/tgcm/download.p hp high-dimension sparse & irregular

15. t-1 t t+1 Forecast Step Use samples!! Initial distribution forecast distribution Model Error Growth Ensemble Kalman filtering (2/3)

16. t-1 t t+1 Update Step Prior Forecast Distribution Ensemble Kalman filtering (3/3) PriorLikelihoodPosterior

17. Deterministic Filter: [Anderson, 2002] Experiment Period: Day 87-91 Year 2002 Observation: “CHAMP density” sampled from “Truth” –with centered Gaussian random error Assimilation cycle: ~90-min (one orbit) Number of ensemble member: 96 Localization: Gaspari and Cohn in horizontal Spin-up time: 2 weeks with perturbed forcing (F10.7 & cross-polar cap potential/HPI) http://www.image.ucar.edu/DARes/DART Observing System Simulation Experiment Strongly forced and Dissipative system stochastic forcing

18. Posterior Mean pressure level 22 ~ 400km Posterior Mean - Prior Mean level 22 ~ 400km level 18 ~ 300km

19. Posterior Mean - Prior Mean Zonal Wind Meridional Wind (level 22 ~ 400km)

20. Driver-response relationship in terms of ensembles Temperature O mixing ratio O2 mixing ratio F10.7CPCP -42.5 lat & -135 lon level 22 ~ 400km

21. State correction via assimilation of density data Driver estimation is a key for improvement (a) EOF-based reduced-state modeling using CTIPe To-Do: Driver-Response Relationship in terms of EOFs Product: EOF-based empirical density model at 400km Real-time CTIPe + EOF-based density correction (b) Ensemble Kalman filtering with TIE-GCM To-Do: Driver Estimation in EnKF framework, Assimilation of ground-/space-based GPS, OSSE with Champ and Grace Product: “OSEE tested” Data assimilation system using a thermosphere-ionosphere general circulation model (TIEGCM) Summary Reanalysis DA data might be useful…

22. Champ 4EOFs+Mean EOF-based regression model Reconstruction of orbit-averaged density using EOFs 2001 2002 2003 2004 2005 2006 2007 Reduced-state modeling using EOFs