1 / 22

VII Driver-Response Relationships

VII Driver-Response Relationships. Tomoko Matsuo (CU) Low dimensional modeling of neutral density Gary Bust (ASTRA) Inference of thermospheric parameters from ionospheric assimilative maps. Low and High Dimensional Modeling of Neutral Density. PRESENTED BY: Tomoko Matsuo (CU)

gyula
Télécharger la présentation

VII Driver-Response Relationships

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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. Lowand HighDimensionalModeling of Neutral Density 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

  3. CTIPe and CHAMP By Courtesy of Mariangel Fedrizzi

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

  5. Sequential non-linear regression analysis of CHAMP data Mean at 400km (2001-2008) 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 pth EOF, minimize With orthonormal constraint for 2

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

  7. By Courtesy of Jiuhou Lei

  8. 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 CHAMP EOF

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

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

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

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

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

  17. Observing System Simulation Experiment http://www.image.ucar.edu/DARes/DART • 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) Strongly forced and Dissipative system stochastic forcing

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

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

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

  21. Summary 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) Reanalysis DA data might be useful…

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

More Related