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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)

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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
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

ctipe and champ
CTIPe and CHAMP

By Courtesy of Mariangel Fedrizzi

ctipe eofs 2005 singular value decomposition
CTIPe EOFs: 2005Singular value decomposition

Diagonalize a sample covariance by SVD

sequential non linear regression analysis of champ data
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

slide11

Density Modeling with CTIPe EOFs (2/3)

EOF-based regression model

CHAMP

EOF

driver response relationship in terms of eof
Driver-response relationshipin terms of EOF

From CHAMP 2001-2008

[Matsuo and Forbes, 2010]

EOF Modes for CIR, CME, northward IMF?

slide14

: 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

slide15

Ensemble Kalman filtering (2/3)

Model Error Growth

t-1

t

t+1

Forecast Step

Use samples!!

Initial distribution forecast distribution

slide16

Ensemble Kalman filtering (3/3)

t-1

t

t+1

Update Step

Forecast

Distribution

Posterior

Likelihood

Prior

Prior

slide17

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

slide18

Posterior Mean - Prior Mean

level 22 ~ 400km

Posterior Mean

pressure level 22 ~ 400km

level 18 ~ 300km

slide19

Posterior Mean - Prior Mean

Zonal Wind Meridional Wind

(level 22 ~ 400km)

driver response relationship in terms of ensembles
Driver-response relationshipin terms of ensembles

-42.5 lat & -135 lon

level 22 ~ 400km

F10.7

CPCP

Temperature

O mixing ratio

O2 mixing ratio

summary
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…

slide22

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