1. 100-km Variations in Ionospheric-Thermospheric Response to Geomagnetic Storms with Data Assimilation
S. Datta-Barua, Illinois Institute of Technology
G. S. Bust, JHUAPL
D. Miladinovich, Illinois Institute of Technology

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Outline
Motivation: better forecasting of disturbed conditions
Objective: data assimilation for ionospheric state
Algorithm updates: Kalman filtering
Conclusions and next steps
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Motivation
During ionospheric storms, disturbance dynamics form plasma structures
At 3 degree down to 100-km scales
Important dynamics contribute to rapid spatial variations in plasma density and total electron content (TEC)
Affects real-time navigation services based on trans- ionospheric signals
TEC gradients cause large differential positioning errors for Global Navigation Satellite System (GNSS) users.
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4. Impact of Space Weather on Aviation
Datta-Barua, S., et al. (2014).
Understanding the physical processes well enough to forecast using data can help operational systems.
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Objective
Want to understand the dynamics forming plasma structures at 3 degree down to 100-km scales
Electric fields Production
Neutral winds Loss
Question: Can we indirectly infer these dynamics from time-varying images of plasma?
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Method
Total electron content (TEC)-based imaging and reconstruction of the ionospheric plasma density N
Ionospheric Data Assimilation 4-Dimensional (IDA4D)
Image-based estimation of plasma motion
Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE)
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EMPIRE Algorithm
Measurements dN/dt
Background models of production, loss and potential P0, L0, F0
Models are IRI 2007, NRL-MSISE00, HWM93, Weimer 2000
Adjustments dP, dL, dF to the background
Corrections may be additive or multiplicative.
Corrections are linear combinations of basis functions.
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Kalman Filter
Measurement update at time tn:
Forecast update:
Analyzed covariance
Forecast covariance
Background model covariance
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October 24-25, 2011 storm TEC
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DMSP F18 at 0145 UT
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12. Estimated DMSP drift components
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13. Conclusions and next steps
We estimate
Stormtime plasma at km resolution regionally.
3-dimensional ion drift and electric field of the F-region.
Kalman filter implemented for drift estimation.
Requires a background model for all drivers being estimated.
Results generally compare favorably to independent data.
Ongoing:
Comparison to drift data (Digisondes, SuperDARN, etc.)
Ingestion of drift measurements into EMPIRE.
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Acknowledgements
NSF Aeronomy ATM
Marc Hairston for DMSP data and support.
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Extra Slides
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EMPIRE Algorithm
[Datta-Barua et al., 2013]
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