1. S. Datta-Barua, Illinois Institute of Technology
D. Miladinovich, Illinois Institute of Technology
G. Bust, John Hopkins University Applied Physics Laboratory
J. Makela, University of Illinois at Urbana-Champaign
URSI AT-RASC
May 18th – 22nd 2015
Gran Canaria

2. Special Thanks:
John Meriwether, for instrumentation and data
Funding Support by:
NSF - AGS-1329383
NSF - AGS-1352602
2

3. Motivation: improved estimation of ionosphere
and thermosphere states (i.e., ion drift velocity,
neutral wind velocity, etc.)
Algorithm Update: Assimilation of neutral wind
measurements for the first time
3

4. The Ionosphere and Thermosphere (I.T.):
4
Figure. [1] “Relationship of the atmosphere and ionosphere”

5. Solar
Storm
Ionosphere
ThermosphereDuring Ionospheric storms the layers
interact dynamically to redistribute
plasma in the ionosphere
One coupling mechanism with
ionospheric plasma is through
collisional drag with neutral winds of
the thermosphere
5

6. Ion continuity equation model:
𝑑𝑁
𝑑𝑡
= 𝑎 𝑝𝑟𝑜𝑑 + 𝑎𝑙𝑜𝑠𝑠 − 𝛻 ⋅ 𝑁 𝑣⊥ − 𝛻 ⋅ 𝑁𝒗∥
𝒗∥ - ion velocity parallel to magnetic field lines
𝒗∥ = 𝒖∥ +
g∥
𝜈 𝑂+
− D
𝛻∥ 𝑁
𝑁
𝒖∥ - neutral wind term
𝑑𝑁
𝑑𝑡
= 𝑎 𝑝𝑟𝑜𝑑 + 𝑎𝑙𝑜𝑠𝑠 − 𝛻 ⋅ 𝑁 𝑣⊥ − 𝛻 ⋅ 𝑁𝒖∥ +
𝑁g∥
𝜈 𝑂+
− D𝛻∥ 𝑁
6

7. Each term can be obtained either from a:
measurement: 𝑦
or from a model: 𝑎
𝑧 = 𝑦 − 𝑎, the difference between a measurement and model
An over determined linear system can be formed
We are motivated by the idea that more measurements may
improve estimation.
7
𝑑𝑁
𝑑𝑡
= 𝑎 𝑝𝑟𝑜𝑑 + 𝑎𝑙𝑜𝑠𝑠 − 𝛻 ⋅ 𝑁 𝑣⊥ − 𝛻 ⋅ 𝑁 𝒖∥ +
𝑁g∥
𝜈 𝑂+
− D𝛻∥ 𝑁
gravity
diffusion
neutral wind
field perpendicular wind
loss
production
Electron density
per time

8. E.M.P.I.R.E. – Estimating Model Parameters from
Ionospheric Reverse Engineering
Solves the linear system:
𝑦 = 𝐻𝑥 + 𝑎 + 𝜈
𝑦 𝑁
⋮
𝑦 𝑁
𝑦𝑢
⋮
𝑦𝑢
=
𝐻 𝑁
⋮
𝐻 𝑢
𝑥 + 𝑎 + [𝜈]
𝑧 = 𝑦 − 𝑎; 𝑧 = 𝐻𝑥 + 𝜈
𝜈 –noise
It is a Kalman filter!
8

9. Global Navigation Satellite System (GNSS) Total
Electron Content (TEC) Measurements [left]
Fabry-Perot Interferometers (FPI) [right]
9
Figure [2] “Slant Total Electron Content” Figure [3] “FPI at ESRANGE, Kiruna Sweden”

10. Ionospheric Data Assimilation
4 Dimensional (IDA4D)
estimates electron density
measurement values at
specified grid points.
These measurements are
finite differenced to obtain
𝑑𝑁
𝑑𝑡
and placed into the
measurement terms (i.e., 𝑦 𝑁)
10

11. Measures the Doppler shift of 𝑂2
+
recombination emissions (630nm) to
obtain neutral wind velocities.
These velocities provide the line of
sight neutral winds 𝑢 𝐿.𝑂.𝑆. winds.
We rotate them using the inclination
and declination angles at the
measurement point to produce 𝑢∥
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Figure [4] “FPI On a Shed”

12. 12
TEC
FPI
Measurements, 𝑦
Models, 𝑎
HWM
Weimer IGRF
𝑎
𝑦 𝑧 = 𝐻𝑥 + 𝜈
Kalman Filter 𝑥 (𝜃, 𝜙, 𝑅, 𝑡)
+
Results:
Adjusted
Models
𝑎
𝐻 𝑥

13. Date: October 25th 2011
Where: South East United States
What: An ionospheric TEC
enhancement lingers on Earth’s night
side during the main phase of an
ionospheric storm
The Pisgah Astronomical Research
Institute FPI
Three different results:
1) No FPI Measurements
2) South and East Measurements
3) All Four Measurements
13
Ingested

14. No FPI assimilation
14

15. Half FPI Assimilation
15

16. Full FPI Assimilation
16

17. 17

18. FPI measurements were assimilated for
the first time to study neutral winds in
the ionosphere
Reduced RMS difference in neutral wind
estimation at the location of ingestion
Suggests that there is an overall
improvement of measurements near the
FPI ingestion point.
18
0
50
100
150
200
250
300
No Ingestion Half Ingestion
RMS
North
West
Ingested

19. 2D maps of horizontal winds in the enhanced TEC region along
with covariance analysis
Assimilation of more FPI instruments in this region.
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20. Images
[1] “Relationship of the atmosphere and ionosphere”
http://en.wikipedia.org/wiki/Ionosphere
[2] “Slant Total Electron Content”
http://gnss.be/ionosphere_tutorial.php
[3] “FPI at ESRANGE, Kiruna Sweden”
https://www.ucl.ac.uk/star/research/planets/terrestrial/
observation
[4] “FPI On a Shed” http://csl.illinois.edu/news/near-
space-study-helping-predict-storms
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