modelling complexity in the upper atmosphere using gps data

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Modelling complexity in the upper atmosphere using GPS data Chris Budd, Cathryn Mitchell, Paul Spencer Bath Institute for Complex Systems, University of Bath

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Modelling complexity in the upper atmosphere using GPS data Chris Budd, Cathryn Mitchell, Paul Spencer Bath Institute for Complex Systems, University of Bath. Ground-receiver tomography. Instrumentation. Have . - PowerPoint PPT Presentation

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Page 1: Modelling complexity in the upper atmosphere using GPS data

Modelling complexity in the upperatmosphere using GPS data

Chris Budd, Cathryn Mitchell, Paul Spencer Bath Institute for Complex Systems, University of Bath

Page 2: Modelling complexity in the upper atmosphere using GPS data

Ground-receiver

tomography

Instrumentation

Have.

Networks of GPS receivers at mid-latitudes over continental regions of the Northern Hemisphere

Problem:

Atmosphere is a highly complex and multi-scale, time-evolving system.

It is vital to know the state of all levels for meteorology and navigation

Page 3: Modelling complexity in the upper atmosphere using GPS data

LATITUDE

Ionospheric Imaging

Measured –

relative values of total electron content TEC

Find –

3D time-evolving

electron density Ne

ALT

ITU

DE

Multi-Instrument Data AnalysiS

Page 4: Modelling complexity in the upper atmosphere using GPS data

Acknowledgements: IGS network

MIDAS – Northern Hemisphere GPS receivers

Page 5: Modelling complexity in the upper atmosphere using GPS data
Page 6: Modelling complexity in the upper atmosphere using GPS data

6 moving satellites S

100 receivers R

Measure the differential phase change between dual frequency radio signals from S to R at 2 minute intervals over one hour

is directly proportional to the total electron content (TEC) of the ionosphere over the path s

Ionosphere

1000kms

sb

sb

Time varying

Electron density Ne

Page 7: Modelling complexity in the upper atmosphere using GPS data

R

S

s dstrNetb ),,,()(

Ne : electron concentration along the I = 6*100 paths s at the initial time (order 100 G electrons/metre cubed)

Set up 3D grid of J = 20 [height] *360*360 [angle] voxels,

x electron density in each voxel, matrix A of path lengths in each voxel

bAx Ill-conditioned .. Use a-priori information to solve

Page 8: Modelling complexity in the upper atmosphere using GPS data

[electron density] = [model electron density] [coefficients]

MIDAS algorithm

The electron density (x) distribution is formed from the weighted (W) sum of orthonormal basis functions, X:

4*50 Spherical Harmonics in latitude and longitude and

3 empirical functions Chapman Profiles in height z

XWx

Page 9: Modelling complexity in the upper atmosphere using GPS data

Chapman functions

z

Page 10: Modelling complexity in the upper atmosphere using GPS data

bAXW 1)(

bAXWAx Obtain least squares best fit for W using the regularised SVD to calculate the generalised inverse

XWx Initial estimate of the electron density

Page 11: Modelling complexity in the upper atmosphere using GPS data

Update this estimate every 2 minutes by assuming small change y in x, c in the measured TEC b and D in the ray path matrix A. To leading order have

Mapping matrix, X, transforms the problem to one for which the unknowns are the linear changes in coefficients G (y = XG) of the orthonormal basis functions

DxceAy

eAXGeA(XG) 1)(

MIDAS – time-dependent inversion

Improve with a Kalman filter

Page 12: Modelling complexity in the upper atmosphere using GPS data

Horizontal Variation

Spherical Harmonics

Model (eg IRI)

Height profile (to create EOFS)

Thin Shell (variable height) Chapman profiles Epstein profiles Models (eg IRI)

TIME:

None Zonal/Meridional Zonal/Meridional & Radial

Co-ordinate frame

Geographic Geomagnetic

Inversion type

2-D (latitude-height or thin shell) 3-D (2-D with time evolution or latitude-longitude-altitude)4-D (latitude-longitude- altitude-time)

Graphics options

Vertical profiles of Ne

Horizontal profiles of Ne

TEC maps

Electron concentration images (latitude vs height) at one longitude.

Electron concentration images (longitude vs height) at one latitude.

TEC movies

Electron concentration movies

MIDAS algorithm

Page 13: Modelling complexity in the upper atmosphere using GPS data

Electron density North America Longitude 70 W

Vertical TEC b Electron density Ne

Page 14: Modelling complexity in the upper atmosphere using GPS data

Vertical TEC b

Page 15: Modelling complexity in the upper atmosphere using GPS data
Page 16: Modelling complexity in the upper atmosphere using GPS data
Page 17: Modelling complexity in the upper atmosphere using GPS data

Observations of mid-latitude ionospheric storms

• Near global view of TEC distributions

• Observations of storm enhanced density

• Uplifts in layer height over Europe and North America

• Poleward movement of the anomaly

Page 18: Modelling complexity in the upper atmosphere using GPS data

Imaging Issues

What is the spatial resolution?

What is the temporal resolution?

What is the accuracy of the imaged electron density?

What scientific information can we derive directly from the images?

Page 19: Modelling complexity in the upper atmosphere using GPS data

Radar backscatter

Verification of the peak height uplift over the USA

MIDAS

Page 20: Modelling complexity in the upper atmosphere using GPS data

Combining imaging with first-principle modeling

How can we relate the images the underlying physics?

• Imaging alone cannot get at the underlying physics

• Simply reproducing localized image features with modeling does not uniquely determine the physical drivers

• Future aim – develop methods that constrain the physical models with full 4D imaging

Page 21: Modelling complexity in the upper atmosphere using GPS data

Acknowledgements to:

GPS RINEX data from SOPAC, IDA3D images from ARLUT, EISCAT

Collaboration with Cornell University

Support from BAE SYSTEMS, the UK EPSRC, BICS and PPARC

Page 22: Modelling complexity in the upper atmosphere using GPS data

MIDAS – Northern Hemisphere

Page 23: Modelling complexity in the upper atmosphere using GPS data

Coverage of Input Data

ionosonde

Polar NIMS

GPS

Page 24: Modelling complexity in the upper atmosphere using GPS data

• Is the TEC movie showing convection?• If so, the plasma over Europe originates from the USA

TEC over the Northern Hemisphere

Page 25: Modelling complexity in the upper atmosphere using GPS data

F2 layer uplifts move horizontally westwards, that is, firstly, in the European sector, then the east coast of the USA, and around an hour later, occurring in the west coast of the USA.

12

3

East-west progression of layer height uplift

Page 26: Modelling complexity in the upper atmosphere using GPS data

Equatorial imaging

(with Cornell University)

Page 27: Modelling complexity in the upper atmosphere using GPS data

Polar imaging

• Observations of patches over ESR

• IDA3D imaging appears to show patches convecting from Sondestrom to ESR

• Imaging alone cannot show the convection

• Combine AMIE convection patterns with trajectory analysis into IDA3D

• Provides strong evidence of plasma transport from Sondestrom to ESR

Page 28: Modelling complexity in the upper atmosphere using GPS data

IDA3D Ne at 400 km 2005 UT

Patch

Page 29: Modelling complexity in the upper atmosphere using GPS data

Results from Europe

Page 30: Modelling complexity in the upper atmosphere using GPS data

Ionospheric Measurements

Page 31: Modelling complexity in the upper atmosphere using GPS data

Observations over ESR

Patch at 20 UT