Estimating currents and electric fields in the high-latitude ionosphere using ground- and

space-based observationsEllen Cousins1, Tomoko Matsuo2,3, Art Richmond1

1NCAR-HAO, 2CU-CIRES, 3NOAA-SWPC

FESD-ECCWES Meeting – 10 Feb 2014 1/13

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High-latitude Ionospheric Currents Currents from magnetosphere close through high-latitude ionosphere Drive currents parallel to and perpendicular to ionospheric electric field

(Pedersen & Hall currents)

E

E

Satellites sample magnetic perturbations

( field-aligned currents) SuperDARN radars sample

plasma drifts ( electric fields)

Goal: Combine the two data sets and estimate complete (2D) current & electric field distribution

FESD-ECCWES Meeting – 10 Feb 2014 2/13

[Brian Anderson]

Active Magnetosphere andPlanetaryElectrodynamics ResponseExperiment

AMPERE: Standard AMPERE: High~1° lat. res. ~ 0.1° lat. res.

3FESD-ECCWES Meeting – 10 Feb 2014

• Magnetometer on every satellite

• 6 orbit planes (12 cuts in local time) ~11 satellites/plane

• 9 minute spacing - re-sampling cadence

• 780 km altitude, circular, polar orbits

Iridium for Science

Using observations of

Inverse procedure to infer maps of

Assimilative Mapping of Ionospheric Electrodynamics [Richmond and Kamide, 1988]

Linear relationships (for a given Σ)

Given 2 of E, Σ, ΔB, can in theory solve for remaining variables

FESD-ECCWES Meeting – 10 Feb 2014 4/13

Electric field (from SuperDARN)

Conductance (height-integrated conductivity) – tensor(no observations for this study)

Magnetic pertubations(from AMPERE)

Ionospheric current density(no observations for this study)

- Electrostatic potential

- Field aligned current density ( )

xa – analysis

y – observations

xb – background model

H – forward operator

K – Kalman gain

Pb – background model error

covariance

R – observational error

covariance

Use the optimal interpolation (OI) method of data assimilation Optimally combine information from observations and a background model, taking into account error properties of both

xa = xb + K (y – H xb )

K = Pb HT (H Pb H

T + R)-1

FESD-ECCWES Meeting – 10 Feb 2014 5/13

Assimilative Mapping Procedure

[From EOF] [analysis]

[physics + Σ]

Use the optimal interpolation (OI) method of data assimilation Optimally combine information from observations and a background model, taking into account error properties of both

Background model and its error properties (from EOF analysis) previously determined for SuperDARN data

Recently did similar analysis for AMPERE data But only have 1 week of data (used years for SuperDARN analysis) Data quality issues

FESD-ECCWES Meeting – 10 Feb 2014 6/13

Assimilative Mapping Procedure

Calculated using just across-track component of ΔBEOF 2mean EOF 1

EOF 5EOF 3 EOF 4

EOF 2mean EOF 1

EOF 5EOF 3 EOF 4

Calculated using just along-track component of ΔB

Relative contribution of mean and each EOF to total observed ΔB2

(more flat spectrum)(more peaked spectrum)

FESD-ECCWES Meeting – 10 Feb 2014 7/13

AMPERE EOFs

xa – analysis

y – observations

xb – background model

H – forward operator

K – Kalman gain

Pb – background model error

covariance

R – observational error

covariance

Use the optimal interpolation (OI) method of data assimilation Optimally combine information from observations and a background model, taking into account error properties of both

xa = xb + K (y – H xb )

K = Pb HT (H Pb H

T + R)-1

FESD-ECCWES Meeting – 10 Feb 2014 8/13

[From EOF] [analysis]

[physics + Σ]

Assimilative Mapping Procedure

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Ionospheric Conductance Height-integrated conductivity (tensor) Assumed infinite along magnetic field lines Pederson/Hall conductance || / to E

Solar-produced component Empirical model – assumed to be reasonably accurate

Auroral component unknown Highly variable in space and time Estimate using empirical model Could adjust using information from observations (have had limited success)

Night-side background level Less well known than day-side Use as fudge factor

€

⊥

SolarNoon45°

Auroral

Background

1st working with the two data sets separately – large disagreement Likely due to errors & biases in the data & errors in conductance model

FESD-ECCWES Meeting – 10 Feb 2014 10/13

Assimilative Mapping Examples

SuperDARN AMPERE Σbgd = 0.3

Σbgd = 3

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AMPERESuperDARN

More agreement if night-side conductance inflated to 3

Solving with both data sets simultaneously

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Assimilative Mapping Examples

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FESD-ECCWES Meeting – 10 Feb 2014 12/13

Assimilative Mapping Examples

BY

BZ

AMPERE SuperDARN

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Next Steps Validation, refinement of procedure by comparing mapped results to independent observations Have begun testing against subset of SuperDARN or AMPERE data excluded from fit

Look at geomagnetic disturbance within the week-long AMPERE data set