lighting-ionosphere coupling workshop, lanl, 20 aug 2008 optical imaging of the mesosphere and...

Lighting-Ionosphere Coupling Workshop, LANL, 20 Aug 2008

Optical imaging of the mesosphere and ionosphereJonathan J. Makela (University of Illinois)

20 Aug 2008Lightning-Ionosphere Coupling Workshop, LANL

Overview

• Imaging as a remote sensing tool• Estimating GW parameters in the

mesosphere• Observing structure and inferring pertinent

parameters in the thermosphere/ionosphere– Airglow emissions of interest– Parameter estimation techniques

• Deployment considerations


Why Imaging?• Many methods exist to probe the upper

atmosphere. Imaging provides several advantages over other techniques:– Large coverage area from a single site (650

650 km in mesosphere; 1750 1750 in thermosphere)

– High spatial resolution (< km in mesosphere; ~km in thermosphere)

– Good temporal resolution (~90 s # filters used)


Why Not Imaging?• As with any observing method, there are also

disadvantages, including:– Passive technique (rely on what Mother Nature gives us)– Measuring (height) integrated quantities– Difficulty in obtaining absolute quantities– Requirement of dark/clear skies

There are many applications where the pros outweigh the cons and imaging is an appropriate technique to probe the upper atmosphere


Airglow• Chemilluminescent processes• Chemistry determines altitude a given emission occurs at

– Perturbations to the medium (AGWs, TIDs, etc) can modify the chemistry and are therefore observed as changes in emission intensity

• Visible from the ground with sensitive CCD cameras


• OH (Hydroxyl)– Peak altitude ~88 km– Broad band emission (770 – 2000 nm)– Bright!

• O2 (Molecular Oxygen)– Peak altitude ~94 km– Narrow band emission (860 – 870 nm)

• Na (Sodium)– Peak altitude ~94 Km– Metal caused by meteor ablation– Used for resonance lidar

• OI (Atomic oxygen: Green Line)– Peak altitude ~98 km– Atomic line emission (557.7 nm)– Weakest of the three but most visible to human eye

Prominent Airglows in Mesosphere


Atmospheric Gravity Waves• Transverse buoyancy

waves– Transport energy across

different regions of the atmosphere (one of the largest sources through mesospshere)

– Perturbations modify mesospheric airglow emission intensities and can thus be imaged

Fenergy =−ρ0ω i

2g2

kz2N2

ρ 'ρ

⎛

⎝⎜⎞

⎠⎟

2

ω z =kh

2 N 2 −ω i2( )

ω i2 − fc

2−

1

4H s2

• Pertinent parameters to know include:– Wave number (kh and kz)– Intrinsic wave frequency (ωi)– Amplitude or perturbation (A or

ρ’/ ρ)


Single-Layer/Single-Site Observation

• Provides horizontal wave numbers and “true” frequency

• No vertical wavelength• No intrinsic frequency

– Need wind or vertical wavelength

• No amplitude– Requires vertical

wavelength

Airglow Layer


Multi-Layer/Single Site Observation

• Vertical wavelength can be estimated by comparing phases in two different layers– Assumes known heights

of layers

• Problems:– Wave does not always

show up in two layers– Potential for 2

ambiguity

Airglow Layers


Single-Layer/Multi-Site Observation

• Provides multiple-angle observations of the same perturbation– Obtain z through standard tomography, tomography of

Fourier Descriptors, or Parameter EstimationAirglow Layer

• Ph.D. work of D. Scott Anderson– Examined these

techniques and their suitability to retrieving estimates of z


Parameter Estimation• If the goal of measurements is to infer a few

parameters of AGWs, full-blown tomography is unnecessary– Parameter estimation can be performed using

multi-site observations and an appropriate forward model without requiring the complexity of tomographic inversion

– Significantly reduces computational requirements and improves end results


Data Model

• Gi is the result of a Gabor filter (a complex band pass filter) on the mapped pixel intensity data which selects the horizontal wavelength to be modeled

Gi x, y( ) =A x,y( )e−σ 2

2wi (x,y)+ωz( )

exp j β x,y( ) + zc −H( )wi x,y( )( )⎡⎣ ⎤⎦

wi x, y( ) =kxx−x0H −z0

+ kyy−y0H −z0

β x,y( ) =kxx+ kyy+ kzz+ω tt+φ x,y( )

x=x'z'

H −z0( ) + x0

y=y'z'

H −z0( ) + y0

where


Phase Analysis (PE-Phase)

• If the layer centroid, zc, is assumed to be known this simplifies to a two-unknown problem

• Vertical wave number, kz, obtained from single-site observations of multiple layers

• Layer centroid obtainable from multi-site observations of a single layer

• Observed frequency, ωt, is obtained from time sequence of images

∠Gi x, y( ) = kxx + kyy + kzz +ω tt + zc − H( )wi x, y( ) +φ(x, y)


Amplitude Analysis (PE-mag)

• Amplitude of wave perturbation, A(x,y), obtained if imaging systems are well calibrated

• Layer thickness, σ, and vertical wave number, kz, obtainable

• Requires multi-site observations of a single (or multiple) layer(s)

Gi x, y( ) =A x,y( )e−σ 2

2wi (x,y)+kz( )


Example Experimental Campaign

OH and OI imagerNa lidarOH and OI imagerNa lidar

OH and OI imagerOH and OI imager

OH and OI imagerOH and OI imager


Campaign Results• Several wave packets observed in the

different imagers

• Basic parameters obtained from the raw images alone

Wave 1 Wave 2


Campaign Results• Using the PE-phase technique, parameters are

estimated– 2 phase ambiguity leads to two solutions– Calculating winds from the dispersion relation tells us

which direction is correct


Campaign Results• Collocated Na lidar measurements at UAO

site confirm downward phase propagation


Considerations• PE-phase contains a 2 ambiguity

– Can be mitigated by using the dispersion relation– Observing additional emission layers would also

help on this front

• PE-mag (not shown) is heavily dependent on proper absolute calibration of each imager– Difficult to do as unknown atmospheric extinction

is non-negligible and non-uniform– Can partially be mitigated by fitting PE-mag

results to PE-phase results (for kz)


• Dissociative Recombination of O2+

– Peak emission below the F peak– Narrow band emission (630.0 nm)– Chemistry depends on both electron and neutral

densities– Long lifetime (~110 s) can cause blurring of

features

• Radiative Recombination of O+

– Peak emission at the F peak– Narrow band emission (777.4 nm)– Assuming an O+ plasma, intensity is proportional to ne

2

– Prompt emission (no blurring)– Very dim emission

Prominent Airglows in Thermosphere/Ionosphere


Ionospheric “Topography”• Using the combination

of the height-dependent 630.0-nm emission and density-dependent 777.4-nm emission can give estimates of F-layer altitude and density

nm = I 7774 ×3.06 ×105 cm-3⎡⎣ ⎤⎦

Hm =e0.171×ln I7774 / I6300( )+6.43 km[ ]


Example from 15-16 Sept 1999


Example from 15-16 Sept 1999

• “Bands” in radar data caused by gradients in electron density; higher densities to the south– Increase in density slightly before local midnight

• F layer is observed to decrease in altitude over time


F-Region Pedersen Conductivity

• Important parameter for understanding:– E- and F-region coupling– Instability processes (e.g., Perkins instability at

mid-latitudes)

• 630.0-nm volume emission rate is similar to the equation for Pedersen conductivity– Both can be shown to have dependence on ne

and O2

630.0-nm intensity is proportional to PF


Pedersen Airglow Technique• Technique allows estimation of F-region Pedersen

airglow over a large area (10001000 km)– Based on modeling study, RMS difference of 0.271 mhos

is expected (0.172 mhos if layer altitude is known)


Comparison to ISR-derived P

F

• Technique validated against estimates of PF

derived from the Arecibo ISR• Estimates were very good, especially given

knowledge of the F-layer altitude


Example During Mid-Latitude Event

• Evolution of structure at mid-latitudes typically understood as Perkins’ instabilities– Depends on variations in conductivities associated with

altitude variations of the F layer that align from NWSE


Possibilities for Technique Improvements

• Uncertainties in techniques from:– Reliance on (climatological) background models– Imperfectly known absolute calibration and systematic

factors (e.g., flat-fielding)– Unknown atmospheric extinction

• Improvements can be gained by either using better models (e.g., assimilative models) or actually integrating images as an assimilated data source– Initial work being performed to integrate into IDA4D


Deployment Requirements

• Dark skies that are typically clear from cloud cover

• Availability of– power– facility for housing instrument– Internet connectivity

• For PE technique, need multiple sites separated by 100-120 km viewing a common volume


Summary• Imaging of the mesosphere and

thermosphere/ionosphere can lead to estimates of parameters important for understanding coupling processes– Provide observations of spatiotemporal dynamics

over a large area

• Integrating images into assimilative models may resolve some of the short comings of current parameter estimation techniques

lighting-ionosphere coupling workshop, lanl, 20 aug 2008 optical imaging of the mesosphere and...

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