Download Tomographic views of the middle atmosphere from a satellite platform Kristoffer Hultgren

Tomographic views of the
middle atmosphere from a
satellite platform
Kristoffer Hultgren
AKADEMISK AVHANDLING
för filosofie doktorsexamen vid Stockholms Universitet
att framläggas för offentlig granskning
den 3 oktober 2014
Department of Meteorology
Stockholm University
Stockholm 2014
Cover image:
Polar Mesospheric Clouds above Vallentuna, Sweden
Photographer: P-M Hedén, www.clearskies.se
Tomographic views of the middle atmosphere
from a satellite platform
Doctoral thesis
Kristoffer Hultgren
ISBN 978-91-7447-974-4
© Kristoffer Hultgren, Stockholm, 2014
Stockholm University
Department of Meteorology
106 91 Stockholm
Sweden
Printed by US-AB
Stockholm, 2014
Summary
The middle atmosphere is a very important part of the Earth
system. Until recently, we did not realize the importance of
the structure of this vaporous shell and of the fundamental
role it plays in both creating and sustaining life on the planet.
Thanks to the development and improvement of new sounding
methods and techniques, our knowledge of the composition of
the atmosphere has become more detailed than ever before. We
have also learned how to reveal complex interactions between
different species and how they react to the incoming solar radiation.
The middle part of the Earth’s atmosphere serves as a host
for the Polar Mesospheric Clouds. These clouds consist of a thin
layer of water-ice particles, only existing during the summer months
and only close to the poles. These clouds can be used as a proxy
for middle atmospheric dynamics. In order to fully utilize Polar
Mesospheric Clouds as tracers for atmospheric variability and
dynamics, we need to better understand their local properties.
The Optical Spectrograph and Infra-Red Imager System
(OSIRIS) is one of two instruments installed on the Odin satellite.
The optical spectrograph of this instrument observes sunlight
scattered by the atmosphere and thus the Polar Mesospheric
Clouds. This thesis deals with a tomographic technique that
can reconstruct both horizontal and vertical structures of the
clouds by using observations from various angles of the atmospheric region. From this information, microphysical properties such as particle sizes and number densities are obtained.
The tomographic technique presented in this thesis also provides a basis for a new satellite concept - MATS. The idea
behind the MATS satellite mission is to analyze wave activity
in the atmosphere over a wide range of spatial and temporal scales, based on the scientific heritage from Odin/OSIRIS
and the tomographic algorithms presented in this thesis.
Contents
1 Introduction
1.1 The Earth’s atmosphere . . . . . . . . . . . . . . . . . . . . .
1.2 The Noctilucent Clouds . . . . . . . . . . . . . . . . . . . . .
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2 Atmospheric Radiation
2.1 Electromagnetic spectrum . . . . . . . . . .
2.2 Basic radiometric quantities . . . . . . . . .
2.2.1 Solid angle . . . . . . . . . . . . . .
2.2.2 Radiant flux . . . . . . . . . . . . .
2.2.3 Irradiance . . . . . . . . . . . . . . .
2.2.4 Radiant intensity . . . . . . . . . . .
2.2.5 Radiance . . . . . . . . . . . . . . .
2.3 Concepts of scattering and absorption . . .
2.3.1 Scattering angle and phase function
2.3.2 Scattering cross-section . . . . . . .
2.3.3 Index of refraction . . . . . . . . . .
2.3.4 Size parameter . . . . . . . . . . . .
2.3.5 Rayleigh and Mie scattering . . . . .
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3 Remote Sensing
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3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2 The inverse problem . . . . . . . . . . . . . . . . . . . . . . . 24
4 Odin/OSIRIS
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4.1 The satellite platform . . . . . . . . . . . . . . . . . . . . . . 27
4.2 The optical spectrograph . . . . . . . . . . . . . . . . . . . . 28
4.3
Original retrievals . . . .
4.3.1 Measured radiance
4.3.2 PMC brightness .
4.3.3 Particle sizes . . .
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5 Tomographic Inversions
5.1 Background . . . . . . . . .
5.2 Inversion method . . . . . .
5.3 A model of Odin/OSIRIS .
5.3.1 Atmospheric grid . .
5.3.2 Coordinate systems
5.3.3 Model description .
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6 The MATS Satellite Concept
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7 Summary of The Papers
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Bibliography
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Acknowledgements
53
Paper I: Hultgren, K., Kornich, H., Gumbel, J., Gerding, M., Hoffmann,
P., Lossow, S., and Megner, L. (2010). What caused the exceptional midlatitudinal Noctilucent Cloud event in July 2009? J. Atmos. Solar-Terr.
Phys., 73, 2125-2131.
Paper II: Hultgren, K., Gumbel, J., Degenstein, D., Bourassa, A., Lloyd,
N., and Stegman, J. (2013). First simultaneous retrievals of horizontal and
vertical structures of Polar Mesospheric Clouds from Odin/OSIRIS tomography. J. Atmos. Solar-Terr. Phys., 104, 213-223.
Paper III: Hultgren, K. and Gumbel, J. (2014). Tomographic and spectral views on the life cycle of polar mesospheric clouds from Odin/OSIRIS.
Submitted to J. Geophys. Res.
Paper IV: Gumbel, J., Hultgren, K., and the MATS Science Team (2014).
Mesospheric Airglow/Aerosol Tomography and Spectroscopy (MATS) - a
satellite mission on mesospheric waves. To be submitted to ESA’s Living
Planet Symposium.
Chapter 1
Introduction
Why are we interested in the Earth’s atmosphere?
Earth - a big round planet, yet so small when viewed from space. The
atmosphere - a thin gaseous layer, just like the crust of an apple. Just
recently, it has been understood that the atmosphere is a very important
part of the Earth system. We did not realize the importance of the
structure of this vaporous shell and of the fundamental role it plays in both
creating and sustaining life on the planet. Up until the mid 20th century,
the mean state of the atmosphere was assumed stable and not likely to be
noticeably affected by anthropogenic activity. Thanks to the development
and improvement of new sounding methods and techniques, our knowledge
of the composition of the atmosphere has become more detailed than ever
before. We have also learned how to reveal complex interactions between
different species and how they react to the incoming solar radiation.
In the atmospheric mixture, a wealth of molecular species have been
discovered and measured. Together, these gases control the balance between
the incoming and outgoing radiation, which, in turn, contributes to the
motion of the atmosphere. Some of these gases act as a shield against
the harmful ultraviolet radiation from the Sun and as a thermal blanket
that traps the heat and insulates the Earth from the cold space. In other
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words, life on Earth, as we know it, is highly dependent on a sparse layer
of molecules floating around above us.
Another part of the mixture is present in the atmosphere only as a
result of human activities. These gases have an effect that modifies the
natural balance of the atmosphere. The long-term consequences of these
alterations are yet to be fully evident.
1.1
The Earth’s atmosphere
The vaporous atmosphere is mainly made up of molecular nitrogen
(N2 ∼ 78%) and molecular oxygen (O2 ∼ 21%), but also of a small part
of noble gases (Ar, He, Ne, Kr, Xe) and minor constituents (CO2 , CO,
CH4 , N2 O, O3 , H2 O) can be found. All of these gases possess very long
lifetimes against chemical destruction and are therefore relatively well
mixed. However, even if most of the gases are well mixed, the atmosphere
itself is highly stratified with significant vertical variations in composition,
temperature, pressure, and density.
In addition to gases, the atmosphere also contains various solid and
liquid particles, such as water droplets, ice crystals, and aerosols. These
particles play an important role in the absorption and scattering of solar
radiation, as well as the physics of clouds and precipitation.
The ratio of molecular species is controlled by either chemistry,
molecular diffusion, or mixing due to fluid motion. Molecular diffusion
works together with gravity to try to order the atmosphere in such a
way that the lightest molecules are present at the top and the heaviest
ones at the bottom. The mixing due to fluid motion of the gases is on
the other hand independent of molecular mass. At lower altitudes, the
time between molecular collisions is so small that the time necessary for
molecular diffusion is many orders of magnitude greater than the time
required for turbulent motion. However, at higher altitudes, the collisions
between molecules occur so seldom that molecular diffusion is the dominant
mechanism. As a consequence, the atmosphere is well mixed at altitudes
below 100km (the homosphere) and stratified and well ordered by mass
above 100 km (the heterosphere).
The magnitude of the atmospheric temperature varies greatly, both
vertically and horizontally. However, the vertical structure is qualitatively
8
Figure 1.1: The temperature profile of Earth’s atmosphere.
similar everywhere. It is therefore meaningful to think of a typical
temperature profile. Figure 1.1 shows a typical example of the vertical
temperature profile of the atmosphere. As can be seen, the profile is divided
into a set of layers based on the different vertical temperature gradients.
These are the troposphere, stratosphere, mesosphere, and thermosphere, each
characterized by substantially different chemical and physical processes.
The boundaries between these layers are called the tropopause, stratopause,
and mesopause. The altitudes at which these boundaries are located are
not fixed but vary in time and location, depending on the amount of energy
input. The regions are commonly also referred to as the lower atmosphere
(troposphere), the middle atmosphere (stratosphere and mesosphere), and
the upper atmosphere (thermosphere and above). The focus of this thesis
will be on the middle atmosphere.
The structure of the temperature profile can be explained by considering
the dominant processes occurring at specific altitudes. From a bottom-up
approach, a large part of the electromagnetic radiation coming from the
9
Sun at wavelengths in or near the visible part of the spectrum reaches the
ground where it is absorbed. From this absorption, the Earth’s surface
is heated and re-emits the energy as black-body radiation which, in turn,
heats the lower atmosphere. This “blocking” of the outgoing radiation
by the atmosphere is referred to as the greenhouse effect and keeps the
surface of the Earth warmer than it would have been in the absence of an
atmosphere. The phenomenon also causes active convection and, as the
air rises, a decrease in temperature with increasing altitude. This gradient
defines the troposphere, which ranges in thickness of about 18 km over
the tropics, 12 km at mid-latitudes, and 6-8 km near the poles. Much
of the variability observed in the atmosphere occurs within this layer,
which accommodates approximately 90% of the atmospheric mass and is
characterized by strong instability with significant vertical exchange of
energy and mass. It is also in this layer most of the weather phenomena
takes place.
Above the troposphere, in the stratosphere, about 90% of the
atmospheric ozone (O3 ) is held (commonly referred to as the ozone layer ).
Most of the solar ultraviolet radiation is absorbed by this “layer”, which
thus acts to protect all living organisms on the planet from this harmful
energy. Due to the absorption by ozone, the stratosphere is heated and,
consequently, the vertical temperature gradient reverses. The increase of
temperature with altitude suppresses vertical motion, leading to very stable
conditions dominated by radiative processes. This leads to a stratified
layer extending up to 50 km, which prevents vertical transport between the
troposphere and the stratosphere. Since only very little water (∼3 ppm)
can cross the cold tropopause, the stratosphere is very dry. An additional
middle atmospheric source of water is the oxidation of methane (CH4 ),
which contributes to another ∼3 ppm of H2 O.
Above the stratosphere, from about 50 km to 90 km, comes the
mesosphere. Since the ozone concentration decreases with altitude, there
is no heating occurring here. Like in the troposphere, the temperature in
the mesosphere again declines with altitude, reaching a minimum at the
mesopause. The temperature at the mesopause can reach as low values as
−180◦ C, which are the lowest temperatures in the whole Earth system.
This allows for the very small amounts of water vapor existing in the
mesopause region to form water-ice particles which gives us the beautiful
Noctilucent Clouds, observable during twilight at the near-polar summer
latitudes. These clouds are discussed further below. The mesosphere
lies above the maximum altitude for aircraft and balloons and below the
10
minimum altitude for orbital spacecraft. It has therefore only been accessed
through the use of sounding rockets. As a result, the mesosphere is the
most poorly understood part of the whole atmosphere.
From about 90 km, in the thermosphere, the temperature once again
rises. This time this mainly happens due to the absorption of solar
radiation (mainly EUV and X-ray radiation) by molecular oxygen (O2 ) and
the stripping of electrons from NO, O2 , O, and N2 (photoionization). This
causes the refractive index to change (see section 2.3.3), which enables radio
waves to be “reflected” and received beyond the horizon. The maximum
temperature values are strongly dependent on the solar activity and can
exceed 2000◦ C. However, even though the temperature is so high, one
would not feel warm in the thermosphere. This is because the pressure
is so low that there is not enough contact with the few atoms of gas to
transfer much heat. A normal thermometer would read significantly below
0◦ C since the energy lost by thermal radiation would exceed the energy
acquired from the atmospheric gas by direct contact.
Above the thermosphere lies the exosphere. Here, the atmosphere
gradually turns into space.
1.2
The Noctilucent Clouds
The atmospheric region mainly discussed in this thesis is the mesosphere.
This region is characterized by a negative gradient of temperature. The
exponential decrease of density with altitude leads to very low pressure,
which causes large particles to rapidly sediment out of this region. The
mesosphere can therefore only host very small particles. Despite this
behavior, the particles that do exist in the mesosphere influence the physics
of the region significantly; it has been shown that particles influence both
the charge balance and the chemistry of the mesosphere. As we shall see,
they also enable ice formation in this very dry region, giving rise to the
Noctilucent Clouds (NLCs).
We know today that NLCs are composed of water-ice particles, possibly
with small nucleation cores. For the small amount of water vapor existing
in the region to be able to condense, the saturation ratio needs to be
sufficiently high and, hence, the temperature needs to be low enough.
This exceptional state only occurs during summer, a counter-intuitive
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phenomenon that can be explained by the general circulation of the
atmosphere; gravity waves, generated in the troposphere by frontal systems
or by airflow over mountains, propagate through the atmosphere and
dissipate at higher altitudes. When the waves break, they transfer their
momentum to the mean flow, being an east-west flow during summer.
This deposition of energy induces a small (but important) north-south
circulation which leads to an upwelling of air at the summer pole and a
downwelling at the winter pole. Air that rises will expand and cool, giving
rise to the extremely cold summer mesopause.
Once the ice particles have formed, they grow by a direct surface
deposition of the surrounding water vapor. Due to gravity, they then
consume the available water vapor while falling through the mesosphere.
Eventually, the particles can reach sizes exceeding 30 nm and they will be
able to scatter light efficiently enough to be observed optically from space
or by ground based instruments (see Paper III).
Figure 1.2: Noctilucent Clouds. Image courtesy of NASA.
The geometry for observing NLCs is illustrated in Figure 1.3. During
daytime, the molecular scattering of sunlight in the lower atmosphere
exceeds the signal scattered by the NLC. When the Sun has set, however,
and the troposphere and stratosphere are in shadow, the mesospheric
clouds still reflect light until the Sun has proceeded even further below the
12
horizon. These short time periods of twilight are the only occasions when
NLCs can be seen by the naked eye.
Figure 1.3: The scattering geometry of Noctilucent Clouds.
When the clouds are observed by space borne instruments, the light
scattered by them can be recorded at all times. When viewed from space,
the clouds are often referred to as Polar Mesospheric Clouds (PMCs),
which is the term used throughout the rest of this thesis.
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Chapter 2
Atmospheric Radiation
Why do we see the things we see?
2.1
Electromagnetic spectrum
The most important physical quantity responsible for energy transfer in the
atmosphere is electromagnetic radiation. Electromagnetic radiation travels
in wave form at the speed of light. Visible light, ultraviolet light, infrared
radiation, microwaves, gamma rays, x-rays, television signals, and radio
waves are all electromagnetic waves and constitute the electromagnetic
spectrum (shown in Figure 2.1).
The human eye is sensitive to electromagnetic radiation with wavelengths
from about 400 to 700 nm (or, equivalently, frequencies between
4.3 − 7.5 × 1014 Hz). This wavelength band is therefore called the visible
region of the electromagnetic spectrum.
15
Figure 2.1: The electromagnetic spectrum.
2.2
Basic radiometric quantities
The whole area of optical techniques can roughly be divided into the two
areas of photometry and radiometry. The central problem of photometry is
the determination of optical quantities closely related to the sensitivity of
the human eye, and radiometry deals with the measurement of energy per
time (power) emitted by light sources or incident on a particular surface.
Therefore, the units of all radiometric quantities are based on watts, [W].
2.2.1
Solid angle
When investigating phenomena connected to a radiation field, there is often
need to consider the amount of radiant energy confined to an element of
solid angle. A solid angle is defined as the ratio of the area η of a spherical
surface intercepted at the core to the square of the radius r and has units
of steradians, [sr]. It can be written as
Ω=
η
.
r2
16
A differential area is, in polar coordinates, given by
dη = (rdθ) (r sin θdφ) ,
where θ and φ represents the zenith and azimuthal angles, respectively.
Hence, the differential solid angle is
dΩ =
dη
= sin θdθdφ
r2
and the solid angle of a sphere is
Z
ΩSphere =
Z
2π
Z
dΩ =
Sphere
π
sin θdθdφ = 4π.
0
0
Figure 2.2: Definition of the solid angle.
2.2.2
Radiant flux
Radiant flux is defined as the radiant energy, W , per unit time and given
in units of watts:
dW
Φ=
[W].
dt
17
2.2.3
Irradiance
Irradiance is defined as the power incident on a surface, also called radiant
flux density, and is given in units of watts per square meter:
E=
2.2.4
dW
dt · dA
[W · m−2 ].
Radiant intensity
Radiant intensity is defined as the power per unit solid angle and given in
units of watts per steradian:
I=
2.2.5
dW
dΩ · dt
[W · sr−1 ].
Radiance
Radiance is defined as the power per unit solid angle per unit projected
source area and is given in units of watts per steradian per square meter:
L=
2.3
dW
dΩ · dt · dA
[W · sr−1 · m−2 ].
Concepts of scattering and absorption
Most of the light that reaches our eyes is not coming directly from the
source but indirectly through the process of scattering. When we look at the
sky, the clouds, land, water, or objects surrounding us, we see the light that
they scatter. In the atmosphere, we see a lot of colorful examples created
by sunlight scattered by molecules, aerosols, and clouds containing water
droplets and ice crystals, such as the blue sky, white clouds, magnificent
rainbows, and halos.
Scattering is a fundamental physical process associated with light
and its interaction with matter which occurs at all wavelengths of the
electromagnetic spectrum. The process is evident when a particle in the
path of an electromagnetic wave absorbs energy from the incident wave and
re-emits that energy in (more or less) all directions.
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2.3.1
Scattering angle and phase function
The scattering angle, θ, is defined as the angle between the incident
direction n and the observed direction n0 , such that cos θ = n · n0 . If
θ < π/2, the incident light is scattered in the forward direction. If, on
the other hand, θ > π/2, the incident light is scattered in the backward
direction.
When discussing scattering patterns, a commonly used concept is the
scattering phase function, which is represented by a probability function
Φ(Θ) of scattering in the direction Θ = cos θ. The scattering phase function
is normalized to the total solid angle by
Z 2π
1
Φ(Θ)dΘ = 1,
4π 0
such that Φ(Θ)dΘ is the probability that a scattered photon is deflected
by an angle θ for which Θ = cos θ is between Θ and Θ + dΘ. Note that
the azimuthal dependence (φ) is removed, which is possible under the
assumption of a spherical particle. For scattering by arbitrarily shaped
particles, Φ(Θ) can become very complex.
2.3.2
Scattering cross-section
In the field of light scattering and radiative transfer, it is customary to
use a term called cross-section, σ [m2 ], to denote the amount of energy
removed from the original beam by the particle (the scattering center). The
scattering cross-section is a hypothetical area which describes the likelihood
of light (or other radiation) being scattered by a particle. In general, the
scattering cross-section is different from the geometrical cross-section of the
particle.
The differential scattering cross-section, σdif f [m2 sr−1 ], is defined as
the amount of radiation scattered into an element of solid angle dΩ in the
direction (θ,φ), or, in terms of the phase function Φ(Θ),
σdif f =
dσ
= σ · Φ(Θ).
dΩ
The concept thus provides a measure of the strength of the interaction
between an individual scatterer and the radiation field. Multiplication with
19
the scatterers’ column density reveals the total scattering along a given
path.
2.3.3
Index of refraction
The properties of the medium that the light is traveling through are fully
given by the complex index of refraction,
m(λ) = n(λ) − ik(λ),
where n(λ) and k(λ) are the real and the imaginary parts, respectively,
given as functions of the wavelength λ. Roughly, n and k can be interpreted
as the scattering and absorption part of the refractive index. A particle
with k = 0 will not absorb light, but scatter light. Typically n has values
larger than 1 but not much bigger than a few.
2.3.4
Size parameter
In the atmosphere, the particles that are responsible for the scattering
range in size from gas molecules (∼ 0.1 nm) to aerosols (∼ 103 nm), water
droplets (∼ 104 nm), ice crystals (∼ 105 nm), and large raindrops and hail
particles (∼ 1 cm). The size parameter, x, is a physical term describing the
relation between particle size and scattering. For a spherical particle, it is
defined as
2πa
x=
,
λ
where a is the particle radius.
2.3.5
Rayleigh and Mie scattering
Figure 2.3 illustrates the scattering patterns of spherical aerosols of sizes
0.1 − 1000 nm illuminated by visible light with a wavelength of 500 nm.
If x 1, the scattering is known as Rayleigh scattering. A small particle
in the Rayleigh regime tends to scatter light equally in the forward and
backward directions. When the particle becomes larger, x & 1, the
scattering is commonly referred to as Mie scattering and the scattered
energy becomes increasingly concentrated in the forward direction with
increasing particle size.
20
Figure 2.3: Angular patterns of scattered intensity from spherical aerosols
of three sizes illuminated by visible light with a wavelength of 500 nm:
(a) 0.1 nm, (b) 100 nm, and (c) 1000 nm. The forward scattering pattern for the 1000 nm aerosol is extremely large and is scaled for illustrative
purposes. Image source: Liou (2002).
A well known everyday example of Rayleigh scattering is the scattering
of visible light by atmospheric molecules, a process leading to the blue color
of the sky. This phenomena can be explained by the heavy wavelength
dependence of the scattering cross-section in the Rayleigh regime, given by
2
2 (2a)6 m2 − 1 σRayleigh = π 5 4 2
.
3
λ
m + 2
The scattering opacity of small particles in the Rayleigh regime therefore
goes roughly as 1/λ4 , making the blue (shorter) part of the spectrum
scatter more strongly than the red (longer) part. This results in the indirect
blue light coming from all regions of the sky.
Mie scattering, on the other hand, is roughly independent of wavelength
(although dependent on particle size) and produces the almost white glare
around the Sun when a lot of particulate material is present in the air.
It also gives us the the white light from mist and fog. Mie theory only
works for spherical particles. The T-matrix method, where matrix elements
are obtained by matching boundary conditions for solutions of Maxwell’s
21
equations, is an extension to non-spherical particles (Mishchenko et al.,
1996). Though this method still works best for relatively simple shapes
such as ellipsoids. Understanding Mie theory is not an easy task, and the
derivation of the equations is somewhat elaborate. For complex-shaped
particles, a fully numerical method has to be employed to solve the Maxwell
equations.
22
Chapter 3
Remote Sensing
What if we look at things from a different angle?
3.1
Overview
Remote sensing satellites can roughly be divided in operational satellites
and scientific satellites. Generally speaking, operational satellites have
a long lifetime and often several near-identical copies, whereas scientific
satellites are unique and have a more limited lifetime, but produce more
advanced data. An example of a scientific satellite is the Odin satellite,
a Swedish led mission being part of an international project involving the
space agencies of Sweden (SNSB), Finland (funded by TEKES), Canada
(CSA) and France (CNES).
Space borne remote sensing instruments have provided the Earth
sciences with a wealth of information in recent decades. The first Earth
observation satellite was the Television Infra-Red Observation Satellite-1
(TIROS-1), a weather satellite launched April 1960 by the National
Aeronautics and Space Administration (NASA), part of the TIROS
23
program and eventually replaced by the satellite series operated by the
National Oceanic and Atmospheric Administration (NOAA) (Rees, 2001).
Scientific and operational Earth observation satellites carry a variety of
passive and active instruments operating in wavelength regions ranging
from microwaves to UV. Data acquired from such instruments is used by
the scientific community, meteorological agencies, and other organizations.
Combining data from different satellites allows for powerful and innovative
applications.
As defined by Rees (2001), remote sensing is the measuring of physical
properties of a system without being in physical contact with it. It can be
divided in active remote sensing, where the instrument transmits a signal
and measures the signal coming back, or passive remote sensing, where the
system simply measures radiation coming from the source. Examples of
common technologies for active remote sensing are RADAR and LIDAR.
Passive remote sensing is commonly done with radiometers or imagers. For
measuring the atmosphere, space borne sensors can either point towards
the limb of the atmosphere, or in the nadir (downward) direction.
3.2
The inverse problem
There is an important difference between in situ and remote sensing
observations. The former can normally be interpreted as true point
measurements, while retrieved satellite quantities always represent a
weighted average over all parts of the atmosphere that contribute to the
signal observed by the satellite instrument.
Following Rodgers (2000), we call the variables that we wish to estimate
the state variables, and assemble them into a state vector,


x1
 x2 
 
x =  . .
 .. 
xn
24
We similarly assemble the measurements into a measurement vector,
 
y1
 y2 
 
y =  . ,
 .. 
ym
where m is not necessarily equal to n. Our understanding of the relationship
between x and y is described by a physical model F, called the forward
model,
y = F(x, b) + ,
(3.1)
where b is a parameter vector including all model variables that we do not
seek to optimize (we call them model parameters) and is the measurement
error, including contributions from the observations, the forward model,
and the model parameters. The function F thus relates the natural state x
to the measurement y and by inverting equation 3.1, we can thus obtain x
given y.
In the presence of error ( 6= 0), the best we can get is an estimate.
We therefore need to weigh the resulting information against our prior (a
priori ) knowledge xa of the state vector before the observations were made.
The optimal solution of x reflecting this ensemble of constraints is called
the a posteriori, the optimal estimate, or the retrieval.
A simple case of the inverse problem can be formulated for a linear
model around the a priori estimate xa and noise-free measurements. The
measurements and the state vector are then simply related:
y=
∂F(x, b)
(x − xa ) + F(xa , b).
∂x
(3.2)
The partial derivative of the model with respect to the state vector is
called the Jacobian matrix of F(x, b) and in the context of an inversion is
often referred to as the kernel matrix, K. In general K is an m × n matrix,
where each column vector represents the derivative of F(x, b) with respect
to a single element of the state vector,
 ∂F1
∂x1

K =  ...
∂Fm
∂x1
...
..
.
...
25
∂F1
∂xn

..  .
. 
∂Fm
∂xn
If m = n, K is a square matrix and equation 3.2 can be solved directly to
yield the retrieved state,
x̂ = xa + K−1 (y − F(xa , b)) .
(3.3)
If F is non-linear, the solution 3.3 must be iterated with recalculations of
the Jacobian around successive guesses for x until the result converges.
26
Chapter 4
Odin/OSIRIS
What if we would have an eye in the sky?
4.1
The satellite platform
The Odin satellite was launched on February 20 in 2001 from Svobodny
in Eastern Siberia. The satellite was placed into an almost circular
Sun-synchronous orbit1 near 600 km with the ascending node2 at 18:00
local time and with a period of approximately 96 min (Murtagh et al.,
2002; Llewellyn et al., 2004). Aboard Odin, two instruments are installed:
the Sub-Millimeter Radiometer (SMR) and the Optical Spectrograph and
Infra-Red Imager System (OSIRIS). The former one, SMR, was designed
to (among other things) provide information about the amount of water
vapor present in both the atmosphere and in space (Nordh et al., 2003).
1 In a Sun-synchronous orbit, the satellite passes over the same section of the Earth
at roughly the same local time each day, thus making it synchronized with the Sun.
2 The ascending node is the point, or longitude, where the satellite passes over the
equator going northward. Similarly, the descending node is the point where the satellite
passes over the equator going southward.
27
Figure 4.1: Odin. Image courtesy of Swedish Space Corporation, SSC.
The latter one, OSIRIS, consists of a spectrograph and three IR imagers;
this instrument is discussed further below.
The Odin mission was designed as a shared mission for both astronomers
and atmospheric scientists by looking out into deep space when performing
astronomy measurements and to Earth for atmospheric observations. The
astronomy part of the mission was, however, completed during 2007. Odin
is now operated as an ESA Third Party Mission and runs in pure aeronomy
mode. For this latter purpose, the satellite is pointed towards the limb of
the Earth, usually in the direction of the satellite track. When performing
limb measurements, the entire satellite is nodded so that the co-aligned
optical axes of SMR and OSIRIS sweep over selected altitude ranges. Both
instruments are designed to retrieve altitude profiles of atmospheric minor
species by observing limb radiance profiles.
4.2
The optical spectrograph
The OS part of OSIRIS is an optical spectrograph which measures sunlight
scattered by the atmosphere at wavelengths between about 275 - 800 nm
and with a resolution of approximately 1 nm (Llewellyn et al., 2004). The
28
geometry of the setup is shown in Figure 4.2. As can be seen in this picture,
the line of sight of Odin crosses a range of altitudes over which the scattered
signal is integrated. The largest contribution to the observed sunlight
do, however, originate at altitudes closest to the tangent point, indicated
with a darker gray shade. Figure 4.2 also shows the three main types of
scattering that have to be dealt with by atmospheric remote sensing. These
are (from left to right in the figure) multiple scattering, which means that
the sunlight is scattered multiple times before arriving at the instrument,
surface reflection, which means that the sunlight is reflected at the surface
of the Earth before returning into the atmosphere and contributing to the
signal, and single scattering, which means that the sunlight is scattered
once before OSIRIS captures it.
Figure 4.2: The three main types of scattering significant for a limb viewing
instrument. Image source: Bourassa et al. (2008).
The instrumental design of OSIRIS is illustrated in Figure 4.3. When
the measurements are performed, sunlight scattered by the atmosphere
enters the telescope through the exit vane, reflects primarily against the
objective mirror, secondly against the field mirror, and then enters the
spectrograph through the slit. Inside the spectrograph, the sunlight is
collimated3 by a convex mirror before it is splitted by a grating into its
different wavelengths. The diffracted light is then redirected further by
another mirror and a prism to finally hit the CCD detector. The CCD
consists of 1353×32 pixels, where the latter number refers to the 32 columns
containing spatial information about the horizontal field of view, which is
approximately 40 km at the tangent point. However, due to the limited
on-board data storage capacity, the image is compressed to 1353 × 1 pixels,
thereby eliminating the horizontal resolution.
3 Collimated light is light whose rays are parallel and therefore will spread minimally
as it propagates.
29
Figure 4.3: A schematic view of OSIRIS. Image source: Llewellyn et al.
(2004).
The pixels of the CCD are exposed to the diffracted sunlight during
a time between 10 ms and 10 s, depending on the observed altitude; at
the mesopause region, the typical exposure time is 1 s. During nominal
aeronomy observations, the vertical field of view is 1 km at the tangent
point and the scan speed is 0,75 km/s. The resulting height resolution is
approximately 1,5 km in this region.
4.3
4.3.1
Original retrievals
Measured radiance
When sunlight entering the optical spectrograph hits the CCD detector, the
information is stored in an on-board data storage. Since the wavelengths of
the light are separated by the optical setup of the instrument, it is possible
to look further into the relationship between the radiance and the different
wavelengths. Figure 4.4 shows an example of a typical spectrum recorded
by OSIRIS. By looking at images of this type, the fact that different
molecules (species) absorb light of different wavelengths (see section 2.3
30
Figure 4.4: An early mission limb image sequence obtained with the optical
spectrograph when Odin was near 80◦ N. The altitude range of the tangent
line of sight of the scan is between approximately 10 and 60 km. Image
source: Llewellyn et al. (2004).
above) provides information about what kind of species the light has been
traveling through (which wavelengths have been absorbed). For example,
the low amount of radiance to the left in Figure 4.4 is due to the presence of
ozone (O3 ) and the small dip centered around 7620 Å is due to absorption
of O2 .
4.3.2
PMC brightness
Figure 4.5 shows the radiance measured at wavelengths 275-310 nm. This
specific wavelength interval has been chosen in order to minimize the
amount of radiation scattered back from the lower atmosphere and the
Earth’s surface. This is possible since light at these wavelengths is absorbed
by the stratospheric ozone layer. As Figure 4.5 shows, the radiance is
increasing substantially at tangent altitudes around 65-85 km. This is
due to the presence of PMCs (see section 1.2) that efficiently scatter
light, making more radiation reach the satellite. The line in Figure 4.5
indicated as “Rayleigh background” is the level of radiance originating from
31
Figure 4.5: A typical radiance measurement by OSIRIS at wavelengths
275 − 310 nm.
the background (in other words: without any PMCs). When analyzing
properties of PMCs, the background therefore has to be subtracted from the
data. The Rayleigh background is calculated using modeled atmospheric
density profiles and is a function of latitude, date, and time of day. PMC
brightness is then retrieved as the difference between the measured limb
radiance and the Rayleigh background for each measurement point between
80 and 88 km at seven different wavelengths: 277, 283, 288, 291, 294, 300
and 304 nm.
Figure 4.5 also shows an excellent example of the need for tomographic
methods; as can be seen, the PMC brightness has a gradually decreasing
appearance below about 80 km. This appearance is simply an artifact of the
observation geometry (depicted in Figure 4.2), since an observation along a
line of sight at any given tangent altitude also includes contributions to the
signal from the fore- and background. This problem is neatly solved by the
32
tomographic techniques discussed in Chapter 5 and Papers II-III.
4.3.3
Particle sizes
To be able to draw further conclusions from the PMC data, for example,
about the particle sizes of the observed cloud, the data must be compared
to theoretical scattering spectra. In order to do this, the retrieved PMC
brightness is converted into a scattering efficiency by relating it to the
incident solar spectrum by the ratio between the radiance scattered towards
the instrument and the solar irradiance at each wavelength (Karlsson and
Gumbel, 2005). The resulting quantity is referred to as a directional albedo,
which can be compared to theoretical scattering spectra calculated for
various particle sizes and scattering angles.
Figure 4.6: Mie scattering simulations for spherical ice particles (solid
lines) together with the directional albedo for various wavelengths
(crosses). The circles show the corresponding results without corrections
for light scattered back from the ground and the lower atmosphere. Image
source: Karlsson and Gumbel (2005).
Figure 4.6 shows theoretical scattering spectra as Mie scattering
simulations for spherical ice particles (solid lines) together with the
directional albedo for various wavelengths (crosses). This shows a basis
33
for fitting a particle size to the measured PMC data. In this example, the
resulting particle radius is 53 nm.
The above method is an example of fitting a single size parameter to
the spectral data. A similar 1-parameter approach has been shown by von
Savigny et al. (2005) who, by assuming that only single scattering occurs,
proposed that the radiation scattered by the PMC particles only depends
on the solar spectrum S(λ) and on the differential scattering coefficient
σdif f (λ, θ) as
IP M C (λ, θ) ∝ σdif f (λ, θ) × S(λ),
where λ is the wavelength and θ is the scattering angle. The differential
scattering coefficient can, in the narrow spectral window used here, be
approximated by a power law: σdif f (λ, θ) ∝ λ−α , where α is the so-called
Ångstrom exponent. By using Mie calculations, the derived Ångstrom
exponents can be related to the PMC particle sizes and, in turn, the
particle sizes to the measured light.
34
Chapter 5
Tomographic Inversions
What if we look at things from every possible angle?
5.1
Background
Tomography refers to imaging by sections, or sectioning, and is usually
the representation of a three dimensional object by means of its two
dimensional cross sections. The technique has been used for many years in
the field of medical imaging, although their application to the atmosphere
is still in its infancy. The mathematical basis for tomographic imaging
was laid down during the early 20th century by Johann Radon (Radon,
1917). The resulting two dimensional Radon transform is used to transform
a two-dimensional field f (r, φ) into a formulation that is based on line
integrals through the field. If it is possible to determine line integrals at
every point within such a field and the angles between successive lines are
infinitesimal, then the inverse transform can be used to obtain a solution of
the field (Herman, 2009).
The Radon transform provides a retrieval of a two dimensional field
from knowledge of line integrals through that field. The atmospheric
35
tomography discussed in this thesis also uses a knowledge of line integrals,
although made by an optical instrument aboard a satellite platform, to
recover the two dimensional volume emission rate profile of the atmosphere.
The application of tomographic techniques for satellite measurements of
the atmosphere induces some particular limitations not experienced by
conventional applications. The solid Earth limits the atmospheric look
directions so that only a very limited interval of viewing angles can provide
line integrals through a given point. Figure 5.1 shows this scenario. The
paths shown are tangent to the solid Earth and therefore represent limiting
paths. For an observational point at 85 km, these limiting paths are
separated by an angle θ ≈ 20◦ . The possible lines of sight intersecting the
given point are then all paths between these boundaries. In a geocentric
system the same angle θ is also determining the movement of the satellite
between positions 1 and 2 and thereby defines the angle along the orbit
(AAO) used throughout this thesis, where 1◦ corresponds to approximately
110 km. For a satellite speed of 7 km/s, this implies that the time
interval between a set of boundary paths for sampling a given point in the
mesopause region is about 5 min. This time interval is consequently also
the temporal scale for sampling a specific volume in space at 85 km.
5.2
Inversion method
This part is a bit more abstract than the previous chapters due to the
mathematical nature of the inversions. The derivations of the equations
given below have therefore been omitted to provide the reader with a
somewhat simplified view. A full description of the technique can be found
in Degenstein (1999).
The inversion method used for the work presented in this thesis has
its roots in an approach to de-blurring ring pattern images collected by
a Fabry-Perot interferometer. The problem was presented by Lloyd and
Llewellyn (1989) as a system of linear equations,
B = AT,
(5.1)
where B, indexed by i, is the measured image of the object T, indexed by j
and distorted by A. The technique, called the Maximum Probability (MP)
method, associated Pij , the most probable contribution of the elements j
36
Figure 5.1: The viewing interval for tangent lines through a point at 85 km
over which line integrals can be calculated. The points 1 and 2 represent
the limiting satellite positions from where this point can be seen (to scale).
to the measurements Bi through the following two equations:
Pij =
(Bi + ni )Aij Tj
P
− 1,
j Aij Tj
(5.2)
P
Pij
,
Tj = P i
i Aij
(5.3)
where ni is the number of elements that contribute to measurement i and
the use of an initial estimate in equation 5.2 gives a set of values Pij to
be used in equation 5.3. This gives a new set of object elements and the
process can be iterated until the result converges.
McDade and Llewellyn (1993) modified the equations 5.2 and 5.3 to
be used for simulations of measurements made with a simple imaging
instrument on a satellite platform:
Pij =
(Oi + ni )Lij Vj
P
− 1,
j Lij Vj
37
(5.4)
P
Pij
Vj = P i
.
L
i ij
(5.5)
Here, Oi is a single measurement, ni is the number of grid cells used,
Lij is the path length of the ith measurement through the j th cell, Vj
is the retrieved volume emission at grid cell j, and Pij is the expected
contribution of the volume emission Vj to the measurement Oi .
Following Degenstein (1999), modifications to the equations can be
made further by substituting equation 5.4 into 5.5 to obtain
P (Oi +ni )Lij Vj(n−1)
−1
P
(n−1)
i
j Lij Vj
(n)
P
,
(5.6)
Vj =
i Lij
where the superscripts (n) and (n − 1) represents the current and previous
iterations, respectively. By assuming that
Oi ni
and that
(n−1)
(Oi + ni )Lij Vj
P
(n−1)
j Lij Vj
1,
equation 5.6 can be approximated as
Oi Lij
P
(n)
Vj
(n−1)
i
P
(n−1)
Lij Vj
j
P
= Vj
i
Lij
.
(5.7)
As described above, Lij is the path length of the ith measurement
through the j th cell. Thus, the denominator in equation 5.7 represents the
sum of the geometric path lengths of the measurements i through the cell j.
Since this sum has no direct dependence on the individual measurements,
we can set
X
Wj =
Lij
i
so that
(n)
Vj
=
(n−1)
Vj
X
i
Oi
Lij
P
(n−1) W
j
j Lij Vj
38
!
.
Using the fact that
X Lij
i
Wj
= 1,
we can assign
Lij
.
Wj
This is then simply a geometry dependent, normalized weighting factor
that is unique for each cell and shows the importance of the individual
measurements of the iterative solution of each cell. This observation
weighting filter determines the influence of each observation on the retrieval
in any given cell. To interpret the relative importance of cells in the
neighborhood, an averaging kernel can be determined from the weighting
filter function value for each observation that intersects the cell. The
observation weighting filter function is fundamental to the accuracy and
speed of convergence of the final solution.
βij =
Now, writing equation 5.1 as O = LV, it is possible to construct
a forward model based on a previous estimate of the volume emission
elements as
X
(n−1)
(n−1)
Oi,est =
Lij Vj
.
j
Together, this gives the final simplified iterative expression
!
X
Oi
(n)
(n−1)
Vj = Vj
β
(n−1) ij
Oi,est
i
(5.8)
that tells us that the next estimate of the volume emission is given by
the previous value multiplied by a weighted average of all measurements
of that cell, divided by their estimates from the previous solution. The
expression 5.8 is the one used as a basis for all tomographic retrievals made
for OSIRIS, used in papers II, III, and IV.
5.3
A model of Odin/OSIRIS
To be able to apply the tomographic technique to measurements made from
a satellite platform, it is necessary to create a model of the platform to be
able to simulate measurements and to provide a basis for the line integrals
Lij . A complete model is described in Degenstein (1999) and Degenstein
et al. (2003) and summarized below.
39
5.3.1
Atmospheric grid
As can be seen in Figure 5.2, the atmosphere can be divided into a discrete
grid. The grid cells are defined by the AAO (angle along the orbit) and
the radial distance from the center of the Earth, creating angular divisions
and radial shells. They are labeled with an index j, the same j as in
the tomographic equations above, increasing with atmospheric layer and
angular division. Associated with the atmospheric grid are the parameters
∆angle and ∆shell , describing the angular width and the radial height of
the grid cells. The number of angles and shells are then given by specifying
intervals in the two dimensions and, subsequently, dividing by ∆angle and
∆shell , respectively.
Figure 5.2: An illustration of a discrete grid used to model the atmosphere
(not to scale).
5.3.2
Coordinate systems
To model the complete scenario where the optical spectrometer of OSIRIS
is observing the atmosphere of the Earth from the Odin satellite, three
different coordinate systems are used. These are the ascending node system,
an Earth centered three dimensional Cartesian space (x̂asc , ŷasc , ẑasc ) with
the x-axis pointing towards the ascending node of the satellite orbit,
the satellite system, an Earth centered three dimensional Cartesian
space (x̂sat , ŷsat , ẑsat ) rotating with the satellite motion and having the
x-axis pointing through the local zenith, and the instrument system
(x̂inst , ŷinst , ẑinst ) with the x-axis corresponding to a preferred direction
within the focal plane and the z-axis pointing in the direction of the optical
axis.
40
The satellite position is at any time given by
Sasc = Rsat (cos α · x̂asc + sin α · ẑasc ) ,
where Rsat is the radial distance of the satellite from the center of the
Earth and α is the angle along the orbit from the ascending node, and
Ssat = Rsat · x̂sat
in the ascending node and satellite systems, respectively.
For the instrument coordinate system, a unit vector for the field of view
in the orbit plane can be defined as
Pinst = sin Ω · x̂inst + cos Ω · ẑinst ,
where Ω is the deviation of the look direction from the optical axis.
A transformation of a look direction vector Pinst into an instantaneous
field of view vector Psat in the satellite system can be performed by using
the three rotation angles ω, θ, and φ, related to the yaw, pitch, and roll of
the optical axis, as
Psat

1
= 0
0
0
cos φ
sin φ

0
cos θ
− sin φ  0
cos φ
sin θ
0
1
0

− sin θ
cos ω
0   sin ω
cos θ
0
− sin ω
cos ω
0

0
0 Pinst .
1
To further transform the unit vector into the ascending node system, a
rotation of the satellite angle α around the ŷsat axis completes the process:
Pasc
5.3.3

cos α
= 0
sin α
0
1
0

− sin α
0  Psat .
cos α
Model description
A radial shell can be represented by the basic formula
r 2 = x2 + y 2 + z 2 .
41
(5.9)
The position of a point at a distance d along a look direction Pasc is, in the
ascending node frame, described as
xasc = Sx + Px d,
(5.10)
yasc = Py d,
(5.11)
zasc = Sz + Pz d,
(5.12)
where Sx and Sz are the satellite coordinates. By putting equations
5.10-5.12 into equation 5.9 a second degree equation is obtained and it
is possible to solve for the distances from the instrument to the shell
intersections by
q
2
2 + r2 ,
ds = − (Sasc · Pasc ) ± (Sasc · Pasc ) − Rsat
where the sign of the square root term indicates if the line of sight intersects
the shell twice or not at all.
To determine the distance of lines of sight to the angular divisions, the
introduction of a plane
z = x tan γasc
is convenient. Here, γasc represents the angle of the plane along the orbit
track in the ascending node coordinate system. The distance from the
instrument to the plane is then given by
da =
Sx tan γasc − Sz
.
Pz − Px tan γasc
The two sets of distances ds and da can then be combined to calculate
all distances needed for the tomographic algorithm, including the parameter
Lij , and hence all the constituents needed to run the retrieval are in place.
42
Chapter 6
The MATS Satellite
Concept
What if we use our knowledge to create something entirely new?
The Swedish effort in space activities has during a long time been
characterized by innovation and cost effectiveness, both by industry and
academia. In the last decades, this has led to several successful scientific
and technical satellite missions (e.g. Viking, Freja, Astrid 1, Astrid 2,
Odin, and Prisma). Through this, the Swedish space program has shown
the ability of being both extremely cost effective and able to deliver good
results in terms of technology demonstrations and new scientific data.
In October 2011, the Swedish National Space Board (SNSB) distributed
a call for ideas to investigate innovative low-cost research missions using
small satellites. The ideas should build on new technology to achieve cost
effective designs and operations. The objective of the call was to “survey for
feasible options for high-quality research missions and assess their potential
for being accomplished to break-through low-cost budgets through the use
of emerging technologies and innovative ways of working”. In order to be
selected, the mission concept thus needed to fulfill requirements on both
scientific quality and technical innovation.
43
In January 2012, twelve proposals of mission concepts had been laid
forward by Swedish research groups within the fields of atmospheric science,
solar-terrestrial interaction, planetary science and astronomy. Based on
the tomographic ideas developed in this thesis, one of these concepts was
MATS - Mesospheric Airglow/Aerosol Tomography and Spectroscopy.
During the spring of 2012, the concepts went through a scientific
evaluation. This was followed by a technical evaluation together with five
leading organizations from the national space industry (OHB Sweden, ÅAC
Microtec, Omnisys Instruments, NanoSpace and RUAG Space).
During 2013, OHB Sweden and ÅAC Microtec continued to collaborate
with the scientific groups and defined a new satellite platform, InnoSat
(Larsson et al., 2014). InnoSat is a universal platform designed to be
“low-cost” and compatible with several of the proposed missions.
With regards to the new InnoSat concept, three of the original ideas
were, in early 2014, invited by SNSB to submit updated proposals based
on the technical evaluation. During spring 2014, SNSB chose the MATS
project to go through a Mission Definition Phase during summer 2014.
The general ideas behind the MATS satellite mission are described in
Paper IV. MATS is to investigate structures and wave activity in the MLT
region over a wide range of spatial and temporal scales. The measurements
are therefore based on optical emissions from the altitude range 80-100 km.
Wave patterns will be analyzed in terms of emitter densities, temperatures
and cloud structures derived from measured radiance from solar scattering
and emissions by both molecules and PMCs. Based on the resulting global
MLT wave climatology, a number of scientific questions will be addressed.
The tomographic analysis techniques described in this thesis have
become a central part of the MATS project. In addition to the tomography
of Polar Mesospheric Clouds in the ultraviolet, described in papers II
and III, this also includes tomography on airglow emissions (dayglow and
nightglow) from excited O2 in the near-infrared.
MATS is today based on an instrument consortium led by the
Department of Meteorology at Stockholm University (MISU), involving
MISU, Chalmers, the Royal Institute of Technology (KTH), and Omnisys
Instruments.
44
Chapter 7
Summary of The Papers
What if we would tell the world what we have seen?
Paper I
Hultgren, K., Kornich, H., Gumbel, J., Gerding, M., Hoffmann, P., Lossow,
S., and Megner, L. (2010). What caused the exceptional mid-latitudinal
Noctilucent Cloud event in July 2009? J. Atmos. Solar-Terr. Phys., 73,
2125-2131.
The existence of PMCs requires, as discussed earlier in this thesis, a
combination of very low temperatures, sufficient water vapor, and nuclei
on which ice can grow. These conditions are occasionally met at latitudes
pole-ward of 50◦ latitude. However, in July 2009, PMCs were observed from
both Paris (48◦ N, 3◦ E) and Nebraska (41◦ N, 100◦ W). Paper I analyzes
the atmospheric conditions on this day using temperature data from the
Microwave Limb Sounder (MLS) aboard the Aura satellite, PMC radiance
data from OSIRIS, winds and tidal data from the Global-Scale Wave
45
Model (GSWM), and microphysical simulations performed by using the
Community Aerosol and Radiation Model (CARMA). The results suggest
that the reason for the formation is a combination of local temperature
variations by diurnal tides, favorably located large-scale planetary waves,
and general mesospheric temperature conditions below the average
compared to previous years.
As Paper I introduces a number of important questions of current
mesospheric research, it serves as a motivation for the new analysis methods
that are the focus of Papers II-IV.
Paper II
Hultgren, K., Gumbel, J., Degenstein, D., Bourassa, A., Lloyd, N., and
Stegman, J. (2013). First simultaneous retrievals of horizontal and vertical
structures of Polar Mesospheric Clouds from Odin/OSIRIS tomography. J.
Atmos. Solar-Terr. Phys., 104, 213-223.
This paper is the main paper of this thesis and focuses on tomographic
retrievals of PMCs. The paper presents the algorithm outlined in chapter
5 and applies it on PMC observations made by OSIRIS. This is done by
introducing local scattering from a cloud element in terms of the volume
scattering coefficient βλ that includes the dependence on the scattering
angle. The results include the first simultaneously retrieved vertical and
horizontal PMC structures and shows that the tomographic algorithm is
able to locate detailed structures such as tilts, stratifications, or holes that
cannot be analyzed by other limb, nadir, or ground-based measurements.
Paper III
Hultgren, K. and Gumbel, J. (2014). Tomographic and spectral views on
the life cycle of polar mesospheric clouds from Odin/OSIRIS. Submitted to
J. Geophys. Res. on 2014/08/14
Paper III applies the tomographic algorithm developed in Paper II to
retrieve microphysical parameters from PMCs, such as particle sizes,
46
particle number densities, and ice mass densities. The results show that the
general structure is consistent with a well established growth-sedimentation
scenario, where large numbers of small particles exist near the top of
the observed cloud layer with a trend towards increasing particle sizes
and decreasing particle number densities with lower altitudes. However,
we find substantial horizontal variation of the clouds’ vertical structure
and properties. A surprising finding is the presence of small numbers of
rather large particles well below the brightest part of the cloud. This has
important implications for PMC microphysics. It also has implications
for the analysis of conventional (non-tomographic) PMC remote sensing
techniques.
Paper IV
Gumbel, J., Hultgren, K., and the MATS Science Team (2014). Mesospheric
Airglow/Aerosol Tomography and Spectroscopy (MATS) - a satellite mission on mesospheric waves. To be submitted to a future ESA Living Planet
Symposium or similar conference.
This paper presents a future application of the tomographic inversions
in terms of a new satellite concept - MATS. The idea behind the MATS
satellite mission is to analyze wave activity in the MLT over a wide range
of spatial and temporal scales, based on optical emissions from altitudes
80-100 km. Based on the resulting global MLT wave climatology, a
number of scientific questions on wave properties and atmospheric coupling
processes can be addressed. The paper describes both scientific ideas and
the instrumental/analysis concepts behind the project.
47
48
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Acknowledgements
I would like to express my special appreciation and thanks to my supervisor
Professor Jörg Gumbel - you have been a tremendous mentor for me. Also,
huge thanks to Heiner for always being a bank of knowledge, to Jonas for
keeping peace in the room, Peggy for acting as a mentor, and to the whole
AP-group for always sharing so much cookies! A big thanks to all PhD
students for making my time at MISU memorable and something I always
will look back to as a special part of my life.
I would like to send out a large amount of gratitude to Doug, Adam,
and Nick for all your help with the algorithms and for the hospitality during
my stay in Saskatoon. Adam, I won’t forget the day when we chased a
balloon over the whole Canadian prairie and actually captured it.
A special thanks to my family. Words cannot express how grateful
I am to my mother and father for all of the sacrifices that you’ve made
on my behalf. At the end I would like to express appreciation to my
beloved wife Sara who always supports me and guides me in the right
direction - I love you!
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