This is a doctoral dissertation about light scattering modeling using spheroidal and ellipsoidal model particles. In this thesis, I have found that ellipsoidally or spheroidally shaped model particles could be used to improve modeling of the light scattering by atmospheric mineral dust, Martian dust analog particles (namely palagonite dust) and volcanic ash particles. Results of these studies have been used to improve ECHAM climate models of the Finnish Meteorological Institute, and plans are also in place to incorporate the results into remote sensing data analysis software of AATSR satellite instrument.

Aalto-D

D 27

/2016

9HSTFMG*aggfhi+

ISBN 978-952-60-6657-8 (printed) ISBN 978-952-60-6658-5 (pdf) ISSN-L 1799-4934 ISSN 1799-4934 (printed) ISSN 1799-4942 (pdf) Aalto University School of Science Biomedical Engineering and Computational Science www.aalto.fi

BUSINESS + ECONOMY ART + DESIGN + ARCHITECTURE SCIENCE + TECHNOLOGY CROSSOVER DOCTORAL DISSERTATIONS

Sini Merikallio

Com

puter modeling of light scattering by atm

ospheric dust particles with spheroids and ellipsoids

Aalto

Unive

rsity

2016

Biomedical Engineering and Computational Science

Computer modeling of light scattering by atmospheric dust particles with spheroids and ellipsoids

Sini Merikallio

DOCTORAL DISSERTATIONS

Computer modeling of light scatteringby atmospheric dust particleswith spheroids and ellipsoids

vii

Computer modeling of light scattering by atmospheric dust particles with spheroids and ellipsoids

viii

Preface

For protons, asteroids, stars

and croissants.

This thesis was prepared in the Finnish Meteorological Institute (FMI)

with the generous support from the Vaisala foundation, the Academy of

Finland and the Magnus Ehrnrooth foundation. The subject has been

shifting from solar power usage on the surface of Mars to plasma physics

measurements in the Earth’s orbit all the way up to the electric solar

wind sailing technologies before finally narrowing into the current theme

of light scattering. At one point the working title was ambitiously ’Pho-

tons, protons and asteroids - scattering events in the Solar system’. The

scattering is still present, but as the focus of this thesis I chose the small-

est of the three: photons.

I express my gratitude for the pre-examiners, Dr. Hester Volten from

the RIVM and Professor Ping Yang from the Texas A & M, for carefully

commenting and approving this thesis. I deeply thank Dr. Gorden Videen

from the US Army Research Lab for agreeing to be the opponent of this

thesis: hope you will enjoy Finland! I feel very honoured and priviledged

to have such great scientists as examiners of my thesis.

I feel huge gratitude towards my supervisors Timo Nousiainen and Ari-

Matti Harri from the Finnish Meteorological Institute. Timo has helped

me immensely in staying in focus; this work and the publications included

would not be the same without his countless constructive comments and

good anger management skills. Not that long time ago, in this galaxy far

far away from any faraway galaxies, Ari-Matti hired me into the Finnish

Meteorological Institute. For this, and for the support ever since, I am

eternally grateful. Professor Jukka Tulkki has graciously supported me

since times of Helsinki University of Technology (now going by a new wavy

Preface

name, Aalto University) and is sincerely thanked for this.

I am also grateful for my boss Gerrit de Leeuw for creating an invigo-

rating working environment. Gerrit is also thanked for scientific insights

and being always the first to join sing-alongs in little Christmas parties!

I also thank Leif Backmann, whose group I had a privilege to be part of

for several great years. I also wish to thank the upper leadership, Unit

Head Ari Laaksonen and Research Director Yrjö Viisanen, for their part

in creating the dynamical scientific working environment of FMI.

I thank all my co-authors for their contributions and fruitful discus-

sions along the way. In addition to previously mentioned, these include

Hanna-Kaisa Lindqvist, Michael Kahnert, Olga Muñoz, Anu-Maija Sund-

strõm, Timo H. Virtanen, Matti Horttanainen and Osku Kemppinen in

the papers that are included in this thesis. Quite a few others have been

involved in work and publications on other topics of research - these ac-

complices are thanked for helping me in keeping perspectives wide; Pekka

Janhunen and other e-sailers deserve a special thanks for building the fu-

ture of the space travel - it has been great to be part of Electric Solar Wind

Sail development since its early days and to see this groundbreaking tech-

nology moving from an idea into realization. I have been lucky for being

able to work in an environment such as FMI and want to thank all my

colleagues for creating this great big family. Especially I thank Markku

Mäkelä for friendly jests and ornithological insights, Johan Silen for his

philosophical take on life and Minna Palmroth for inspiring attitude.

Great many colleagues in the international light-scattering community

have taken part in shaping my understanding of the world. Especially I

thank David Crisp from NASA JPL for advises and encouragement, not

to forget tips on car tuning. Olga Muñoz and Hester Volten are thanked

for providing their publicly available measurements. Zhaokai Meng and

Oleg Dubovik are thanked for the use of their model particle databases.

Both measurements and databases were vital to this study.

For friends and family, especially the furry ones, I owe an apology and

will promise to shift my attention your way right now as this project is

done. Huge hug and thank you for being here!

Myrskylään paljon lämpimiä ajatuksia: kiitos Margit, Birgit ja Ull-

Brit Högström varauksettomasta tuestanne ja aivojen hengähdysmahdol-

lisuuksista pitkin lumisia metsiä suomenhevosten kyydissä matkaten.

Helsinki, January 28th, 2016

Sini Merikallio

x

Contents

Contents xi

Original publications xiii

1. Review of papers and the author’s contribution xv

2. Introduction 12.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2.2 Mineral aerosols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.3 Scope and Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3. Theory 113.1 Properties of light . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Microphysical properties . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.3 Optical properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

4. Laboratory measurements 194.1 Mineral dust samples . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4.2 Size Distribution Measurements . . . . . . . . . . . . . . . . . . . . 20

4.3 Scattering Measurements . . . . . . . . . . . . . . . . . . . . . . . . 21

5. Modelling 255.1 Spheroids and ellipsoids . . . . . . . . . . . . . . . . . . . . . . . . . 27

5.2 Summing over shapes . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

5.3 Fitting and simulated annealing . . . . . . . . . . . . . . . . . . . . 32

5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

6. Summary of results and discussion 35

References 47

Errata 73

xi

Contents

xii

Original publications

This thesis consists of an overview, followed by four internationally peer-

reviewed research articles. These papers are cited by roman numerals in

the introductory part as follows:

I Merikallio, S., Lindqvist, H., Nousiainen, T., and Kahnert, M. (2011).

Modeling light scattering by mineral dust using spheroids: assessment

of applicability,

Atmospheric Chemistry and Physics, 11:5347–5363.

http://dx.doi.org/10.5194/acp-11-5347-2011

II Merikallio, S., Nousiainen, T., Kahnert, M. and Harri, A.-M. (2013).

Light scattering by the Martian dust analog, palagonite, modeled with

ellipsoids,

Optics Express, 21, 15:17972–17985.

http://dx.doi.org/10.1364/OE.21.017972

III Merikallio, S., Muñoz, O., Sudström, A.-M., Virtanen, T. H., Hort-

tanainen, M., de Leeuw, G., and Nousiainen, T. (2014). Optical modeling

of volcanic ash particles using ellipsoids,

Journal of Geophysical Research: Atmospheres, 11:5347–5363.

http://dx.doi.org/10.1002/2014JD022792

IV Kemppinen, O., Nousiainen, T., Merikallio, S. and Räisänen, P. (2015).

Retrieving microphysical properties of dust-like particles using ellip-

soids: the case of refractive index,

Atmospheric Chemistry and Physics, 15:11117–11132.

http://dx.doi.org/10.5194/acp-15-11117-2015

xiii

Original publications

xiv

1. Review of papers and the author’scontribution

Paper I assessed whether and how single-scattering properties of mineral

dust particles could be modeled using spheroidal model particles. Model

performance was evaluated by comparing simulated scattering matrix el-

ements with measurements from five dust samples, namely feldspar, red

clay, green clay, Saharan dust and loess. The best performing ensem-

ble of various spheroidal shapes was sought at two wavelengths, 632.8

and 441.6 nm. Spheroidal model particles were shown to work well for

the purpose, but no single best-fit shape distribution could be found that

would consistently perform well for all the samples at both wavelengths.

A previously suggested power-law shape distribution was also tested and

found to be suitable. Also a simple equiprobable distribution was found

to work well on most cases. My contributions to this paper included: (i)

accessing database for model particle scattering characteristics, (ii) devel-

oping the computer codes required, (iii) performing the modeling required,

(iv) analyzing the results, and (v) writing the majority of the paper.

Paper II studies the performance of ellipsoidal model particles in sim-

ulating light scattering from palagonite dust particles. Palagonite is an

often used Martian dust analog material and a good candidate for mod-

eling light scattering from, as real Martian dust was unavailable at the

time this thesis was written. Ellipsoidal model particles were found to

be well suited for modeling light scattering characteristics of palagonite

and, with optimization, very good fits between the measurements and

the model were obtained. The simple equiprobable distribution of ellip-

soids performed relatively well. Particle shape was found to be an impor-

tant parameter affecting the asymmetry parameter and single scattering

albedo, as its variations could be used to deviate their values as much as

typical uncertainties associated with particle sizes and refractive indices.

Both single scattering albedo and asymmetry parameter are important

xv

Review of papers and the author’s contribution

parameters in radiative transfer models, indicating that scattering parti-

cle shape could thus be of high importance for them. My contributions to

this paper included: (i) accessing database for model particle scattering

characteristics, (ii) developing the computer codes required, (iii) perform-

ing the modeling required, (iv) analyzing the results, and (v) writing the

majority of the paper.

Paper III turns back to Earth and studies the modeling of light scatter-

ing by volcanic ash. Modelling volcanic ash single scattering character-

istics using ellipsoidal model particles was performed and the modelling

results compared to the measurements from several real volcanic dusts.

The ash was collected and its light scattering characteristics measured

from vicinities of multiple volcanoes. Two of these volcanic ash samples,

Eyjafjallajökull and Puyehue, were previously unpublished. The results

were encouraging in that ellipsoidal model particles seem to work well

in modelling light scattering by volcanic ash. The work continues now

in building look-up tables to be used with algorithms to retrieve volcanic

ash properties from satellite remote sensing data. My contributions to

this paper included: (i) accessing database for model particle scattering

characteristics, (ii) developing the computer codes required, (iii) perform-

ing the modeling required, (iv) analyzing the results, and (v) writing the

majority of the paper.

Paper IV studies the usage of ellipsoidal model particles in refractive

index retrievals. This was done by first modeling synthetic random par-

ticles single scattering parameters with set refractive indices. The scat-

tering matrices of these synthetic particles were then fitted by scattering

matrices of ellipsoidal model particles calculated with several different

refractive indices. The best-fitting refractive index were then compared

with the original set refractive indices of the synthetic random particles.

A striking discrepancy between set and retrieved refractive index was

found, leading to a conclusion that ellipsoidal model particles can not be

trusted in retrievals of refractive indices of the atmospheric dust. My con-

tributions to this paper included preparing the optical characteristics of

the ellipsoidal model particles and writing a part of the paper.

xvi

2. Introduction

Beware lest you lose the

substance by grasping at the

shadow

Aesop, 620 - 560 BCE

2.1 Background

The Universe is a dust-ridden place with a dusty past. From the birth

of the stars to tiny particles traversing vast empty spaces, it is hard to

find any area or topic that does not share some connection with small

solid particles of matter that can be called dust. Small dust particles

exist in the vicinity of most solar system bodies — from our own atmo-

sphere to cometary comas. Our own solar system is riddled with dust

particles, called the zodiacal cloud, best seen as a halo of scattered light

in the ecliptic plane right before and after the Sun makes its appear-

ance in the sky (Gustafson, 1994; May, 2007). Interstellar dust parti-

cle clouds blur our view of parts of the Universe; the light absorbed and

scattered by them, as well as by the exo-zodiacal clouds around other

stars are major sources of noise in detection of exoplanets (Kaltenegger

et al., 2006). Even the design of space technology has been influenced by

threat of small dust particle impacts. For example, the Electric solar wind

sail, a novel technology envisioned to be able to move asteroids from their

tracks (Merikallio and Janhunen, 2010), has its tethers manufactured out

of multiple wires to make them more resistant to micrometeorite impacts

(Janhunen et al., 2010; Seppänen et al., 2013).

Also lifeforms on the planet Earth are influenced by the dustiness of

their environment, be it through destructively invading their lungs, stick-

ing to and abrading their mucous membranes (Nkhama et al., 2015; Zeleke

1

Introduction

et al., 2010), impairing the growth of their leaves (Farmer, 1993; Wu

and Wang, 2014; Zia-Khan et al., 2014), acting as fertilizer (Rodríguez

et al., 2011; Bristow et al., 2010; Yu et al., 2015) or indirectly by scat-

tering the radiation in the atmosphere, thus affecting the climate (Quaas

et al., 2008; Prospero and Lamb, 2003). Climate then affects the whole

biosphere (Bahn et al., 2014; Peñuelas et al., 2013), but the biosphere also

influences the climate (Chapin III et al., 2014; Paasonen et al., 2013), so

the climate-biosphere system is complex and atmospheric dust one of its

components (Carslaw et al., 2010).

Aerosol is a mixture of gas and particles suspended in it, but the term

aerosol is commonly generalized also to refer to those aerosol particles

suspended in gas. The particles can be of a liquid or a solid composi-

tion, or a mixture of both. There are several types of aerosols in the at-

mospheres of Earth and other planets. In more humid environments of

Earth, aerosols are formed by sea spray and evaporation products from

vegetation. Dryer aerosol species include volcanic ash, black carbon pol-

lution and wind-blown dust. The sources of these particles are various;

some are lifted from the eroding surface by wind (Pye and Tsoar, 2009),

while others are spewed high into the atmosphere by volcanoes or me-

teoric impacts. Yet others get hit by the particles dropping back to the

surface and get lifted by the impact in a process called saltation (Kok

et al., 2012; Beladjine et al., 2007). Saltation is an important mechanism

especially on dry bodies with weak gravity and plenty of dust on their

surface, such as the Moon, Mars and comets. Especially on Mars, the

process is more efficient than on Earth and is the dominating dust lift-

ing mechanism (Greeley, 2002; Almeida et al., 2008; Ayoub et al., 2014).

On the Earth, several types of aerosol particles are emitted into the at-

mosphere by both natural and human activities, the latter including, e.g.

fossil fuel combustion associated with transportation and energy produc-

tion, and biomass burning (Pikridas et al., 2013; Dordevic et al., 2014;

Beecken et al., 2015; Jalkanen et al., 2015; Sakamoto et al., 2015). These

emissions are affecting the climate (Ramanathan et al., 2001; Mahowald,

2011; Spracklen and Rap, 2013; Charlson et al., 1992), but there is con-

siderable uncertainty in estimating these effects (Boucher et al., 2013).

Mineral dust is a very abundant aerosol species in the Earth’s atmo-

sphere, with a considerable and largely uncertain radiative impact (Stocker

et al., 2013). This impact is caused by scattering, absorption and re-

emission of radiation, resulting in a measurable change in the spectrum

2

Introduction

and angular dependence of solar and thermal radiation that can tell us

much about the properties of these culprit particles (Kaufman et al., 2002,

2005). Dust also has an indirect effect on the solar radiation field, by mod-

ifying reflectivity, formation and lifetime of clouds, which it does by acting

as condensation nuclei (Nenes et al., 2014; Garimella et al., 2014; Määt-

tänen et al., 2005; Klüser and Holzer-Popp, 2010; Spiegel et al., 2014)

and ice-forming nuclei (DeMott et al., 2003; Atkinson et al., 2013). Again

here, the effect is not one sided. Clouds also affect the physical proper-

ties, namely composition, shape and size of the dust particles and particle

distributions in exchange (Matsuki et al., 2010). Understanding, and be-

ing able to accurately model, the interaction between solar radiation and

atmospheric particles is vital in order to produce reliable models and pre-

dictions of weather and climate, both of which are important when consid-

ering the future, both short term and long term, of our societies and the

biosphere around us (Revesz et al., 2014; Field et al., 2014; Barros et al.,

2014).

Modelling interaction of light with atmospheric particles is not a trivial

task, especially considering the heterogeneous populations of dust parti-

cles with hugely varying compositions, shapes and sizes. Exact analysis

of the scattering event lies often beyond our theoretical or computational

capabilities. Fortunately modeling provides tools and methods that have

proven useful in improving the understanding of this phenomenon. Previ-

ously, however, the models have been much too simplistic. As an example,

approximating real mineral dust particles as spherical, homogeneous and

isotropic, i.e. Mie scatterers, is a widely used approach in remote sensing

of trace gases and aerosols, utilized for example in analyzing data from

the Ozone Monitoring Instrument (OMI) (Veihelmann et al., 2007), in a

NASA GISS GCM ModelE (Li et al., 2010) and in lidar observations of

atmospheric desert dust (Pitari et al., 2015), but is also present in other

areas like modeling the scattering by the dust around the Moon (Glenar

et al., 2011). This approximation has long been known to produce inaccu-

rate results (Mishchenko et al., 1995; Yi et al., 2011; Mishchenko et al.,

2003), which was also comfirmed by Papers I, II and III. Although it is

a working assumption for small liquid particles, using spheres to model

light scattering from atmospheric dust and ash particles has increased

the estimation errors and thus impaired forecast accuracies of the mod-

els, producing potentially misleading results (Wang et al., 2003; Kylling

et al., 2014), such as overestimation of ash cloud optical depth by 25%

3

Introduction

(Krotkov et al., 1999).

There is thus a profound need for more accurate models to describe light

scattering by real particles, development of which, nonetheless, is not a

simple task: on the one hand we would desire them to be as accurate as

possible, but then also be governed by only a few variables, so that their

optimization and analysis could be manageable and meaningful (Kahnert

et al., 2014). Moreover, the models should not be too straining computa-

tionally.

In the mid-90’s, models of light scattering by spheroidal particles were

introduced as a possible tool for improving the accuracy of the single-

scattering modeling schemes of irregular particles (Voshchinnikov and

Farafonov, 1993; Voshchinnikov et al., 2000; Mishchenko et al., 1996b;

Schulz et al., 1998, 1999). With the development of more advanced mea-

surement technologies, the whole scattering matrix data of real dust par-

ticles became available in the early 2000s (Volten et al., 2001; Muñoz

et al., 2001). A couple of years later, in 2003, it was first shown that

spheroids could be used to simulate laboratory measured data from light

scattering by micron-scale mineral dust particles (Nousiainen and Ver-

meulen, 2003). It was then logical to question whether spheroids would

provide adequate means for modeling all atmospheric dusts, or if some-

thing more complex was needed. This thesis presents further progress

along this path, seeking to add a layer of both depth and width on the

knowledge of the topic. This is done by computer modeling, taking use

of available measurements (see Chapter 4.) and precomputed databases

of optical characteristics of different model particles (Chapter 5). Before

that, however, the target particles are discussed below, and some central

theoretical aspects are introduced in Chapter 3.

2.2 Mineral aerosols

Mineral aerosols form a part of the solid-form aerosol particles, which ex-

hibit various shapes, compositions and mixtures depending on their ori-

gins (Taylor et al., 2015). Mineral composition, type of lifting event and

forcing endured while suspended, i.e. by acids, radiation, oxidicing agents,

collisions and agglomeration with other particles, all influence the time-

varying form of atmospheric mineral aerosol (Fitzgerald et al., 2015; Sul-

livan et al., 2007). Typically, these aerosols are very irregular in shape

and vary substantially in mineral composition between different locales

4

Introduction

and from particle to particle (Kandler et al., 2011; Formenti et al., 2011;

Linke et al., 2006). The atmospheric mineral dust content depends on

the relative humidity and wind speed (Csavina et al., 2014), as well as on

the surface characteristics, biggest sources being deserts, arid and semi-

arid lands (Tegen and Schepanski, 2009). Climate warming and changing

land use is foreseen to affect the amount of dust in the atmosphere in the

future by changing the vegetational cover and weather conditions (Muhs

et al., 2014).

Shape, surface roughness, mineralogical composition, size and internal

structure affect the way single dust particles scatter light. In nature,

these particles occur in various size-, shape-, compositional, structural

and spatial distributions. Ideally all of these particle properties would

have to be taken into account in assessing the influence dust has on the

thermal and solar radiation fields, but this is usually impractical. Remote

sensing observations used in efforts to retrieve these quantities often rely

heavily on assumptions on some of the particle characteristics and mainly

concentrate on retrievals of the total amount of particles present in the

atmosphere (Dubovik et al., 2011; Su et al., 2014; Mishchenko and Travis,

1997; Tanré et al., 1997; Kokhanovsky and de Leeuw, 2009).

Mineral dust and ash can also impare health of Earth’s inhabitants via

a process called silicosis (a condition that has been quite humorously re-

ferred to as pneumonoultramicroscopicsilicovolcanoconiosis (Oxford Uni-

versity Press, 2015; Occupational and Environmental Health Department

of Protection of the Human Environment, 1999). The causative link be-

tween silica and quartz dusts and lung cancer has been confirmed (Sogl

et al., 2012; Koskela et al., 1994; Yu and Tse, 2014). Moreover, other

symptoms, such as esophageal cancer, cardiovascular disease and chronic

obstructive pulmonary disease (COPD) have been linked with exposure to

dust (Yu et al., 2005; Pan et al., 1999; Chen et al., 2012; Wang et al., 2013)

and dusty atmospheres might even have shaped our genomics (Borzan

et al., 2014). Strenghtening air pollution measures could minimize these

adverse health effects, but on the downside might then lead to acceler-

ated climate change, since airborne sulphate and dust particles increase

the albedo of the Earth, mitigating the impact of greenhouse gas warm-

ing by reflecting solar radiation back to space (Makkonen et al., 2012;

Mickley et al., 2012; Arneth et al., 2009). Black carbon particles, on the

other hand, always have a strong warming effect on the climate (Jacob-

son, 2000; Ramanathan and Carmichael, 2008); minimising their emis-

5

Introduction

sions is thus always worthwhile. Climate change is perceived to be one

of the biggest challenges for humanity to tackle and adapt to in the fu-

ture and will affect, among other things, agriculture (World Bank Group,

2014; Nelson et al., 2014), food security (Wheeler and von Braun, 2013;

Schmidhuber and Tubiello, 2007), marine ecosystems (Doney et al., 2012;

O’Neil et al., 2012), spread of diseases (Altizer et al., 2013; Pautasso et al.,

2012), species diversity (Thuiller et al., 2011; Bellard et al., 2012; Moritz

and Agudo, 2013), economy (Tol, 2012; Stern, 2013-09-01T00:00:00), and

even sex determination in some species (Neuwald and Valenzuela, 2011;

Holleley et al., 2015). Presumably there also exists effects we are not yet

even aware of.

Then, we go to Mars. I have sought to provide a modeling tool which

could be used to improve dust particle treatment in models designed to

study the radiative transfer in the Martian atmosphere (Paper II). Air-

borne dust is an especially prevalent feature in the otherwise optically

thin atmosphere of Mars. Dust can also have a substantial impact on the

thermal properties of the atmosphere, global circulation and climate on

Mars. Due to the low gas density of Mars, only about one hundredth of

that of the air close to the Earth’s surface, the force of wind is very feeble

and majority of dust particles are presumably lifted by dust devils (Basu

et al., 2004; Jackson and Lorenz, 2015). Dust devils in Mars are bigger

than those found on Earth, reaching typically heights of 2 - 6 kilometers

(on Earth 250 - 1500 m) and having a diameter of around a quarter of

a kilometer (only 5 - 30 m on Earth) (Cantor et al., 2006; Sinclair, 1964;

Metzger, 1999; Ryan and Carroll, 1970; Ryan and Lucich, 1983; Thomas

and Gierasch, 1985). This difference in size is thought to be due to input

of heat by the aerosol particles that are warmed by solar radiation. Solar

heating of the dust increases the upward drift within the dust devil (Fuer-

stenau, 2006). Studies of the absorption and scattering of solar radiation

by this dust provide the principle source of information on the processes

generating and maintaining the dust distribution, and its effect on the

climate. Interpretation of remote sensing observations can also be com-

plicated by processes of scattering, absorption and emission by airborne

dust. Physical and optical properties of the atmospheric dust are still

inadequately understood despite ongoing efforts in the field.

Naturally, most of the measurements and data available from atmo-

spheric mineral dust particles concern terrestrial dust. Retrievals of air-

borne dust properties in the atmospheres of other planets is even more

6

Introduction

challenging, starting from the fact that remote sensing options are far

fewer and then also the measurement technology available is heavily dic-

tated by mass and environmental design constraints. Nevertheless, our

neighbouring planet, Mars, has had several instruments flown in its orbit

or delivered on its surface. Measurements performed by these instru-

ments have given us some constraints on the properties of the Martian

aerosols, see e.g. (Dlugach et al., 2003; Korablev et al., 2005; Smith, 2008;

Wolff et al., 2006) and surface dust deposits, e.g. (Vaniman et al., 2014;

Bish et al., 2013; Morris et al., 2004; Christensen et al., 2004; Ruff and

Christensen, 2002). These have still only given us a sneak peek on the

full range of parameters still unknown to us.

There are currently also small research laboratories on Mars: NASA

has two rovers, named Opportunity and Curiosity (Arvidson et al., 2011;

Grotzinger et al., 2012), operational on the surface. There are also many

future missions, including Insight, which will be concentrating on seis-

mology (Panning et al., 2012) and Exomars, which will focus on preparing

for manned flights (Bost et al., 2015). Opportunity and Curiosity have

been active on the surface of Mars since 2004 and 2012, respectively,

but are more focused on geology and not aimed at examining the atmo-

spheric particles (Squyres et al., 2003; Mahaffy et al., 2012; Grotzinger

et al., 2012). A photo of Martian dust, taken by MSL Curiosity, is shown

in Figure 2.1. In the absence of samples returned to Earth, where more

thorough laboratory investigations could be run, the shapes and charac-

teristics of the Martian dust particles are particularly loosely constrained.

Noteworthily, preparation for human exploration of Mars has been men-

tioned as one of the goals in a recent NASA report (Mars Exploration

Program Analysis Group (MEPAG), 2015) and novel technologies might

make it possible and economically feasible in the near future (Janhunen

et al., 2015). Challenges of this endeavor include health effects by dust

(Ahmadli et al., 2014), as well as trouble it may cause to machinery by

invading into small spaces and stucking on the surfaces. Accurate models

of Martian dust particles’ single scattering parameters help in retrieving

Martian atmospheric dust properties from remote sensing measurements.

Being able to analyze the amount and quality of atmospheric dust might

then be important considering the safety of operations on the Martian

surface.

7

Introduction

Figure 2.1. Martian surface dust particles that have passed through a 150 μm sieve.Picture is about 6.5 mm wide and taken by NASA’s Mars rover Curiosity’sMars Hand Lens Imager (MAHLI) on Oct. 20, 2012. Image credit: NASA

2.3 Scope and Objectives

In this work, I take a closer look into scattering of light by small atmo-

spheric dust particles with the underlying goal of improving the treat-

ment of scattering by atmospheric dust particles in climate and radiative

transfer models. By comparing measured and modeled scattering matrix

elements, I have found that quite simple models incorporating an assem-

bly of different spheroidal or ellipsoidal model particles perform quite well

in comparison. This provides a useful compromise between a low compu-

tational burden and a high modeling performance.

In Paper I we demonstrated the utility of spheroidal model particles

in modeling light scattering from mineral dust aerosols. Paper II then

studied how slightly more complex shapes, ellipsoids, perform in modeling

light scattering from palagonite dust, often used Martian dust analogue.

After this, we returned to Earth and studied the use of ellipsoids on mod-

eling dust particles from volcano eruptions (Paper III). In all these ap-

plications, the chosen model particles improve tremendously on the still

often used spherical model particles, but also weaknesses for the tested

models were identified. For example, assessing the best possible shape

distribution proved to be a complex issue, with no clear one favoured dis-

tribution standing up for all cases. This indicated that neither spheroids

nor ellipsoids were performing perfectly or even consistently, and raised

8

Introduction

a concern about their usage in retrievals. This was the subject of the Pa-

per IV, where the use of ellipsoidal model particles in retrievals of optical

parameters of dust was discussed. We found that, at least in the case of re-

fractive index, the ellipsoidal model produces erroneous retrieval results.

One should thus be cautioned and not trust the results blindly when us-

ing such model particles in retrievals of particle refractive indices. While

models based on ellipsoids generally outperform those assuming spheri-

cal or spheroidal particles, further studies are called for, both to assess

the usefulness of ellipsoids in retrievals of other parameters and also in

search of even better performing modeling methods.

9

Introduction

10

3. Theory

Croyez ceux qui cherchent la

vérité, doutez de ceux qui la

trouvent

André Gide

A homogeneous particle’s microphysical properties include its size, shape

and composition. Out of these the optical properties, i.e. scattering and

absorption cross-sections, scattering matrix and quantities retrieved from

those such as single scattering albedo and asymmetry parameter, can

be calculated. Doing this is called solving the direct, or forward single-

scattering problem. Going the other way, retrieving the microphysical

properties from the optical properties, is called an inverse problem. Be-

ing able to accurately relate the optical and microphysical properties en-

ables solving these problems. Should the solution for the forward problem

have errors, we should expect even more trouble with solutions of the in-

verse problem. It is important to be able to model light single scattering

by atmospheric particles accurately as it has direct consequences on how

well we can retrieve the properties of these particles from remote sensing

measurements and on how accurately we can estimate their impacts on

forward problems.

In this section, first we look at what light is, after which relevant op-

tical properties of matter are introduced, followed by the microphysical

material properties used, including effective size and effective refractive

index.

3.1 Properties of light

Electromagnetic (EM) radiation has a so-called dual wave - particle na-

ture and can be modeled as either a propagating wavefront of transverse

11

Theory

electromagnetic fields or as a particle of light, a photon (Einstein, 1905).

EM radiation is characterized by its wavelength, λ, which determines the

energy of a photon as E = hc/λ, where h is the Planck constant, 6.6 ·10−34 Js, and c the speed of light in vacuum, 3.00 · 108 m/s. When the

wavelength is roughly between 400 and 700 nm, the electromagnetic ra-

diation can trigger photoreceptor cells in our eyes and is thus called visible

light. Radiation with wavelengths just below this range are called ultra-

violet (UV) light. UV radiation is invisible to us, but can be seen by some

other animal species (Osorio and Vorobyev, 2005; Lind et al., 2013). More

energetic shorter wavelengths beyond this include X-ray and gamma ra-

diation, important especially in medical and astronomical imaging. Light

with longer than visible wavelengths of up to 1 mm, is called infrared (IR)

light. Beyond IR, with decreasing photon energy, there exist microwaves

and radio waves, both used in various contemporary technologies.

On inner planets of the solar system, like Earth and Mars considered in

this study, the main source of light is our own star, the Sun. Our eyes, as

well as plants using light for photosynthesis, have evolved to have their

best sensitivity to the very wavelengths that are both emitted by the Sun

and transmitted through the atmosphere. When the black body radiation

emitted by the Sun propagates through the atmosphere, the atmospheric

molecules and aerosols adjust its intensity, polarization and spectrum.

They do this by absorbing, scattering and emitting electromagnetic radi-

ation.

Light can be described as mutually perpendicular fluctuating magnetic

and electric fields, B and E, both of which are also perpendicular to the

direction of the light propagation. Magnetic and electric fields are related

with each other by four so called Maxwell’s equations, which all electro-

magnetic fields must satisfy. The first one, Gauss’s law, relates the electric

charge to the electric field it produces (Grant and Phillips, 1990):

∇ · �D = ρ, (3.1)

where ρ is the charge density and D the electric displacement. D is relat-

ing E with its environment by a constitutive relation:

�D = εrε0 �E + p, (3.2)

where ε0 (= 8.85 · 10−12 F/m) is the permittivity of free space, εr is the

relative permittivity of the medium and p is the polarization, which is

non-zero for dielectric mediums in an electric field. In vacuum, D is equal

to ε0E.

12

Theory

The second law, Gauss’s law for magnetism is

∇ · �B = 0. (3.3)

It states in effect that magnetic monopoles do not exist. The third law

of Maxwell’s, Faraday’s law of induction, describes how time-varied mag-

netic field induces an electric field:

∇× �E = − ∂

∂tB, (3.4)

where t denotes time. Similarly Ampère’s law describes how an electric

current produces a magnetic field circling it:

∇× �H = J +∂

∂t�D, (3.5)

where J the current density and H the magnetic intensity. H can be

related to B by another constitutive relation, similarly as D is to E in Eq

(3.2), as�H =

B

μrμ0−M, (3.6)

where μ0 = 4π · 10−7 N/A2 is the permeability of the free space, μr the

wavelength dependent relative permeability of the medium and M the

magnetization of the medium. Both εr and μr are dependent on the wave-

length of radiation.

Speed of light can be calculated from:

c = (μrμ0εrε0)−1/2. (3.7)

For vacuum both μr and εr equal unity. The rate of energy trasported by

light is described by a so called Poynting vector N [W/m2] as

�N = �E × �H, (3.8)

and if the light faces a surface, it exerts a radiation pressure amounting

to N/c on it (Grant and Phillips, 1990).

In vacuum, absence of charges and currents leads to both ρ and J to

equal zero, respectively. We can then reach the so called wave equations

by applying a curl (∇×) to both Faraday’s and Ampère’s laws, and using

both Gauss’s laws:μ0ε0

∂2

∂2t�E −∇2 �E = 0,

μ0ε0∂2

∂2t�B −∇2 �B = 0.

(3.9)

Solutions to these equations describe time harmonic fields in vacuum. A

plane wave solution can be expressed as

E = E0ei(ωt−kz), (3.10)

13

Theory

where E0 is the amplitude of the wave, ω = 2πc/λ is the angular velocity

of the radiation, k = ω/c is the wave number of the radiation, t describes

time and z is the distance propagated into direction perpendicular to both�E and �B.

Alas, we are not living in a vacuum, nor is our environment homoge-

neous, and the previously shown Maxwell’s equations that include source

terms can be very hard to solve around real scattering particles. In Sec-

tion 3.3 a look is taken into how optical properties of matter, i.e. how it

interacts with the electromagnetic radiation, can be calculated from the

microphysical properties (3.2) - as we will see, often via some approxima-

tion.

From the field vector �E two important properties of light can be cal-

culated: intensity from the magnitude and polarization state from the

direction and the movement of the electric field vector. �E, however, is

changing its direction at such a fast rate that direct measurements of

its values are unfeasible and some measurable derived quantities are

called for. Sir George Gabriel Stokes, an Irish mathematician working

in Cambridge, introduced in 1852 four parameters that could be used to

describe light (Stokes, 1852). These parameters have been widely used

ever since. The so called Stokes vector, �S, consists of four components:�S = [I,Q, U, V ], the first one of which describes the total intensity (polar-

ized and unpolarized) while others describe polarization state; Q and U

linear polarizations at 45◦ angles with respect to each other, and V the

circular polarization. It is common for the light to have non-zero compo-

nents of all of these so called Stokes parameters simultaneously. However,

for atmospheric scatterers, V is usually vanishingly small. In a cartesian

(x, y, z) coordinate system, for a light ray propagating in the z direction,

the Stokes parameters can be expressed with electric field strength com-

ponents, Ex and Ey, as:

I = k2μ0ωf

(|Ex|2 + |Ey|2),Q = k

2μ0ωf(|Ex|2 − |Ey|2),

U = kμ0ωf

Re(ExE∗y),

V = − kμ0ωf

Im(ExE∗y),

(3.11)

where ωf denotes the angular frequency, asterisk ∗ the complex conju-

gate operation, Re() the real part, and Im() the imaginary part of the

complex-valued object, respectively. For a fully polarized monochromatic

radiation the Stokes parameters fulfill Q2+U2+V 2 = I2. In nature, light

is rarely fully polarized and the degree of light polarization can then be

14

Theory

calculated as Ip/I =√

Q2 + U2 + V 2/I. The parameter for intensity, I, is

thus always positive, while polarization elements can have either sign.

For natural, unpolarized light, I is the only element differing from zero.

Light is said to be coherent, if it is monochromatic and has a constant

phase difference between all sources. Laser (Light Amplification by Stim-

ulated Emission of Radiation) emits coherent radiation and is often used

in various measurements, including scattering measurements described

later in Chapter 4.

3.2 Microphysical properties

In this section quantities used to describe microphysical properties of dust

particles in this thesis are introduced. An important parameter describ-

ing the material properties of the scatterer is its refractive index m. The

refractive index is a complex number; the real part describes the ratio of

the speed of light in vacuum, c, to the phase velocity of the light, v, as it

traverses the material: Re{m} = c/v. The imaginary part of the refractive

index quantifies the relative rate of light that is absorbed into the mat-

ter. Thus, for vacuum, the refractive index equals unity, as light moves at

c and no absorption takes place. Refractive index can also be calculated

from the permeability μ and permittivity ε of the material, as:

m =με

μ0ε0, (3.12)

where μ0 and ε0 are the permeability and permittivity of free space, re-

spectively.

For an ensemble of particles, often also the refractive index should be

considered as a distribution of different values as it can change quite sig-

nificantly in-between individual particles. Also the porosity and inhomo-

geneity of the particles leads to various refractive index regions within

the particles and can be taken into account by calculating the weighted

average of constituent material refractive indices. Still we didn’t find this

approach to work very well and thus did not use it any further in our

studies. For simplicity a homogenous material, and thus also a singular

m, is almost always assumed.

Absolute sizes of the particles are not needed to calculate the scattering

matrix elements. In the databases used the optical parameters are conve-

niently tabulated as a function of the so called size parameter X, quantity

15

Theory

relating a particle radius r to the wavelength λ as:

X =2πr

λ. (3.13)

In natural dusts, particle sizes vary a lot and are described by a size dis-

tribution. Conveniently, as shown by Hansen and Travis (1974), different

size distributions of similar particles with the same effective radii, reff ,

and effective variances, νeff , have comparable ensemble-averaged optical

characteristics such as single-scattering albedo and asymmetry parame-

ter. This makes the use of reff and νeff very suitable for characterizing size

distributions. The geometric cross-section-weighted mean radius, or the

effective radius, is defined as

reff =

∑r rπr

2n(r)∑r πr

2n(r), (3.14)

where r is the particle radius and n(r) is the number of particles with ra-

dius r. Dimensionless geometric cross-section-weighted effective variance

νeff is defined as:

νeff =

∑r(r − reff)

2πr2n(r)

r2eff∑

r πr2n(r)

. (3.15)

The effective standard deviation of the radius of a distribution of particle

sizes is then defined as σeff =√νeff (Hansen and Travis, 1974).

3.3 Optical properties

Optical properties of scattering particles describe the way particles in-

teract with incoming light. Quantities discussed in this section are scat-

tering and absorption cross-sections, single scattering albedo, scattering

matrix, and asymmetry parameter, but we first take a brief look into how

these can be calculated.

There is no general analytical solution for calculating optical properties

from the microphysical properties of the particles. Exact analytical so-

lutions for Maxwell’s equations only exist for certain specific geometries,

such as a sphere (described by Mie theory, Mie (1908)), infinite cylinders

(Stratton, 1941) and spheroids (S.Asano and Yamamoto, 1975). Thus, as-

sumptions often have to be made either about the target particle proper-

ties, physics involved, or computational accuracy has to be relaxed.

One of the most used computational methods for calculating the op-

tical properties is the T-Matrix method, where boundary conditions for

Maxwell’s equations are used for obtaining the solution (Mishchenko et al.,

1996b). Also the finite-difference time-domain (FDTD) method (Taflove

16

Theory

et al., 2013; Kunz, 1993), where the electric and magnetic fields are iter-

ated by solving them in a discretized grid alternatingly from each other,

i.e. in a leap-frog manner, is popular. Approximations in particle char-

acteristics lead for example to Rayleigh approximation, which is derived

by assuming scattering particles to be much smaller than wavelength (i.e.

X � 1). Similarly, for very large particles ray-tracing with geometric op-

tics provides a solution. A Discrete Dipole Approximation (DDA), where

the scattering particle is described by finite number of individual electric

dipoles, is used to model light scattering from small particles of general

shape, but its accuracy is constrained by computational capacity available

(Purcell, 1973; Draine and Flatau, 1994).

Only part of the light is affected by the scatterers. The relative rate of

energy scattered by a particle is indicated by the scattering cross section,

Csca. The scattering cross section also depends on the incident wavelength

and often differs greatly from the geometrical cross section of the particles

(van de Hulst, 1981). Similarly, Cabs indicates the absorption cross-section

and summed together Csca and Cabs form the extinction cross section Cext.

The single-scattering albedo ω then indicates scattering efficiency relative

to the total extinction:

ω =Csca

Csca + Cabs. (3.16)

The single-scattering albedo equals unity when scattering particles are

non-absorbing. In contrast, highly absorbing particles are described by

very small values of ω.

A scattering event can be described by a four-by-four scattering phase

matrix P, which transforms the Stokes vector describing the state of the

incoming radiation, �Sin into one describing the scattered radiation, �Ssca:

�Ssca =Csca

4πr2P�Sin, (3.17)

where r is the distance from the scatterer. The scattering matrix depends

on the wavelength of the radiation, particle size, composition and shape,

and can be written open as:

P(θ) =

⎛⎜⎜⎜⎜⎜⎝

P11(θ) P12(θ) P13(θ) P14(θ)

P21(θ) P22(θ) P23(θ) P24(θ)

P31(θ) P32(θ) P33(θ) P34(θ)

P41(θ) P42(θ) P43(θ) P44(θ)

⎞⎟⎟⎟⎟⎟⎠

, (3.18)

where θ denotes the scattering angle, i.e. the angle between directions of

the incident and the scattered electromagnetic radiation. The scattering

17

Theory

matrix can be reduced to only six independent non-zero matrix elements

when an ensemble of randomly oriented particles with their mirror par-

ticles in equal numbers is considered (van de Hulst, 1981; Bohren and

Huffman, 1983):

P(θ) =

⎛⎜⎜⎜⎜⎜⎝

P11(θ) P12(θ) 0 0

P12(θ) P22(θ) 0 0

0 0 P33(θ) P34(θ)

0 0 −P34(θ) P44(θ)

⎞⎟⎟⎟⎟⎟⎠

. (3.19)

Here we have used the so-called phase matrix, P, as a scattering matrix,

which is normalized such that the integral of the first element, P11, over

the scattering angle θ is

1

2

∫ π

0P11(θ) sin(θ)dθ = 1. (3.20)

P11 is called the phase function and for unpolarized incident light, it de-

scribes the angular distribution of intensity for scattered radiation. The

other non-zero elements of the matrix, namely P12, P22, P33, P34 and P44,

relate the different polarization components between the incident and

scattered rays.

Even in the simplest of atmospheric radiative transfer models, three key

parameters are needed to describe the single scattering characteristics of

the scattering particles. The rate of light scattered and absorbed can be

calculated from single scattering albedo, ω, and scattering cross section

Csca, related by Equation 3.16. Also a measure for the direction of light

scattering is needed. This is often provided by the asymmetry parameter,

g, which is a cosine-weighted integral of the phase function P11 over the

scattering angle θ:

g =1

2

∫ π

0P11 cos(θ) sin(θ)dθ. (3.21)

It describes the relative amount of forward scattering occurring in the

media. If the light is scattered isotropically, g = 0. If it is scattered mostly

in the forward direction, g approaches unity. If it is strongly scattered

back toward the source, g approaches −1. Due to their central role in

radiative flux computations, we have in Paper II used g and ω as key

quantities in comparing the performance of different models.

18

4. Laboratory measurements

A man with one watch knows

what time it is; a man with two

watches is never quite sure.

Lee Segall

First necessary condition for validating the usage of spheroidal and el-

lipsoidal model particles for modelling light scattering is their ability to

accurately reproduce the measured optical parameters of sample parti-

cles. This however, is not sufficient by itself, but also the microphysical

particle properties that are input in the models, should correspond with

the measured material properties. To assess these qualifications, mea-

surements of both real particle microphysical and optical parameters are

needed. In this chapter, an overview is presented about the apparatus

and principles of these measurements.

The measurements have been performed in Amsterdam and Granada

by suspending sample dust in air and measuring the scattering of a laser

beam in various scattering angles (Muñoz et al., 2012). Both intensity and

polarization parameters, i.e. all scattering matrix elements as described

in Chapter 3, have been measured. The measurements for Puyehue and

Eyjafjallajökull are originally published in Paper III and a palagonite

dust sample was used as a proxy for the real Martian dust (Laan et al.,

2009). Other measurements of mineral and volcanic dusts used in this

study have been published by Volten et al. (2001); Muñoz et al. (2002,

2004, 2011) and Laan et al. (2009).

4.1 Mineral dust samples

The samples have either been collected from the surface or they have been

ground from blocks of solid material. In Figure 4.1, Scanning electron mi-

19

Laboratory measurements

Figure 4.1. SEM-pictures of different sample types discussed in this thesis; mineral dust,volcanic ash, and Martian analog dust are shown. Each of the red bars is100 μm in length.

croscope (SEM) images of three types of samples discussed in this thesis

are shown: mineral dust typical for Earth’s astmosphere (Sahara, Pa-

per I), volcanic ash (from Puyehue, Paper III), and palagonite (proxy

for Martian dust, Paper II). The reader should note the characteristic

shapes of volcanic ash particles with sharp edged crystals and vesicular

structures (Maria and Carey, 2002; Riley et al., 2003) as opposed to more

homogenous and rounded shapes of studied mineral dusts.

4.2 Size Distribution Measurements

Light scattering characteristics of particles are strongly influenced by

their sizes (van de Hulst, 1981). This is why before being able to model

light scattering from an ensemble of particles, it is vital to have estimates

of their sizes. Laser sizing is done by measuring the intensity distribution

pattern from the particles and comparing that with scattering patterns

for various sizes provided by the instrument software. A spherical par-

ticle shape is assumed and either exact Mie theory or more approximate

Fraunhofer theory are used for calculating the lookup-tables. The Fraun-

hofer diffraction theory assumes large distance from the scatterer and

a large particle size in addition to spherical shape, which is also the as-

sumption made by the Mie theory. Two instruments were used to measure

the sizes of particles in the samples: a Fritsch laser particle sizer (Konert

and Vandenberghe, 1997) was used for samples measured in Amsterdam

and a Mastersizer2000 (Malvern instruments) was used for samples that

were measured later in Granada, Spain.

The samples considered in this thesis presented various particle sizes:

the effective radiuses are tabulated in Table (4.1) according to the theo-

20

Laboratory measurements

Table 4.1. Effective radiuses of sample particle grouped in volcanic (first) and mineraldust (later) groups.

Mie Fraunhofer

Sample reff [μm] reff [μm]

Eyjafjallajökull 7.8 4.0

Lokon 7.0

Pinatubo 8.0 2.9

Puyehue 8.6 5.0

Redoubt A 4.1

Spurr Ashton 5.2 2.6

St. Helens 8.9 4.1

feldspar 1.0

green clay 1.55

loess 3.9

palagonite 11.1 4.5

red clay 1.5

Saharan dust 8.2

ries used in their calculation. To grasp the scale of these dust particles,

one can compare them with e.g. human hair, radius of which varies be-

tween individuals typically around 8 - 60 μm (Robbins, 1988), whereas

particularly soft furred vicuña (Vicugna vicugna) has an average hair ra-

dius of only 7 μm (Bergen and Krauss, 1942) equaling that of a volcanic

particle from Lokon. Normalized projected-surface-area, number and vol-

ume size distribution tables for all the samples are freely available in the

Amsterdam-Granada Light Scattering Database, www.iaa.es/scattering/

(Muñoz et al., 2010).

4.3 Scattering Measurements

Light is said to be singly scattered, when it has undergone only one scat-

tering event by independently scattering particles. Multiple scattering,

on the contrary, involves light getting scattered sequentially by multiple

targets, e.g. when passing through an optically thick atmophere. For the

light to be singly scattered requires an optically thin sample. Neverthless,

understanding, and being able to quantify, single scattering behaviour of

the atmospheric particles is essential also when using multiple scattering

21

Laboratory measurements

Figure 4.2. Dust is levitated by brushing it gently from the reservoir into an air streamso that it can be measured when falling in the air.

radiative transfer applications, which require parameters describing sin-

gle scattering, such as scattering cross sections, single scattering albedo

and phase functions or asymmetry parameter, as an input (Merikallio,

2003).

Here light scattering measurements from the dust particle samples seek

to determine the scattering parameters for that particular sample so that

those can be compared with or used in the light scattering, radiative

transfer or climate models. Here we briefly outline the principles of these

measurements. In order to acquire Stokes parameters of the sample in

the single scattering regime, target dust must first be suspended in air

in a sufficiently sparse amounts. This is done in the way pictured in Fig-

ure 4.2 by brushing the sample little by little into an air stream, which

transports it in front of the measuring laser beams path.

A photomultiplier tube, which multiplies the signal produced by a pho-

ton, is used as a detector. In this experimental setup used in Amsterdam

and Granada, the photomultiplier moves along a ring around the target

measuring scattering angles from at most 3◦ to 177◦ (Figure 4.3). The

signal fluctuates slightly due to changes in measured particle flow size

and amount. These fluctuations are detected and corrected for by using

a reference signal from another fixed-location photomultiplier tube. By

using various optical filters and electro-optic modulators, both between

the transmitter and the sample, and between the sample and the detec-

tor, different scattering matrix parameters can then be deduced from the

measured intensities (Berry et al., 1977).

22

Laboratory measurements

Figure 4.3. Measurent apparatus showing the sample in the middle and detector able torotate around it.

Special tests, as described by Muñoz et al. (2011), are used to ensure

that the experiment stays within the single scattering regime. Checks are

also performed to assure that the measurements fulfill at all measured

scattering angles the so called Cloude coherency matrix test (Hovenier

et al., 1986, 2004) within the experimental errors, assuring that the mea-

sured matrix is a sum of scattering matrices of single particles. A detailed

description of the data acquisition, calibration process and experimental

apparatus is available in Muñoz et al. (2010).

The phase matrix, P, is related to the measured scattering matrix, F, by

some unknown normalization factor, a, as F = aP. Because the normal-

ization factor is unknown, values of F11(θ) in the Amsterdam - Granada

database are normalized simply by setting F11 to equal unity for scat-

tering angle θ=30◦. This makes database samples comparable with each

other, but it should be noted that in the modelling part of my studies I

renormalized these measurements by extrapolating them with modelling

results so that Eq.(3.20) could be applied. Similarly, in the database,

synthetic scattering matrices are now provided for newer samples mea-

sured in Granada. These are produced in the way described in Muñoz

et al. (2007) by extending the measurements to the whole scattering angle

range and including conditions at exact forward and backward directions

as proposed in Hovenier and Guirado (2014). The Amsterdam-Granada

23

Laboratory measurements

light scattering database, http://www.iaa.es/scattering/, provides experi-

mental data and the corresponding extrapolated matrices for all samples

discussed in this work (Muñoz et al., 2012).

24

5. Modelling

Alea iacta est

Julius Cesar, Jan 10th, 49 BCE

Being able to model our environment grants us the ability to compare

the evolution of different environmental states, thus giving us tools for

decision making on future actions. Regarding climate change, this aspect

is crucial, as without good models there is no way to make trustworthy

assessments of our actions into the future of Earths’ climate. Modelling

light scattering by suspended dust particles is only one part of the whole

picture, but there is a very large uncertainty in the amount, and even in

direction, of aerosols’ impact in radiative forcing of our atmosphere. These

uncertainties are larger than with other factors such as trace gas concen-

trations or solar activity especially when considering cloud albedo changes

that can be considerably affected by atmospheric dust particles (Stocker

et al., 2013). A model can, however, all too easily end up being an over-

simplification, in which case the details of real events get either blurred

under the assumptions, or perhaps more gravely, downright wrong con-

clusions are reached.

I have investigated whether the optical properties of mineral dust and

volcanic ash particles could be simulated by using spheroidal and ellip-

soidal model particles. By using these model particles, I am not sug-

gesting that dust particles really have ellipsoidal or spheroidal forms,

but rather that the single-scattering properties of dust particle ensembles

might be simulated numerically by using suitable ensembles of ellipsoidal

or spheroidal model particles.

Scattering matrix measurements of real sample particles were compared

with simulations based on ellipsoids and spheroids. In Paper I we per-

formed a study of spheroids, whereas in Papers II, III and IV we have

progressed toward using ellipsoids as model particles. In this chapter,

25

Modelling

the model particles are introduced and the modelling procedures clari-

fied. As pointed out by Nousiainen (2009), spheroids perform quite well

in modelling light single scattering from mineral dust. They have also

been shown to work pretty well in simulating optical properties of ter-

restrial clays [Paper I]. Moreover, spheroidal particles optical properties

can be computed within acceptable computational time and pre-calculated

databases exist (Dubovik et al., 2006), making them easy to apply. Al-

though good, spheroids are not perfect, so the logical ’next step’ upgrade

from them was to add one more degree of freedom, most naturally achieved

by using ellipsoids instead of spheroids. The optical properties of terres-

trial feldspar particles have been shown to be fairly well reproducable by

models employing ellipsoidal particles (Bi et al., 2009).

We have adopted spheroidal and ellipsoidal model particles for their

simple parametrization (see Section 5.1) and due to the availability of

databases where their optical properties are tabulated (Dubovik et al.,

2006; Meng et al., 2010), but also for the promise they have shown in

previous studies (Bi et al., 2009; Nousiainen and Vermeulen, 2003; Nou-

siainen et al., 2006; Veihelmann et al., 2006). Moreover, the databases

provided for both spheroids and ellipsoids cover such a wide parameter

space of particle sizes and refractive indices, that they can be readily ap-

plied to modelling of atmospheric dust. Other models for particle shapes

have also been considered, be they cylinders (Mishchenko et al., 1996a),

Chebyshev particles (Mishchenko and Travis, 1994; Ding and Xu, 1999;

Petrov et al., 2007), polyhedral prisms (Nousiainen et al., 2006), non-

symmetric hexahedra (Bi et al., 2010), convex polyhedra and deformed

spheroids (Gasteiger et al., 2011), Gaussian random spheres (Muinonen

et al., 1996; Nousiainen et al., 2003; Muinonen et al., 2007, 2009), random

blocks (Kalashnikova et al., 2005), irregular rhombohedra (Dabrowska

et al., 2013), concave fractal polyhedra (Liu et al., 2013), spatial Poisson-

Voronoi tessellation (Ishimoto et al., 2010; Zubko et al., 2013), or stere-

ogrammetric shapes (Lindqvist et al., 2014) to name a few. However,

databases of sufficiently wide parameter space are not available for these

more refined model shapes. The choice of model particle shape depends on

the problem, target particles and resources at hand, as different shapes

and sizes of particles require different means to calculate their light scat-

tering behaviour.

26

Modelling

5.1 Spheroids and ellipsoids

When spheres are elongated or squashed (one of their dimension varied),

we reach spheroidal shapes. These shapes are called oblate, when one

axis is shorter than the other two, or prolate, when the varied axis is

lengthened. Examples of both an oblate and a prolate spheroid are shown

in Figure 5.1. An assembly of different spheroidal particles can be used to

create different ensemble-averaged optical properties by varying the rela-

tive amounts of differently shaped spheroids, i.e. by varying the spheroid

axial ratio distribution. The three dimensional analogue of ellipse, with

three differing main axes, is called an ellipsoid; spheroids are a subset of

ellipsoids with two equal semi-principal axes. An example of an ellipsoid

is shown in Figure 5.2, although it has to be remembered that both oblate

and prolate spheroids (Figure 5.1), and spheres are all ellipsoids as well.

Any ellipsoids surface can be described in Cartesian coordinates, [x, y, z],

by three perpendicular semi-principal axes of length ax, ay and az, as

x2

a2x+

y2

a2y+

z2

a2z= 1. (5.1)

When ay is set equal to ax = axy, a spheroid is reached:

x2 + y2

a2xy+

z2

a2z= 1. (5.2)

All ellipsoids are mirror-symmetric and spheroids are also rotation sym-

metric; a sphere is an ellipsoid with three equal semi-principal axes. Vol-

ume of an ellipsoid can be calculated as

V =4

3πaxayaz, (5.3)

which for a sphere becomes the familiar 4/3 πr2.

The aspect ratio ε is used in describing the form of a spheroid. Following

conventions used by Nousiainen et al. (2006), the one main-axis length

that differs from the others is placed in the denominator when calculating

the ε, so that ε = axy/az. The shape of a spheroid can thus be described

also by a so called shape parameter ξ as:

ξ =

⎧⎪⎪⎨⎪⎪⎩

ε− 1 ε > 1 (oblate)

1− 1/ε ε < 1 (prolate)

0 ε = 1 (sphere).

(5.4)

Unlike with ε, the response of shape parameter ξ to the adjustment of

the longest axes’ lengths for both prolates and oblates is similar and lin-

ear. This symmetrical behaviour leads to ξ suiting for shape distribution

parametrisation more easily than ε.

27

Modelling

Figure 5.1. An oblate spheroid (left) is an ellipsoid with two of its largest semimajor axisbeing of the same length, whereas a prolate spheroid (right) is an ellipsoidwith two of its shortest semimajor axis being of the same length. Also shownas shadows are the projections of the spheroids on the x = 0, y = 0 and z = 0

planes.

Figure 5.2. All of the three main axis lengths of an ellipsoid are independent from eachother. Here an ellipsoid is shown with semimajor axis of length 0.5, 1 and 2.Also projections are shown similarly to Figure 5.1.

28

Modelling

5.2 Summing over shapes

The scattering properties for individual spheroid shapes and sizes were

acquired from the dust optical database of Dubovik et al. (2006), while the

properties for ellipsoids were acquired from the database of Meng et al.

(2010). When considering an ensemble of particles, quantities such as the

cross sections (Cxx, where ’xx’ stands for ’abs’, ’sca’ or ’ext’) can simply be

summed over all participating individual particles shapes and sizes as

Cxx =∑i

ηi∑r

nrCxx(r, i), (5.5)

where nr and ηi are the relative weights for the size, r, and shape, i, bins in

the distribution, respectively, and are naturally normalized to unity. Ad-

ditional weighting with the corresponding Csca of each particle is needed

in order to calculate the ensemble averages of asymmetry parameter and

the scattering matrix elements. For example,

g =

∑i ηi

∑r nrCsca(r, i)g(r, i)∑

i ηi∑

r nrCsca(r, i). (5.6)

The model ensemble scattering matrix P (θ) is similarly obtained by in-

tegrating over the measured size distribution and assumed shape distri-

bution as

P(θ) =

∑i ηi

∑r nrCsca(r, i)P(θ, r, i)∑

i ηi∑

r nrCsca(r, i),, (5.7)

where i is the index of shape, r the particle size, and Csca(r, i) the scatter-

ing cross-section. Sum of shape-distribution weights,∑

i ηi, is normalized

to unity.

In the case of spheroids, we have mainly used the power law distribution

suggested by Nousiainen et al. (2006) for shape distribution, f(ξ), which

is defined as:

f(ξ) = |ξn| , (5.8)

where n is a free parameter defining the shape distribution’s form. For

ellipsoids, no equally simple best-guess shape distribution could be identi-

fied, so we have been using either the retrieved best-fit shape distribution

or an equiprobable distribution.

Figure 5.3, which has the same content as Figure 1 of Paper I, demon-

strates spheroidal model particles scattering matrices for one sample with

a particular wavelength, refractive index and size distribution. It can be

seen that whilst scattering matrices for different spheroidal forms differ

quite a lot between each other, at many instances they do not overlap

with measurements. This is most notable on scattering matrix elements

29

Modelling

0 45 90 135 180

10-1

100

101P11

0 45 90 135 180-0.5

0

0.5-P12/P11

0 45 90 135 1800

0.2

0.4

0.6

0.8

1P22/P11

0 45 90 135 180-1

-0.5

0

0.5

1P33/P11

0 45 90 135 180-0.5

0

0.5-P34/P11

0 45 90 135 180-1

-0.5

0

0.5

1P44/P11

Figure 5.3. Simulated and measured scattering-matrix elements at wavelength λ = 632.8nm for the sample of loess. Small black dots represent the measurements,whilst the black line represents the Mie simulation for a sphere. Resultsfor different spheroidal model particles, all with refractive indices of m =

1.55+ 0.001i are shown in colors and range from prolate (red) to oblate (blue)aspect ratios. This Figure is reproduction of the Figure 1 from Paper I.

P22 and P44, but other elements also have such regions especially on small

scattering angles. For these, no ensemble of spheroidal model particles

will be able to exactly reproduce the measurements. Nonetheless, the re-

sults for a sphere (black line) often fall even further away from the truth,

demonstrating how often any combination of spheroids will improve the

model when compared with using plain spheres.

The performance of some other generalized shape distributions besides

the previously discussed equiprobable and power law distributions were

also investigated. Simply leaving the most spherical particle shapes out

altogether was found to improve the results slightly. With spheroids, shift-

ing the distribution towards prolates or oblates would also be an easily

applied small adjustment that was tested, as well as different cosine-

weighted distributions, where the sphere had the heaviest weight of all

the particles. Nevertheless, the results were quite inconsistent and if

for some scattering matrix elements the fits were better, they usually de-

teoriated for the others so that any significant overall improvement was

30

Modelling

rarely reached. Moreover, the cosine-weighted distribution often under-

performed the equiprobable distribution. It thus became quite clear that

spherical particles are far from the optimal choice in modelling real min-

eral dust particles.

Among themselves, the oblates (bluish lines in the Figure 5.3) have

lightly more variations in their scattering matrix elements than do the

prolates (reddish lines in the Figure 5.3). This might be the reason why

purely oblate shape distributions outperform by a small margin those con-

sisting of purely prolate particles. A distribution consisting of both pro-

lates and oblates usually performs best overall when good fits are pursued

for the whole scattering matrix and for the whole scattering angle range

(0 to 180 degrees). Adding more weight to either the oblate or prolate end

of the distribution occasionally yielded better fits. To do this, however, an

additional modelling parameter would need to be introduced to quantify

the bias between oblates and prolates. As this did not consistently, or even

notably, improve the results the focus with spheroids was kept with the

original ξn distribution.

As the database for ellipsoids contains tabulations for over 40 different

shapes, and also the measurements are available at more than 40 scat-

tering angles for each of the six independent scattering matrix elements,

the task of optimization is not a trivial one. So for computational reasons

the number of shapes used in fitting was constrained to a manageable

amount. Thus, from both databases, we then carefully selected a subset

of shapes that was used for fitting. We excluded from the analysis shapes

closely resembling the sphere (values of ax/az and ay/az close to unity),

but included the sphere itself (ax = ay = az). This selection was based on

the work done for Paper I, where it was determined that best-fit shape

distributions for mineral dusts consist mostly of the shapes that have the

largest axial ratios. We finally adopted 34 shapes for the ellipsoids, in-

cluding six oblate and six prolate spheroids as well as the sphere.

When summing over sizes, we followed the measured size distributions

of the corresponding samples, but acknowledge that making the measure-

ments over particle ensembles is not a simple task and the measurements

might have error in them (Reid et al., 2003). Real refractive indices of the

samples are also not known, which is why we have analyzed many differ-

ent values for m, but the question still remains if we might be completely

missing the right values. We also considered whether the dependence on

refractive index, m, over small intervals was sufficiently linear so that

31

Modelling

our computed values were not missing anything unexpected. To put it the

other way, when the m-value is bracketed, are the single-scattering char-

acteristics, that would be obtained with the same model, also bracketed?

Nousiainen (2007) studied this problem and found that the dependence

between these quantities is monotonic when calculated over shape distri-

bution, so it seems m-bracketing, even with relatively large intervals, is

sufficient.

5.3 Fitting and simulated annealing

To test the performance of our models, we assessed how well scattering

matrices measured in a laboratory can be reproduced with simulated dis-

tributions of spheroids and ellipsoids. In the case of the Martian dust ana-

logue, palagonite, this turns out to be a demanding test, due to the fact

that the analogue particles have larger sizes than those that are typically

found in the Martian atmosphere. This is because we can expect simple

model particles to perform better in mimicking scattering behaviour of

samples with smaller reff (Eq. 3.14), as was shown in Paper I. To quan-

tify the suitability of model particles in reproducing the observations, we

needed to find relative shape distribution proportions of different model

particles such that the measurements could be optimally matched by the

ensemble-averaged scattering properties. As negative weights would have

no physical meaning and thus could not be allowed, simple linear regres-

sion algorithms were not suitable for fitting. On the other hand, non-

linear fitting algorithms do not perform well or are not adequately fast in

problems with a large number of degrees of freedom.

We applied a classic Monte Carlo simulated annealing algorithm to find

the shape distribution providing the best fit (Robert and Casella, 2004;

Zelinka et al., 2012). This method starts with a randomized distribution

of shapes and proceeds by varying it stepwise with random deviations,

evaluating the cost function after each step. Should the varied shape

distribution provide a better fit, it is accepted with some probability as

the new starting distribution for the next round of variations. Thus we

slowly move into the direction of better performing shape distributions.

To avoid getting stuck in local minima, the algorithm sporadically accepts

also variations of the shape distribution which increase the cost function

and thus take the fit further away. The probability at which it accepts or

declines the next varied distribution depends on the simulation time in

32

Modelling

an inverse fashion so that this likelihood of acceptance is high at the be-

ginning (i.e. the system is ’hot’) whereas with time the system is ’cooled’

and stray steps are fewer. As the use of the Monte Carlo optimization

method here requires merely summing up precomputed values with dif-

ferent weights, it can be easily performed by computers in a reasonable

time. More over, this method provided very consistent and acceptable re-

sults in all the cases where it was applied.

5.4 Discussion

Optical properties of dust depend on composition, homogeneity, surface

roughness, sizes and shapes of the particles. They are also varying with

the wavelength of the incoming light. As always in modelling real world

phenomena, not all characteristics can fully be accounted for. We have

here restricted ourselves into using homogeneous, isotropic, smooth sur-

faced and highly symmetrical model particles. However, real mineral par-

ticles are more often than not inhomogenous and most of them are also

anisotropic and birefringent. Moreover, we have assumed the same char-

acteristics all through the size and shape distributions. In real particles,

the refractive index changes with particle size as do other scattering char-

acteristics, not to mention the shape distribution. To fully account for

these features the model particle characteristics would have needed to

vary individually for each size-bin, but this would have vastly expanded

degrees of freedom in our model and its implementation was beyond our

resources (for the time being). In the future the models will get better,

measurement data sets more numerous, computational power greater and

coding skills ampler, which will all contribute in making possible the ever

more precise modelling of particle characteristics. This will increase our

understanding on our surroundings, perhaps most pressingly on the ef-

fects atmospheric dust particles are having on climate.

The models discussed in this thesis are increasing the modelling prowess

for forward applications (Papers I, II and III). There is, however, also a

question raised in Paper IV, whether the ellipsoidal model is so flexible

that it can too easily be bent around any problem to produce misleading

results. There it was found that retrieving the refractive index by using

ellipsoidal model particles produces erroneous results. Specifically, ellip-

soids reproduce the target optical properties best with erroneous material

parameters: the refractive index used in calculating ellipsoidal model par-

33

Modelling

ticles optical parameters bears no resemblance to the real world counter-

parts refractive index [Paper IV]. These model particles might thus not

be suitable for use in retrievals, but they are, nonetheless, the best model

particles that at the moment have extensive databases available for the

required parameter space.

34

6. Summary of results and discussion

Kilteinkin Volta vihaa pölyä.

(Even the nicest Volta hates dust.)

A Finnish vacuum cleaner ad

In modelling light scattering by various atmospheric mineral dust par-

ticles, there often is a need for something simple, fast, and yet more ac-

curate than the often used approximation of spherical isotropic particles,

the so called Mie spheres. In this work, the usefulness of spheroidal and

ellipsoidal model particles for this purpose has been assessed. It has to be

emphasized that these model particles are only used as a tool to assess, es-

timate or forecast the scattering behaviour of the particles and thus their

shape does not, and is not intended to, correlate with the real shapes of

the particles. Ellipsoids, as well as their spheroidal subset, were indeed

found to be superior to the Mie spheres in almost any combinations and

applications. Nevertheless, there were still various issues identified with

the models as well, such as their unreliability in retrievals, which calls for

even better methods to be developed in the future.

The usage of spheroidal model particles was investigated and partially

validated for atmospheric mineral dust (Paper I). It was also studied

whether best-fit shape distributions bear any similarities between differ-

ent samples. Also, consistency of the best-fitting solution with varying

wavelength was tested for. Positive results from these tests, i.e. that

the best working shape distributions for different wavelengths would be

similar, would allow us to propose a generic, first-guess shape distribu-

tion to be used for any suspended dust of similar type as the samples

studied. Unfortunately, but not unexpectedly, although spheroids produce

good results for separate samples, it seems that there is much variation

between best-fit shape distributions of different samples, optimized pa-

rameters as well as between different wavelengths. This makes sugges-

35

Summary of results and discussion

tions for a shape distribution difficult to make even when considering a

specific type of particles, such as volcanic ash. Nonetheless, both ensem-

bles of different spheroids and ellipsoids are found to fit all of the studied

measurements significantly better than Mie spheres. This might be be-

cause the light scattering characteristics of the individual spheroids and

ellipsoids differ from each other sufficiently so as to provide a good base

ensemble for replicating real measurements of various real particle popu-

lations, while spheres do not, due to their perfectly symmetric shapes. A

good starting point for any modelling application using either spheroids

or ellipsoids was found to be the use of an equiprobable shape distribu-

tion. In an equiprobable distribution all different model particle shapes

included in the study are present in equal proportions. With spheroids,

even a better solution is available though: a power-law shape distribution

with an exponent around three, as previously suggested by Nousiainen

et al. (2006). This kind of power-law distribution favours the particles

with the largest axial ratio differences.

There is a clear trend indicating that spheroids work best for modelling

particles of the smallest sizes, whereas scattering by ensembles with big-

ger grain sizes seemed to be more challenging and often impossible to

mimic well. Interestingly, the size dependent performance is strong with

all the other scattering matrix elements except for the polarization ele-

ments P12 and P34; spheroids perform quite well on reproducing both of

them regardless of the size range. When a generic shape distribution

(Nousiainen n = 3) is used to model the scattering behaviour of any of the

clay types studied, there were huge improvements found when compared

with the Mie particles (Paper I). This is demonstrated by Figure 6.1, iden-

tical in content to the Figure 6 of Paper I, where errors are compared

for different samples, wavelengths, quantities and models. The quantity

used for assessing the goodness of models in Paper I was called ψ - in

essence it describes the area in the plot between the measurement and

model curves. As a main message of Figure 6.1 it can be seen that already

an equiprobable distribution (n = 0) of spheroids almost universally re-

duces the fitting errors when compared with spheres. However, further

improvements can be achieved by using larger values of shape distribu-

tion exponent. Performance of spheroids can also be linked with sample

particle size, as results are clearly better for smallest particles. Impres-

sively, when best-fit distributions of spheroids are used instead of the ex-

ponential ones in Figure 6.1 (not shown), the error of Mie models could be

36

Summary of results and discussion

reduced by 60− 100% with the exception of large grained Saharan sand.

We then moved our attention further out into the solar system, to our

neighbouring planet Mars (Paper II). As we do not have the full scatter-

ing-matrix measurements of the real Martian dust particles, we had to

rely on an analogue material from Earth, namely palagonite dust. We also

relaxed all axis of our model particles, i.e. moved from spheroidal particles

to generic ellipsoids. We then investigated whether these model shapes

could reproduce the light-scattering measurents performed on palagonite.

Indeed, the fits were found to be very good, as can also be seen in Figure

6.2, which is similar to that of Figure 4 of Paper II. There results are

shown for when the whole scattering matrix of ellipsoidal shape distribu-

tion is fitted simultaneously into measurements of palagonite. When the

matrix elements are fitted individually (Figure 2 of Paper II) the fitting

results are close to perfect, but here we can see that trying to find a single

shape distribution of ellipsoids to describe the whole matrix leads into fit-

ting errors. Not surprisingly, using only spheroids, a subset of ellipsoids,

leads to overall worse performance.

Ellipsoidal model particles were shown to be able to effectively repro-

duce features of the measured scattering matrix elements of the palag-

onite Mars analog dust. However, each scattering matrix element de-

manded the use of a unique best-fit shape distribution and these differed

considerably and apparently arbitrarily from each other as well as from

the best-fit shape distribution for when the whole scattering matrix was

optimized simultaneously. This can be seen in Figure 6.3, which is similar

to that of Figure 3 of Paper II, where best-fitting shape distributions for

Palagonite dust are shown both for individual scattering matrix elements

(black circles) as well as for the whole matrix (red crosses). These results

suggest, similarly to the results of Paper I for mineral dust, that using a

standard shape distribution for modelling light single scattering from var-

ious dust particles is not an optimal solution. Delightfully however, using

equiprobable shape distribution was found to produce scattering parame-

ter values very close to those produced by using the best-fit shape distri-

bution. This suggests that, if the palagonite can be thought of as being a

representive proxy for Martian dust, an equiprobable shape distribution

of ellipsoids might indeed be a reasonable starting point in modelling the

scattering behaviour of real Martian atmospheric dust particles. Com-

pared with using the best-fit distribution for Palagonite this would pro-

vide a more general approach; best-fit shape distribution for palagonite is

37

Summary of results and discussion

Figure 6.1. Almost anything works better than a sphere in modelling light scatteringfrom mineral dust particles: deviation of asymmetry parameter and ψ val-ues of modeled scattering matrix elements compared for five samples, twowavelengths, and four models. Each row corresponds to a different sampleranging from smallest (feldspar in the first row) to the largest (Saharan dustin the last row). There are seven bar groups in each row, the leftmost (withlight blue background) of which describes the deviation in asymmetry param-eter and the others are for each independent scattering matrix element. Asmodels compared, the three first bars represent different exponential shapedistributions, with exponents n having values of 0, 3 and 10, and the right-most dark bar describes the error when using a sphere. Moreover, results fortwo different wavelengths are presented, the 632.8 nm shown in wider andcolored bars, while 441.6 nm results are represented by thinner dark bars ontop of the others. This figure is identical in content with the Figure 6 fromPaper I.

38

Summary of results and discussion

Figure 6.2. Comparison of measured (red error bars) and modeled scattering matrix ele-ments of palagonite dust - black line shows the result when whole of the scat-tering matrix has been fitted simultaneously. Individual ellipsoidal shapesproduce the scattering matrix elements drawn in light pink color and spherecreates the blue line. For comparison, also shown in dashed green line is thesolution for when only spheroidal shapes are used. This figure is similar tothat of Figure 4 from Paper II.

39

Summary of results and discussion

in any case bound to differ somewhat from the best-fit distribution for the

real Mars dust.

When considering volcanic dust, we also assessed the best working shape

distribution on the view points of satellite instrument data analysis. This

we did by considering only scattering matrix elements P11 and P12 on a

limited angle span visible to the instrument. Encouragingly these results

showed only modest variation in-between different wavelengths studied,

as can be seen in Figure 6.4, adapted from Figure 9 of Paper III. Inter-

estingly, pure prolate shapes were missing from the best-fit shape distri-

butions and majority of the favoured shapes had relatively modest aspect

ratios.

It has become very clear, that it is almost impossible to pick a specific

shape distribution for either ellipsoids or spheroids which would be the

best choice for modelling scattering behaviour for all samples and circum-

stances, even when quite similar samples are compared, as was done in

Paper I (mineral dust) and III (volcanic ash). Instead, the best shape dis-

tribution and the performance of the model depend on the sample that is

modeled, on the wavelength the modelling is performed and on the quan-

tity of interest, e.g. which scattering matrix elements or angle spans are

emphasized. For climate modelling applications, the success in modelling

the asymmetry parameter is used to test the particle shape model. In

such situations, a power-law shape distribution, Eq. (5.8), with an expo-

nent n = 3 is, on average, the best choice when using spheroids [Paper

I]. Here as well this generic shape distribution produces significant im-

provements when compared to Mie models. On the other hand, when the

whole phase function is of concern, a very low value of n (n < 1), or even

an equiprobable distribution (n = 0) seems to work best. Higher values of

n work best when all the elements of the scattering matrix are optimized

simultaneously; in half of the cases the upper limit of n available in our

models, 18, was reached. This aptly demonstrates the high variability

between optimal shape distributions for different uses.

Overall, a simple equiprobable distribution in all studied cases for both

the spheroids and ellipsoids produced good fits to the scattering parame-

ters, which makes an equiprobable distribution a sensible initial approx-

imation for a generic problem. This might be a symptom indicating that

these kinds of model particles provide a sufficiently multiform basis for

reproducing almost any real measurements, e.g. the scattering character-

istics by individual shapes differ sufficiently from each other and their

40

Summary of results and discussion

Figure 6.3. Optimally working shape distribution weights when the whole scattering ma-trix is fitted with the same shape distribution (red crosses) and for when eachscattering matrix is fitted individually (black circles) as a function of largestaxis, be and ce, with respect to the shortest axis, ae. Thus, symbols on diago-nal represent oblate spheroids whilst those on x-axis prolate spheroids. Thesize of the marker is directly proportional to the weight of the correspondingshape. This Figure is similar to the Figure 3 of Paper II.

41

Summary of results and discussion

Figure 6.4. Best-fit shape distributions for volcanic ash averaged over different wave-lengths: blue for the shorter and red for the longer wavelengths considered.Shaded area in the background corresponds to the region spanned by themodel ellipsoids used and otherwise symbols are similar to those in Figure6.3. This Figure is adapted from Figure 9 of Paper III.

ensemble smoothes any characteristics, finally providing an adequate,

generic matrix that never falls too far away from reality. As an exam-

ple, spheroids can be used to describe light scattering from very different

shapes, such as cubes (Nousiainen et al., 2011). As we have seen, both

spheroidal and ellipsoidal model particles work well in forward modelling

applications.

For retrieval purposes, however, an ability to produce close to real opti-

cal parameters is not a sufficient demand for model particles. There also

needs to be a correlation between the microphysical characteristics of the

best performing model particles and those of the retrievals target parti-

cles. Alas, it was found that often ellipsoid distributions with incorrect

refractive indices were producing significantly better fits than those with

real set parameter values. Moreover, the retrieval of the refractive in-

dex failed also when a fixed shape distribution was used. Ellipsoids are

thus not necessarily well suited for retrieving real particle refractive in-

dex from remote sensing data and this most likely extends to other aerosol

properties as well.

Calculated optical properties are influenced by assumptions about the

size distribution, particle shapes, and imaginary part of the refractive in-

dex. In Paper II, the effects of different assumptions on size, shape and

m on scattering by palagonite particles were also assessed. Shape was

found to impact ω and g by amounts comparable to that caused by size

distribution and imaginary index variations when applied within reason-

42

Summary of results and discussion

Δ

Δ

λ

λ

λ

Δ

Δ

Δ

Δ

Δ

Δ

ΔΔ

Δ

Δ

Δ

Figure 6.5. The asymmetry parameters that are obtained with various size and shapedistributions, wavelengths, and refractive indices. Different shape distribu-tion models are shown in x-axis; a sphere, three ellipsoid distributions andan exponential spheroid distribution. n3 refers for the exponent of the shapedistribution weights being 3. Different colors indicate different wavelengthsand each group of boxes show results for different varying size distributions(left) and imaginary parts of the refractive index (right) so that the top of thebox corresponds to the highest, and the bottom to the lowest value used. Themidline represents the values for the default case. This Figure is a reproduc-tion from Figure 6 of Paper II.

able bounds. This is shown for g in Figure 6.5 that is a reproduction from

Figure 6 of Paper II. It even seems to be possible in various situations

to reach in the modelling the same values for ω and g by altering the

shape of the scattering particles rather than adjusting refractive indices

or sizes. An exception to this was found at infrared wavelengths, where

ω is quite insensitive to the shape of the scattering particles. The effect

of particle shape on scattering behaviour has previously been mostly ne-

glected in retrieval algorithms used to analyze remote sensing data of the

Martian atmosphere. Such an omission might well have led to added un-

certainties and misinterpretations in the retrieved characteristics of the

Martian atmospheric dust particles. Refractive indices and sizes of the

Martian particles might thus not be even as constrained as we currently

assume and reanalyzing existing data might prove productive.

In this work, I have sought to assess and validate the applicability of

simple particle shape models in replicating the measurements of vari-

ous dust and ash samples. Atmospheric mineral dust was successfully

43

Summary of results and discussion

modeled with spheroidal model particles and ellipsoidal model particles

were applied for volcanic ash and Mars analog dust. It was found that

both spheroidal and ellipsoidal model particles could greatly improve on

spherical Mie-models as expected. Finding a generalized shape distri-

bution proved to be difficult and a conclusion was reached that using an

equiprobable distribution is a good first trial option for most usages where

no prior optical parameter measurements of the modeled particles exist.

When such measurements exist, the adopted shape distribution should be

optimized to suit the purpose - in such a case, very good agreement of the

model with the measurements can almost always be reached. Finally, el-

lipsoidal particle shape model performance was tested in the retrieval of

preset synthetic particle refractive index. This test failed miserably, indi-

cating that we can not assume that these model particles can consistently

and unambiguously be used to link the scattering dust particles optical

properties with their microphysical properties.

Model particles studied in this work, ellipsoids and their subset of spher-

oids with lesser deviations from the sphere, provide dramatic improve-

ments in reproducing measured scattering matrices, when compared with

Mie-particles. There are databases available of precomputed values for

the optical parameter of both ellipsoids and spheroids, making their us-

ages in modelling feasible. Nevertheless, there are shortcomings in these

models’ capability when used in solving the inverse problem, i.e. in re-

trievals. Clinging into some hope however, it is not far fetched to assume

that some other particle model might work better. Such a model should

nevertheless be sought, as humanity is depending heavily on truthful re-

mote sensing measurements of the atmosphere (Yang et al., 2013), or as

we say in Finland: ”Lohi on niin hieno kala, että sitä kannattaa pyytää,

vaikkei kiinni saisikaan” (Big fish are worth fishing even if you don’t catch

one).

This work has increased our ability to understand the light scatter-

ing by atmospheric mineral aerosol and volcanic ash particles. Results

of this thesis can be utilized in both terrestrial and space sciences to

better understand and anticipate the effects of small dust particles on

climate and weather systems. Here on Earth, the results have already

been adopted in the ECHAM 5 climate model’s (Stier et al., 2005; Roeck-

ner et al., 2003) lookup tables for atmospheric aerosols (Räisänen et al.,

2013). Work is also in progress to incorporate the ellipsoidal model in

Advanced Along-Track Scanning Radiometer, AATSR (European Space

44

Summary of results and discussion

Agency, 2002), satellite instrument data analysing algorithm look-up ta-

bles for atmospheric volcanic ash. Ellipsoidal and spheroidal model parti-

cles can also be used for increasing the reliability of remote sensing analy-

sis of the atmospheres of Earth and other planets. Further away in space,

the results might be usable in other planets, moons and objects having

an atmosphere or coma with suspended non-spherical dust particles in it.

Being able to model light scattering in the environments of these objects is

important for deciphering accurately any optical remote sensing measure-

ments to obtain information on their atmospheres or surface properties.

Accurate remote sensing data increases the scientific knowledge but then

also guides future mission planning and technology development as well

as political decision making, as is the case with climate change monitor-

ing. It is thus imperative that this data represents reality as truthfully as

possible.

With improving measurement technologies and more measurements,

the future will bring us better knowledge of the real world atmospheric

dust particles and their scattering behaviour. Especially evident this

progress will be with other planetary bodies as each new spacecraft or-

biter, probe or lander with suitable instrumentation will increase our

knowledge considerably. With the development of computational capac-

ities and modelling tools these measurements can be more reliably inter-

preted. There is a need for all this, as understanding our dusty environ-

ment has direct consequences for us on various fronts, such as in com-

batting climate change, increasing public health and planning of manned

Mars flights.

45

Summary of results and discussion

46

References

G. Ahmadli, R. Schnabel, A. Jokuszies, P. M. Vogt, U. Zier, and U. Mirastschi-

jski. Einfluss von Mars- und Mondstaubanaloga auf die Wundheilung hu-

maner Haut im ex-vivo Modell. Handchirurgie, Mikrochirurgie, Plastische

Chirurgie, 46(6):361 – 368, 2014. doi:10.1055/s-0034-1394419. URL https:

//www.thieme-connect.com/DOI/DOI?10.1055/s-0034-1394419.

M. P. Almeida, E. J. R. Parteli, J. S. Andrade, and H. J. Herrmann. Giant salta-

tion on Mars. Proceedings of the National Academy of Sciences, 105(17):6222–

6226, 2008. doi:10.1073/pnas.0800202105. URL http://www.pnas.org/content/

105/17/6222.abstract.

S. Altizer, R. S. Ostfeld, P. T. J. Johnson, S. Kutz, and C. D. Harvell. Cli-

mate change and infectious diseases: From evidence to a predictive frame-

work. Science, 341(6145):514–519, 2013. doi:10.1126/science.1239401. URL

http://www.sciencemag.org/content/341/6145/514.abstract.

A. Arneth, N. Unger, M. Kulmala, and M. O. Andreae. Clean the air, heat the

planet? Science, 326(5953):672–673, 2009. doi:10.1126/science.1181568. URL

http://www.sciencemag.org/content/326/5953/672.short.

R. E. Arvidson, J. W. Ashley, J. F. Bell, M. Chojnacki, J. Cohen, T. E. Economou,

W. H. Farrand, R. Fergason, I. Fleischer, P. Geissler, R. Gellert, M. P.

Golombek, J. P. Grotzinger, E. A. Guinness, R. M. Haberle, K. E. Herken-

hoff, J. A. Herman, K. D. Iagnemma, B. L. Jolliff, J. R. Johnson, G. Klin-

gelhöfer, A. H. Knoll, A. T. Knudson, R. Li, S. M. McLennan, D. W. Mittle-

fehldt, R. V. Morris, T. J. Parker, M. S. Rice, C. SchrÃuder, L. A. Soderblom,

S. W. Squyres, R. J. Sullivan, and M. J. Wolff. Opportunity Mars Rover mis-

sion: Overview and selected results from Purgatory ripple to traverses to

Endeavour crater. Journal of Geophysical Research: Planets, 116(E7), 2011.

ISSN 2156-2202. doi:10.1029/2010JE003746. URL http://dx.doi.org/10.1029/

2010JE003746. E00F15.

J. D. Atkinson, B. J. Murray, M. T. Woodhouse, T. F. Whale, K. J. Baustian, K. S.

Carslaw, S. Dobbie, D. O’Sullivan, and T. L. Malkin. The importance of feldspar

47

References

for ice nucleation by mineral dust in mixed-phase clouds. Nature, 498(7454):

355–358, 2013. ISSN 0028-0836. doi:10.1038/nature12278.

F. Ayoub, J.-P. Avouac, C.E. Newman, M.I. Richardson, A. Lucas, S. Leprince,

and N.T. Bridges. Threshold for sand mobility on Mars calibrated from

seasonal variations of sand flux. Nature Communications, 5, Sep. 2014.

doi:10.1038/ncomms6096.

M. Bahn, M. Reichstein, J. S. Dukes, M. D. Smith, and N. G. McDowell. Climate–

biosphere interactions in a more extreme world. New Phytologist, 202(2):356–

359, 2014. ISSN 1469-8137. doi:10.1111/nph.12662. URL http://onlinelibrary.

wiley.com/doi/10.1111/nph.12662/full. 2013-16311.

V. R. Barros, C. B. Field, D. J. Dokken, M. D. Mastrandrea, K. J. Mach, T. E.

Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S.

Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea, and L. L. White, edi-

tors. Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B:

Regional Aspects. Contribution of Working Group II to the Fifth Assessment

Report of the Intergovernmental Panel on Climate Change. Cambridge Univer-

sity Press, Cambridge, United Kingdom and New York, NY, USA, 2014. URL

http://ipcc-wg2.gov/AR5/report/final-drafts/.

S. Basu, M. I. Richardson, and R. J. Wilson. Simulation of the Martian dust

cycle with the GFDL Mars GCM. Journal of Geophysical Research: Planets,

109(E11), 2004. ISSN 2156-2202. doi:10.1029/2004JE002243. URL http://

onlinelibrary.wiley.com/doi/10.1029/2004JE002243/full. E11006.

J. Beecken, J. Mellqvist, K. Salo, J. Ekholm, J.-P. Jalkanen, L. Johansson,

V. Litvinenko, K. Volodin, and D. A. Frank-Kamenetsky. Emission factors

of SO2, NOx and particles from ships in Neva Bay from ground-based and

helicopter-borne measurements and AIS-based modeling. Atmospheric Chem-

istry and Physics, 15(9):5229–5241, 2015. doi:10.5194/acp-15-5229-2015. URL

http://www.atmos-chem-phys.net/15/5229/2015/.

D. Beladjine, M. Ammi, L. Oger, and A. Valance. Collision process between an

incident bead and a three-dimensional granular packing. Phys. Rev. E, 75:

061305, Jun 2007. doi:10.1103/PhysRevE.75.061305. URL http://link.aps.org/

doi/10.1103/PhysRevE.75.061305.

C. Bellard, C. Bertelsmeier, P. Leadley, W. Thuiller, and F. Courchamp. Impacts

of climate change on the future of biodiversity. Ecology Letters, 15(4):365–

377, 2012. ISSN 1461-0248. doi:10.1111/j.1461-0248.2011.01736.x. URL http:

//dx.doi.org/10.1111/j.1461-0248.2011.01736.x.

W. Von Bergen and W. Krauss. Textile Fiber Atlas: A Collection of Photomi-

crographs of Common Textile Fibers. Americal Wool Handbook Company,

1942. URL http://www.sil.si.edu/digitalcollections/HistoryCultureCollections/

HST_title.cfm?bib_id=SIL7-236.

48

References

H. G. Berry, G. Gabrielse, and A. E. Livingston. Measurement of

the Stokes parameters of light. Appl. Opt., 16(12):3200–3205, Dec

1977. doi:10.1364/AO.16.003200. URL http://ao.osa.org/abstract.cfm?URI=

ao-16-12-3200.

L. Bi, P. Yang, G.W. Kattawar, and R. Kahn. Single-scattering prop-

erties of triaxial ellipsoidal particles for a size parameter range from

the Rayleigh to geometric-optics regimes. Applied Optics, 48(1), 2009.

doi:10.1016/j.jaerosci.2010.02.008.

L. Bi, P. Yang, G. W. Kattawar, and R. Kahn. Modeling optical properties of

mineral aerosol particles by using nonsymmetric hexahedra. Appl. Opt., 49(3):

334–342, Jan 2010. doi:10.1364/AO.49.000334. URL http://ao.osa.org/abstract.

cfm?URI=ao-49-3-334.

D. L. Bish, D. F. Blake, D. T. Vaniman, S. J. Chipera, R. V. Morris, D. W. Ming,

A. H. Treiman, P. Sarrazin, S. M. Morrison, R. T. Downs, C. N. Achilles,

A. S. Yen, T. F. Bristow, J. A. Crisp, J. M. Morookian, J. D. Farmer, E. B.

Rampe, E. M. Stolper, N. Spanovich, and MSL Science Team. X-ray diffrac-

tion results from Mars Science Laboratory: Mineralogy of Rocknest at Gale

Crater. Science, 341(6153), 2013. doi:10.1126/science.1238932. URL http:

//www.sciencemag.org/content/341/6153/1238932.

C. F. Bohren and D. R. Huffman. Absorption and Scattering of Light by Small

Particles. John Wiley & Sons, New York, 1983. 530 pp.

V. Borzan, B. Tomaševic, and S. Kurbel. Hypothesis: Possible respiratory advan-

tages for heterozygote carriers of cystic fibrosis linked mutations during dusty

climate of last glaciation. Journal of Theoretical Biology, 363(0):164 – 168,

2014. ISSN 0022-5193. doi:http://dx.doi.org/10.1016/j.jtbi.2014.08.015. URL

http://www.sciencedirect.com/science/article/pii/S002251931400469X.

N. Bost, C. Ramboz, N. LeBreton, F. Foucher, G. Lopez-Reyes, S. De Ange-

lis, M. Josset, G. Venegas, A. Sanz-Arranz, F. Rull, J. Medina, J.-L. Jos-

set, A. Souchon, E. Ammannito, M.C. De Sanctis, T. Di Iorio, C. Carli,

J.L. Vago, and F. Westall. Testing the ability of the exomars 2018 pay-

load to document geological context and potential habitability on mars.

Planetary and Space Science, 108(0):87 – 97, 2015. ISSN 0032-0633.

doi:http://dx.doi.org/10.1016/j.pss.2015.01.006. URL http://www.sciencedirect.

com/science/article/pii/S0032063315000070.

O. Boucher, D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-

M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh,

S. Sherwood, B. Stevens, X.-Y. Zhang, G. Bala, N. Bellouin, A. Benedetti,

S. Bony, K. Caldeira, A. Del Genio, M.C. Facchini, M. Flanner, S. Ghan,

C. Granier, C. Hoose, A. Jones, M. Koike, B. Kravitz, B. Laken, M. Lebsock,

N. Mahowald, G. Myhre, C. OâAZDowd, A. Robock, B. Samset, H. Schmidt,

49

References

M. Schulz, G. Stephens, P. Stier, T. Storelvmo, D. Winker, and M. Wyant,

editors. Clouds and Aerosols. In: Climate Change 2013: The Physical Sci-

ence Basis. Contribution of Working Group I to the Fifth Assessment Report

of the Intergovernmental Panel on Climate Change. Cambridge University

Press, Cambridge, United Kingdom and New York, NY, USA, 2013. URL

https://www.ipcc.ch/report/ar5/wg1/.

C. S. Bristow, K. A. Hudson-Edwards, and A. Chappell. Fertilizing the Amazon

and equatorial Atlantic with West African dust. Geophysical Research Letters,

37(14), 2010. ISSN 1944-8007. doi:10.1029/2010GL043486. URL http://dx.doi.

org/10.1029/2010GL043486. L14807.

B. A. Cantor, K. M. Kanak, and K. S. Edgett. Mars orbiter camera observa-

tions of Martian dust devils and their tracks (september 1997 to january 2006)

and evaluation of theoretical vortex models. Journal of Geophysical Research:

Planets, 111(E12), 2006. ISSN 2156-2202. doi:10.1029/2006JE002700. URL

http://onlinelibrary.wiley.com/doi/10.1029/2006JE002700/full.

K. S. Carslaw, O. Boucher, D. V. Spracklen, G. W. Mann, J. G. L. Rae, S. Wood-

ward, and M. Kulmala. A review of natural aerosol interactions and feedbacks

within the Earth system. Atmospheric Chemistry and Physics, 10(4):1701–

1737, 2010. doi:10.5194/acp-10-1701-2010. URL http://www.atmos-chem-phys.

net/10/1701/2010/.

F. S. Chapin III, J. T. Randerson, A. D. McGuire, J. A. Foley, and C. B. Field.

Changing feedbacks in the climate - biosphere system. Frontiers in Ecology

and the Environment, 6(6):313–320, 2014. doi:10.1890/080005. URL http:

//www.esajournals.org/doi/abs/10.1890/080005.

R. J. Charlson, S. E. Schwartz, J. M. Hales, R. D. Cess, J. A. Coakley, J. E.

Hansen, and D. J. Hofmann. Climate forcing by anthropogenic aerosols.

Science, 255(5043):423–430, 1992. doi:10.1126/science.255.5043.423. URL

http://www.sciencemag.org/content/255/5043/423.abstract.

W. Chen, Y.i Liu, H. Wang, E. Hnizdo, Y. Sun, L. Su, X. Zhang, S. Weng,

F. Bochmann, F. J. Hearl, J. Chen, and T. Wu. Long-term exposure to silica

dust and risk of total and cause-specific mortality in Chinese workers: A co-

hort study. PLoS Med, 9(4), 2012. doi:10.1371/journal.pmed.1001206.

P. R. Christensen, M. B. Wyatt, T. D. Glotch, A. D. Rogers, S. Anwar, R. E. Arvid-

son, J. L. Bandfield, D. L. Blaney, C. Budney, W. M. Calvin, A. Fallacaro, R. L.

Fergason, N. Gorelick, T. G. Graff, V. E. Hamilton, A. G. Hayes, J. R. John-

son, A. T. Knudson, H. Y. McSween, G. L. Mehall, L. K. Mehall, J. E. Moersch,

R. V. Morris, M. D. Smith, S. W. Squyres, S. W. Ruff, and M. J. Wolff. Mineral-

ogy at Meridiani Planum from the mini-TES experiment on the Opportunity

rover. Science, 306(5702):1733–1739, 2004. doi:10.1126/science.1104909. URL

http://www.sciencemag.org/content/306/5702/1733.abstract.

50

References

J. Csavina, J. Field, O. Félix, A. Y. Corral-Avitia, A. E. Sáez, and E. A. Betterton.

Effect of wind speed and relative humidity on atmospheric dust concentra-

tions in semi-arid climates. Science of The Total Environment, 487:82 – 90,

2014. ISSN 0048-9697. doi:http://dx.doi.org/10.1016/j.scitotenv.2014.03.138.

URL http://www.sciencedirect.com/science/article/pii/S0048969714004902.

D. D. Dabrowska, O. Muñoz, F. Moreno, T. Nousiainen, E. Zubko, and A. C.

Marra. Experimental and simulated scattering matrices of small calcite parti-

cles at 647.0 nm. Journal of Quantitative Spectroscopy and Radiative Transfer,

124(0):62 – 78, 2013. ISSN 0022-4073. doi:10.1016/j.jqsrt.2013.02.010. URL

http://www.sciencedirect.com/science/article/pii/S0022407313000642.

P. J. DeMott, K. Sassen, M. R. Poellot, D. Baumgardner, D. C. Rogers, S. D.

Brooks, A. J. Prenni, and S. M. Kreidenweis. African dust aerosols as atmo-

spheric ice nuclei. Geophysical Research Letters, 30(14), 2003. ISSN 1944-

8007. doi:10.1029/2003GL017410. 1732.

J. Ding and L. Xu. Convergence of the T-matrix approach for randomly ori-

ented, nonabsorbing, nonspherical Chebyshev particles. Journal of Quanti-

tative Spectroscopy and Radiative Transfer, 63(2âAS6):163 – 174, 1999. ISSN

0022-4073. doi:10.1016/S0022-4073(99)00013-8.

Zh. M. Dlugach, O.I. Korablev, A.V. Morozhenko, V.I. Moroz, E.V. Petrova, and

A.V. Rodin. Physical properties of dust in the Martian atmosphere: Analysis

of contradictions and possible ways of their resolution. Solar System Research,

37:1–19, 2003. doi:10.1023/A:1022395404115.

S. C. Doney, M. Ruckelshaus, J. Emmett D., J. P. Barry, F. Chan, C. A. English,

H. M. Galindo, J. M. Grebmeier, A. B. Hollowed, N. Knowlton, J. Polovina,

N. N. Rabalais, W. J. Sydeman, and L. D. Talley. Climate change impacts

on marine ecosystems. Annual Review of Marine Science, 4(1):11–37, 2012.

doi:10.1146/annurev-marine-041911-111611. PMID: 22457967.

D. Dordevic, A.M. Stortini, D. Relic, A. Mihajlidi-Zelic, J. Huremovic, C. Bar-

bante, and A. Gambaro. Trace elements in size-segregated urban aerosol in

relation to the anthropogenic emission sources and the resuspension. Envi-

ronmental Science and Pollution Research, 21(18):10949–10959, 2014. ISSN

0944-1344. doi:10.1007/s11356-014-2998-1.

B. T. Draine and P. J. Flatau. Discrete-dipole approximation for scat-

tering calculations. J. Opt. Soc. Am. A, 11(4):1491–1499, Apr 1994.

doi:10.1364/JOSAA.11.001491. URL http://josaa.osa.org/abstract.cfm?URI=

josaa-11-4-1491.

O. Dubovik, A. Sinyak, T. Lapyonok, B. N. Holben, M. Mishchenko, P. Yang, T. F.

Eck, H. Volten, O. Muñoz, B. Veihelmann, W. J. van der Zande, J.-F. Leon,

M. Sorokin, and I. Slutsker. Application of spheroid models to account for

51

References

aerosol particle nonsphericity in remote sensing of desert dust. Journal of

Geographic Research, 111:D11208, 2006. doi:10.1029/2005JD006619.

O. Dubovik, M. Herman, A. Holdak, T. Lapyonok, D. Tanré, J. L. Deuzé, F. Ducos,

A. Sinyuk, and A. Lopatin. Statistically optimized inversion algorithm for

enhanced retrieval of aerosol properties from spectral multi-angle polarimetric

satellite observations. Atmospheric Measurement Techniques, 4(5):975–1018,

2011. doi:10.5194/amt-4-975-2011. URL http://www.atmos-meas-tech.net/4/

975/2011/.

A. Einstein. Über einen die Erzeugung und Verwandlung des Lichtes betre-

ffenden heuristischen Gesichtspunkt. Annalen der Physik, 322(6):132–148,

1905. ISSN 1521-3889. doi:10.1002/andp.19053220607. URL http://dx.doi.org/

10.1002/andp.19053220607.

European Space Agency. The AATSR product handbook, 2002.

https://earth.esa.int/handbooks/aatsr/, Last accessed: 27 August 2014.

A. M. Farmer. The effects of dust on vegetation - a review. Environmental Pollu-

tion, 79(1):63 – 75, 1993. ISSN 0269-7491. doi:http://dx.doi.org/10.1016/0269-

7491(93)90179-R.

C. B. Field, V. R. Barros, D. J. Dokken, K. J. Mach, M. D. Mastrandrea, T. E. Bilir,

M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel,

A. N. Levy, S. MacCracken, P. R. Mastrandrea, , and L. L. White, editors.

Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global

and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment

Report of the Intergovernmental Panel on Climate Change. Cambridge Univer-

sity Press, Cambridge, United Kingdom and New York, NY, USA, 2014. URL

http://ipcc-wg2.gov/AR5/report/final-drafts/.

E. Fitzgerald, A. P. Ault, M. D. Zauscher, O. L. Mayol-Bracero, and K. A. Prather.

Comparison of the mixing state of long-range transported Asian and African

mineral dust. Atmospheric Environment, 115(0):19 – 25, 2015. ISSN 1352-

2310. doi:http://dx.doi.org/10.1016/j.atmosenv.2015.04.031. URL http://www.

sciencedirect.com/science/article/pii/S1352231015300340.

P. Formenti, L. Schütz, Y. Balkanski, K. Desboeufs, M. Ebert, K. Kandler, A. Pet-

zold, D. Scheuvens, S. Weinbruch, and D. Zhang. Recent progress in under-

standing physical and chemical properties of African and Asian mineral dust.

Atmospheric Chemistry and Physics, 11(16):8231–8256, 2011. doi:10.5194/acp-

11-8231-2011. URL http://www.atmos-chem-phys.net/11/8231/2011/.

S. D. Fuerstenau. Solar heating of suspended particles and the dynamics of

Martian dust devils. Geophysical Research Letters, 33(19), 2006. ISSN 1944-

8007. doi:10.1029/2006GL026798. URL http://onlinelibrary.wiley.com/doi/10.

1029/2006GL026798/full.

52

References

S. Garimella, Y.-W. Huang, J. S. Seewald, and D. J. Cziczo. Cloud conden-

sation nucleus activity comparison of dry- and wet-generated mineral dust

aerosol: the significance of soluble material. Atmospheric Chemistry and

Physics, 14(12):6003–6019, 2014. doi:10.5194/acp-14-6003-2014. URL http:

//www.atmos-chem-phys.net/14/6003/2014/.

J. Gasteiger, M. Wiegner, S. Groß, V. Freudenthaler, C. Toledano, M. Tesche,

and K. Kandler. Modelling lidar-relevant optical properties of complex

mineral dust aerosols. Tellus B, 63(4):725–741, 2011. ISSN 1600-0889.

doi:10.1111/j.1600-0889.2011.00559.x. URL http://onlinelibrary.wiley.com/doi/

10.1111/j.1600-0889.2011.00559.x/abstract.

D. A. Glenar, T. J. Stubbs, J. E. McCoy, and R. R. Vondrak. A reanalysis

of the Apollo light scattering observations, and implications for lunar exo-

spheric dust. Planetary and Space Science, 59(14):1695 – 1707, 2011. ISSN

0032-0633. doi:10.1016/j.pss.2010.12.003. URL http://www.sciencedirect.com/

science/article/pii/S0032063310003636. Lunar Dust, Atmosphere and Plasma:

The Next Steps.

I. S. Grant and W. R. Phillips. Electromagnetism. John Wiley & Sons

Ltd, 1990. ISBN 978-0-471-92712-9. URL http://eu.wiley.com/WileyCDA/

WileyTitle/productCd-0471927120.html.

R. Greeley. Saltation impact as a means for raising dust on Mars.

Planetary and Space Science, 50(2):151 – 155, 2002. ISSN 0032-

0633. doi:http://dx.doi.org/10.1016/S0032-0633(01)00127-1. URL http://www.

sciencedirect.com/science/article/pii/S0032063301001271.

J. P. Grotzinger, J. Crisp, A. R. Vasavada, R. C. Anderson, C. J. Baker, R. Barry,

D. F. Blake, P. Conrad, K. S. Edgett, B. Ferdowski, R. Gellert, J. B. Gilbert,

M. Golombek, J. Gómez-Elvira, D. M. Hassler, L. Jandura, M. Litvak, P. Ma-

haffy, J. Maki, M. Meyer, M. C. Malin, I. Mitrofanov, J. J. Simmonds, D. Van-

iman, R. V. Welch, and R. C. Wiens. Mars Science Laboratory mission and

science investigation. Space Science Reviews, 170(1-4):5–56, 2012. ISSN

0038-6308. doi:10.1007/s11214-012-9892-2. URL http://dx.doi.org/10.1007/

s11214-012-9892-2.

B. A. S. Gustafson. Physics of zodiacal dust. Annual Re-

view Of Earth And Planetary Sciences, 22:553–595, 1994.

doi:doi:10.1146/annurev.ea.22.050194.003005.

J. E. Hansen and L. D. Travis. Light scattering in planetary atmospheres. Space

Sci. Rev., 16:527–610, 1974. doi:10.1007/BF00168069.

C. E. Holleley, D. O’Meally, Stephen D. Sarre, J. A. Marshall Graves, T. Ezaz,

K. Matsubara, B. Azad, X. Zhang, and A. Georges. Sex reversal triggers

the rapid transition from genetic to temperature-dependent sex. Nature, 523

53

References

(7558):79–82, 2015. doi:10.1038/nature14574. URL http://dx.doi.org/10.1038/

nature14574.

J.W. Hovenier and D. Guirado. Zero slopes of the scattering function and scat-

tering matrix for strict forward and backward scattering by mirror sym-

metric collections of randomly oriented particles. Journal of Quantitative

Spectroscopy and Radiative Transfer, 133(0):596 – 602, 2014. ISSN 0022-

4073. doi:10.1016/j.jqsrt.2013.09.023. URL http://www.sciencedirect.com/

science/article/pii/S0022407313004032.

J.W. Hovenier, H. C. van de Hulst, and C. V. M. van der Meer. Conditions for the

elements of the scattering matrix. Astron. Astrophys., 157:301–310, 1986.

J.W. Hovenier, C. V. M. van der Mee, and H. Domke. Transfer of Polarized Light

in Planetary Atmospheres. Springer, 2004. ISBN 1-4020-2889-X. URL http:

//www.springer.com/us/book/9781402028557.

H. Ishimoto, Y. Zaizen, A. Uchiyama, K. Masuda, and Y. Mano. Shape

modeling of mineral dust particles for light-scattering calculations using

the spatial Poisson Voronoi tessellation. Journal of Quantitative Spec-

troscopy and Radiative Transfer, 111(16):2434 – 2443, 2010. ISSN 0022-

4073. doi:10.1016/j.jqsrt.2010.06.018. URL http://www.sciencedirect.com/

science/article/pii/S0022407310002633.

B. Jackson and R. Lorenz. A multiyear dust devil vortex survey using an auto-

mated search of pressure time series. Journal of Geophysical Research: Plan-

ets, 120(3):401–412, 2015. ISSN 2169-9100. doi:10.1002/2014JE004712. URL

http://onlinelibrary.wiley.com/doi/10.1002/2014JE004712/full.

M. Z. Jacobson. Strong radiative heating due to the mixing state of black car-

bon in atmospheric aerosols. Nature, 409(6821):695–697, 2000. ISSN 0028-

0836. doi:10.1038/35055518. URL http://www.nature.com/nature/journal/

v409/n6821/suppinfo/409695a0_S1.html.

J.-P. Jalkanen, L. Johansson, and J. Kukkonen. A comprehensive inventory of

ship traffic exhaust emissions in the European sea areas in 2011. Atmospheric

Chemistry and Physics Discussions, 15(5):7459–7491, 2015. doi:10.5194/acpd-

15-7459-2015. URL http://www.atmos-chem-phys-discuss.net/15/7459/2015/.

P. Janhunen, P. K. Toivanen, J. Polkko, S. Merikallio, P. Salminen, E. Haeg-

gström, H. Seppänen, R. Kurppa, J. Ukkonen, S. Kiprich, G. Thornell,

H. Kratz, L. Richter, O. Kramer, R. Rosta, M. Noorma, J. Envall, S. Lätt,

G. Mengali, A. A. Quarta, H. Koivisto, O. Tarvainen, T. Kalvas, J. Kauppinen,

A. Nuottajärvi, and A. Obraztsov. Invited article: Electric solar wind sail:

Toward test missions. Review of Scientific Instruments, 81(11):111301, 2010.

doi:10.1063/1.3514548. URL http://scitation.aip.org/content/aip/journal/rsi/81/

11/10.1063/1.3514548.

54

References

P. Janhunen, S. Merikallio, and M. Paton. EMMI – Electric solar wind

sail facilitated Manned Mars Initiative. Acta Astronautica, 113(0):22 – 28,

2015. ISSN 0094-5765. doi:10.1016/j.actaastro.2015.03.029. URL http://www.

sciencedirect.com/science/article/pii/S0094576515001290.

M. Kahnert, T. Nousiainen, and H. Lindqvist. Review: Model particles in atmo-

spheric optics. Journal of Quantitative Spectroscopy and Radiative Transfer,

146(0):41 – 58, 2014. ISSN 0022-4073. doi:10.1016/j.jqsrt.2014.02.014. URL

http://www.sciencedirect.com/science/article/pii/S0022407314000715.

O. V. Kalashnikova, R. Kahn, I. N. Sokolik, and W.-H. Li. Ability of multiangle

remote sensing observations to identify and distinguish mineral dust types:

Optical models and retrievals of optically thick plumes. Journal of Geograph-

ical Research, 110:D18S14, 2005. doi:10.1029/2004JD004550.

L. Kaltenegger, M. Fridlund, and A. Karlsson. Interferometric space missions

for the search for terrestrial exoplanets: Requirements on the rejection ra-

tio. Astrophysics and Space Science, 306(3):147–158, 2006. ISSN 0004-640X.

doi:10.1007/s10509-006-9183-z.

K. Kandler, K. Lieke, N. Benker, C. Emmel, M. Küpper, D. Müller-Ebert,

M. Ebert, D. Scheuvens, A. Schladitz, L. Schütz, and S. Weinbruch. Elec-

tron microscopy of particles collected at Praia, Cape Verde, during the Saha-

ran Mineral Dust Experiment: particle chemistry, shape, mixing state and

complex refractive index. Tellus B, 63(4), 2011. ISSN 1600-0889. URL

http://www.tellusb.net/index.php/tellusb/article/view/16241.

Y. J. Kaufman, D. Tanré, and O. Boucher. A satellite view of

aerosols in the climate system. Nature, 419(6903):215–223, 2002.

doi:10.1038/nature01091. URL http://www.nature.com/nature/journal/v419/

n6903/abs/nature01091.html.

Y. J. Kaufman, I. Koren, L. A. Remer, D. Tanré, P. Ginoux, and S. Fan. Dust

transport and deposition observed from the Terra-Moderate Resolution Imag-

ing Spectroradiometer (MODIS) spacecraft over the Atlantic ocean. Journal of

Geographical Research, 110:D10S12, 2005. doi:10.1029/2003JD004436.

L. Klüser and T. Holzer-Popp. Relationships between mineral dust and cloud

properties in the West African Sahel. Atmospheric Chemistry and Physics,

10(14):6901–6915, 2010. doi:10.5194/acp-10-6901-2010. URL http://www.

atmos-chem-phys.net/10/6901/2010/.

J. F. Kok, E. J. R. Parteli, T. I. Michaels, and D. B. Karam. The physics of wind-

blown sand and dust. Reports on Progress in Physics, 75(10):106901, 2012.

URL http://stacks.iop.org/0034-4885/75/i=10/a=106901.

55

References

A. A. Kokhanovsky and G. de Leeuw. Satellite Aerosol Remote Sensing Over

Land. Springer-Verlag Berlin Heidelberg, 2009. ISBN 978-3-540-69396-3.

doi:10.1007/978-3-540-69397-0. 388 pp.

M. Konert and J. Vandenberghe. Comparison of laser grain size analy-

sis with pipette and sieve analysis: a solution for the underestimation

of the clay fraction. Sedimentology, 44(3):523–535, 1997. ISSN 1365-

3091. doi:10.1046/j.1365-3091.1997.d01-38.x. URL 10.1046/j.1365-3091.1997.

d01-38.x.

O. Korablev, V.I. Moroz, E.V. Petrova, and A.V. Rodin. Optical properties of dust

and the opacity of the Martian atmosphere. Advances in Space Research, 35

(1), 2005.

R.-S. Koskela, M. Klockars, H. Laurent, and M. Holopainen. Silica dust exposure

and lung cancer. the Scandinavian Journal of Work, Environment & Health,

20(6):407–416, 1994. URL http://www.jstor.org/stable/40966286.

N. A. Krotkov, D. E. Flittner, A. J. Krueger, A. Kostinski, C. Riley,

W. Rose, and O. Torres. Effect of particle non-sphericity on satellite

monitoring of drifting volcanic ash clouds. Journal of Quantitative Spec-

troscopy and Radiative Transfer, 63(2–6):613–630, 1999. ISSN 0022-

4073. doi:http://dx.doi.org/10.1016/S0022-4073(99)00041-2. URL http://www.

sciencedirect.com/science/article/pii/S0022407399000412.

K. S. Kunz. The finite difference time domain method for electromagnetism. CRC

Press, 1993. ISBN 0-8493-8657-8.

A. Kylling, M. Kahnert, H. Lindqvist, and T. Nousiainen. Volcanic ash infrared

signature: porous non-spherical ash particle shapes compared to homogeneous

spherical ash particles. Atmospheric Measurement Techniques, 7(4):919–929,

2014. doi:10.5194/amt-7-919-2014. URL http://www.atmos-meas-tech.net/7/

919/2014/.

E. C. Laan, H. Volten, D. M. Stam, O. Muñoz, J. W. Hovenier, and T. L. Roush.

Scattering matrices and expansion coefficients of Martian analogue palagonite

particles. Icarus, 199:219–230, 2009.

J. Li, L. Liu, A.A. Lacis, and B.E. Carlson. An optimal fitting approach

to improve the GISS ModelE aerosol optical property parameterization us-

ing AERONET data. Journal of Geophysical Research, 115:D16211, 2010.

doi:10.1029/2010JD013909.

O. Lind, M. Mitkus, P. Olsson, and A. Kelber. Ultraviolet vision in birds:

the importance of transparent eye media. Proceedings of the Royal Soci-

ety of London B: Biological Sciences, 281(1774), 2013. ISSN 0962-8452.

doi:10.1098/rspb.2013.2209.

56

References

H. Lindqvist, O. Jokinen, K. Kandler, D. Scheuvens, and T. Nousiainen. Sin-

gle scattering by realistic, inhomogeneous mineral dust particles with stere-

ogrammetric shapes. Atmospheric Chemistry and Physics, 14(1):143–157,

2014. doi:10.5194/acp-14-143-2014. URL http://www.atmos-chem-phys.net/14/

143/2014/.

C. Linke, O. Möhler, A. Veres, Á. Mohácsi, Z. Bozóki, G. Szabó, and M. Schnaiter.

Optical properties and mineralogical composition of different Saharan min-

eral dust samples: a laboratory study. Atmospheric Chemistry and Physics,

6(11):3315–3323, 2006. doi:10.5194/acp-6-3315-2006. URL http://www.

atmos-chem-phys.net/6/3315/2006/.

C. Liu, R. L. Panetta, P. Yang, A. Macke, and A. J. Baran. Modeling the scattering

properties of mineral aerosols using concave fractal polyhedra. Appl. Opt.,

52(4):640–652, Feb 2013. doi:10.1364/AO.52.000640. URL http://ao.osa.org/

abstract.cfm?URI=ao-52-4-640.

A. Määttänen, H. Vehkamäki, A. Lauri, S. Merikallio, J. Kauhanen, H. Sav-

ijärvi, and M. Kulmala. Nucleation studies in the Martian atmosphere.

Journal of Geophysical Research: Planets, 110(E2), 2005. ISSN 2156-2202.

doi:10.1029/2004JE002308. URL http://dx.doi.org/10.1029/2004JE002308.

E02002.

P. R. Mahaffy, C. R. Webster, M. Cabane, P. G. Conrad, P. Coll, S. Atreya, R. Ar-

vey, M. Barciniak, M. Benna, L. Bleacher, W. B. Brinckerhoff, J. L. Eigenbrode,

D. Carignan, M. Cascia, R. A. Chalmers, J. P. Dworkin, T. Errigo, P. Ever-

son, H. Franz, R. Farley, S. Feng, G. Frazier, C. Freissinet, D. P. Glavin, D. N.

Harpold, D. Hawk, V. Holmes, C. S. Johnson, A. Jones, P. Jordan, J. Kellogg,

J. Lewis, E. Lyness, C. A. Malespin, D. K. Martin, J. Maurer, A. C. McAdam,

D. McLennan, T. J. Nolan, M. Noriega, A. A. Pavlov, B. Prats, E. Raaen,

O. Sheinman, D. Sheppard, J. Smith, J. C. Stern, F. Tan, M. Trainer, D. W.

Ming, R. V. Morris, J. Jones, C. Gundersen, A. Steele, J. Wray, O. Botta, L. A.

Leshin, T. Owen, S. Battel, B. M. Jakosky, H. Manning, S. Squyres, R. Navarro-

González, C. P. McKay, F. Raulin, R. Sternberg, A. Buch, P. Sorensen, R. Kline-

Schoder, D. Coscia, C. Szopa, S. Teinturier, C. Baffes, J. Feldman, G. Flesch,

S. Forouhar, R. Garcia, D. Keymeulen, S. Woodward, B. P. Block, K. Arnett,

R. Miller, C. Edmonson, S. Gorevan, and E. Mumm. The sample analysis

at Mars investigation and instrument suite. Space Science Reviews, 170(1-

4):401–478, 2012. ISSN 0038-6308. doi:10.1007/s11214-012-9879-z. URL

http://dx.doi.org/10.1007/s11214-012-9879-z.

N. Mahowald. Aerosol indirect effect on biogeochemical cycles and climate.

Science, 334(6057):794–796, 2011. doi:10.1126/science.1207374. URL http:

//www.sciencemag.org/content/334/6057/794.abstract.

R. Makkonen, A. Asmi, V.-M. Kerminen, M. Boy, A. Arneth, P. Hari, and M. Kul-

mala. Air pollution control and decreasing new particle formation lead to

57

References

strong climate warming. Atmospheric Chemistry and Physics, 12(3):1515–

1524, 2012. doi:10.5194/acp-12-1515-2012. URL http://www.atmos-chem-phys.

net/12/1515/2012/.

A. Maria and S. Carey. Using fractal analysis to quantitatively char-

acterize the shapes of volcanic particles. Journal of Geophysical Re-

search: Solid Earth, 107(B11):ECV 7–1–ECV 7–17, 2002. ISSN 2156–2202.

doi:10.1029/2001JB000822.

Mars Exploration Program Analysis Group (MEPAG). Mars science goals, ob-

jectives, investigations, and priorities: 2015 active version. Technical report,

MEPAG, Jun 2015. URL http://mepag.nasa.gov/reports.cfm.

A. Matsuki, A. Schwarzenboeck, H. Venzac, P. Laj, S. Crumeyrolle, and L. Gomes.

Cloud processing of mineral dust: direct comparison of cloud residual and clear

sky particles during AMMA aircraft campaign in summer 2006. Atmospheric

Chemistry and Physics, 10(3):1057–1069, 2010. doi:10.5194/acp-10-1057-2010.

URL http://www.atmos-chem-phys.net/10/1057/2010/.

B. May. A Survey of Radial Velocities in the Zodiacal Dust Cloud. PhD thesis,

Imperial College, London, 2007.

Z. Meng, P. Yang, G.W. Kattawar, L. Bi, K.N. Liou, and I. Laszlo. Single-

scattering properties of tri-axial ellipsoidal mineral dust aerosols: A database

for application to radiative transfer calculations. Journal of Aerosol Science,

41:501–512, 2010. doi:10.1016/j.jaerosci.2010.02.008.

S. Merikallio. Available solar energy on the dusty Martian atmosphere and sur-

face. Master’s thesis, Helsinki University of Technology, Espoo, Finland, 2003.

S. Merikallio and P. Janhunen. Moving an asteroid with Electric Solar

Wind Sail. Astrophysics and Space Sciences Transactions, 6(1):41–48, 2010.

doi:10.5194/astra-6-41-2010. URL http://www.astrophys-space-sci-trans.net/6/

41/2010/.

S. M. Metzger. Dust devils as aeolian transport mechanisms in southern Nevada

and the Mars Pathfinder landing site. PhD thesis, Univ. of Nev., Reno, 1999.

L. J. Mickley, E. M. Leibensperger, D. J. Jacob, and D. Rind. Regional warm-

ing from aerosol removal over the united states: Results from a tran-

sient 2010-2050 climate simulation. Atmos. Environ, 46:545–553, 2012.

doi:10.1016/j.atmosenv.2011.07.030. URL http://www.atmos-chem-phys.net/

12/1515/2012/.

G. Mie. Beiträge zur Optik trüber Medien, speziell kolloidaler Metal-

lösungen. Annalen der Physik, 330(3):377–445, 1908. ISSN 1521-

3889. doi:10.1002/andp.19083300302. URL http://dx.doi.org/10.1002/andp.

19083300302.

58

References

M. I. Mishchenko and L. D. Travis. Light scattering by polydisperse, ro-

tationally symmetric nonspherical particles: Linear polarization. Journal

of Quantitative Spectroscopy and Radiative Transfer, 51(5):759–778, 1994.

doi:10.1016/0022-4073(94)90130-9.

M. I. Mishchenko and L. D. Travis. Satellite retrieval of aerosol properties over

the ocean using polarization as well as intensity of reflected sunlight. Journal

of Geophysical Research: Atmospheres, 102(D14):16989–17013, 1997. ISSN

2156-2202. doi:10.1029/96JD02425. URL http://onlinelibrary.wiley.com/doi/10.

1029/96JD02425/full.

M. I. Mishchenko, A. A. Lacis, B. E. Carlson, and L. D. Travis. Nonspheric-

ity of dust-like tropospheric aerosols: Implications for aerosol remote sens-

ing and climate modeling. Geophysical Research Letters, 22(9):1077–1080,

1995. ISSN 1944-8007. doi:10.1029/95GL00798. URL http://dx.doi.org/10.

1029/95GL00798.

M. I. Mishchenko, L. Travis, and A. Macke. Scattering of light by polydis-

perse, randomly oriented, finite circular cylinders. Applied Optics, 35(24):

4927–4940, 1996a. URL https://www.osapublishing.org/ao/abstract.cfm?uri=

ao-35-24-4927.

M. I. Mishchenko, L. D. Travis, and D. W. Mackowski. T-matrix computations

of light scattering by nonspherical particles: A review. Journal of Quantita-

tive Spectroscopy and Radiative Transfer, 55(5):535–575, 1996b. ISSN 0022–

4073. doi:10.1016/0022-4073(96)00002-7. URL http://www.sciencedirect.com/

science/article/pii/0022407396000027.

M. I. Mishchenko, I. V Geogdzhayev, L. Liu, J. A. Ogren, A. A. Lacis, W. B. Rossow,

J. W. Hovenier, H. Volten, and O. Muñoz. Aerosol retrievals from AVHRR

radiances: effects of particle nonsphericity and absorption and an updated

long-term global climatology of aerosol properties. Journal of Quantitative

Spectroscopy and Radiative Transfer, 79–80(0):953–972, 2003. ISSN 0022–

4073. doi:http://dx.doi.org/10.1016/S0022-4073(02)00331-X. URL http://www.

sciencedirect.com/science/article/pii/S002240730200331X.

C. Moritz and R. Agudo. The future of species under climate change: Resilience

or decline? Science, 341(6145):504–508, 2013. doi:10.1126/science.1237190.

URL http://www.sciencemag.org/content/341/6145/504.abstract.

R. V. Morris, G. Klingelhöfer, B. Bernhardt, C. Schröder, D. S. Rodionov, P. A.

de Souza, A. Yen, R. Gellert, E. N. Evlanov, J. Foh, E. Kankeleit, P. Gütlich,

D. W. Ming, F. Renz, T. Wdowiak, S. W. Squyres, and R. E. Arvidson. Min-

eralogy at Gusev Crater from the Mössbauer Spectrometer on the Spirit

Rover. Science, 305(5685):833–836, 2004. doi:10.1126/science.1100020. URL

http://www.sciencemag.org/content/305/5685/833.abstract.

59

References

D. R. Muhs, J. M. Prospero, M. C. Baddock, and T. E. Gill. Identifying sources

of aeolian mineral dust: Present and past. In P. Knippertz and J.-B. W. Stuut,

editors, Mineral Dust, pages 51–74. Springer Netherlands, 2014. ISBN 978-94-

017-8977-6. doi:10.1007/978-94-017-8978-3_3. URL http://dx.doi.org/10.1007/

978-94-017-8978-3_3.

K. Muinonen, T. Nousiainen, P. Fast, K. Lumme, and J. I. Peltoniemi. Light

scattering by Gaussian random particles: Ray optics approximation. Journal

of Quantitative Spectroscopy and Radiative Transfer, 55:577–601, 1996.

K. Muinonen, E. Zubko, J. Tyynelä, Yu. Shkuratov, and G. Videen. Light scatter-

ing by Gaussian random particles with discrete-dipole approximation. Jour-

nal of Quantitative Spectroscopy and Radiative Transfer, 106:360–377, 2007.

doi:10.1016/j.jqsrt.2007.01.049.

K. Muinonen, T. Nousiainen, H. Lindqvist, O. Muñoz, and G. Videen. Light scat-

tering by Gaussian particles with internal inclusions and roughened surfaces

using ray optics. Journal of Quantitative Spectroscopy and Radiative Transfer,

110:1628–1639, 2009. doi:10.1016/j.jqsrt.2009.03.012.

O. Muñoz, H. Volten, J. F. de Haan, W. Vassen, and J. W. Hovenier. Experimental

determination of scattering matrices of randomly oriented fly ash and clay

particles at 442 and 633 nm. Journal of Geographical Research, 106(D19):

22833–22844, 2001.

O. Muñoz, H Volten, JF. de Haan, W. Vassen, and JW. Hovenier. Experimen-

tal determination of the phase function and degree of linear polarization of

El Chichón and Pinatubo volcanic ashes. J. Geophys. Res., 107(4174), 2002.

doi:10.1029/2001JD000983.

O. Muñoz, H. Volten, J. W. Hovenier, B. Veihelmann, W. J. van der Zande, L. B.

F. M. Waters, and W. I. Rose. Scattering matrices of volcanic ash particles of

Mount St. Helens, Redoubt, and Mount Spurr volcanoes. Journal of Geograph-

ical Research, 109, 2004. doi:10.1029/2004JD004684.

O. Muñoz, H. Volten, J. W. Hovenier, T. Nousiainen, K. Muinonen, D. Guirado,

F. Moreno, and L. B. F. M. Waters. Scattering matrix of large Saharan dust

particles: Experiments and computations. Journal of Geophysical Research:

Atmospheres, 112(D13), 2007. ISSN 2156-2202. doi:10.1029/2006JD008074.

O. Muñoz, F. Moreno, D. Guirado, J.L. Ramos, A. Lopez, F. Girela, J.M. Jerónimo,

L.P. Costillo, and I. Bustamante. Experimental determination of scattering

matrices of dust particles at visible wavelengths: The IAA light scattering

apparatus. Journal of Quantitative Spectroscopy and Radiative Transfer, 111

(1):187 – 196, 2010. ISSN 0022–4073. doi:10.1016/j.jqsrt.2009.06.011. URL

http://www.sciencedirect.com/science/article/pii/S002240730900226X.

60

References

O. Muñoz, F. Moreno, D. Guirado, J.L. Ramos, H. Volten, and J.W. Hov-

enier. The IAA cosmic dust laboratory: Experimental scattering matri-

ces of clay particles. Icarus, 211(1):894 – 900, 2011. ISSN 0019-1035.

doi:10.1016/j.icarus.2010.10.027. URL http://www.sciencedirect.com/science/

article/pii/S001910351000415X.

O. Muñoz, F. Moreno, D. Guirado, D. D. Dabrowska, H. Volten, and J.W. Hove-

nier. The Amsterdam – Granada light scattering database. Journal of Quan-

titative Spectroscopy and Radiative Transfer, 113(7):565 – 574, 2012. ISSN

0022-4073. doi:10.1016/j.jqsrt.2012.01.014. URL http://www.sciencedirect.

com/science/article/pii/S0022407312000386.

G. C. Nelson, H. Valin, R. D. Sands, P. Havlík, H. Ahammad, D. Deryng, J. El-

liott, S. Fujimori, T. Hasegawa, E. Heyhoe, P. Kyle, M. Von Lampe, H. Lotze-

Campen, D. Mason dCroz, H. van Meijl, D. van der Mensbrugghe, C. Müller,

A. Popp, R. Robertson, S. Robinson, E. Schmid, C. Schmitz, A. Tabeau, and

D. Willenbockel. Climate change effects on agriculture: Economic responses to

biophysical shocks. Proceedings of the National Academy of Sciences, 111(9):

3274–3279, 2014. doi:10.1073/pnas.1222465110. URL http://www.pnas.org/

content/111/9/3274.abstract.

A. Nenes, B. Murray, and A. Bougiatioti. Mineral dust and its microphysi-

cal interactions with clouds. In P. Knippertz and J.-B. W. Stuut, editors,

Mineral Dust, pages 287–325. Springer Netherlands, 2014. ISBN 978-94-

017-8977-6. doi:10.1007/978-94-017-8978-3_12. URL http://dx.doi.org/10.1007/

978-94-017-8978-3_12.

J. L. Neuwald and N. Valenzuela. The lesser known challenge of cli-

mate change: Thermal variance and sex-reversal in vertebrates with

temperature-dependent sex determination. PLoS ONE, 6(3):e18117, 03 2011.

doi:10.1371/journal.pone.0018117. URL http://dx.doi.org/10.1371%2Fjournal.

pone.0018117.

E. Nkhama, M. Ndhlovu, J. T. Dvonch, S. Siziya, and K. Voyi. Prevalence

and determinants of mucous membrane irritations in a community near

a cement factory in Zambia: A cross sectional study. International Jour-

nal of Environmental Research and Public Health, 12(1):871–887, 2015.

doi:10.3390/ijerph120100871. URL http://www.mdpi.com/1660-4601/12/1/871/

htm.

T. Nousiainen. Impact of particle shape on refractive-index dependence of scat-

tering in resonance domain. Journal of Quantitative Spectroscopy and Radia-

tive Transfer, 108:464–473, 2007. doi:10.1016/j.jqsrt.2007.07.008.

T. Nousiainen. Optical modeling of mineral dust particles: A review. Journal

of Quantitative Spectroscopy and Radiative Transfer, 110:1261–1279, 2009.

doi:10.1016/j.jqsrt2009.03.002.

61

References

T. Nousiainen and K. Vermeulen. Comparison of measured single-scattering ma-

trix of feldspar particles with T-matrix simulations using spheroids. Jour-

nal of Quantitative Spectroscopy and Radiative Transfer, 79âAS80(0):1031

– 1042, 2003. ISSN 0022-4073. doi:10.1016/S0022-4073(02)00337-0. URL

http://www.sciencedirect.com/science/article/pii/S0022407302003370.

T. Nousiainen, K. Muinonen, and P. Räisänen. Scattering of light by large Saha-

ran dust particles in a modified ray optics approximation. Journal of Geophys-

ical Research, 108:4025, 2003. doi:10.1029/2001JD001277.

T. Nousiainen, M. Kahnert, and B. Veihelmann. Light scattering modeling of

small feldspar aerosol particles using polyhedral prisms and spheroids. Jour-

nal of Quantitative Spectroscopy and Radiative Transfer, 101:471–487, 2006.

doi:10.1016/j.jqsrt.2006.02.038.

T. Nousiainen, M. Kahnert, and H. Lindqvist. Can particle shape information be

retrieved from light-scattering observations using spheroidal model particles?

Journal of Quantitative Spectroscopy and Radiative Transfer, 112(13):2213 –

2225, 2011. ISSN 0022-4073. doi:10.1016/j.jqsrt.2011.05.008.

Occupational and Environmental Health Department of Protection of the

Human Environment. Hazard Prevention and Control in the Work

Environment: Airborne Dust. World Health Organization, 1999.

URL www.who.int/occupational_health/publications/en/oehairbornedust.pdf.

WHO/SDE/OEH/99.14.

J. M. O’Neil, T. W. Davis, M. A. Burford, and C. J. Gobler. The rise of

harmful cyanobacteria blooms: The potential roles of eutrophication and cli-

mate change. Harmful Algae, 14(0):313 – 334, 2012. ISSN 1568-9883.

doi:http://dx.doi.org/10.1016/j.hal.2011.10.027. URL http://www.sciencedirect.

com/science/article/pii/S1568988311001557.

D. Osorio and M. Vorobyev. Photoreceptor sectral sensitivities in terrestrial an-

imals: adaptations for luminance and colour vision. Proceedings of the Royal

Society of London B: Biological Sciences, 272(1574):1745–1752, 2005. ISSN

0962-8452. doi:10.1098/rspb.2005.3156.

Oxford University Press. Oxford Dictionaries, 2015. URL http://http://www.

oxforddictionaries.com/.

P. Paasonen, A. Asm, T. Petäjä, M. K. Kajos, M. Äijälä, H. Junninen, T. Holst,

J. P. D. Abbatt, A. Arneth, W. Birmili, H. D. van der Gon, A. Hamed, A. Hof-

fer, L. Laakso, A. Laaksonen, W. R. Leaitch, C. Plass-Dülmer, S. C. Pryor,

P. Räisänen, E. Swietlicki, A. Wiedensohler, D. R. Worsnop, V.-M. Kermi-

nen, and M. Kulmala. Warming-induced increase in aerosol number con-

centration likely to moderate climate change. Nature Geoscience, 6:438–442,

2013. doi:10.1038/ngeo1800. URL www.nature.com/ngeo/journal/v6/n6/full/

ngeo1800.html.

62

References

G. Pan, K. Takahashi, Y. Feng, L. Liu, T. Liu, S. Zhang, N. Liu, T. Okubo, and

D. F. Goldsmith. Nested case-control study of esophageal cancer in relation to

occupational exposure to silica and other dusts. American Journal of Indus-

trial Medicine, 35(3):272–280, 1999. ISSN 1097-0274. doi:10.1002/(SICI)1097-

0274(199903)35:3<272::AID-AJIM7>3.0.CO;2-T.

M. P. Panning, W. B. Banerdt, E. Beucler, L. Boschi, C. Johnson, P. Lognonne,

A. Mocquet, and R. C. Weber. InSight: Single station broadband seismology for

probing Mars’ interior. Technical report, NASA Marshall Space Flight Center,

Huntsville, AL, United States, Mar. 2012. URL http://ntrs.nasa.gov/search.

jsp?R=20120013652. M12-1478.

M. Pautasso, T. F. Döring, M. Garbelotto, L. Pellis, and M. J. Jeger. Impacts of

climate change on plant diseases – opinions and trends. European Journal of

Plant Pathology, 133(1):295–313, 2012. ISSN 0929-1873. doi:10.1007/s10658-

012-9936-1. URL http://dx.doi.org/10.1007/s10658-012-9936-1.

J. Peñuelas, J. Sardans, M. Estiarte, R. Ogaya, J. Carnicer, M. Coll, A. Bar-

beta, A. Rivas-Ubach, J. Llusiá, M. Garbulsky, I. Filella, and A. S. Jump.

Evidence of current impact of climate change on life: a walk from genes to

the biosphere. Global Change Biology, 19(8):2303–2338, 2013. ISSN 1365-

2486. doi:10.1111/gcb.12143. URL http://onlinelibrary.wiley.com/doi/10.1111/

gcb.12143/abstract.

D. Petrov, Y. Shkuratov, and G. Videen. Analytical light-scattering solution

for Chebyshev particles. J. Opt. Soc. Am. A, 24(4):1103–1119, Apr 2007.

doi:10.1364/JOSAA.24.001103. URL http://josaa.osa.org/abstract.cfm?URI=

josaa-24-4-1103.

M. Pikridas, A. Tasoglou, K. Florou, and S. N. Pandis. Characterization of

the origin of fine particulate matter in a medium size urban area in the

mediterranean. Atmospheric Environment, 80(0):264 – 274, 2013. ISSN 1352-

2310. doi:10.1016/j.atmosenv.2013.07.070. URL http://www.sciencedirect.com/

science/article/pii/S1352231013006018.

G. Pitari, G. Di Genova, E. Coppari, N. De Luca, P. Di Carlo, M. Iar-

lori, and V. Rizi. Desert dust transported over europe: Lidar observa-

tions and model evaluation of the radiative impact. Journal of Geophys-

ical Research: Atmospheres, 120(7):2881–2898, 2015. ISSN 2169-8996.

doi:10.1002/2014JD022875. URL http://onlinelibrary.wiley.com/doi/10.1002/

2014JD022875/abstract. 2014JD022875.

J. M. Prospero and P. J. Lamb. African droughts and dust transport to the

Caribbean: Climate change implications. Science, 302(5647):1024–1027,

2003. doi:10.1126/science.1089915. URL http://www.sciencemag.org/content/

302/5647/1024.abstract.

63

References

C. R. Purcell, E. M.and Pennypacker. Scattering and absorption of light by

nonspherical dielectric grains. Astrophysical Journal, 186:705–714, 1973.

doi:10.1086/152538. URL http://adsabs.harvard.edu/abs/1973ApJ...186..705P.

K. Pye and H. Tsoar. Aeolian Sand and Sand Dunes. Springer Berlin Heidelberg,

2009. ISBN 978-3-540-85909-3. doi:http://dx.doi.org/10.1007/978-3-540-85910-

9.

J. Quaas, O. Boucher, N. Bellouin, and S. Kinne. Satellite-based estimate of the

direct and indirect aerosol climate forcing. Journal of Geophysical Research:

Atmospheres, 113(D5), 2008. ISSN 2156-2202. doi:10.1029/2007JD008962.

URL http://dx.doi.org/10.1029/2007JD008962. D05204.

P. Räisänen, P. Haapanala, C. E. Chung, M. Kahnert, R. Makkonen, J. Tonttila,

and T. Nousiainen. Impact of dust particle non-sphericity on climate simula-

tions. Quarterly Journal of the Royal Meteorological Society, 139(677):2222–

2232, 2013. ISSN 1477-870X. doi:10.1002/qj.2084. URL http://onlinelibrary.

wiley.com/doi/10.1002/qj.2084/abstract.

V. Ramanathan and G. Carmichael. Global and regional climate changes

due to black carbon. Nature Geosci, 1(4):221–227, 2008. ISSN 1752-0894.

doi:10.1038/ngeo156.

V. Ramanathan, P. J. Crutzen, J. T. Kiehl, and D. Rosenfeld. Aerosols,

climate, and the hydrological cycle. Science, 294(5549):2119–2124, 2001.

doi:10.1126/science.1064034. URL http://www.sciencemag.org/content/294/

5549/2119.abstract.

J. S. Reid, H. H. Jonsson, H. B. Maring, A. Smirnov, D. L. Savoie, S. S.

Cliff, E. A. Reid, J. M. Livingston, M. M. Meier, O. Dubovik, and S.-C.

Tsay. Comparison of size and morphological measurements of coarse mode

dust particles from Africa. Journal of Geographic Research, 108, 2003.

doi:10.1029/2002JD002485.

R. L. Revesz, P. H. Howard, K. Arrow, L. H. Goulder, R. E. Kopp,

M. A. Livermore, M. Oppenheimer, and T. Sterner. Global warm-

ing: Improve economic models of climate change. Nature, 508

(7495), 2014. doi:10.1038/508173a. URL http://www.nature.com/news/

global-warming-improve-economic-models-of-climate-change-1.14991.

C. M. Riley, W. I. Rose, and G. J. S. Bluth. Quantitative shape measurements of

distal volcanic ash. Journal of Geophysical Research: Solid Earth, 108(B10),

2003. ISSN 2156-2202. doi:10.1029/2001JB000818.

C. R. Robbins. Chemical and Physical Behavior of Human Hair. Springer New

York, 1988. ISBN 978-1-4757-2009-9. doi:10.1007/978-1-4757-2009-9.

64

References

C. Robert and G. Casella. Monte Carlo Statistical Methods. Springer-Verlag

New York, 2004. ISBN 978-0-387-21239-5. doi:http://dx.doi.org/10.1007/978-1-

4757-4145-2.

S. Rodríguez, A. Alastuey, S. Alonso-Pérez, X. Querol, E. Cuevas, J. Abreu-

Afonso, M. Viana, N. Pérez, M. Pandolfi, and J. de la Rosa. Transport of

desert dust mixed with North African industrial pollutants in the subtropi-

cal Saharan air layer. Atmospheric Chemistry and Physics, 11(13):6663–6685,

2011. doi:10.5194/acp-11-6663-2011. URL http://www.atmos-chem-phys.net/

11/6663/2011/.

E. Roeckner, G. Bäuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta,

S. Hagemann, I. Kirchner1, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese,

U. Schulzweida, and A. Tompkins. The atmospheric general circulation model

ECHAM5, part 1, model description. Technical Report 349, Max-Planck-

Institut für Meteorologie, Hamburg, 11 2003. URL http://www.mpimet.mpg.

de/fileadmin/publikationen/Reports/max_scirep_349.pdf.

S. W. Ruff and P. R. Christensen. Bright and dark regions on mars: Particle

size and mineralogical characteristics based on thermal emission spectrometer

data. Journal of Geophysical Research: Planets, 107(E12):2–1–2–22, 2002.

ISSN 2156-2202. doi:10.1029/2001JE001580. URL http://onlinelibrary.wiley.

com/doi/10.1029/2001JE001580/abstract. 5119.

J. A. Ryan and J. J. Carroll. Dust devil wind velocities: Mature state.

Journal of Geophysical Research, 75(3):531–541, 1970. ISSN 2156-

2202. doi:10.1029/JC075i003p00531. URL http://onlinelibrary.wiley.com/doi/

10.1029/JC075i003p00531/full.

J. A. Ryan and R. D. Lucich. Possible dust devils, vortices on Mars. Journal

of Geophysical Research: Oceans, 88(C15):11005–11011, 1983. ISSN 2156-

2202. doi:10.1029/JC088iC15p11005. URL http://onlinelibrary.wiley.com/doi/

10.1029/JC088iC15p11005/abstract.

K. M. Sakamoto, J. D. Allan, H. Coe, J. W. Taylor, T. J. Duck, and J. R. Pierce.

Aged boreal biomass-burning aerosol size distributions from bortas 2011. At-

mospheric Chemistry and Physics, 15(4):1633–1646, 2015. doi:10.5194/acp-15-

1633-2015. URL http://www.atmos-chem-phys.net/15/1633/2015/.

S.Asano and G. Yamamoto. Light scattering by a spheroidal particle. Appl.

Opt., 14(1):29–49, Jan 1975. doi:10.1364/AO.14.000029. URL http://ao.osa.

org/abstract.cfm?URI=ao-14-1-29.

J. Schmidhuber and F. N. Tubiello. Global food security under climate

change. Proceedings of the National Academy of Sciences, 104(50):19703–

19708, 2007. doi:10.1073/pnas.0701976104. URL http://www.pnas.org/content/

104/50/19703.abstract.

65

References

F. M. Schulz, K. Stamnes, and J. J. Stamnes. Modeling the radiative transfer

properties of media containing particles of moderately and highly elongated

shape. Geophysical Research Letters, 25(24):4481–4484, 1998. ISSN 1944-

8007. doi:10.1029/1998GL900184. URL 10.1029/1998GL900184.

F. M. Schulz, K. Stamnes, and J. J. Stamnes. Shape dependence of the

optical properties in size-shape distributions of randomly oriented pro-

late spheroids, including highly elongated shapes. Journal of Geophysi-

cal Research: Atmospheres, 104(D8):9413–9421, 1999. ISSN 2156-2202.

doi:10.1029/1998JD200107. URL http://onlinelibrary.wiley.com/doi/10.1029/

1998JD200107/abstract.

H. Seppänen, T. Rauhala, S. Kiprich, J. Ukkonen, M. Simonsson, R. Kurppa,

P. Janhunen, and E. Häggström. One kilometer (1 km) electric solar wind sail

tether produced automatically. Review of Scientific Instruments, 84(9):095102,

2013. doi:10.1063/1.4819795. URL http://scitation.aip.org/content/aip/journal/

rsi/84/9/10.1063/1.4819795.

P. C. Sinclair. Some preliminary dust devil measurements.

Monthly Weather Review, 92:363 – 367, 1964. doi:10.1175/1520-

0493(1964)092<0363:SPDDM>2.3.CO;2.

M.D. Smith. Spacecraft observations of the Martian atmosphere. An-

nual Review of Earth and Planetary Sciences, 36:191–219, 2008.

doi:10.1146/annurev.earth.36.031207.124334.

M. Sogl, D. Taeger, D. Pallapies, T. Bruning, F. Dufey, M. Schnelzer, and

M. Kreuzer. Quantitative relationship between silica exposure and lung

cancer mortality in German uranium miners, 1946 – 2003. British Jour-

nal of Cancer, 107(7):1188âAS–1194, 2012. doi:10.1038/bjc.2012.374. URL

http://www.nature.com/bjc/journal/v107/n7/full/bjc2012374a.html.

J. K. Spiegel, N. Buchmann, O. L. Mayol-Bracero, L. A. Cuadra-Rodriguez, C. J.

Valle Díaz, K. A. Prather, S. Mertes, and W. Eugster. Do cloud properties in a

Puerto Rican tropical montane cloud forest depend on occurrence of long-range

transported African dust? Pure and Applied Geophysics, 171(9):2443–2459,

2014. ISSN 0033-4553. doi:10.1007/s00024-014-0830-y. URL http://dx.doi.org/

10.1007/s00024-014-0830-y.

D. V. Spracklen and A. Rap. Natural aerosolâASclimate feedbacks suppressed by

anthropogenic aerosol. Geophysical Research Letters, 40(19):5316–5319, 2013.

ISSN 1944-8007. doi:10.1002/2013GL057966. URL http://onlinelibrary.wiley.

com/doi/10.1002/2013GL057966/abstract. 2013GL057966.

S. W. Squyres, R. E. Arvidson, E. T. Baumgartner, J. F. Bell, P. R. Chris-

tensen, S. Gorevan, K. E. Herkenhoff, G. Klingelhöfer, M. B. Madsen, R. V.

66

References

Morris, R. Rieder, and R. A. Romero. Athena Mars rover science investiga-

tion. Journal of Geophysical Research: Planets, 108(E12), 2003. ISSN 2156-

2202. doi:10.1029/2003JE002121. URL http://onlinelibrary.wiley.com/doi/10.

1029/2003JE002121/full.

N. Stern. The structure of economic modeling of the potential impacts of climate

change: Grafting gross underestimation of risk onto already narrow science

models. Journal of Economic Literature, 51(3):838–859, 2013-09-01T00:00:00.

doi:doi:10.1257/jel.51.3.838. URL http://www.ingentaconnect.com/content/aea/

jel/2013/00000051/00000003/art00006.

P. Stier, J. Feichter, S. Kinne, S. Kloster, E. Vignati, J. Wilson, L. Ganzeveld,

I. Tegen, M. Werner, Y. Balkanski, M. Schulz, O. Boucher, A. Minikin, and

A Petzold. The aerosol-climate model ECHAM5-HAM. Atmos. Chem. Phys, 5:

1125–1156, 2005.

T.F. Stocker, D. Qin, G.-K. Plattner, L.V. Alexander, S.K. Allen, N.L. Bindoff, F.-

M. BreÌAon, J.A. Church, U. Cubasch, S. Emori, P. Forster, P. Friedlingstein,

N. Gillett, J.M. Gregory, D.L. Hartmann, E. Jansen, B. Kirtman, R. Knutti,

K. Krishna Kumar, P. Lemke, J. Marotzke, V. Masson-Delmotte, G.A. Meehl,

I.I. Mokhov, S. Piao, V. Ramaswamy, D. Randall, M. Rhein, M. Rojas, C. Sabine,

D. Shindell, L.D. Talley, D.G. Vaughan, and S.-P. Xie. Technical Summary,

book section TS, page 33âAS115. Cambridge University Press, Cambridge,

United Kingdom and New York, NY, USA, 2013. ISBN 978-1-107-66182-

0. doi:10.1017/CBO9781107415324.005. URL http://www.climatechange2013.

org.

G. G. Stokes. On the change of refrangibility of light. Philosophical Transactions

of the Royal Society of London, 142:463–562, 1852. doi:10.1098/rstl.1852.0022.

URL http://rstl.royalsocietypublishing.org/content/142/463.short.

A. J. Stratton. Electromagnetic Theory. Mcgraw Hill Book Company, 1941. URL

https://archive.org/details/electromagnetict031016mbp.

J. Su, Y. Wu, M. P. McCormick, L. Lei, and III Lee, R. B. Improved method

to retrieve aerosol optical properties from combined elastic backscatter and

Raman lidar data. Applied Physics B, 116(1):61–67, 2014. ISSN 0946-2171.

doi:10.1007/s00340-013-5648-2. URL 10.1007/s00340-013-5648-2.

R. C. Sullivan, S. A. Guazzotti, D. A. Sodeman, and K. A. Prather. Direct ob-

servations of the atmospheric processing of Asian mineral dust. Atmospheric

Chemistry and Physics, 7(5):1213–1236, 2007. doi:10.5194/acp-7-1213-2007.

URL http://www.atmos-chem-phys.net/7/1213/2007/.

A. Taflove, S. G. Johnson, and A. Oskooi. Advances in FDTD Computational

Electrodynamics: Photonics and Nanotechnology. Artech House Publishers,

2013. ISBN 978-1608071708.

67

References

D. Tanré, Y. J. Kaufman, M. Herman, and S. Mattoo. Remote sensing of aerosol

properties over oceans using the MODIS/EOS spectral radiances. Journal

of Geophysical Research: Atmospheres, 102(D14):16971–16988, 1997. ISSN

2156-2202. doi:10.1029/96JD03437. URL http://onlinelibrary.wiley.com/doi/10.

1029/96JD03437/abstract.

M. Taylor, S. Kazadzis, V. Amiridis, and R.A. Kahn. Global aerosol

mixtures and their multiyear and seasonal characteristics. At-

mospheric Environment, 116(0):112 – 129, 2015. ISSN 1352-

2310. doi:http://dx.doi.org/10.1016/j.atmosenv.2015.06.029. URL

http://www.sciencedirect.com/science/article/pii/S1352231015301709.

I. Tegen and K. Schepanski. The global distribution of mineral dust. IOP Con-

ference Series: Earth and Environmental Science, 7(1):012001, 2009. URL

http://stacks.iop.org/1755-1315/7/i=1/a=012001.

P. Thomas and P. J. Gierasch. Dust devils on Mars. Science, 230(4722):175–177,

1985. doi:10.1126/science.230.4722.175. URL http://sciencepubs.org/content/

230/4722/175.abstract.

W. Thuiller, S. Lavergne, C. Roquet, I. Boulangeat, B. Lafourcade, and M.. B.

Araujo. Consequences of climate change on the tree of life in Europe. Nature,

470(7335):531–534, 2011. doi:10.1038/nature09705. URL http://dx.doi.org/10.

1038/nature09705.

R. S. J. Tol. On the uncertainty about the total economic impact of climate

change. Environmental and Resource Economics, 53(1):97–116, 2012. ISSN

0924-6460. doi:10.1007/s10640-012-9549-3. URL http://dx.doi.org/10.1007/

s10640-012-9549-3.

H. C. van de Hulst. Light Scattering by Small Particles. Dover Publications, Inc.,

New York, 2nd edition, 1981.

D. T. Vaniman, D. L. Bish, D. W. Ming, T. F. Bristow, R. V. Morris, D. F. Blake,

S. J. Chipera, S. M. Morrison, A. H. Treiman, E. B. Rampe, M. Rice, C. N.

Achilles, J. P. Grotzinger, S. M. McLennan, J. Williams, J. F. Bell, H. E. New-

som, R. T. Downs, S. Maurice, P. Sarrazin, A. S. Yen, J. M. Morookian, J. D.

Farmer, K. Stack, R. E. Milliken, B. L. Ehlmann, D. Y. Sumner, G. Berger,

J. A. Crisp, J. A. Hurowitz, R. Anderson, D. J. Des Marais, E. M. Stolper,

K. S. Edgett, S. Gupta, N. Spanovich, and MSL Science Team. Mineralogy

of a mudstone at Yellowknife Bay, Gale Crater, Mars. Science, 343(6169),

2014. doi:10.1126/science.1243480. URL http://www.sciencemag.org/content/

343/6169/1243480.

B. Veihelmann, T. Nousiainen, M. Kahnert, and W. J. van der Zande. Light

scattering by small feldspar particles simulated using the Gaussian random

sphere geometry. Journal of Quantitative Spectroscopy and Radiative Trans-

fer, 100:393–405, 2006. doi:10.1016/j.jqsrt.2005.11.053.

68

References

B. Veihelmann, P.F. Levelt, P. Stammes, and J.P. Veefkind. Simulation study

of the aerosol information content in OMI spectral reflectance measurements.

Atmospheric Chemistry and Physics Discussion, 7:1785–1821, 2007.

H. Volten, O. Muñoz, J. F. de Haan, W. Vassen, J. W. Hovenier, K. Muinonen,

and T. Nousiainen. Scattering matrices of mineral aerosol particles at 441.6

nm and 632.8 nm. Journal of Geographical Research, 106(D15):17375–17401,

2001. doi:10.1029/2001JD900068.

N. V. Voshchinnikov and V. G. Farafonov. Optical properties of spheroidal par-

ticles. Astrophysics and Space Science, 204(1):19–86, 1993. ISSN 0004-

640X. doi:10.1007/BF00658095. URL http://link.springer.com/article/10.

1007%2FBF00658095.

N. V. Voshchinnikov, V. B. Il’in, Th. Henning, B. Michel, and V. G. Fara-

fonov. Extinction and polarization of radiation by absorbing spheroids:

shape/size effects and benchmark results. Journal of Quantitative Spec-

troscopy and Radiative Transfer, 65(6):877 – 893, 2000. ISSN 0022-

4073. doi:10.1016/S0022-4073(99)00159-4. URL http://www.sciencedirect.com/

science/article/pii/S0022407399001594.

J. Wang, X. Liu, S. A. Christopher, J. S. Reid, E. Reid, and H. Maring.

The effects of non-sphericity on geostationary satellite retrievals of dust

aerosols. Geophysical Research Letters, 30(24), 2003. ISSN 1944-8007.

doi:10.1029/2003GL018697. URL http://dx.doi.org/10.1029/2003GL018697.

Z. Wang, Y. Liu, M. Hu, X. Pan, J. Shi, F. Chen, K. He, P. Koutrakis, and

D. C. Christiani. Acute health impacts of airborne particles estimated from

satellite remote sensing. Environment International, 51:150 – 159, 2013.

ISSN 0160-4120. doi:http://dx.doi.org/10.1016/j.envint.2012.10.011. URL http:

//www.sciencedirect.com/science/article/pii/S0160412012002358.

T. Wheeler and J. von Braun. Climate change impacts on global food security.

Science, 341(6145):508–513, 2013. doi:10.1126/science.1239402. URL http:

//www.sciencemag.org/content/341/6145/508.abstract.

M. J. Wolff, M. D. Smith, R. T. Clancy, N. Spanovich, B.A. Whitney, M.T. Lem-

mon, J.L. Lemmon, J.L. Bandfield, D. Bandfield, A. Ghosh, G. Landis, P.R.

Christensen, J.F. Bell III, and S.W. Squyres. Constraints on dust aerosols from

the Mars Exploration rovers using MGS overflights and Mini-TES. Journal of

Geophysical Research, 111, 2006. doi:10.1029/2006JE002786.

World Bank Group. Turn down the heat : Confronting the new climate normal.

Technical report, Washington, DC, 2014. URL http://hdl.handle.net/10986/

20595.

C. Wu and X. Wang. Effects of foliar dust on plant reflectance spectra and phys-

iological ecology: A review. Chin. J. Appl. Environ. Biol., 20(6):1132–1138,

69

References

2014. doi:10.3724/SP.J.1145.2014.03044. URL www.cibj.com/en/AdsClick.

aspx?AdsId=266.

J. Yang, P. Gong, R. Fu, M. Zhang, J. Chen, S. Liang, B. Xu, J. Shi,

and R. Dickinson. The role of satellite remote sensing in climate change

studies. Nature Climate Change, 3(10):875–883, 2013. ISSN 1758-678X.

doi:10.1038/nclimate1908. URL http://www.nature.com/nclimate/journal/v3/

n10/abs/nclimate1908.html.

B. Yi, C. N. Hsu, P. Yang, and S.-C. Tsay. Radiative transfer simulation

of dust-like aerosols: Uncertainties from particle shape and refractive in-

dex. Journal of Aerosol Science, 42(10):631 – 644, 2011. ISSN 0021-

8502. doi:http://dx.doi.org/10.1016/j.jaerosci.2011.06.008. URL http://www.

sciencedirect.com/science/article/pii/S0021850211001030.

H. Yu, M. Chin, T. Yuan, H. Bian, L. A. Remer, J. M. Prospero, A. Omar,

D. Winker, Y. Yang, Y. Zhang, Z. Zhang, and C. Zhao. The fertilizing role of

African dust in the Amazon rainforest: A first multiyear assessment based

on data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observa-

tions. Geophysical Research Letters, 42(6):1984–1991, 2015. ISSN 1944-8007.

doi:10.1002/2015GL063040. URL http://dx.doi.org/10.1002/2015GL063040.

2015GL063040.

I. T. Yu and L. A. Tse. Joint association of smoking and silica dust with lung

cancer risk: a population-based case-referent study in Hong Kong men. Occup

Environ Med, 71(A4), 2014. doi:10.1136/oemed-2014-102362.11.

I. T. S. Yu, L. A. Tse, T. W. Wong, C. C. Leung, C. M. Tam, and A. C..K.

Chan. Further evidence for a link between silica dust and esophageal can-

cer. International Journal of Cancer, 114(3):479–483, 2005. ISSN 1097-

0215. doi:10.1002/ijc.20764. URL http://onlinelibrary.wiley.com/doi/10.1002/

ijc.20764/abstract.

Z. K. Zeleke, B. E. Moen, and M. Bråtveit. Cement dust exposure and acute

lung function: A cross shift study. BMC Pulmonary Medicine, 10(19), 2010.

doi:10.1186/1471-2466-10-19. URL http://www.biomedcentral.com/1471-2466/

10/19.

I. Zelinka, V. Snášel, and A. Abraham. Handbook of Optimization : From Classi-

cal to Modern Approach. Springer Berlin Heidelberg, 2012. ISBN 978-3-642-

30503-0.

S. Zia-Khan, W. Spreer, Y. Pengnian, X. Zhao, H. Othmanli, X. He, and

J. Müller. Effect of dust deposition on stomatal conductance and leaf temper-

ature of cotton in northwest China. Water, 7(1):116, 2014. ISSN 2073-4441.

doi:10.3390/w7010116. URL http://www.mdpi.com/2073-4441/7/1/116.

70

References

E. Zubko, K. Muinonen, O. Muñoz, T. Nousiainen, Y. Shkuratov, W. Sun, and

G. Videen. Light scattering by feldspar particles: Comparison of model ag-

glomerate debris particles with laboratory samples. Journal of Quantita-

tive Spectroscopy and Radiative Transfer, 131(0):175 – 187, 2013. ISSN

0022-4073. doi:10.1016/j.jqsrt.2013.01.017. URL http://www.sciencedirect.

com/science/article/pii/S0022407313000368.

71

References

72

Errata

In Paper II, Eq.(15) should read:

ξe =√(1− ce/be)2 + (1− be/ae)2. (6.1)

73

This is a doctoral dissertation about light scattering modeling using spheroidal and ellipsoidal model particles. In this thesis, I have found that ellipsoidally or spheroidally shaped model particles could be used to improve modeling of the light scattering by atmospheric mineral dust, Martian dust analog particles (namely palagonite dust) and volcanic ash particles. Results of these studies have been used to improve ECHAM climate models of the Finnish Meteorological Institute, and plans are also in place to incorporate the results into remote sensing data analysis software of AATSR satellite instrument.

Aalto-D

D 27

/2016

9HSTFMG*aggfhi+

ISBN 978-952-60-6657-8 (printed) ISBN 978-952-60-6658-5 (pdf) ISSN-L 1799-4934 ISSN 1799-4934 (printed) ISSN 1799-4942 (pdf) Aalto University School of Science Biomedical Engineering and Computational Science www.aalto.fi

BUSINESS + ECONOMY ART + DESIGN + ARCHITECTURE SCIENCE + TECHNOLOGY CROSSOVER DOCTORAL DISSERTATIONS

Sini Merikallio

Com

puter modeling of light scattering by atm

ospheric dust particles with spheroids and ellipsoids

Aalto

Unive

rsity

2016

Biomedical Engineering and Computational Science

Computer modeling of light scattering by atmospheric dust particles with spheroids and ellipsoids

Sini Merikallio

DOCTORAL DISSERTATIONS