assessing precipitation mechanisms on kilimanjaro and

Assessing Precipitation Mechanisms onKilimanjaro and Mount Kenya:

an Idealized Modeling Study

Master’s Thesis

in Atmospheric Sciences

Submitted to the

Faculty of Geo- and Atmospheric Sciences

of the

University of Innsbruck

in Partial Fulfillment of the Requirements for the Degree of

Master of Science

by

Federico Covi

Advisors

Assoc. Prof. Dr. Alexander Gohm and Univ. Prof. Dr. Georg Kaser

Innsbruck, November 2016

To my grandfather

i

Abstract

Tropical glaciers have proved to be fundamental in the understanding of the climate

behaviour and its change in the tropical mid-troposphere, where measurements have

been only recently collected. Glaciers on Kilimanjaro and Mount Kenya in tropi-

cal East Africa are among the best studied sites in the tropics and their general

behaviour is nowadays well understood. The two mountains, located 370 km away

from each other, are often considered as typically influenced by the same air masses.

Yet, their precipitation patterns and glaciers behaviour differ considerably. This

indicates that either different air masses are at play or that precipitation processes

are considerably different. The present study aims to investigate the most relevant

mechanisms of precipitation over Kilimanjaro and Mount Kenya.

First, in-situ observations and ERA-Interim reanalysis data are used to charac-

terize the atmospheric background conditions during precipitation events at the two

mountains. Next, idealized vertical profiles are constructed and used as an atmo-

spheric reference state for simulations with the Weather Research and Forecasting

(WRF) model. Two types of model topography are used, a semi-realistic topography

constructed from a high-resolution digital elevation dataset (SRTM) and an ideal

topography obtained by a parametric formula. A series of sensitivity simulations is

carried out with modified topography, atmospheric reference state and surface heat

fluxes to asses the dominant factors governing precipitation over the two mountains.

The analysis of atmospheric background conditions confirms the hypothesis that

Kilimanjaro and Mount Kenya are locally influenced by the same air mass during

precipitation events. Numerical simulations show that the mesoscale circulation

over the two mountains is the result of a complex interaction of the large-scale flow

with the topography and the surface heat fluxes. Precipitation distribution and

magnitude are very sensitive to the orientation of the mountain respect to the large-

scale flow. Moreover the precipitation magnitude and the shift upslope, towards the

summit, of the precipitation maximum are strongly controlled by the surface heat

fluxes. With this, we aim to enhance the climate information from the differently

behaving glaciers on the two East African mountains.

iii

Contents

Abstract iii

Contents v

1 Introduction 1

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 State of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2.1 Tropical Glaciology and Meteorology . . . . . . . . . . . . . . 3

1.2.2 Atmospheric Processes over Complex Terrain . . . . . . . . . 9

1.3 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

1.4 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2 Methods 15

2.1 Reanalysis Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.1.1 Classification of Precipitation Events . . . . . . . . . . . . . . 15

2.1.2 ERA-Interim Reanalysis . . . . . . . . . . . . . . . . . . . . . 18

2.2 Numerical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2.1 Model Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.2.2 Input Sounding . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2.3 Topography . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2.4 Simulations Overview . . . . . . . . . . . . . . . . . . . . . . . 28

3 Reanalysis Study 31

3.1 Classification of Precipitation Events . . . . . . . . . . . . . . . . . . 31

3.2 ERA-Interim Reanalysis . . . . . . . . . . . . . . . . . . . . . . . . . 34

4 Idealized Numerical Simulations 39

4.1 Kilimanjaro and Mount Kenya Comparison . . . . . . . . . . . . . . . 39

4.2 Sensitivity Study for Kilimanjaro . . . . . . . . . . . . . . . . . . . . 53

4.3 Idealized Ridge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

v

vi CONTENTS

5 Discussion 61

5.1 Reanalysis Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.2 Idealized Numerical Simulations . . . . . . . . . . . . . . . . . . . . . 63

5.3 Comparison to Previous Studies . . . . . . . . . . . . . . . . . . . . . 67

5.4 Limitations of the Study and Possible Improvements . . . . . . . . . 68

6 Conclusions 71

A Description of WRF Model Modifications 73

B Model Testing 77

Bibliography 81

Acknowledgments 89

Curriculum Vitae 91

Chapter 1

Introduction

1.1 Motivation

The presence of glaciers in tropical East Africa, close to the equator, has always

been regarded as a peculiarity that caused interest in the scientific community since

the first ascents to the main summits of the region in the Rwenzori Mountains, on

Mount Kenya and on Kilimanjaro (Hastenrath and Reidel 1984; Kaser and Osmas-

ton 2002; Kaser et al. 2004; Cullen et al. 2012). During the last decades, glaciologists

and climatologists have become increasingly aware of the fact that tropical glaciers

provide important proxy data in climate change research (Cruikshank 2001). A de-

tailed knowledge of glaciers evolution allows insight into past climate conditions, if

the atmospheric processes controlling the observed changes are understood (Hasten-

rath and Kruss 1992; Molg et al. 2009a; Prinz et al. 2016). In this sense, glaciers

on Kilimanjaro and on Mount Kenya, because of their location and exceptional el-

evation, offer a unique setup to sample atmospheric conditions in the tropical mid-

troposphere, which is extremely valuable given the influence of low latitude weather

and climate processes on global circulation (Chiang 2009; Cullen et al. 2012).

Hastenrath and Reidel (1984) comprehensively reported and documented glacier

retreat in the three presently glacierized massif of tropical East Africa: Rwenzori,

Mount Kenya and Kilimanjaro. Specific details for Mount Kenya are provided by,

e.g., Kruss and Hastenrath (1987), Hastenrath et al. (1989), Hastenrath (1995, 2005)

and Prinz et al. (2011, 2012); for the Rwenzori by, e.g., Kaser and Noggler (1991,

1996), Kaser and Osmaston (2002) and Molg et al. (2003); and for Kilimanjaro by,

e.g., Hastenrath and Greischar (1997), Thompson et al. (2002), Kaser et al. (2004)

and Cullen et al. (2012).

On a global scale, the main cause of glacier retreat and mass loss is attributed to

changes in air temperature, but this has been demonstrated to not be the unique case

on tropical glaciers (IPCC 2001). The factors governing tropical glaciers fluctuations

1

2 Introduction

are considered to be an interactive combination of changes in air temperature and

atmospheric moisture content, jointly driving precipitation, cloudiness and incoming

shortwave radiation (Kaser 1999). Process resolving studies, based on and evaluated

with in situ observations, revealed that glaciers on Kilimanjaro are most sensitive

to changes in atmospheric moisture and precipitation (Molg and Hardy 2004; Molg

et al. 2008, 2009b). A similar study by Prinz et al. (2016) show analogous results

for Lewis glacier on Mount Kenya.

Climate variability in East Africa is well documented on different time scales.

Specifically several climate proxies, e.g., historical accounts of lake levels, glacier

variations analysis (Hastenrath and Reidel 1984), and water balance models of lakes

(Nicholson and Yin 2001), indicate the climatic evolution over East Africa during

the last 150 years. According to these proxies there is evidence of a transition to a

drier climate, starting around 1880. Drier conditions were maintained throughout

the 20th century (Rodhe and Virji 1976). This drying is critically depending on

moisture supply from the Indian Ocean (Molg et al. 2006; Chan et al. 2008), which

strongly influences the precipitation amount and frequency over tropical East Africa.

These findings led to particular interest in precipitation processes on Kiliman-

jaro (Molg et al. 2008) and on Mount Kenya (Nicholson et al. 2013; Prinz et al.

2016). In particular local precipitation patterns on the summits, inferred by glaciers

mass and energy balance studies, are well understood as well as the large-scale

circulation that controls weather and climate in East Africa (Molg et al. 2009a).

However the link between these two scales remains still as a gap in our knowledge.

The mesoscale interaction of the huge mountains, such as Kilimanjaro and Mount

Kenya, with the large-scale flow is not yet totally clear despite the pioneering study

by Molg et al. (2009a). Filling this gap represents the next step forward in the

scientific understanding of glacier retreat in tropical East Africa.

It is for all of these reasons that a better understanding of the precipitation

mechanisms over the glaciated mountains of tropical East Africa is necessary. This

study targets this gap and aims to improve the understanding of the relevant pro-

cesses responsible for precipitation on the summits of Kilimanjaro and Mount Kenya.

1.2 State of Research

In the previous Chap. 1.1 a general summary and overview of more than a 100

years of scientific research on tropical glaciers in East Africa was presented. This

chapter will now focus on specific aspects of previous studies relevant for this work.

It is organized in two separated parts: the first one about tropical glaciology and

meteorology and the second one about orographic precipitation.

1.2 State of Research 3

30°S

20°S

10°S

0°

10°N

20°N

30°N

40°N

10°W 10°E 30°E 50°E

5°S

0°

5°N

30°E 35°E 40°E

Kilimanjaro

Mt. KenyaRwenzori

0 250 500

km

Figure 1.1: Glaciated areas in tropical East Africa.

1.2.1 Tropical Glaciology and Meteorology

Glaciated areas in tropical East Africa are nowadays limited to three specific geo-

graphic locations (Fig. 1.1): Kilimanjaro in Tanzania, Mount Kenya in Kenya and

the Ruwenzori on the border between Uganda and Congo (Kaser and Osmaston

2002).

Kilimanjaro

The scientific exploration of Kilimanjaro started when Hans Meyer first ascended

the mountain in 1887. Publications reporting the drastic retreat of the glaciers on

the summit were abundant during the whole 20th century (Meyer 1891; Volkens

1897; Klute 1920; Gillman 1923; Jager 1931; Geilinger 1936; Hunt 1947; Spink 1949;

Humphries 1959; Downie and Wilkinson 1972; Hastenrath and Reidel 1984; Osmas-

ton 1989; Hastenrath and Greischar 1997; Kaser and Osmaston 2002). Kilimanjaro

is the highest mountain in Africa. It is located at the boarder between Tanzania

and Kenya, about 370 km south of the equator and about the same distance from

the Indian Ocean. The mountain is a huge stratovolcano, about 80 km by 50 km on

its base, and it consists of three single peaks: Shira (4005 m), Mawenzi (5140 m)

and Kibo (5893 m). The latter, Kibo, is the only peak with glaciers (Hastenrath

and Reidel 1984; Kaser and Osmaston 2002; Kaser et al. 2004). The summit region

of Kibo (Fig. 1.2) is formed by a series of concentric craters of different age, 1.9

km by 2.4 km in diameter enclosing the innermost Reusch Crater. Glaciers on the

summit represent the remaining parts of an ice cap which previously covered the

entire summit of Kibo (Humphries 1959). These ice bodies typically have vertical

4 Introduction

Figure 1.2: Glacier extent on Kibo in 2003 as in Cullen et al. (2006). AWSs locations

and vertical ice cliffs are indicated. The highlighted 5700 m contour approximately follows

the outer crater rim and reasonably delineates the almost flat summit plateau. Taken from

Molg et al. (2008).

walls mainly along their north and south margins and show a strong east-west ori-

entation (Kaser et al. 2004). As noted by Osmaston (1989) this asymmetry is not

only confined to the most recent glacier extent, there is in fact evidence of similar

behaviour also in the past.

Since the year 2000, the scientific efforts on Kilimanjaro have been strengthen-

ing within the framework of several projects and a long going cooperation between

the universities of Innsbruck, Massachusetts and Otago. Three automatic weather

stations (AWSs) are operated on Kibo (see Fig. 1.2). AWS1 has been running since

February 2000 on the flat surface of the Northern Icefield and it is designed for long-

term monitoring of the high-altitude climate; AWS2, running since 2005, addresses

the special case of vertical ice walls on the summit plateau (Molg 2003), AWS3 is


located on Kersten glacier and it is explicitly designed to run models of the glacier-

climate interaction (Molg et al. 2008). Studies combining in-situ measurements from

AWS1 (Molg and Hardy 2004) and from AWS3 (Molg et al. 2008) with mass and

energy balance modelling showed that fluctuations of both slope glaciers and the

horizontal surfaces of the plateau glaciers on Kilimanjaro primarily reflect precipita-

tion variability. This is a direct cause of the snowfall albedo feedback, which is much

stronger than on other locations such as mid-latitude glaciers. Glaciers on Kibo are

in fact located above the mean freezing level, thus effects of local air temperature

changes on mass balance are almost negligible (Molg et al. 2008).

Mount Kenya

Mount Kenya is is also subject of extensive scientific studies (Hastenrath 2005).

As on Kilimanjaro, the scientific interest in the mountain started in the late 19th

and early 20th century with the first expeditions to the peak region. These trips

produced sketches and photographs of the glaciers at that time (Gregory 1894;

Mackinder 1900; Dutton 1929). Following, several scientific campaigns were carried

out, undertaking measurements of ice surface velocity and mapping the glaciers

surface (Troll and Wien 1949; Charnley 1959). An exhaustive summary of all the

scientific expeditions on the glaciers of Mount Kenya is presented in Hastenrath

(2005).

Lewis glacier (number 4 in Fig. 1.3) is the biggest of Mount Kenya massif and it

is one of the best documented tropical glaciers (Prinz et al. 2011). It is located about

370 m below the summit of Mount Kenya in a south-westerly exposed, quasi cirque

location between the main summit and a secondary peak (Fig. 1.3). The retreat of

Lewis glacier is recorded both over the Quaternary, by downvalley moraines, and in

recent decades, by discontinuous field campaigns (see Hastenrath (2005) for detailed

references).

Pioneering studies using limited meteorological data and simple modeling tools

attributed the observed retreat of Lewis glacier to combined changes in radiation

geometry, air temperature, precipitation, albedo and cloudiness (Kruss and Hasten-

rath 1987, 1990; Hastenrath and Kruss 1992; Hastenrath 2009). Since September

2009 an AWS is operated by the University of Innsbruck on Lewis glacier at 4828

m, about 30 m below the upper limit of the glacier. Detailed point (Nicholson et al.

2013) and distributed (Prinz et al. 2016) surface energy and mass balance model-

ing studies revealed that nowadays Lewis glacier is most sensitive to atmospheric

moisture, in a complex interaction of solid precipitation, cloudiness and albedo, de-

spite the much warmer conditions at the summit of Mount Kenya compared to to

Kilimanjaro, with a mean regional freezing level close to the altitude of the glaciers.

6 Introduction

Figure 1.3: Map of the glaciers on Mount Kenya. Shading and solid lines refer to Septem-

ber 1987 and dashed lines indicate glaciers that disappeared earlier. Topography contours

at 200 m intervals. Lewis glacier is indicated with number 4. Taken from Hastenrath

(2005).

Tropical Meteorology

Meteorological conditions in tropical East Africa are the results of complex interac-

tions between multiple convergences zones and topographic and marine influences

(Nicholson 1996). The most dominant factor is the solar driven Intertropical Con-

vergence Zone (ITCZ) which is driven by solar radiation and, moving from south

to north during boreal spring and from north to south during boreal autumn, is

responsible for the typical tropical seasonality (Chan et al. 2008). The confluence

of the trade winds along the ITCZ causes a seasonal wind change from northeast-

erlies in January to easterlies in March, southeasterlies in July and again easterlies

in October, as shown in Table 1.1 (Gatebe et al. 1999). The mean annual rain-

fall is typically divided into four periods: January to February (rather dry), March

to May (long rains), June to September (rather dry), October to December (short


Period Season Wind Prec.

January-February (JF) Dry Northeasterlies 18%

March-May (MAM) Long rains Easterlies 42%

June-September (JJAS) Dry Southeasterlies 15%

October-December (OND) Short rains Easterlies 25%

Table 1.1: Seasonality in tropical East Africa and related large-scale wind direction and

percentage of the mean annual precipitation (Prec.). Taken from Indeje et al. (2000) and

Chan et al. (2008)

rains), accounting respectively for roughly 18%, 42%, 15%, and 25% of the mean

annual rainfall (Table 1.1) (Indeje et al. 2000). Precipitation events during the long

rains are heavier and longer in duration, and more likely associated to local factors

(Chan et al. 2008) compared to events during the short rains which are less intense

and shorter in duration. Precipitation amounts in East Africa critically depend on

moisture supply from the Indian Ocean and its connection with intraseasonal and

interannual variability has been strongly investigated (Webster et al. 1999; Mutai

and Ward 2000; Hastenrath 2001; Molg et al. 2006).

Recently, few studies investigated how the synoptic scale meteorological condi-

tions influence precipitation events on the glaciarized summits of Kilimanjaro and

Mount Kenya. Chan et al. (2008) correlated global circulation patterns with sig-

nificant snowfall events recorded by AWS1 on Kilimanjaro (Fig. 1.2). The results

shows that both short rains and long rains precipitation events on the summit are

dominated by a east to west propagation of moisture, with the Indian Ocean playing

a major role. Only during the long rains season there is evidence of moisture con-

tribution from the interior of the African continent. Furthermore, largest snowfall

events on the summit tend to be associated with low wind speed, favorable for the

development of surface radiative heating and thereby deep convection. High specific

humidity near the surface is another necessary ingredient to trigger moist convection

during large snowfall events.

Molg et al. (2009a) performed atmospheric numerical simulations to study the

mesoscale interaction of the topography of Kilimanjaro with the air masses of the

large scale flow. A high moisture content in the atmospheric boundary layer is

identified to be the most important precondition for significant precipitation on the

summit (as in Chan et al. (2008)). Furthermore the elevation band of maximum

precipitation is usually located at mid elevations, as typically observed on tropical

high mountains (Hastenrath 1991). This can be shifted upslope during moister

events. The simulations shows that the precipitation maximum is located on the

leeward side of the mountain, favoured by the formation of a lee side flow reversal

8 Introduction

Figure 1.4: Different convective patterns on Mount Kenya and on Kilimanjaro (Kibo).

Taken from Kaser and Osmaston (2002), pag. 139.

after the lateral deflection of the impinging flow.

Pepin et al. (2010) suggests a new perspective in the present literature affirming

that the vegetation belt and the land cover around Kilimanjaro is the main source of

moisture for precipitation on the summit. The study, supported by a field campaign

to measure different atmospheric variables along the slope of Kilimanjaro, shows

evidence of a strong diurnal cycle allowing the formation of a local slope winds

circulation which brings moisture from the forested zone below to the top of the

mountain.

A recent work of Nicholson et al. (2013) investigated the micrometeorological

conditions recorded by the AWS on Lewis glacier on Mount Kenya and compared

it with the conditions at AWS3 on Kersten glacier on Kilimanjaro. Meteorological

conditions at Lewis glacier show little interannual variability, in accordance with the

expected regional hygric seasonality. The comparison with the data from the summit

of Kilimanjaro suggests contrasts between the two locations, JF (JJAS) is the more

arid of the two dry seasons and OND (MAM) is the more humid of the two wet

seasons at Mount Kenya (Kilimanjaro). Furthermore the occurrence of convective

clouds is found to be more frequent over the summit of Mount Kenya, leading to an

higher amount of accumulated snow at this location. Both mountains appear to be

influenced by the same synoptic conditions (Ehrengruber 2011), although only 40%

of days with precipitation during the study period occurred simultaneous at both

sites. This suggests that probably the precipitation driving mechanism is different.

Accumulated precipitation at Lewis glacier was 35% larger than at Kersten glacier

during the sampled period.

An hypothesis proposed by Kaser and Osmaston (2002), illustrated in Fig. 1.4,


suggests that different convective patterns on the two mountains are caused by

differences in mountain shape and height. The convex shape of Kibo essentially

prevents convective precipitation at the summit plateau. Convective cells cannot,

as on Mt. Kenya, meet aloft the summit but arrange themselves annularly around

the peak. Furthermore Kilimanjaro is about 700 m higher than Mount Kenya. This

difference in height may strongly reduce the number of precipitation events that

reach the summit of Kilimanjaro compared to to Mount Kenya.

Finally the concomitant effect of a more frequent cloud cover and a higher

accumulation on Mount Kenya could provide a possible explanation for glaciers

remaining at a lower elevation compared to Kilimanjaro (Nicholson et al. 2013).

Limited-area atmospheric modeling, such as conducted by Molg et al. (2009a), is

required to fully understand the physical processes that govern the formation of

convective precipitation over the two mountain.

1.2.2 Atmospheric Processes over Complex Terrain

Flow Regimes for Isolated Mountains

In a dry atmosphere a stratified flow past an isolated mountain is typically controlled

by the characteristics of the large-scale flow impinging the obstacle and by the terrain

geometry (Epifanio 2015). Typically the large-scale flow is assumed uniform, with a

uniform cross-mountain component of the upstream flow U and a constant upstream

buoyancy frequency (or Brunt-Vaisala frequency) N . Such a model gives a rough

first approximation to many atmospheric flows but excludes phenomena such as

trapped lee waves which depend on vertical variations in N and U . The factors

governing the terrain geometry of an isolated mountain are the stream-wise length

scale a, the cross-stream length scale b, and the maximum height h.

The non-dimensional parameters governing the behavior of the flow for constant

N and U are then: the non-dimensional mountain height ε = Nh/U (Smolarkiewicz

and Rotunno 1989, 1990), which measures the amplitude of the disturbance; the

vertical aspect ratio δ = U/Na, which measures the importance of non-hydrostatic

effects; and c) the horizontal aspect ratio β = b/a. If the vertical aspect ratio δ is

small (< 0.1) the flow is essentially hydrostatic and the set of control parameters

then reduces to ε and β.

Four classes of flow regimes are identified for stratified flow over topography

with uniform upstream N and U : small-amplitude waves, wave breaking, upstream

stagnation and flow-splitting, and lee vortices. The schematic flow regimes diagram

in Fig. 1.5 summarizes the occurrence of these phenomena as a function of ε and β.

Detailed descriptions of the flow classes can be found in Epifanio (2015).

When ε 1 the mountain induced disturbance for all β takes the form of a

10 Introduction

Figure 1.5: Schematic flow regimes diagram for stratified flow past an isolated ridge as

a function of ε and β. Note that the actual shapes and positions of the curves will depend

on obstacle shape. Taken from Epifanio (2015).

small-amplitude mountain wave. For β ≥ 1 (i.e., for elongated ridges) streamlines

above the lee slope overturn when ε exceeds a critical value usually in the range

0.7 − 1.2 depending on obstacle shape. For all β exists a critical non-dimensional

mountain height at which upstream flow stagnation occurs inducing flow-splitting

(Smith 1988). Upstream stagnation is often accompanied by the formation of a pair

of counter-rotating lee vortices, which may become unstable causing a transition to

vortex shedding and the formation of Karman vortex streets (Schar and Smith 1993;

Schar and Durran 1996). When flow splitting occurs and the flow turn around the

obstacle (ε > 1) the flow regime is often referred to as “flow around”. When the flow

rises over the obstacle (ε < 1) the flow regime is often referred to as “flow over”.

Orographic Precipitation

The influence of orography on patterns of precipitation is a well studied in at-

mospheric sciences: mountains significantly modify the large scale atmospheric flow

creating some of the most pronounced climate gradients on Earth (Roe 2005). Nowa-

days what is referred to “orographic precipitation” is the alteration or reorganization

of one of the three major storm types when it encounters topographic features, with

the three major types of storm being convective clouds, frontal system and tropical

cyclones (Houze 2012).

Three main factors are traditionally considered important in orographic precip-

itation (Fig. 1.6): (1) moist, large-scale flow towards an obstacle (hill, mountain or

mountain chain), (2) mesoscale orographically induced lifting of the large-scale flow

(which cools the air to saturation and induces condensation), and (3) conversion

of the condensate to precipitable particles (by some combination of smaller-scale


Figure 1.6: Schematic diagram of the elements of orographic precipitation: (1) large-

scale flow, (2) orographic lifting and condensation, and (3) conversion of condensate to

precipitation. Taken from Rotunno and Houze (2007).

convection, turbulent air motions, and cloud microphysics) (Rotunno and Houze

2007).

Regarding the large-scale flow, two properties of the atmosphere are important:

the lifting condensation level (LCL) and the level of free convection (LFC) (Banta

1990). The LCL is the level at which a parcel of moist air, lifted dry-adiabatically,

would become saturated. The LFC is the level at which a parcel of air, lifted dry-

adiabatically until saturated and moist-adiabatically thereafter, would first become

warmer than its surroundings in a conditionally unstable atmosphere (definitions

from American Meteorological Society (2016)). Thus, the properties of the imping-

ing flow determine the amount of lifting needed to produce clouds. If air is lifted to

its LCL but not to its LFC, stable or stratiform clouds will be produced. If air is

lifted to its LFC, unstable or convective clouds will be produced (Banta 1990).

Unstable cumulus clouds grow when lifting releases moist instability. There are

two type of inherent moist instability. Potential instability occurs when the equiv-

alent potential temperature θe decreases with height (dθe/dz < 0) in a layer, forced

lifting to saturation makes the layer unstable and produces cumulus convection.

Conditional instability occurs when the saturation equivalent potential temperature

θes decreases with height (dθes/dz < 0) in a layer, if processes such as surface heating

or surface convergence lift a parcel to its LFC this type of instability is released. The

two types of moist instability are not mutually exclusive, before saturation occurs a

sounding can in fact exhibit both types (Banta 1990).

It is evident that the many degree of freedom governing the interaction between

the large-scale flow and the orography make orographic precipitation a difficult pro-

cess to study. However extensive research in different area of complex terrain allows

nowadays to identify several types of precipitation mechanisms, collected and sum-

marized by Houze (2012). Below, the mechanisms which are relevant for this study

12 Introduction

(a) (b)

(c) (d)

Figure 1.7: Mechanisms by which a high mountain affect precipitating clouds. Taken

from Houze (2012).

are summarized.

Figure 1.7a illustrates what happens when stably stratified air rises over an

obstacle or a barrier. In this case the vertical component of motion of the air

following the terrain upward produces or strengthens a cloud on the windward side

of the mountain. On the leeward side the cloud is evaporated due to flow descent.

Figure 1.7b represents a situation similar to Fig. 1.7a, but in this case the airflow

impinging on the obstacle is unstable. The result is that the air ascending the terrain

overturns on subscale of the obstacle. This overturning may be caused by several

different factors: a deep layer of very unstable air lifted above its LFC, a shallow layer

containing buoyant instability lifted over the obstacle, a slightly potentially unstable

layer embedded in a preexisting widespread cloud system, a layer presenting strong

shear. Figure 1.7c shows how the daily cycle affects convection over a mountain.

Solar heating during the day draws air upward to converge at the mountain top,

causing air parcels to rise above their LFC. These effects lead to a maximum of

convective precipitation in the warm part of the day. Figure 1.7d illustrates a

mechanism involving more effects at once. In this case vertically propagating wave

initiated by the large-scale flow over the mountain induces wave motions tilting

upward and upstream. On the lee side of the mountain the upward motion phase of

the wave may favor precipitating convective clouds. This process may be enforced

by diurnal heating, by gravity waves induced by the airflow over the terrain as well

as by convergence due to the formation of lee vortices. Finally other precipitation

mechanisms mentioned by Houze (2012) are not considered relevant for this study.

1.3 Goals 13

1.3 Goals

More than a 100 years of scientific studies have focused on the understanding of

ongoing glaciers retreat in tropical East Africa and its complex interaction with

climate. A lot is nowadays known about the processes governing glaciers melt,

concomitant climate variability in East Africa, local precipitation pattern on Kili-

manjaro and Mount Kenya as well as large-scale circulation that controls weather in

the region, but there is still a lack of knowledge about the local processes governing

precipitation formation over the two mountains. Filling this gap is the next step in

the understanding of the complex climate-glacier interaction in tropical East Africa.

Three hypothesis, emerged from previous studies, are particularly focusing

on the mechanisms producing precipitation over Kilimanjaro and Mount Kenya.

Nicholson et al. (2013) and Ehrengruber (2011) stated that precipitation events at

the two locations are determined by the same air mass. Kaser and Osmaston (2002)

considered the mountains shape and altitude to be responsible of the particular con-

vective patterns on Kilimanjaro and Mount Kenya (Fig. 1.4). Pepin et al. (2010)

suggested that a strong diurnal cycle, allowing the development of slope winds cir-

culation, is the most important mechanism in the production of precipitation on the

summit of Kilimanjaro.

For these reasons the goals of this thesis are:

(1) To investigate the background conditions favourable for precipitation events,

testing the hypothesis that the air mass is the same at the two site.

(2) To investigate the impact of the two different topographies on the spatial

distribution of precipitation, testing the hypothesis that the mountains shape

and altitude are playing a major role in determining the particular convective

patterns.

(3) To investigate the impact of the diurnal cycle on the formation of precipitation

at the top of the two mountains, testing the hypothesis that this is the most

important mechanism.

1.4 Outline

In order to tackle the three different goals listed in Chap. 1.3, this thesis is divided

in two different but complementary parts. The first part (Chap. 3) is address-

ing the first goal and it consists of a reanalysis study. In-situ observations and

ERA-Interim reanalysis data are used to characterize the atmospheric background

conditions favouring precipitation events on Kilimanjaro and Mount Kenya. The

14 Introduction

second part (Chap. 4) is addressing the second and the third goals and it con-

sists of idealized numerical simulations performed with the Weather Research and

Forecasting (WRF) model.

The methods used for both parts are described in Chap. 2, the results are

discussed in Chap. 5 and the conclusions are drawn in Chap. 6.

Chapter 2

Methods

2.1 Reanalysis Study

The first fundamental prerequisite for precipitation is a sufficient moisture content

in the atmosphere. When investigating the precipitation mechanisms it is thus

important that the source of moisture and the nature of the air mass during the

event is well constrained.

This reanalysis study focuses on testing the hypothesis (goal (1) in 1.3) that dur-

ing precipitation events the synoptic background conditions are the same at the two

study sites . For this purpose, all precipitation events on Kilimanjaro and Mount

Kenya are classified based on in-situ observations (Chap. 2.1.1). Consequently

ERA-Interim reanalysis data are used to characterized the atmospheric background

conditions of each precipitation class (Chap. 2.1.2). Then, these background condi-

tions are used as initial conditions for the WRF idealized numerical simulations.

2.1.1 Classification of Precipitation Events

The first challenge when studying precipitation on Kilimanjaro and Mount Kenya

is to select and isolate all those events happening at the summit of the two moun-

tains. Furthermore in this study it is also important to appropriately capture the

differences between the two locations. For these reasons three classes of precipita-

tion events are defined describing events that occurred simultaneously at both sites,

events that occurred only on Kilimanjaro and events that occurred only on Mount

Kenya. Data from two automatic weather stations, on Kersten glacier (Kilimanjaro)

and on Lewis glacier (Mount Kenya), are used.

15

16 Methods

Data

Three automatic weather stations (AWSs) are operated on the summit plateau of

Kilimanjaro (Fig. 1.2). Two of them, one from the University of Massachussetts

(since 2000) and one from the University of Innsbruck (since 2005), are located on

the Northern Ice Field while a third one (since 2005) is located on Kersten glacier

(AWS KG) at 5873 m a.s.l, almost directly at Africa’s highest point (Molg et al.

2008). Data from AWS KG are used in this study. In 2009 the University of

Innsbruck installed an AWS on Lewis glacier (AWS LG) on Mount Kenya at 4828

m a.s.l., filling the gap of long-term meteorological data close to the summit of the

mountain (Nicholson et al. 2013).

Both stations are equipped to measure the following variables: air temperature,

relative humidity, radiation fluxes (shortwave incoming radiation SWI, shortwave

outgoing radiation SWO, longwave incoming radiation LWI and longwave outgoing

radiation LWO), wind speed and wind direction, air pressure and surface height.

Unfortunately the stations are not equipped with precipitation gauges, which would

be hard to operate, due to the solid nature of precipitation (typically snow and/or

graupel), and hard to maintain. The only available instrument to detect snowfall

events is the sonic ranger which measures surface height changes and therefore de-

tects accumulation. The sensor mounted on AWS KG is a Campbell Scientific SR50

sonic ranger while the one on AWS LG is a Campbell Scientific SR50a. Both sensors,

often used in nivological and glaciological application, are capable of measure the

distance from a fixed reference with an accuracy of ±0.01 m. The characteristics of

the instrument are making the use of it quite challenging, especially in tropical high

mountains. There are in fact several known issues:

• Data collected during snowfall events are particularly noisy, snowflakes big

enough between the sensor and the surface could in fact interfere with the

measurements. This is particularly true for the two study sites since the typical

observed precipitation type is graupel (from field observations).

• The typical daily accumulation amounts reported during fieldwork at the two

sites are a few centimeters. Due to the low accuracy of the sonic ranger, light

precipitation events are almost impossible to be detected.

• The mast of the AWSs on which the sensor is mounted is drilled into the ice

at both locations. During strong melting periods it can happen that the ice

surrounding the mast melts, allowing it to turn and change position affecting

the reliability of the data.

The best gap-free period with good quality data is October 2010 − February

2012. Data are quality checked and already used in other publications (e.g., Nichol-

2.1 Reanalysis Study 17

son et al. (2013)). For these reasons this study will furthermore focus only on this

period.

Classification

The methodology used for the classification of precipitation events is here explained.

This procedure is based on the analysis of snow height measurements recorded by

the sonic ranger of the two AWSs. Thus, all the accumulated values given below

and following in Chap. 3 are referred to snow height and not to liquid precipitation.

Data from other sensors are not used in the process. A detailed description of the

classification procedure is found below:

• First of all SR50 raw data of surface height are processed to obtain daily snow

accumulation values. Mean and standard deviation of measurements around

midnight (22:00 − 02:00) are calculated for each day of the study period. The

mean value is considered as reference surface height of the day if the standard

deviation does not exceed the 90th percentile of the whole data set. If the the

standard deviation exceeds the 90th percentile the mean value is rejected and

the reference surface height of the day is interpolated from neighbouring, not

rejected, values. Thus, a set of daily reference surface heights is generated,

from which daily snow accumulation or ablation is calculated. Choosing mid-

night as reference time seems to be the best choice. In fact during the night

both sites show evidence of cloud free sky which ensure a better quality of the

measurement due to a object-free path between the sensor and the surface.

• Significant precipitation events are defined as events exceeding a threshold of 2

cm daily snow accumulation. The definition of this threshold allows to exclude

from the analysis all the measured signals that are smaller than the accuracy

of the sensor (±0.01 m). Note that the first step described above is already

filtering the noisy data.

• All significant precipitation days are sorted into 3 different classes as follow:

1. BOTH: this class includes all days with precipitation occurred simulta-

neously at both locations.

2. MTK: this class includes all days with precipitation occurred only on

Mount Kenya.

3. KIBO: this class includes all days with precipitation occurred only on

Kilimanjaro.

The classification is based on a 3-days window comparing daily snow accumu-

lation for the two mountains. For example an event is included in the class

18 Methods

BOTH if precipitation is occurring at Mount Kenya on a certain day and at

Kilimanjaro on the same day or one day before or after; an event is included in

the class MTK if precipitation is occurring at the Mount Kenya on an certain

day and at Kilimanjaro no precipitation is occurring the same day, the day

before and the day after.

The aim of this procedure is to produce a list of dates of significant (if not

extreme) precipitation events at the two study site. It does not want to be a complete

and exhaustive catalogue of all precipitation events recorded by the two AWSs but

rather a set of representative situations for the reanalysis data study, which should

help to test the hypothesis that both mountains are influenced by the same air

masses (goal (1), Nicholson et al. (2013)).

2.1.2 ERA-Interim Reanalysis

ERA-Interim reanalysis data from ECMWF are used to characterize the synoptic

conditions favouring each precipitation class. Results are compared to verify whether

the two mountains are influenced by the same air mass during precipitation events

(goal (1), Chap. 1.3).

Data

ERA-Interim is one the latest global atmospheric reanalysis produced by the Eu-

ropean Centre for Medium-Range Weather Forecasts (ECMWF) and it covers the

data period since 1979. The ERA-Interim atmospheric model and reanalysis system

uses cycle 31r2 of ECMWF’s Integrated Forecast System (IFS) configured for the

following spatial resolution (Dee et al. 2011; Berrisford et al. 2011):

• 60 levels in the vertical, with the top level at 0.1 hPa.

• A reduced Gaussian grid with approximately uniform 79 km spacing for surface

and other grid-point fields (Fig. 2.1).

Parameter Units

Geopotential m2 s−2

Logarithm of surface pressure Pa

Temperature K

Specific humidity kg/kg

Eastward wind component m s−1

Northward wind component m s−1

Table 2.1: ERA-Interim upper air parameters on model level used in this study.

2.1 Reanalysis Study 19

35.25

°E

36.00

°E

36.75

°E

37.50

°E

38.25

°E

39.00

°E

39.75

°E

Kilimanjaro

Mt. Kenya

0 125 250

km

500

m

1000 m

1500

m

2000 m

(a)

35.25

°E

36.00

°E

36.75

°E

37.50

°E

38.25

°E

39.00

°E

39.75

°E

Kilimanjaro

Mt. Kenya

0

100

200

300

400

500

600

700

800

m

(b)

Figure 2.1: (a) ERA-Interim reduced Gaussian grid, black dotted grid lines, and topogra-

phy, white contour, and (b) standard deviation of model topography from real topography.

Gridded data products include a large variety of 3-hourly surface parameters,

describing atmospheric as well as ocean-wave and land-surface conditions, and 6-

hourly upper air parameters covering the troposphere and the stratosphere. The

upper air parameters on model levels used to characterize the vertical structure of

the large-scale flow are summarized in Table 2.1. Two aspects of the model are

important to be highlighted:

• Kilimanjaro and Mount Kenya are only few grid points apart.

• The topography which is resolved by the model is not depicting the two isolated

mountains. This is clearly visible Fig. 2.1b which shows the standard deviation

of the model topography from the real topography. The model grid is able to

resolve only a gradually rising plateau that reach its highest point around 2000

m to the west of Mount Kenya (Fig. 2.1a).

Due to the absence of the two peaks in the topography, the ERA-Interim model

is not resolving the local and small-scale circulation resulting form the terrain-

atmosphere interaction, thus the upper air parameters such temperature, wind and

humidity are well representing the large-scale characteristics of the air mass im-

pinging on the two mountains. For this reason the upper air parameters used in

20 Methods

this study are extracted at the closest grid point to Kilimanjaro and Mount Kenya,

which should be most representative for the atmospheric background conditions at

the two sites.

Methodology

The basic idea of this study is to calculate mean and standard deviation of at-

mospheric vertical profiles for each of the precipitation classes. The atmospheric

variables considered relevant for this study and, thus, used to calculate means and

standard deviations of vertical profiles are summarized in Table 2.2. Below, the step

by step procedure is described:

• The atmospheric variables are calculated from the ERA-Interim upper air

parameters shown in table 2.1 extracted at the closest grid point to Kilimanjaro

and Mount Kenya, thus obtaining vertical profiles for every event in each of

the precipitation classes.

• All the vertical profiles, which in the previous step are calculated on model

levels, are then interpolated to constant height levels, spacing from 50 m at

the model bottom to few km at the model top.

• Based on these profiles the arithmetic average and the standard deviation

of all atmospheric variables, except wind direction, are calculated for each

precipitation class. The vector average of wind direction below 8 km is cal-

culated for all precipitation events in each class and then assigned to one of

the reference cardinal directions (N, NE, E, SE, S, SW, W, NW). In order to

avoid meaningless vertical averages, the absence of turning wind with height

Symbol Unit Variable Calculation

Θ K Potential temperature Bolton (1980), eq. 7

Θe K Equivalent pot. temp. Bolton (1980), eq. 43

Θes K Saturation equivalent pot. temp. Bolton (1980), eq. 43

RH % Relative humidity rh = (w/ws)

Wsp m s−1 Wind speed From wind components

Wdr Wind direction From wind components

Table 2.2: Atmospheric variables used in this study and relative formula used in the calcu-

lation. The saturation equivalent potential temperature is computed using the equivalent

potential temperature formula by assuming the air parcel is saturated. w = mass mixing

ratio of water vapor at actual value, ws = mass mixing ratio of water vapor at saturation

value.

2.2 Numerical Model 21

is checked. It is important to note that such a calculation, which comprises

averaging of atmospheric variables over different dates throughout different

seasons, is meaningful only because the study site is located in the tropics,

where seasonality is rather weak.

The described procedure is performed for the three precipitation classes and

also for all the events exceeding 2 cm daily snow mean accumulation. The ERA-

Interim reanalysis parameters necessary for the calculations are available at 00, 06,

12 and 18 UTC, which correspond to 03, 09, 15, 21 East Africa local time (LT).

Only the night time profiles at 00 UTC (03 LT) are analysed which are not affected

by diurnal heating and a convective boundary layer. The idea behind this selection

is that these profiles are also used as initial conditions for the simulations to study,

amongst others, the effect of surface heating on precipitation.

2.2 Numerical Model

In order to asses the role and the impact of the background flow, of the topography

(goal (2) in Chap. 1.3), and of the diurnal cycle (goal (3) in Chap. 1.3) in the

precipitation mechanism over Kilimanjaro and Mount Kenya, idealized numerical

simulation are performed. An idealized numerical model setup typically consists

of a highly idealized topography, e.g., a parametric function, an input sounding,

which is horizontally homogeneous and represents the state of the atmosphere at

the beginning of the simulation, and very few parameterization schemes. Due to its

semplicity an idealized model set-up is the perfect numerical tool to isolate single

effects contributing to precipitation formation. It is in fact possible to perform a

series of simulations with different settings, e.g. turning off diurnal heating at the

surface or slightly changing the input sounding, in order to asses the role of each of

the precipitation mechanisms.

Details about the model setup, the topography and the input sounding used in

the numerical simulations are described as follow.

2.2.1 Model Setup

Numerical simulations are performed with the Weather Research and Forecasting

(WRF) Model, version 3.7., based on the Advanced Reasearch WRF core (Ska-

marock et al. 2008). All simulations run on a single domain with an horizontal grid

spacing of ∆x = ∆y = 1 km to explicitly resolve convective processes. The domain

size is Lx = 1500 km, Ly = 1000 km, and Lz = 25 km in the x, y, and z directions,

respectively. The terrain-following vertical grid uses 100 levels with spacings that

increase from ∆z ≈ 76 m at the surface to 240 m at the top. The lateral boundaries

22 Methods

are open (radiative) and the upper boundary is a rigid wall with a 7-km Rayleigh-

damping layer below to absorb vertically propagating gravity waves. Such a large

domain is used to minimize lateral-boundary interferences in the horizontal (e.g.,

Kirshbaum and Fairman (2015)) and to avoid undesired effects of the damping layer

in the troposphere (see Appendix B). For convenience, the model bottom is fixed

at a reference level of 1300 m above sea level (a.s.l.), since the height of the plains

surrounding the two mountains is overall above 1300 m a.s.l.. From now on for the

rest of the thesis the term above reference level (a.r.l.) will refer to vertical distances

above the reference level, which is 1300 m a.s.l.. Input sounding and topography

will be related to this reference level as a starting point. A detailed description of

the topography used for Kilimanjaro and Mount Kenya and of the input sounding

will follow.

Cloud microphysics is parameterized using the WSM 6-class graupel scheme,

which is a single moment scheme for water vapor, cloud water, cloud ice, snow,

rain and graupel (Hong and Lim 2006). Since graupel is the most expected type of

precipitation on the summit of the two mountains (personal communication) it is

important that this class is well represented in the microphysics scheme. Cumulus

convection is supposed to be resolved explicitly at ∆x = 1 km.

The atmospheric boundary layer is parameterized using the MYNN 2.5 level

TKE scheme (Nakanishi and Niino 2004, 2006). For horizontal smoothing, two-

dimensional Smagorinsky first-order closure is used. Surface momentum fluxes are

parameterized based on the MM5 Monin-Obukhov similarity theory (Monin and

Obukhov 1954). Surface heat fluxes, both sensible and latent, are prescribed as a

sinusoidal-like function, null before 6:00 and after 18:00 and with maximum value

at noon, to simulate an idealized daily cycle. Maximum values at noon of 500 W

m−2, for the surface sensible heat flux, and of 400 W m−2, for the surface latent

heat flux, are used in this study. These values are based on ERA-Interim Reanalysis

data (Fig. 2.2) over the study period October 2010 − February 2012 (same as

in Reanalysis Study). It is important to note that the surface fluxes prescribed

are homogeneous over the whole model domain and thus the impacts of land cover

variability and topography shading are not considered with this idealized approach.

In order to explicitly prescribe the surface heat fluxes a modification to the MM5

Monin-Obukhov scheme was necessary. A detailed description of this modification

can be found in Appendix A.

Other parameterizations such as longwave and shortwave radiation and land-

surface schemes are not necessary since the surface fluxes are prescribed explicitly.


00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:002000

200400600800

100012001400

W m

−2

KilimanjaroRnHLE

(a)

00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:002000

200400600800

100012001400

W m

−2

Mount KenyaRnHLE

(b)

Figure 2.2: Surface net radiation (Rn), surface sensible (H) and latent (LE) heat flux.

Mean and standard deviation, in color shadings, over the period October 2010 − Febru-

ary 2012. Data is from the ERA-Interim Reanalysis. The model grid points used for

Kilimanjaro and Mount Kenya are the same as in the reanalysis study (Chap. 2.1.2).

2.2.2 Input Sounding

The simulations are initialized with a single sounding based on the Reanalysis Study

results. Specifically the input sounding is idealized based on the results of class

BOTH (Fig. 3.5a) at 03 LT (00 UTC), which is also the starting time of the

simulations.

Figure 2.3 shows the idealized input sounding with the required variables to

initialize the simulations: potential temperature, water vapor mixing ratio, relative

humidity and wind speed along both x and y direction. The Reanalysis Study showed

that the most common wind direction during precipitation events is southeasterlies

(SE). In order to maximize the benefit of the channel-like nature of the domain and

to minimize disturbances at the boundaries this wind direction is rotated by 125

clockwise so that the wind is blowing in the x direction of the domain. Following

the model domain and the topography are rotated correspondingly.

The potential temperature profile consists of three layers with different Brunt-

Vaisala frequency N (Table 2.3) with a reference potential temperature at the model

24 Methods

300

350

400

450

500

550

600

650

700

Θ (K)

0

5

10

15

20

25

Heig

ht (k

m a

.r.l.)

0 10 20 30 40 50 60 70 80 90 100

RH (%)

0 2 4 6 8 10 12 14 16qv (g kg−1 )

0 3 6 9 12 15u (m s−1 )

0 3 6 9 12 15v (m s−1 )

Figure 2.3: Idealized input sounding showing potential temperature Θ (K), water vapor

mixing ratio qv (g kg−1), red line, relative humidity (%), green line, wind speed along the

x direction u (m s−1) and wind speed along the y direction v (m s−1). To be noted that

u and v are not the west and south component of the wind but the wind along the x and

y direction of the rotated model domain.

reference level of Θ = 302.5 K. The relative humidity profile is linearly decreasing

from 98% at the model reference level to 0% at 9750 m a.r.l.. Following the water

vapor mixing ratio profile is calculated from the relative humidity profile. The wind

speed in the x direction, u, is linearly increasing from 0 m s−1 at the model reference

level to 9 m s−1 at 2000 m a.r.l. and then it is kept constant. The wind speed in

the y direction, v, is zero since the domain was rotated.

Most of the simulations performed in this study are initialized with the input

sounding here presented. Few simulations are initialized without wind and few

with a relative humidity profile decreasing from 75% (instead of 98%) at the model

reference level to 0% at 7500 m a.r.l..

Vertical structure of potential temperature

Layer 1 z1 = 0 m a.r.l. z2 = 6700 m a.r.l. N1 = 0.0125 s−1



Table 2.3: Vertical structure of potential temperature as represented in the input sound-

ing.

2.2.3 Topography

Two types of approaches are used to represent the topography of Kilimanjaro and

Mount Kenya in this study. First a realistic topography, obtained by a digital

elevation model (DEM), is used to compare the impact of differences in the real


terrain geometry of the two mountains. Secondly an idealized topography, obtained

by a parametric formula, is used to simplify the model setup, excluding small scale

topographic features, in order to better understand the general mesoscale circulation

characterizing precipitation events over the two mountains.

Real topography

A 1 km mesh size topographic dataset generated from NASA’s Shuttle Radar To-

pography Mission (SRTM) is used to idealize the topography of Kilimanjaro and

Mount Kenya (Farr et al. 2007). First of all the digital elevation model (DEM) of

the two isolated mountains is extracted from the surrounding plains. The DEM of

Kilimanjaro is cut at 1406 m a.s.l and the DEM of Mount Kenya is cut at 1997

m a.s.l.. These two “cut” heights are representing the mean elevation of the plains

surrounding the two mountains. Both topographies are then related to the model

reference level, subtracting 1300 m to each pixel, and placed in the middle of the

model domain. Thus, in the simulations of Kilimanjaro the surface is at 106 m a.r.l.

and in those of Mount Kenya at 697 m a.r.l..

Subsequently, each topography is smoothed using a Gaussian blur in order to

filter noise in the DEM and to ensure that the slope angle does not exceed 30,

which could affect the model stability. Finally the topography is rotated by 125

clockwise in order to adjust to the wind direction of the input sounding. Figures

2.4a and 2.4b show the center of the model domain and the corresponding terrain

for the two mountains.

Table 2.4 shows the important parameters of the topography before and after

the smoothing process. Notice that the final topography used in the simulations

(smoothed) is not completely representing the heights reached by the real mountains;

this is especially true for Mount Kenya. Furthermore the heights of the plains

surrounding the two mountains (“cut” height) differ by about 600 m.

Kilimanjaro (5895 m) Mount Kenya (5199 m)

DEM smoothed DEM smoothed

summit height a.s.l. 5862 m 5629 m 4865 m 4584 m

α 45.00 25.05 45.00 14.79

“cut” height a.s.l. 1406 m 1997 m

model bottom a.s.l. 1300 m 1300 m

Table 2.4: Height, maximum slope angle α and “cut” height as depicted by the SRTM

DEM and after the smoothing process.

26 Methods

h0 x0 y0 B a b

BELL 4000 m a.r.l. 750 500 0 km 3.5 km 3.5 km

YL/XL 4000 m a.r.l. 750 500 10 km 2.5 km 3.5 km

Table 2.5: Parameters of eq. 1 and eq. 2 in Kirshbaum and Durran (2005) used to

construct the three ideal topographies.

Ideal topography

Three ideal topographies are constructed using the parametric formula presented in

Kirshbaum and Durran (2005): BELL, a symmetric bell-shaped mountain, XL, an

elliptically-shaped mountain with an elongated axis on the x direction, and YL, an

elliptically-shaped mountain with an elongated axis on the y direction. Table 2.5

summarizes the parameters of eq. 1 and eq. 2 in Kirshbaum and Durran (2005)

used to construct the three topographies. Notice that XL and YL are obtained

using the same set of parameters, rotating the final topography by 90. XL simplify

and idealize the topography of Kilimanjaro while YL the topography of Mount

Kenya. Figures 2.4c, 2.4d, and 2.4e show the center of the model domain and the

corresponding topography for XL, YL and BELL.


(a) (b)

(c) (d)

(e)

0 300 600 900 1200 1500km

0

200

400

600

800

1000

km

Model Domain

(f)

Figure 2.4: Center of the model domain with topography contours at 500 m intervals

for: (a) Kilimanjaro, (b) Mount Kenya, (c) XL, (d) YL, and (e) BELL. (f) whole model

domain with Mount Kenya topography, in red the center of the model domain.

28 Methods

2.2.4 Simulations Overview

In this section an overview of the simulations performed is given. All the simulations

start at 03 LT, which is exactly the time of the idealized input sounding, and end at

21 LT for a total of 18 hours running time. This time frame allows for an affordable

computational effort and ensures a spin-up time of 3 hours before surface fluxes

start.

All the simulation using the Kilimanjaro topography will be noted with the

name kibo and those using the Mount Kenya topography with mtk. Three types of

experiments are carried out: a comparison between Kilimanjaro and Mount Kenya,

a sensitivity study for Kilimanjaro and an idealized topography experiment named

Idealized Ridge. Details about the initialization of all the simulations performed in

this study are summarized in Table 2.6 and a brief description of the most important

aspects of each experiment is following below.

Kilimanjaro and Mount Kenya comparison

Name Topo Wind RH S. H. F.

kibo ALL kibo yes 98% yes

kibo F kibo no 98% yes

kibo W kibo yes 98% no

mtk ALL mtk yes 98% yes

mtk F mtk no 98% yes

mtk W mtk yes 98% no

mtk ALLsh mtk sh yes 98% yes

Sensitivity Study for Kilimanjaro


kibo ALL kibo yes 98% yes

kibo rh75 kibo yes 75% yes

kibo F0.5 kibo yes 98% halved

kibo F0.5rh75 kibo yes 75% halved

Idealized Ridge


BELL BELL yes 98% yes

XL XL yes 98% yes

YL YL yes 98% yes

Table 2.6: Overview summarizing the simulations performed in this study. Details about

the acronym (name), topography (topo), input sounding (Wind and RH at the model

reference level), and surface heat fluxes (S. H. F.) used are given.


Kilimanjaro and Mount Kenya comparison

This experiment aims to investigate the differences in processes producing precipi-

tation on Kilimanjaro and Mount Kenya, with particular focus on the topography.

It consists in a set of 3 simulations for each mountain (Table 2.6). ALL is the refer-

ence simulation for both mountains, it is initialized with the surface heat fluxes and

the input sounding described in Chap. 2.2.1 and Chap. 2.2.2. Its purpose is to re-

produce the typical situation favouring precipitation at the two study site, allowing

the evaluation of the combined effect of the topography, of the background flow and

of the surface heat fluxes. F is a type of simulation similar to ALL, with the only

difference that the wind specified in the input sounding is set to zero. Its purpose is

to evaluate the pure effect of the surface fluxes in the precipitation mechanisms. W

is a type of simulation similar to ALL, with the only difference that the surfaces heat

fluxes are set to zero. Its purpose is to evaluate the potential of the background flow

in the precipitation mechanisms. Finally ALLsh is a type of simulation performed

only for Mount Kenya. It is called ALLsh, standing for ALL same height, and it

consists in the same type of simulation as ALL but with the height of the plains

surrounding Mount Kenya lowered to be same as for Kilimanjaro.

Sensitivity study for Kilimanjaro

In order to investigate the sensitivity of each of the processes producing precipitation,

a set of few simulations are performed for Kilimanjaro only. They are summarized

in Table 2.6. rh75 is a type of simulation in which the prescribed relative humidity

at the surface is 75% instead of 98%, as already explained in Chap. 2.2.2. F0.5 is

a type of simulation in which the intensity of the surface heat fluxes prescribed is

halved. In particular the maximum values at noon of the surface sensible and latent

heat flux are halved from 500 W m−2 to 250 W m−2 and from 400 W m−2 to 200 W

m−2, respectively. Finally F0.5rh75 is a type of simulation in which surface heat

fluxes are halved, like in F0.5, and relative humidity is prescribed as in rh75. The

purpose of these simulations is to investigate the role of the moisture content of the

background flow and to further evaluate the effect of the surface heat fluxes in the

precipitation mechanisms.

Idealized Ridge

Three simulations are performed with the three ideal topographies described in

Chap. 2.2.3 and the same input sounding as well as surface heat fluxes of the

simulation type ALL (Table 2.6). The basic idea of the experiment is to idealize

the real topographies of Kilimanjaro and Mount Kenya with same idealized ridge.

The only difference between the simulations XL and YL is the orientation of the

30 Methods

ridge respect to the impinging background flow. The purposes of this experiment

are mainly two: to further simplify the atmospheric setting simulated by the model

in order gain a better understanding of the basic mechanisms of precipitation and

to investigate the role of the background flow direction.

Chapter 3

Reanalysis Study

3.1 Classification of Precipitation Events

This chapter summarizes the main outcomes of the classification of precipitation

events over Kilimanjaro and Mount Kenya.

Figure 3.1 shows a summary of the total seasonal snow accumulation recorded

by the AWSs at the two summits. For the analysis the solar year is divided into four

periods, as already presented in Chap. 1.2.1, two rain seasons, MAM (long rains)

and OND (short rains), and two dry seasons, JF and JJAS. Over the whole study

period, October 2010 − February 2012, the AWS on Kilimanjaro recorded a total

of 2.51 m of snow accumulation while the AWS on Mount Kenya recorded a total

of 3.15 m. Snow accumulation on Kersten glacier (Kilimanjaro) is almost equally

distributed over the study period. Rain seasons (grey shadings) exhibit higher values

of snow accumulation while dry seasons lower values. Snow accumulation at Lewis

glacier (Mount Kenya) is less equally distributed, with more than 60% of the total

OND 2010 JF 2011 MAM 2011 JJAS 2011 OND 20110.0

0.2

0.4

0.6

0.8

1.0

1.2

Snow

Acc

umul

atio

n (m

)

23%

19%

6%

15% 10%

24%

27%

14%

34%

28%

Mt. Kenya: 3.15 m Kilimanjaro: 2.51 m

Figure 3.1: Total seasonal snow accumulation on Kilimanjaro (red) and Mount Kenya

(blue) as recorded by AWSs on the summits. On top of the bar is shown the percentage

relative to the study period October 2010− February 2012. Grey shadings indicate tropical

East Africa rain seasons.

31

32 Reanalysis Study

Nov 2010Jan 2011

Mar 2011

May 2011Jul 2011

Sep 2011

Nov 2011Jan 2012

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Snow

Acc

umul

atio

n (m

) Mt. Kenya: 51 eventsKilimanjaro: 40 events

(a)

Nov 2010Jan 2011

Mar 2011

May 2011Jul 2011

Sep 2011

Nov 2011Jan 2012

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Snow

Acc

umul

atio

n (m

) Mt. Kenya: 19 eventsKilimanjaro: 7 events

(b)

Figure 3.2: Precipitation events on Kilimanjaro (red) and Mount Kenya (blue) exceeding

(a) 2 cm and (b) 4 cm of daily snow accumulation during the study period October 2010

− February 2012. Grey shadings indicate tropical East Africa rain seasons, the green line

indicates the 2 cm and 4 cm threshold.

snow accumulation recorded during the last two periods, JJAS 2011 and OND 2011.

Notice, the exceptional high amount of snow accumulation recorded during the dry

season JJAS 2011 compared to the very little amount recorded during the rain

season MAM 2011. The most wet period is for both location the rain season OND

2011; the most dry period is the dry season JF 2011 for Mount Kenya and both dry

seasons, JF 2011 and JJAS 2011, for Kilimanjaro.

Figure 3.2a shows a summary of all the precipitation events exceeding 2 cm

snow accumulation. The total number of precipitation days on Kilimanjaro is 40

and on Mount Kenya 51, resulting in 27.5% more events on Mount Kenya. During

the study period precipitation events do not only occur in the rain seasons but also in

the dry seasons. Precipitation events reaching the two summits are homogeneously

distributed throughout the whole study period, a part from two very dry months

(June 2011 and February 2012).

Figure 3.2b is showing the same as Fig. 3.2a but with a 4 cm threshold used

in the analysis. In this case the number of precipitation events on Kilimanjaro is

3.1 Classification of Precipitation Events 33

Nov 2010Jan 2011

Mar 2011

May 2011Jul 2011

Sep 2011

Nov 20110.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Snow

Acc

umul

atio

n (m

) 9 periods with accumulation on both mountains

Class BOTH

Mt. KenyaKilimanjaro

(a)

Nov 2010Jan 2011

Mar 2011

May 2011Jul 2011

Sep 2011

Nov 20110.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Snow

Acc

umul

atio

n (m

) 5 periods with accumulation only on Mount Kenya

Class MTK


(b)

Nov 2010Jan 2011

Mar 2011

May 2011Jul 2011

Sep 2011

Nov 20110.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

Snow

Acc

umul

atio

n (m

) 8 periods with accumulation only on Kilimanjaro

Class KIBO


(c)

Figure 3.3: Daily snow accumulation during precipitation events on Kilimanjaro (red)

and Mount Kenya (blue) for: (a) class BOTH, (b) class MTK, and (c) class KIBO. Grey

shadings indicate tropical East Africa rain seasons, the green line indicates the 2 cm

threshold.

34 Reanalysis Study

7 and on Mount Kenya 19, resulting in more than double events recorder by the

AWS on Lewis glacier. In this case most of the precipitation events focuses during

the rain seasons OND 2010 and OND 2011; the rain season MAM 2011 presents

only two case, one on Kilimanjaro and one on Mount Kenya, with more than 4 cm

accumulation.

Figure 3.3 shows the final results of the classification of precipitation events

over Kilimanjaro and Mount Kenya. Class BOTH shows evidence of similarity with

Fig. 3.2, most of the precipitation events occur in the the rain seasons OND 2010

and OND 2011 while only few events occur in the other periods. On the other hand

class MTK and class KIBO seems to reveal opposite behaviour. All the precipitation

events in the class MTK occur in the two periods JJAS 2011 and OND 2011 while

most of the precipitation events in the class KIBO occur in the periods OND 2010,

JF 2011, and MAM 2011. Notice, that both class MTK and class KIBO count less

events than class BOTH.

3.2 ERA-Interim Reanalysis

This chapter summarizes the results of the ERA-Interim reanalysis data study for

the three precipitation classes, class BOTH, class MTK, and class KIBO, and for

all the precipitation events exceeding 2 cm of snow accumulation, class ALL.

First of all it is important to highlight the processing of the wind direction in this

analysis. Vector average and standard deviation of wind direction on constant height

levels are calculated for each of the four classes. Figure 3.4 shows the results of the

averaging process. The wind direction is nearly constant in the lower troposphere

for each class, with no evidence of wind turning in the lower 8 km except from close

to the surface. Class BOTH and class ALL show a strong evidence of constant wind

direction in the lower atmosphere. For class MTK and class KIBO the evidence is

not so strong, but it must be noted that for this two classes the number of events on

which the average is based is relatively small. Since the wind direction is not turning

in the lower troposphere it is possible to calculate the vector average of wind direction

below 8 km for each precipitation events in each class. The vertically averaged wind

direction is then assigned to one of the reference cardinal directions (N, NE, E, SE,

S, SW, W, NW). Finally a bar plot showing the number of precipitation events for

each cardinal direction is created for all the four precipitation classes.

Figure 3.5 summarizes the main results of the ERA-Interim reanalysis data

study for the four precipitation classes. For each class mean vertical profiles and

standard deviation of Θ, Θe, Θes, RH and Wsp are shown for Kilimanjaro (red) and

Mount Kenya (blue). The wind direction bar plot is as described above.

3.2 ERA-Interim Reanalysis 35

N E S W NWind Direction

0

2

4

6

8

10

12He

ight

(km

a.s

.l.)

Class BOTH

(a)


0

2

4

6

8

10

12

Heig

ht (k

m a

.s.l.

)

Class MTK

(b)


0

2

4

6

8

10

12

Heig

ht (k

m a

.s.l.

)

Class KIBO

(c)


0

2

4

6

8

10

12

Heig

ht (k

m a

.s.l.

)

Class ALL

(d)

Figure 3.4: Vector average and standard deviation of wind direction at 00 UTC for: (a)

class BOTH, (b) class MTK, (c) class KIBO, and (d) class ALL.

• Class BOTH: vertical profiles at the two locations are very similar. Profiles

of Θ are almost identical. A decreasing Θe and Θes below 4 km denote a po-

tentially and a conditionally unstable layer respectively. RH and Wsp profiles

show a greater standard deviation but a very similar behaviour. Mean wind

speed in the lower 4 km at Kilimanjaro is about 2 m s−1 to 3 m s−1 higher

than at Mount Kenya. The most common wind directions during the events

are SE and E.

• Class MTK: vertical profiles at the two locations are similar, but not as similar

as in class BOTH. Profiles of Θ are almost identical. The most important

difference is that RH is by about 10% to 20% higher at Mount Kenya than

at Kilimanjaro. A decreasing Θe, between 2 km and 5 km at Mount Kenya

and below 3 km at Kilimanjaro, denotes a potentially unstable layer. Overall

higher Θe for Mount Kenya is a result of higher RH, especially in the lower

4 km. A decreasing Θes, below 4 km at Mount Kenya and between 3 km and

4 km at Kilimanjaro, denotes a conditionally unstable layer; notice that at

Kilimanjaro Θes increases with height between 2 km and 3 km . Mean wind

speed at Mount Kenya is the lower of the whole study. The dominant wind

direction at Mount Kenya is SE.

• Class KIBO: vertical profiles at the two locations are very similar. The pro-

files of this class are almost identical to the ones of class BOTH. The most

important difference is that RH between 4 km and 8 km is by about 10% to

20% higher at Kilimanjaro than at Mount Kenya. A decreasing Θe and Θes be-

low 4 km denote a potentially and a conditionally unstable layer respectively.

The dominant wind direction at Kilimanjaro is SE.

36 Reanalysis Study

• Class ALL: vertical profiles at the two locations are very similar. A decreasing

Θe and Θes below 4 km denote a potentially and a conditionally unstable layer

respectively. The dominant wind directions during the events are S, SE and

E, with a relative high number of events with SW wind direction at Mount

Kenya.

3.2 ERA-Interim Reanalysis 37

300

310

320

330

340

350

Θ (K)

0

2

4

6

8

10

12He

ight

(km

a.s

.l.)

320

330

340

350

360

Θe (K)

330

340

350

360

370

Θes (K)

0 20 40 60 80 100

RH (%)

0 5 10 15 20 25 30

Wsp (m s−1 )

0 5 10 15 20

Number of Events

NEE

SES

SWW

NWN

Win

d Di

rect

ion

KilimanjaroMt. Kenya

Class BOTH

(a)

300

310

320

330

340

350

Θ (K)

0

2

4

6

8

10

12

Heig

ht (k

m a

.s.l.

)

320

330

340

350

360

Θe (K)

330

340

350

360

370

Θes (K)

0 20 40 60 80 100

RH (%)0 5 10 15 20 25 30

Wsp (m s−1 )

0 5 10 15 20

Number of Events

NEE

SES

SWW

NWN

Win

d Di

rect

ion


Class MTK

(b)

300

310

320

330

340

350

Θ (K)

0

2

4

6

8

10

12

Heig

ht (k

m a

.s.l.

)

320

330

340

350

360

Θe (K)

330

340

350

360

370

Θes (K)

0 20 40 60 80 100

RH (%)

0 5 10 15 20 25 30

Wsp (m s−1 )

0 5 10 15 20

Number of Events

NEE

SES

SWW

NWN

Win

d Di

rect

ion


Class KIBO

(c)

300

310

320

330

340

350

Θ (K)

0

2

4

6

8

10

12

Heig

ht (k

m a

.s.l.

)

320

330

340

350

360

Θe (K)

330

340

350

360

370

Θes (K)

0 20 40 60 80 100

RH (%)

0 5 10 15 20 25 30

Wsp (m s−1 )

0 5 10 15 20

Number of Events

NEE

SES

SWW

NWN

Win

d Di

rect

ion


Class ALL

(d)

Figure 3.5: Mean vertical profiles and standard deviation at 00 UTC of Θ, Θe, Θes, RH

and Wsp at Kilimanjaro (red) and Mount Kenya (blue) for: (a) class BOTH, (b) class

MTK, (c) class KIBO, and (d) class ALL. The wind direction bar plot is showing the

number of precipitation events for each cardinal direction.

Chapter 4

Idealized Numerical Simulations

The results of the numerical simulations are here presented. All the types of simu-

lations as well as the model setup are described in Chap. 2.2.

A note about the figures presented has to be made. Being the aim of this study

to identify main differences as well as different precipitation mechanisms most of

the figures show fields of atmospheric parameters averaged in time over the whole

simulation. This is done in order to highlight stationary patterns. For example

figures showing mean of water vapor mixing ratio are used to identify the location

where clouds typically form and thus where the occurrence of precipitation is max-

imized. Furthermore it is important to keep in mind that the real topographies of

the two mountains are rotated by 125 clockwise and that the reference level of the

model is fixed at 1300 m a.s.l.. Finally an area of interest (AOI) is introduced, it

is represented for both mountains by a 300 km by 300 km centered in the relative

summits. This AOI is used to calculate mean of accumulated precipitation.

4.1 Kilimanjaro and Mount Kenya Comparison

The results of the comparison experiment between Kilimanjaro and Mount Kenya

are here presented. A total of 7 simulations are performed in this comparison, 3

for Kilimanjaro (kibo ALL, kibo F and kibo W) and 4 for Mount Kenya (mtk ALL,

mtk F, mtk W and mtk ALLsh). First the results of the simulations type W are

presented, followed by type F and type ALL, mtk ALLsh will be presented apart.

Figures are at the end of the chapter.

Simulations type W

The simulation type W focuses on studying the effects of the interaction between

the background flow and the topography, prescribed surface heat fluxes are 0. The

main results are shown in Fig. 4.1 for Kilimanjaro and in Fig. 4.2 for Mount Kenya.

39

40 Idealized Numerical Simulations

β ε

Kilimanjaro 0.68 6.02

Mount Kenya 1.54 3.69

Table 4.1: Horizontal aspect ratio β and non-dimensional mountain height ε for Kili-

manjaro and Mount Kenya.

In both cases the interaction of the background flow with the topography lead

to the “flow around” regime with formation of lee vortices (Fig. 4.1d and 4.2d).

The flow reversal on the lee side of the mountain is rather weak, with very low wind

speeds for Kilimanjaro (Fig. 4.1b) while for Mount Kenya it is stronger (Fig. 4.2b).

The non-dimensional mountain height ε and the horizontal aspect ratio β, as

described by Epifanio (2015), are shown in Table 4.1. The non-dimensional moun-

tain height ε = Nh/U is calculated assuming that the atmosphere is unsaturated,

considering mean values of N and U below crest height. If these parameters are

compared with the flow regimes diagram for stratified flow past an isolated moun-

tain (Fig. 1.5), the regime expected for both Kilimanjaro and Mount Kenya is flow

splitting with lee vortices, which is exactly what can be observed in the simula-

tions. Furthermore the horizontal aspect ratio β shows that both mountains have

an overall similar elliptical shape but with different orientations respect the main

background flow direction.

The precipitation distribution is different on the two mountain. For Kilimanjaro

the accumulated precipitation (Fig. 4.1a) exhibits higher rates and it is located

mainly on the lee side concurrently with the lee vortices, precipitation occurs also

on the windward side most likely due to orographic lifting. For Mount Kenya the

accumulated precipitation over the whole simulation (Fig. 4.2a) is very low, it is

located only on the windward side of the mountain and it is occurring all in the first

hour of the simulation (Fig. 4.8a), this indicates that precipitation is occurring due

to orographic lifting while the model reach the steady state.

This type of simulation is the one with the lowest accumulated precipitation of

the whole set for both mountains (Fig. 4.8a). Accumulated precipitation at the end

of the simulation averaged over the AOI is about 0.03 mm at Kilimanjaro and less

than 0.01 mm at Mount Kenya. Furthermore significant precipitation, more than

0.01 mm, does not reach the summit areas (Fig. 4.9a).

Simulations type F

The simulation type F focuses on studying the effects of the surface heat fluxes,

parameterizing the daily cycle, on the precipitation mechanisms. The main results

are shown in Fig. 4.3 for Kilimanjaro and in Fig. 4.4 for Mount Kenya.

4.1 Kilimanjaro and Mount Kenya Comparison 41

In both cases results show evidence of a well developed slope wind circulation

driven by the the surface sensible heat flux. Accumulated precipitation is found on

the slopes with its maximum over the summits (Fig. 4.3a and 4.4a). Both the cross

sections (Figs. 4.3b-c and 4.4b-c) exhibit the strongest updrafts close to the two

summits leading to the development of sustained clouds during the whole simulation.

Finally Figs. 4.3d-e and 4.4d-e show the average location of vertical updraft and

cloud formation, with a higher water vapor mixing ratio close to summits elevations.

This type of simulation is the one with the highest accumulated precipitation

(Fig. 4.8b). Accumulated precipitation at the end of the simulation averaged over

the AOI is about 2.28 mm at Kilimanjaro and about 1.70 mm at Mount Kenya.

Furthermore the precipitation maximum is located close to the two summit area

(Fig. 4.9b).

Simulations type ALL

The simulation type ALL focuses on studying the combined effect resulting from the

interaction of the surface fluxes and the background flow with the topography, and

it is the closest to reality. The main results are shown in Fig. 4.5 for Kilimanjaro

and in Fig. 4.6 for Mount Kenya.

In both cases the interaction of the background flow with the topography lead

to the “flow around” regime, as in the case W but with some difference. In the

lee of Mount Kenya two counter-rotating lee vortices form (Fig. 4.6d); in the lee

of Kilimanjaro there is no evidence of lee vortices (Fig. 4.5d), nevertheless, the

orientation of the streamlines indicates convergence on the leeward side. For Mount

Kenya the flow reversal on the lee side (Fig. 4.6b), due to the vortices, is evident and

well developed while for Kilimanjaro it is not (Fig. 4.5b). Accumulated precipitation

exhibits its maximum on the lee side for both mountains, with two drier flanks

originating from the lateral sides of the mountains (Fig. 4.5a and 4.6a). Cross

sections for Kilimanjaro shows a strong asymmetry in the mean cloud structure

(Fig. 4.5b and 4.5c). Clouds are almost only located on the lee side, which is

also reflected in the accumulated precipitation. This asymmetry is not so strong

for Mount Kenya (Fig. 4.6b and 4.6c) where clouds and precipitation also form

on the windward side of the mountain. Furthermore the cross sections across flow

direction highlight another evident difference: an upslope/downslope circulation is

well developed on the lateral slope of Kilimanjaro/Mount Kenya.

Accumulated precipitation in this type of simulation is lower than in the type F

but significantly higher than in the type W (Fig. 4.8c). Accumulated precipitation

at the end of the simulation averaged over the AOI is about 1.94 mm at Kilimanjaro

and about 1.39 mm at Mount Kenya. Precipitation maximum is located close to

the two summit (Fig. 4.9c).


Simulations type ALLsh

The simulation type ALLsh is a particular simulation for Mount Kenya in which the

reference height of the topography is the same as for the Kilimanjaro simulation The

purpose, is to evaluate the impact of the altitude of the plateau surrounding the two

mountains. From the simulations type W, F, and ALL (Figs. 4.8a-c) there is evi-

dence that accumulated precipitation at Mount Kenya is always lower. In particular

in the two simulations with significant accumulated precipitation, type F and ALL,

the accumulated precipitation at the end of the simulation averaged over the AOI

at Mount Kenya is in both cases about 0.5 mm lower than at Kilimanjaro. This

could be the result of a higher “cut” height, which means that the surface is placed

at an higher altitude in the model for the simulation on Mount Kenya, resulting in

a lower moisture content of the lower part of the atmosphere that interacts with the

terrain.

The main results of the simulation type ALLsh for Mount Kenya are shown in

Fig. 4.7. The comparison with Fig. 4.6 leads to the conclusion that the processes

characterizing the two simulations are the same (formation of two counter-rotating

lee vortices, flow reversal, etc.) although the accumulated precipitation is higher in

the simulation ALLsh (Fig. 4.8d). In particular the accumulated precipitation at

the end of the simulation ALLsh averaged over the AOI is about 1.85 mm, much

closer to the one at Kilimanjaro (1.94 mm). From Fig. 4.9d can be clearly seen how

the accumulated precipitation close to the surface (altitude band 0−700 m a.r.l.) is

almost the same between the two locations. At higher altitude bands (700−2700 m

a.r.l.) Mount Kenya shows a higher median. Accumulated precipitation maximum

is located both on the windward side and on the lee side of Mount Kenya (Fig.

4.7a), showing contrast with the strong asymmetry of Kilimanjaro.


(a)

(b) (c)

(d) (e)

Figure 4.1: Simulation kibo W: (a) accumulated precipitation (mm) at the end of the

simulation, as color contour, and cross sections path, as red lines; cross section (b) parallel

and (c) perpendicular to the background flow direction showing the mean over the whole

simulation of θ (K), as red contour lines, total hydrometeor mixing ratio (g kg−1), as color

contours, and wind component parallel to the cross section (m s−1), as vector; mean over

the whole simulation of streamlines and vertical velocity (m s−1), as color contours, at (d)

950 m. a.r.l. and (e) 2950 m. a.r.l..


(a)

(b) (c)

(d) (e)

Figure 4.2: Simulation mtk W: as in Fig. 4.1.


(a)

(b) (c)

(d) (e)

Figure 4.3: Simulation kibo F: (a), (b), and (c) as in Fig. 4.1; mean over the whole

simulation of (d) vertical velocity (m s−1), as color contour, and (e) total hydrometeor

mixing ratio (g kg−1), as color contour, at 4200 m a.r.l..


(a)

(b) (c)

(d) (e)

Figure 4.4: Simulation mtk F: (a), (b), and (c) as in Fig. 4.1; mean over the whole

simulation of (d) vertical velocity (m s−1), as color contour, and (e) total hydrometeor

mixing ratio (g kg−1), as color contour, at 3450 m a.r.l..


(a)

(b) (c)

(d) (e)

Figure 4.5: Simulation kibo ALL: as in Fig. 4.1.


(a)

(b) (c)

(d) (e)

Figure 4.6: Simulation mtk ALL: as in Fig. 4.1.


(a)

(b) (c)

(d) (e)

Figure 4.7: Simulation mtk ALLsh: as in Fig. 4.1.


03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 2103 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21Time of the day

0.00

0.01

0.02

0.03

m. a

. p. (

mm

)

kibo = 0.0277 mmmtk = 0.0009 mm

Kilimanjaro Mt. Kenya

(a)

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 2103 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21Time of the day

0.00.51.01.52.02.5

m. a

. p. (

mm

)

kibo = 2.28 mmmtk = 1.70 mm


(b)

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 2103 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21Time of the day

0.00.51.01.52.02.5

m. a

. p. (

mm

)



(c)

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 2103 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21Time of the day

0.00.51.01.52.02.5

m. a

. p. (

mm

)



(d)

Figure 4.8: Accumulated precipitation averaged over the AOI for: (a) simulation type

W, (b) simulation type F, (c) simulation type ALL, and (d) simulation mtk ALLsh with

simulation kibo ALL. The text box is showing the accumulated precipitation averaged

over the AOI at the end of the simulation at Kilimanjaro (kibo) and Mount Kenya (mtk).


0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6Accumulated Precipitation (mm)

0

700

1700

2700

3700

4700

Altit

ude

Band

s (m

a.r.

l.)


(a) Simulation type W

0 50 100 150 200 250Accumulated Precipitation (mm)

0

700

1700

2700

3700

4700

Altit

ude

Band

s (m

a.r.

l.)Mt. KenyaKilimanjaro

(b) Simulation type F

0 10 20 30 40 50 60Accumulated Precipitation (mm)

0

700

1700

2700

3700

4700

Altit

ude

Band

s (m

a.r.

l.)


(c) Simulation type ALL

0 10 20 30 40 50 60Accumulated Precipitation (mm)

0

700

1700

2700

3700

4700

Altit

ude

Band

s (m

a.r.

l.)


(d) Simulation type ALLsh.

Figure 4.9: Boxplot of accumulated precipitation averaged over altitude bands in the

AOI for: (a) simulation type W, (b) simulation type F, (c) simulation type ALL, and (d)

simulation mtk ALLsh with simulation kibo ALL. The boxplot shows median, 25th and

75th percentile, wiskers show the minimum and the maximum values. Altitude bands are

the following: 0 − 700 m a.r.l., 700 − 1700 m a.r.l., 1700 − 2700 m a.r.l., 2700 − 3700 m

a.r.l. and 3700− 4700 m a.r.l..

4.2 Sensitivity Study for Kilimanjaro 53

4.2 Sensitivity Study for Kilimanjaro

The results of the sensitivity study on Kilimanjaro are here presented. A total of 4

simulations are performed with varying surface sensible and latent heat fluxes and

moisture content as described in Chap. 2.2

Figure 4.10 shows a boxplot of accumulated precipitation over the whole simu-

lation on altitude bands. The simulation type ALL is showing the highest median

at the lowest elevation band (0 − 700 m a.r.l.) and at the highest (3700 − 4700

m a.r.l.), while at mid elevations (700 − 4700 m a.r.l.) the median is very close

to the one of simulation type F0.5. The simulation type rh75 shows surprisingly

high accumulated precipitation values at high altitudes, with a median very close to

the type ALL. These two type of simulations (ALL and rh75) show an increase in

median with height, indicating that the precipitation maximum is located close to

the summit of Kilimanjaro. On the other hand simulation type F0.5 and F0.5rh75

show a precipitation maximum located at mid elevations. As expected the simula-

tion type F0.5rh75 shows the lowest accumulated precipitation values at all altitude

bands. However only few grid points are located in the highest elevation band

(3700 − 4700 m a.r.l.), for this reasons the representativeness of Fig. 4.10 at this

height is questionable.

Figure 4.11 shows accumulated precipitation averaged over the AOI (Fig. 4.11a)

and over the summit area of Kilimanjaro (Fig. 4.11b). With summit area it is in-

tended the area covering 1000 m in vertical below the highest point of the topography

(altitude band 3300 m a.r.l. - 4300 m a.r.l.). Notice that the mean accumulated pre-

cipitation at the end of the simulations changes between the AOI and the summit.

For the AOI the maximum value is found in the simulation types ALL, followed by

the simulation type F0.5; while for the summit area the maximum value is found in

the simulation type rh75, followed by the simulation type ALL.

Figure 4.12 shows the distribution of the accumulated precipitation at the end

of each simulation. The overall U-shaped pattern is very similar in all simulations

except from the magnitude.


0 10 20 30 40 50 60 70Accumulated Precipitation (mm)

0

700

1700

2700

3700

4700

Altit

ude

Band

s (m

a.r.

l.)

88230

1046

510

191

23

#

ALLrh75F0.5F0.5rh75

Figure 4.10: As in Fig. 4.9 but for the simulation type ALL, rh75, F0.5, and F0.5rh75 at

Kilimanjaro. On the right the number of grid points (#) at each altitude band is shown.

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 2103 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21Time of the day

0.0

0.5

1.0

1.5

2.0

m. a

. p. (

mm

)

AOI

ALL = 1.94 mmrh75 = 0.29 mmF0.5 = 0.87 mmF0.5rh75 = 0.03 mm

(a)

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 2103 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21Time of the day

05

1015202530

m. a

. p. (

mm

)

SUMMIT AREA (3300 - 4300 m a.r.l.)

ALL = 20.56 mmrh75 = 25.42 mmF0.5 = 10.76 mmF0.5rh75 = 8.17 mm

(b)

Figure 4.11: Accumulated precipitation averaged (a) over the AOI and (b) over the

summit area (altitude band 3300−4300 m a.r.l.) for the simulation type ALL, rh75, F0.5,

and F0.5rh75 at Kilimanjaro. In the legend values of averaged accumulated precipitation

at the end of each simulation are shown.

4.2 Sensitivity Study for Kilimanjaro 55

(a) (b)

(c) (d)

Figure 4.12: Accumulated precipitation (mm) at the end of the simulation for: (a) sim-

ulation kibo ALL, (b) simulation kibo F0.5, (c) simulation kibo rh75, and (d) simulation

kibo F0.5rh75.


4.3 Idealized Ridge

The results of the idealized topography experiment are here presented. A total of

three simulations are performed as described in Chap. 2.2. The simulation BELL

uses a symmetric bell-shaped mountain, XL uses an elliptically-shaped mountain

with an elongated axis on the x direction and YL uses an elliptically-shaped moun-

tain with an elongated axis on the y direction.

Figure 4.13 shows the accumulated precipitation averaged over the mountain for

the three simulations. Grid points located on the plain surrounding the topography

are excluded from the average. It is clearly visible how XL is experiencing by far

the highest accumulated precipitation, with a value of 5.67 mm at the end of the

simulation, while BELL and YL are very similar in behaviour, with values of 3.39

mm and 3.41 mm, respectively.

Figures 4.14 and 4.15 show the most important results of the 3 simulations,

including accumulated precipitation, streamlines and vertical velocity and cross sec-

tions along and across flow direction passing through the summit and the center

point of the domain (cross section paths are shown Figs. 4.14a,c,e). In these figures

the similarity between the simulations BELL and YL can be observed again. They

both show an evident “flow around” regime with formation of lee vortices, larger in

YL than in BELL (Figs. 4.14b,f). The accumulated precipitation distribution has it

maximum on the lee side of the mountain, with a characteristic “U” shape pattern,

determined by the circulation driven by the two counter rotating lee vortices (Figs.

4.14b,f). In the leeward side of the mountain the wind is in fact either very low

(BELL) or directed towards the mountain (YL) contributing to the upslope trans-

port of moist air already driven by the surface sensible heat flux (Figs. 4.15a,e).

Two symmetric flanks can be observed also in the precipitation minimum, which

again appear to be wider in the simulation YL. These dry zones are to be associated

with subsidence which can be seen in the vertical velocity in both Figs. 4.14b,f and

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 2103 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21Time of the day

0123456

m. a

. p. (

mm

)

BELL = 3.39 mmXL = 5.67 mmYL = 3.41

BELL XL YL

Figure 4.13: Accumulated precipitation averaged over the mountain for the simulations:

BELL, XL, and YL. The text box is showing the accumulated precipitation averaged over

the mountain at the end of the simulation.

4.3 Idealized Ridge 57

Figs. 4.15b,f.

The situation is different for the simulation XL, the flow regime is again of

the type “flow around” but without lee vortices (Fig. 4.14d). The precipitation

maximum is located on leeward side of the mountain (Fig. 4.14c) at the same

location as the vertical velocity maximum (Fig. 4.15c) and it is not showing the

particular symmetric shape seen in BELL and YL. The precipitation minimum is

organized in two symmetric flanks also in this case, but they are less pronounced

than in the previous two cases. The cross section perpendicular to the background

flow is showing a well developed updraft along the lateral slope (Fig. 4.15d), which

is in contrast to the flow pattern in BELL and YL. The cross section parallel to

the background flow is showing a strong downslope wind on the leeward side of the

mountain, which is again in contrast to the flow pattern in BELL and YL.


(a) (b)

(c) (d)

(e) (f)

Figure 4.14: Results of the simulations: (a-b) BELL, (c-d) and XL, and (e-f) YL. (a),

(c), and (e) accumulated precipitation (mm) at the end of the simulation, as color contour,

and cross sections path, as red lines; (b), (d), and (f) mean over the whole simulation of

streamlines and vertical velocity (m s−1), as color contours, at (d) 1950 m. a.r.l..

4.3 Idealized Ridge 59

(a) (b)

(c) (d)

(e) (f)

Figure 4.15: Results of the simulations: (a-b) BELL, (c-d) and XL, and (e-f) YL. Cross

section (a), (c), and (e) parallel and (b), (d), and (f) perpendicular to the background

flow direction showing the mean over the whole simulation of θ (K), as black contour

lines, vertical velocity (m s−1), as color contours, total hydrometeor mixing ratio, as

colored contour lines (green 0.1 kg−1, purple 0.4 g kg−1 and indigo 1.0 g kg−1), and wind

component parallel to the cross section (m s−1) as vector.

Chapter 5

Discussion

5.1 Reanalysis Study

In this chapter the results of the reanalysis study, presented in Chap. 3, are dis-

cussed. The results of the classification of precipitation events and of the ERA-

Interim reanalysis are discussed separately.

The classification of precipitation events on Kilimanjaro and Mount Kenya is the

result of an analysis of in-situ observations collected at Kersten and Lewis glacier.

The analysis covers a study period of only 16 months, which are definitely not

enough for deducing a climatological behaviour. Furthermore during the years 2010

and 2011 East Africa was struck by a severe drought (Lyon and DeWitt 2012; Yang

et al. 2014), clearly evident in the long rains season MAM 2011 in Fig. 3.1. Thus,

the period covered by the reanalysis study (October 2010 − February 2012) may not

be the best representative period, but it is the only one when AWSs measurements

were collected at both locations.

Despite the expectations, precipitation events on the two summits during the

study period do not strictly follow the typical East Africa seasonality. Figure 3.2

shows clearly that precipitation events exceeding 2 and 4 cm daily snow accumu-

lation are happening throughout the whole study period. However, they are more

frequent and exhibit higher daily snow accumulation during the short rains seasons

(OND 2010 and OND 2011). For these reasons accumulation on the two summits

may be also influenced by local mechanisms and not only by synoptic conditions

due to the typical seasonality of the tropics. A similar behaviour was already noted

by (Molg et al. 2009a), who performed a similar analysis using Kersten glacier AWS

data but for a different period.

The second finding that emerges from Fig. 3.2 is that events with higher daily

accumulation (> 4 cm) are happening twice as much on Mount Kenya (19 events)

than on Kilimanjaro (7 events), while events with a lower daily accumulation (> 2

61

62 Discussion

cm) are showing less difference between the two sites (51 events on Mount Kenya

and 40 on Kilimanjaro). This is showing evidence that stronger precipitation events

are more easily happening on Mount Kenya rather than on Kilimanjaro and it could

be a possible explanation to the reported higher accumulation on Lewis glacier

(Nicholson et al. 2013).

The distribution of precipitation events during the study period for class BOTH

(Fig. 3.3a) is very similar to the one of all precipitation events exceeding 2 cm. Class

MTK (Fig. 3.3b) and class KIBO (Fig. 3.3c) show a really different behaviour. Most

of the events at Kilimanjaro are happening during the first half of the study period

while those at Mount Kenya are limited only to the second half. This behaviour

may be due to seasonal synoptic conditions that favour precipitation on one site

rather than on the other depending on the time of the year. Furthermore this

behaviour could also be the results of a combination of several exceptional factors

thus not reflecting the typical climatology. Further investigations are required to

better understand this aspect.

The ERA-Interim reanalysis data study is developed in order to test the hy-

pothesis in goal (1) in Chap. 1.3. The results (Fig. 3.5) show some differences and

several analogies between the mean vertical profiles of the four classes:

• First of all class ALL and class BOTH are almost identical, thus revealing

that the synoptic conditions favouring precipitation simultaneously on both

mountains are those characterizing also most of the events.

• All the four classes show evidence of a high moisture content of the atmosphere

close to the surface, with values of relative humidity between 80% and 100%.

Furthermore the vertical profiles of Θe and Θes exhibit a potentially and a

conditionally unstable layer, respectively, thus indicating that the atmospheric

conditions are favourable for convection.

• Class MTK is the one showing most differences from the others. The relative

humidity in the lower troposphere are between 10% and 20% lower at Kiliman-

jaro than at Mount Kenya. Furthermore the wind speed in the first 5 km of

atmosphere is on average few m s−1 lower than in the other classes, especially

at Mount Kenya. In this case it could be possible that the combined effects

of low wind speed and higher humidity at Mount Kenya favour precipitation

only at this site. Nevertheless it must be taken into account that the number

of events in this class is 5 and thus drawing major conclusions from such a

small sample may be inappropriate.

• Class KIBO is almost identical to class BOTH and ALL. The only small dif-

ference is in the relative humidity above 4 km, which is slightly higher (about

5.2 Idealized Numerical Simulations 63

10%) at Kilimanjaro. The reason for different precipitation patters in this case

have to be most likely searched elsewhere.

• The most common wind directions are S, SE and E for all the classes, thus

confirming previous results of, e.g., Nicholson et al. (2013) and Ehrengruber

(2011). Furthermore only few precipitation events are characterized by SW,

W and NW wind direction, 11 at Mount Kenya and 5 at Kilimanjaro over

the whole study period. Thus, the influence of a west to east propagation of

convective activity at Kilimanjaro during precipitation events may not be that

important as previously stated by Chan et al. (2008).

Most of the differences between the four classes are well restricted in the range of

the relative standard deviations (Fig. 3.5), thus revealing that variability between

events of the same class may overwhelm differences between the classes. For all

these reasons the conclusion of this reanalysis study is that the synoptic background

conditions favouring precipitation events on Kilimanjaro and Mount Kenya are very

similar and are not responsible for the difference between the two sites highlighted

by the in-situ observations. Finally the hypothesis that during precipitation events

the air mass at the two mountains is the same (goal (1) in Chap. 1.3) is confirmed.

5.2 Idealized Numerical Simulations

The results of the idealized numerical simulations, presented in Chap. 4, are here

discussed. First of all the comparison of simulated precipitation with measurements

at the two summits is not one of the main goal of this study. Due to the highly

idealized model setup and to the quality of the AWSs data, a close match between the

model results and the observations cannot be expected. However a rough estimation

of the simulated precipitation at the two summits could be used to check whether

the order of magnitude of the results is comparable with the measurements.

Measured daily snow accumulation at the two summits during the study period

October 2010 − February 2012 range between 2 cm (lower threshold) and 13 cm

(Fig. 3.2a). Considering a snow density of 250 kg m−3 these values can be converted

into a snow water equivalent of 5 mm and 32.5 mm, respectively. Considering the

results of the simulations kibo ALL and mtk ALLsh, the median of accumulated

precipitation close to the summit (Fig. 4.9d, altitude band 3700 − 4700 m a.r.l.

for Kilimanjaro and 1700 − 2700 a.r.l. for Mount Kenya) is about 10 mm at both

location, thus in the range of the measurements. Furthermore the results of the

simulations of type F show much higher accumulated precipitation (Fig. 4.9b), out

of the measurements range and, thus, indicating that this type of simulations may

not represent the reality.

64 Discussion

The most important finding of the numerical simulations is that the mesoscale

circulation characterizing precipitation events at Kilimanjaro and Mount Kenya is

the result of a complex interaction of surface heat fluxes, background flow and

topography.

The Role of Terrain Geometry

Both mountains have an overall similar elliptical shape but with a totally different

positioning respect the main background flow direction. For Kilimanjaro the long

axis is aligned parallel to the background flow whereas for Mount Kenya it is aligned

perpendicular. This orientation of the mountains has an impact on both the dy-

namically and thermally driven flows. Simulations of type ALL (Fig. 4.5 and 4.6),

the ones closer to reality, give an overview of the mesoscale circulation, quite similar

to the one describe by Houze (2012) and shown in Fig. 1.7d.

At Mount Kenya the flow regime is “flow around” with lee vortices formation,

which lead to flow reversal in the leeward side of the mountain. At this spot surface

heat fluxes induce an upslope flow on the lee slope (Fig. 4.6b). The results of

the two simultaneous processes is a strong updraft of moisture condensating along

the lee slope; precipitation is confined to the leeward side of the mountain by the

impinging background flow which at the height of the summit advects it downstream.

Orographic lifting on the windward side of the mountain causes precipitation also

on the windward side of Mount Kenya (Fig. 4.6a and 4.6b).

At Kilimanjaro the flow regime is “flow around” without lee vortices formation.

Strong upslope circulations develop on the lateral slopes, contrary to the Mount

Kenya case, forcing moist air to converge on the summit plateau from where it

is further advected downstream by the large-scale flow. The combination of these

effects cause a strong asymmetry in precipitation on Kilimanjaro (Fig. 4.5a and

4.5b).

The results of the simulations XL and YL (Figs. 4.14 and 4.15), which use

an idealized ridge to represent the topography of Kilimanjaro and Mount Kenya

are showing a very similar mesoscale circulation. Similar results are also shown in

the studies by Crook and Tucker (2005) and Tucker and Crook (2005), allowing a

close comparison. Accumulated precipitation is maximum when the long axis of the

mountain is aligned parallel to the background flow, as in the case of Kilimanjaro

and XL. Precipitation develops in a narrow band on the lee slope of the mountain

in the simulation XL and in a “U” shape pattern in the simulation YL. Similar

precipitation patterns are also observed by Tucker and Crook (2005).

The reasons for these patterns, as explained by Crook and Tucker (2005), are

shown in Fig. 5.1. First of all the effect of the heating gradient induced by the

surface heat fluxes is maximized when the flow is parallel to the long axis of the

5.2 Idealized Numerical Simulations 65

Figure 5.1: Schematic showing the different forcing that occurs when the flow is along

or across a heated obstacle. Taken from Crook and Tucker (2005).

mountain. This is clearly visible in both the simulations kibo ALL and XL (Fig. 4.5c

and 4.15d), where an evident upslope circulation develops and persists throughout

the whole simulation. Secondly the orographic effect is maximized when the flow is

parallel to the long axis of the mountains, with the downward velocity forced by the

lee slope minimized.

Furthermore the flow regime seems to play an important role in determining the

final precipitation distribution. This aspect is not address in the study by Tucker

and Crook (2005). At Kilimanjaro (simulations kibo ALL and XL) the absence of

lee vortices lead to results very similar to the ones by Tucker and Crook (2005). At

Mount Kenya (simulations mtk ALL and YL) the formation of lee vortices induces

flow reversal on the leeward side of the mountain (Fig. 4.6b and 4.15e), which enforce

the upslope circulation already driven by the surface sensible heat flux. In this case

the precipitation maximum is shifted upslope compared to the study by Tucker and

Crook (2005), with a “U” shape pattern rather than a “V” shape pattern.

The first possible reason for the “U” shape pattern is the effect of the flow re-

versal induced by the lee vortices, which tends to push the air back to the mountain,

force it to raise and then spread to the lateral sides. The second possible reason is

that the “U” shape precipitation pattern is related to the updrafts induced by grav-

ity waves (Fig. 4.15). Further investigations are necessary to distinguish between

the two cases.

Finally one of the most important findings of this study is that precipitation

distribution and magnitude are very sensitive to the orientation of the mountain

respect the background flow. Notice that all the simulations are performed without

changing the wind direction of the background flow. This is a major limitation

of the present study. Although the wind direction used (SE) is the most frequent

66 Discussion

during precipitation events, it represent less than 50% of all cases (Fig. 3.5). Further

investigations, e.g. simulations changing the wind direction by ±45, are necessary

to fully understand the precipitation mechanisms.

The Role of Surface Heat Fluxes and Moisture Content

Surface heat fluxes are one of the most important mechanisms producing precipita-

tion on Kilimanjaro and Mount Kenya. From the comparison of the simulations of

type W, ALL and F (Fig. 4.8 and 4.9) it is clear that the magnitude of precipitation

is strongly controlled by the surface heat fluxes. In fact the highest accumulated

precipitation is found in the simulations of type F, while precipitation does not occur

at the summit of the two mountains in the simulations of type W. Thus, the atmo-

spheric conditions favouring precipitation events on the summits are characterized

by strong surface heat fluxes and absence of wind, as already found by Chan et al.

(2008).

The main result of the sensitivity study for Kilimanjaro (Fig. 4.10) is that

the precipitation maximum, typically located at mid elevations (simulations F0.5

and F0.5rh75), is shifted towards the summit when the intensity of the surface heat

fluxes is stronger (simulations ALL and rh75). Furthermore the sensitivity study

shows also that the simulations of type ALL and rh75, which are characterized

by different moisture content of the background flow, experience similar values of

accumulated precipitation at the summit (Fig. 4.11b). This indicates that the

moisture content of the atmosphere may be less important than the surface heat

fluxes in the precipitation mechanisms.

However these results have to be carefully interpreted. In fact, both surface

sensible and latent heat fluxes are prescribed, this means that two effects occur

at the same time. The surface sensible heat flux develops the upslope circulation

while the surface latent heat flux contributes to rise the moisture content of the

atmosphere. This is a major limitation of the sensitivity study. In order to better

understand the role of surface heat fluxes and moisture content in the precipitation

mechanisms further investigations are required, e.g. simulations activating only one

of the two surface heat fluxes.

Finally the moisture content of the atmosphere is also the reason why accumu-

lated precipitation at Mount Kenya is lower than at Kilimanjaro for the simulations

of type ALL, W and F. Due to the fact that the plains surrounding Mount Kenya

are higher than the one surrounding Kilimanjaro, the moisture content of the at-

mosphere is lower, since the input sounding used to initialize the simulations is the

same. The simulation mtk ALLsh (Fig. 4.7) confirms this hypothesis. In this case

the mean accumulated precipitation at Kilimanjaro and at Mount Kenya is basically

the same (Fig. 4.8d).

5.3 Comparison to Previous Studies 67

5.3 Comparison to Previous Studies

The numerical simulations performed by Molg et al. (2009a) show a very similar

mesoscale circulation to the one observed in this study. Nevertheless this study

leads to two important advances in the understanding of the precipitation mecha-

nisms. First the precipitation distribution and magnitude is mainly determined by

the orientation of the topography related to the background flow direction. Sec-

ondly the surface heat fluxes are determining the shifting towards the summit of

the precipitation maximum. This hypothesis was already presented by Pepin et al.

(2010).

A recent study by Cullen et al. (2012) shows that the asymmetry of glaciers

distribution on the summit of Kilimanjaro is not confined to the most recent glacier

extents (Osmaston 1989). Patterns of ice distribution established from former

moraines indicate in fact that recent climate controls on glacier behaviour are simi-

lar to those in the past. Hence, it is conceivable that the precipitation mechanisms

responsible for this asymmetry remained unchanged during the last century.

If the precipitation mechanisms remained unchanged, the cause of glacier retreat

needs to be searched in either less frequent events reaching the summit or less snow

accumulation during the events. Both these reasons may be related to the main

precipitation mechanisms: direction and strength of the background winds, surface

heat fluxes and moisture content of the atmosphere. While evidence of a drying

climate in tropical East Africa during the last 150 years is well documented by

several studies (Hastenrath and Reidel 1984; Nicholson and Yin 2001), less is know

about changes in surface heat fluxes and in large-scale flow direction and speed.

A study by Molg et al. (2012) argued that glacier retreat on Kilimanjaro is

unlikely to be influenced by local land-cover change, closely linked to surface fluxes.

In the light of the results of the present work it is likely that glacier retreat may be

driven by a complex interaction of moisture content of the atmosphere and surface

fluxes. A reduced moisture supply from the Indian Ocean (Molg et al. 2006) is re-

ducing the moisture content of the background flow, while surface fluxes determines

the uptake of moisture to the summit. Furthermore changes in the large-scale flow

direction and speed may change the precipitation pattern and, hence, the location

of its maximum. For these reasons changes in surface heat fluxes and in large-scale

flow direction and speed need to be further studied.

One of the main purpose of this study was to investigate possible reasons for

different precipitation patterns at the two mountains, as evinced from in-situ ob-

servations comparison by Nicholson et al. (2013). The results of the numerical

simulations are not totally answering this question. First of all the simulation of

type ALLsh shows very similar accumulated precipitation values at both location.

68 Discussion

Figure 4.9d shows that the median values of accumulated precipitation for the al-

titude band 2000 m − 4000 m are higher at Mount Kenya (about 10 mm) than at

Kilimanjaro (about 5 mm). This is most likely due to the orographycally induced

precipitation on the windward side of Mount Kenya.

Furthermore the results here presented apparently do not support the hypothe-

sis advanced by Kaser and Osmaston (2002) (goal (2) in Chap. 1.3). The particular

convective patterns on Kilimanjaro and Mount Kenya (Fig. 1.4) are not reproduced

by the model. In the simulations of type F, which are probably the most similar to

the situations presented in Fig. 1.4, the precipitation maxima are both located at

the summit, in particular there is no evidence of a dry spot at the plateau of Kil-

imanjaro as it is suggested by Kaser and Osmaston (2002). Part of this mismatch

may be related to the model mesh size of 1 km which is not sufficient to resolve the

convective clouds in detail. However, the background flow is causing a displacement

of the clouds downstream.

5.4 Limitations of the Study and Possible Im-

provements

The limitations of this study, as well as possible improvements and new research

directions are here summarized and discussed.

First of all the reanalysis study has two main limitations. The classification of

the precipitation events on Kilimanjaro and Mount Kenya is based on measurements

at a single site on each summit. The limitations of this type of measurements have

already been highlighted in Chap. 2.1.1. Furthermore the period with good quality

data from both AWSs is restricted to 16 months. For these reasons the classification

is not intended to derive a climatological overview of precipitation events on the two

summits but rather a pragmatic approach to determine precipitation events used

for the ERA-Interim reanalysis data study.

Although the ERA-Interim is presumably one of the best atmospheric data

reanalysis available, the resolution of 0.75 is hardly enough for the purpose of this

study. The two mountains are located only a few grid points apart, thus distinct

difference between the two locations cannot be expected.

Both this limitations are related to the remoteness of tropical East Africa moun-

tains. Significant progress in the knowledge about the true distribution of precip-

itation in the region may be achieved with new remote sensing instruments, e.g.,

the Global Precipitation Measurements (GPM) with its improved spatial resolu-

tion. Finally intensive field campaigns, with the support of aircraft measurements

as well as land based remote sensing instrumentations, may significantly improve

5.4 Limitations of the Study and Possible Improvements 69

the understanding of atmospheric processes over tropical high mountains.

Concerning the idealized numerical simulations, two major limitations of this

study have already been highlighted in these discussions. Further simulations, e.g.,

changing the direction of the background wind by ±45 , are necessary to fully

understand the mesoscale circulation given by the interaction of the large-scale flow

and the orientation of the mountain. Furthermore the effect of the single surface

sensible heat flux or surface latent heat flux is not yet clear, since in the simulations

here presented they are both prescribed at the same time. Thus, two effects occur

simultaneously: the upslope circulation driven by the surface sensible heat flux and

the rise of the moisture content of the atmosphere due to the surface latent heat flux.

In order to understand which of the two effects is the most relevant in determining

the final precipitation distribution and magnitude further simulations are required.

Moreover the homogeneity of the prescribed surface heat fluxes is another im-

portant limitation of the idealized approach used in this study. In fact the sinu-

soidal like function, representing the surface heat fluxes, is the same in the plains

surrounding the mountains as well as on the summits. This leads to some important

differences to the reality. First of all topographic shading is not considered and thus

the forcing of the surface heat fluxes is the same on all the slopes. The impact of

clouds on the surface heat fluxes is not considered, since a radiation scheme is not

used in this approach. Finally differences due to different land cover properties are

also not considered. Land cover properties may in fact vary between the two loca-

tions and between different altitude bands, e.g., passing from the savannah of the

surrounding plains to the vegetation belt and to the arid summit zone. For these

reasons the mesoscale circulation described in this study as a result of the idealized

numerical simulation may differ from the reality.

In order to further improve the knowledge about atmospheric processes over

tropical East Africa mountains real case numerical simulations may be a possible

solution. In this case fully coupled numerical simulations, with radiation and land

surface schemes, may significantly improve the reality of model setup, allowing the

understanding of more complex mechanisms that are not captured in this study.

Chapter 6

Conclusions

In this thesis in-situ observations, ERA-Interim reanalysis data and idealized numer-

ical simulations are combined to asses the precipitation mechanisms on Kilimanjaro

and Mount Kenya. Precipitation on the two mountains is driven by a complex in-

teraction of the large-scale flow with the topography and the surface heat fluxes.

The most important conclusions of this study are summarized below.

• The reanalysis study confirms the hypothesis that the two mountains are typ-

ically influenced by the same air mass during precipitation events.

• Precipitation distribution and magnitude are very sensitive to the orientation

of the mountain respect to the large-scale flow. Both the thermally and dy-

namically driven flows characterizing precipitation events are determined by

the interaction of the topography and the background flow. When the long

axis of the mountain is aligned parallel to the background flow the accumu-

lated precipitation is maximum and it develops in a narrow band on the lee

slope. When the long axis of the mountain is aligned perpendicular to the

background flow the accumulated precipitation is less and the distribution is

organized in a “U” shape pattern.

• The magnitude of precipitation is strongly controlled by the surface heat fluxes.

The upslope circulation driven by the surface sensible heat flux and the contri-

bution of the surface latent heat flux to the moisture content of the atmosphere

are responsible for the shift upslope of the precipitation maximum.

In order to fully understand the precipitation mechanisms over the two moun-

tains further investigations on the role of different wind direction of the large-scale

flow and on the impact of the surface sensible and latent heat flux are necessary.

Moreover the differences between the two mountains described by the numerical

simulations are not corresponding to the ones typically observed. Data collected at

71

72 Conclusions

the two summits shows in fact that Mount Kenya is experiencing typically more pre-

cipitation than Kilimanjaro, thus permitting glaciers to survive at lower elevations

at this location. In order to understand these observations, further studies, better

representing the differences between the two mountains, need to be carried out.

Appendix A

Description of WRF Model

Modifications

In this Appendix the modifications implemented in the WRF model, version 3.7

(Skamarock et al. 2008), are described. This part of the manuscript is intended

for expert WRF users, which are already familiar with the WRF model general

architecture and module structure and the FORTRAN syntax. First of all all the

simulations performed in this study are based on a compilation of the LES (large

eddy simulation) ideal test case of the WRF. Despite the name of the test case the

simulations here presented are run in a non LES setup as described in Chap. 2.2.

Two main modifications were necessary to the WRF model .

A small modification was necessary to allow the initialization program ideal.exe

to read the topography from an external file. This is not allowed in the standard

LES ideal case, which normally initializes the model grid topography with a para-

metric formula which can be prescribed in m o d u l e i n i t i a l i z e l e s .F. This module

contains a section in which the topography is specified in the variable g r id%ht, this

section is replaced with the code shown in A.1: mnt on is a new namelist variable,

which the user can use to specify whether the topography is read from an external

file or not, dem . txt is the external file from which the topography is read. It is

important to note that this file need to be placed in the run directory of the model

and need to be formatted to contain the DEM information in a matrix with the

same size of the model grid specified via the namelist (model domain).

The second modification is allowing the user to specify directly the surface

sensible and latent heat fluxes. Surface heat fluxes can be specified either as a

constant value or as a sinusoidal like function, which is 0 before 6:00 and af-

ter 18:00 and has maximum value at noon. This modification involves the fol-

lowing modules: m o d u l e f i r s t r k s t e p p a r t 1 .F, m o d u l e s u r f a c e d r i v e r .F and

m o d u l e s f s f c l a y .F. First of all in m o d u l e f i r s t r k s t e p p a r t 1 .F (A.2), before

the call of the function s u r f a c e r d r i v e r , the surface sensible ( s s h f ) and latent

73

74 Description of WRF Model Modifications

( s l h f ) heat fluxes are specified through the newly introduced namelist variables

mtn hfx and mtn lh. The switcher f l u x e s t y p e , editable in the namelist, allows

the user to choose which type of surface heat fluxes to prescribe: 1 for constant

fluxes, 2 for sinusoidal like fluxes and any other number for switching off this mod-

ification. The surface heat fluxes s s h f and s l h f are then passed through the func-

tion s u r f a c e r d r i v e r , which is defined in m o d u l e s u r f a c e d r i v e r .F, and following

through the function SFCLAY (called also in function SFCLAY SEAICE WRAPPER in the

same module). Finally the function SFCLAY is defined in m o d u l e s f s f c l a y .F which

is modified as shown in A.3 to account for the prescribed surface heat fluxes.

It is important to note that all the variables which are newly introduced in func-

tions and subroutines must be properly defined as the FORTRAN syntax requires,

step which is not shown here. Furthermore new namelist variables such as mtn on,

f l u x e s t y p e , mtn hfx and mtn lh have to be defined in Reg i s t ry .EMCOMMON.

Listing A.1: Topography modification: m o d u l e i n i t i a l i z e l e s .F

! TOPOGRAPHY, modi f ied by Federico Covi 02/02/16

! Two opt ion f o r the topography i n i t i a l i z a t i o n depending on the

! name l i s t parameter mtn on

! 1) mtn on=0, NO TOPOGRAPHY

IF ( m o d e l c o n f i g r e c%mtn on == 0) THEN

DO j=j t s , j t e

DO i=i t s , i t e

g r id%ht ( i , j ) = 0 .

ENDDO

ENDDO

! 2) mtn on=1, TOPOGRAPHY READ FROM dem. t x t f i l e

ELSE IF ( m o d e l c o n f i g r e c%mtn on == 1) THEN

open (unit=17, f i l e=’dem . txt ’ , status=’ old ’ , action=’ read ’ )

DO j=jds , jde

READ( 1 7 ,∗ ) ( g r id%ht ( i , j ) , i=ids , ide )

ENDDO

! 3) mtn on=∗ wr i t e an ERROR statement

ELSE

write ( 6 ,∗ ) ’ ∗∗∗ not v a l i d mtn on opt ion ∗∗∗ ’

ENDIF

! ELSE

! DO j=j t s , j t e

! DO i=i t s , i t e

! g r i d%ht ( i , j ) = 0 .

! ENDDO

! ENDDO

! ENDIF

! END of MODIFIED PART

75

Listing A.2: Surface heat fluxes mod.: m o d u l e f i r s t r k s t e p p a r t 1 .F

! SURFACE FLUXES, modi f ied by Federico Covi 10/05/16

! cons tant su r f a c e heat f l u x e s

IF ( c o n f i g f l a g s%f l u x e s t y p e == 1) THEN

s s h f = c o n f i g f l a g s%mtn hfx

s l h f = c o n f i g f l a g s%mtn lh

! s i nu so i da l− l i k e su r f a c e heat f l u x e s

ELSE IF ( c o n f i g f l a g s%f l u x e s t y p e == 2) THEN

IF ( s i n (2∗4 .∗ atan ( 1 . ) / 2 4∗ ( hr+minute /60.+ sec /3600.−6.)) &

.LE. 0 . ) THEN

s s h f = 0 .0

s l h f = 0 .0

ELSE

s s h f = c o n f i g f l a g s%mtn hfx∗ &

s i n (2∗4 .∗ atan ( 1 . ) / 2 4∗ ( hr+minute /60.+ sec /3600.−6.))

s l h f = c o n f i g f l a g s%mtn lh∗ &

s i n (2∗4 .∗ atan ( 1 . ) / 2 4∗ ( hr+minute /60.+ sec /3600.−6.))

ENDIF

ENDIF


CALL s u r f a c e d r i v e r ( . . . . . . . . . . . . . . . . . . . . . . . &

& . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . &

! Modi f ied by Federico Covi 12/03/2016

& , f l u x e s t y p e=c o n f i g f l a g s%f l u x e s t y p e &

& , mtn hfx=s s h f &

& , mtn lh=s l h f )


76 Description of WRF Model Modifications

Listing A.3: Surface heat fluxes modification: m o d u l e s f s f c l a y .F

!−−−−−COMPUTE SURFACE MOIST FLUX:

!

! IF (IDRY.EQ.1 )GOTO 390

IF ( PRESENT(SCM FORCE FLUX) ) THEN

IF (SCM FORCE FLUX.EQ. 1 ) GOTO 405

ENDIF

!

DO 370 I=i t s , i t e

QFX( I )=FLQC( I )∗ (QSFC( I )−QX( I ) )

QFX( I )=AMAX1(QFX( I ) , 0 . )

LH( I )=XLV∗QFX( I )


IF ( f l u x e s t y p e .EQ. 1 . or . f l u x e s t y p e .EQ. 2) THEN

LH( I ) = mtn lh

QFX( I ) = LH( I ) / XLV

ENDIF


370 CONTINUE

!

!−−−−−COMPUTE SURFACE HEAT FLUX:

!

390 CONTINUE

DO 400 I=i t s , i t e

IF (XLAND( I )−1.5 .GT. 0 . )THEN

HFX( I )=FLHC( I )∗ (THGB( I )−THX( I ) )

! IF ( PRESENT(ISFTCFLX) ) THEN

! IF ( ISFTCFLX.NE.0 ) THEN

! AHW: add d i s s i p a t i v e hea t ing term (commented out in 3 . 6 . 1 )

! HFX( I)=HFX( I)+RHOX( I )∗USTM( I )∗USTM( I )∗WSPDI( I )

! ENDIF

! ENDIF

ELSEIF(XLAND( I )−1.5 .LT . 0 . )THEN

HFX( I )=FLHC( I )∗ (THGB( I )−THX( I ) )

HFX( I )=AMAX1(HFX( I ) ,−250.)

ENDIF


IF ( f l u x e s t y p e .EQ. 1 . or . f l u x e s t y p e .EQ. 2) THEN

HFX( I ) = mtn hfx

! THGB( I ) = HFX( I ) / FLHC( I ) + THX( I )

! TSK( I ) = THGB( I )∗(PSFCPA( I )/P1000mb)∗∗ROVCPENDIF


Appendix B

Model Testing

In this Appendix an issue related to the boundary conditions, found during the

WRF model testing phase, is presented with its momentary solution. As described

in Chap. 2.2 the nature of the simulation, with a background flow forcing over a non

symmetric domain, requires the use of open boundary conditions. It is know from

previous works (Kirshbaum and Fairman 2015) that this type of lateral boundary

conditions tend to develop noises and disturbances close to the domain boundaries.

This is particularly true in case of a very moist atmosphere, deep convection situ-

ations and strong surface heat fluxes forcing. The typical solution to this problem

is to size the model domain in a way that disturbances propagation does not affect

the area of interest (AOI).

In order to properly size the domain used in this study two test simulation

are performed: OPEN, with open boundary conditions, and PER, with periodic

boundary conditions. Both simulations are run with the same identical model setup,

consisting in surface heat fluxes and input sounding without background wind as

in the type F. In the simulation PER it would in fact be inconvenient to prescribe

a background flow due to the nature of the periodic boundary conditions. The

topography used is the one of Kilimanjaro.

The results of the two simulations are compared with particular interest in

the AOI, in this case defined as a 300 km by 300 km square in the center of the

domain, where the mountain is placed. Finally the domain is sized in order to nullify

differences in accumulated precipitation in the AOI between the two simulation.

Figures B.2 and B.3 show the results of the simulations PER and OPEN. The

boundary conditions issue affecting OPEN is clearly visible in both the accumulated

precipitation and the vertical velocity. Disturbances generates at the lateral bound-

aries due to huge gradients, especially in moisture, induced by the strong surface

heat fluxes and following are propagating towards the center of the domain. Figure

B.1 shows evidence of a strong correlation in accumulated precipitation between

OPEN and PER, especially over the mountain (bottom panel), where differences

77

78 Model Testing

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 2103 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21Time of the day

0.00.51.01.52.02.5

m. a

. p. (

mm

) OPEN PER

03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 2103 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21Time of the day

0

2

4

6

8

m. a

. p. (

mm

) OPEN PER

Figure B.1: Mean accumulated precipitation (mm) over the AOI (top panel) and over

the mountain (bottom panel) for the simulation OPEN and PER.

are not appreciable.

Following this comparison the model setup and domain used in OPEN are

considered to reproduce reliable results in the AOI. Furthermore the domain used

in the simulations previously shown in this study is even larger than the one used

in OPEN. The final domain size is in fact Lx = 1500 km and Ly = 1000 km (Chap.

2.2), about 200 km larger in the y direction, ensuring a good quality of the results.

79

Figure B.2: Accumulated precipitation (mm) and vertical velocity (m s−1) close to the

surface at the end of the simulation PER.

80 Model Testing

Figure B.3: Accumulated precipitation (mm) and vertical velocity (m s−1) close to the

surface at the end of the simulation OPEN.

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Acknowledgments

First of all I would like to thank Assoc. Prof. Dr. Alexander Gohm for supervising

this thesis. He always found the time for meetings and discussions which helped a

lot to improve this work. His modus operandi has been a true example of rigorous

scientific working. I would also like to thank Prof. Dr. Georg Kaser for making

this interesting and challenging topic available for me. When he trusted me to

join glaciological field works for the first time I was nothing more than a student;

he gave me the opportunity to gain skills and knowledge which go far behind the

master studies. The computational results presented have been achieved (in part)

using the HPC infrastructure LEO of the University of Innsbruck. Additionally I

would like to thank Simon Siedersleben, Lukas Umek, Daniel Leukaf and Johannes

Wagner for their help and fruitful discussions about the WRF model. I also want

thank Carsten Maass, from ECMWF, which provided useful information about the

ERA-Interim reanalysis. I would like to thank Lindsay Nicholson, Rainer Prinz and

Thomas Molg for the precious discussions about Kilimanjaro and Mount Kenya.

A really big thanks goes to my family, which supported me all these years and

waited too long for this moment, and to my italian friends, especially Luca and

Andrea, which have not been forgotten during my Innsbruck adventure. A big hug

goes to Stephan Peter Galos, which in these 3 years has been a teacher, a colleague

but above all a friend. Finally I am really sorry that Veronica had to be that patient

with me.

89

Curriculum Vitae

Federico Covi

Dr. Stumpfstrasse 117, 6020 Innsbruck, Austria

Born on March 30 1990 in Rovereto, Italy

Education:

2015–2016 Master’s thesis under the guidance of Assoc. Prof. Dr. Alexander

Gohm and Univ. Prof. Dr. Georg Kaser, Institute of Atmospheric

and Cryospheric Science, University of Innsbruck: “Assessing Precip-

itation Mechanisms on Kilimanjaro and Mount Kenya: an Idealized

Modeling Study”.

2013–2016 Master of Science at the University of Innsbruck. Master of Science

in Atmospheric Science.

2012–2013 Bachelor’s thesis under the guidance of Prof. Dr. Claudio Della Volpe,

Department of Physics, University of Trento: “Working Procedure and

Data Analysis for Weather Radar on Macaion Site”.

2009–2013 Bachelor of Science at the University of Trento. Bachelor of Science

in Physics.

2004–2009 Highschool, Rovereto Matura.

Work Experience:

05/2015–10/2016 Student assistant at the University of Innsbruck in the glaciers mass

balance monitoring program of the Institute of Atmospheric and

Cryospheric Science.

10/2015–01/2016 Tutor for the course “Mountain Meteorology” at the Institute of At-

mospheric and Cryospheric Science (Assoc. Prof. Dr. Alexander

Gohm).

10/2012–03/2013 Internship at the meteorological office of Meteotrentino, Provincia Au-

tonoma di Trento, Italy.

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assessing precipitation mechanisms on kilimanjaro and

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