Fabian Lächler

Optimization of Regionalized Precipitation with Radar and Rain Gauge Data for Trentino-Alto Adige

MASTER’S THESIS Submitted to the Institute of Geography, University of Innsbruck In partial fulfilment of requirements for the degree Master

Advisors: Univ.-Prof. Dr. Ulrich Strasser Ass. Prof. Dr. Thomas Marke

September 2017

Abstract

The accurate measurement of rainfall is an important prerequisite for various applications of meteorology, hydrology and their subsections. The reliable detection of precipitation fields is difficult, particularly in mountainous regions. To provide the best possible measurement, rain gauge and precipitation radar data are combined in state-of-the-art weather models. However, these state-of- the-art methods have only been extensively used for a short period of time. For the study area, which is mostly located in the central eastern Alps in the Italian federal state Trentino-Alto Adige, no radar data from Gantkofel mountain had been integrated between 2004 and 2009. Therefore the aim of this master thesis was to develop a state-of-the-art algorithm which calculates retrospective precipitation fields for the years from 2004 to 2009. The algorithm, which is strongly orientated towards the INCA model developed by ZAMG (Haiden et al., 2011), computes a weighted combination of rain gauge and radar data. It takes into account the reduced visibility of radar in mountainous areas. Furthermore the elevation dependence of precipitation is considered. Three evaluation methods were selected to determine the accuracy of the developed model. Firstly, image differencing between the self- developed and the INCA model without the Gantkofel radar was carried out. Secondly, a leave-one- out cross-validation was executed. And thirdly an image differencing between the self-developed algorithm and the state-of-the-art INCA, which includes the Gantkofel radar, was performed for 2014. The results showed that the present algorithm is capable in computing accurate precipitation fields with a significantly improved spatial resolution of 500 meters. However the computed precipitation values are inflated in regions with poor radar accessibility. A further integration of all available radar data would be an advantage to eliminate regions that are affected by topographic shielding.

ii

Acknowledgements

First of all, I would like to thank my advisors Ulrich Strasser and Thomas Marke for supporting me and creating the idea for this master thesis. While working on my thesis, I learned a lot about programming and scientific working. I am also grateful to the whole AHC team for supporting me and providing me with helpful advice. Especially Marcel Siegmann helped me with dozens of code snippets and supported me whenever I needed it. Also Daniel Günther and Florian Hanzer always helped me find a solution to solve problems. Furthermore, I would like to thank Mauro Tollardo for the provision of the radar data and his expertise.

In addition I would like to thank Erin Naismith and Dominik Scheuer for proofreading the thesis. I would also like to thank Carina Miggitsch, Francesca Pierri and Bettina Wildauer for being the best flatmates and motivating me all the time. Many thanks also to Nicky de Leeuw for treating my back pain during the work for this thesis.

A special thank is dedicated to my parents and to my sister, for helping me in every possible way. They enabled me my studies and motivated me every time anew when there were problems.

iii

Table of Contents 1. Background of precipitation analysis ...... 2

1.1 Precipitation measurement techniques...... 2

1.1.1 Precipitation Radar ...... 3

1.1.2 Rain gauge ...... 9

1.1.3 Multi-sensor precipitation estimates ...... 10

1.2 INCA ...... 11

2 Data and Methods ...... 13

2.1 Study area ...... 13

2.2 Data ...... 15

2.2.1 Radar data ...... 15

2.2.2 Rain gauge data ...... 16

2.3 Approach of combined precipitation Analysis (CPF) ...... 17

2.3.1 Pre-Processing ...... 18

2.3.2 Main precipitation analysis ...... 19

2.4 Evaluation methods ...... 23

2.4.1 Differences of CPF – INCA 2008 ...... 23

2.4.2 Leave-one-out cross-validation...... 24

2.4.3 Differences of CPF – INCA 2014 ...... 25

3 Results ...... 26

3.1 Precipitation results ...... 26

3.2 Results of differences CPF – INCA in 2008 ...... 27

3.3 Results of Leave-one-out cross-validation ...... 29

3.4 Results of differences CPF – INCA in 2014 ...... 34

4 Discussion ...... 35

4.1 Discussion differences of CPF – INCA in 2008 ...... 35

4.2 Discussion of leave-one-out cross-validation...... 36

4.3 Discussion differences of CPF – INCA in 2014 ...... 38

iv

5 Conclusion and Outlook...... 39

References ...... 42

v

List of Figures

Figure 1: Marshall and Palmer drop-size distribution functions (dashed lines) compared with the results

of Laws and Parsons (solid lines). (Rinehart, 2010 – based on Marshall and Palmer, 1948) ...... 5

Figure 2: Schematic sketch showing the effects of particle coalescence, melting, and changes in

terminal velocity on radar reflectivity through the bright band. Zero height is the melting level. The

radar reflectivity Ƞ is given along the x-axis (Rinehart, 2010)...... 7

Figure 3: Schema of CAPPI and CMAX. (Yoon et al., 2014) ...... 8

Figure 4: Terrain height with radar site in the study area. For better orientation the main sub regions

of the central eastern Alps are labelled...... 15

Figure 5 shows the increase of rain gauges from 2004 to 2009 ...... 17

Figure 6: Monthly accumulated precipitation for January 2008. The maximum precipitation amounts

at the Rieserferner group (north-east) are clearly visible...... 26

Figure 7: Monthly accumulated precipitation for June 2008. The highest precipitation values in the

Zillertal Alps and the Rieserferner group (north-east) are clearly visible...... 27

Figure 8: Difference image of CPF and INCA in January 2008...... 28

Figure 9: Difference image between CPF and INCA in June 2008...... 28

Figure 10: Distribution and altitude of all in the leave-one-out cross-validation used stations...... 29

Figure 11: Accumulated precipitation for February 2008. Station Hochserfaus was out of order in

February...... 30

Figure 12: Accumulated precipitation for August 2008. The accumulated precipitation is much stronger

than in February...... 30

Figure 13: Station recorded and modelled accumulated precipitation for February. The x-axis marks

the days in February and the y-axis represents the accumulated precipitation in millimeter...... 31

Figure 14: Station recorded and modelled accumulated precipitation for August. The x-axis marks the

days in August and the y-axis represents the accumulated precipitation in millimeter...... 32

Figure 15: Difference image of CPF and INCA in January 2014...... 34

vi

Figure 16: Difference image of CPF and INCA in June 2014...... 35

vii

List of Tables

Table 1: Radar wave lengths and frequency bands. (Emeis, 2010) ...... 4

Table 2: All stations used for leave-one-out cross-validation ...... 24

Table 3 shows the computed efficiency criteria for CPF and INCA in February 2008. NSE values of 1 are

optimal, values from 0 to 1 are acceptable and values ≤ 0 representing an insufficient model

performance. In PBIAS values a perfect fit between model and observed data is represented by 0.

A positive value indicates a model underestimation, while a negative value indicates a model

overestimation. The RMSE labels a perfect model fit with 0. The RMSE value is the average error in

mm...... 33

Table 4 shows the computed efficiency criteria for CPF and INCA in August 2008. NSE values of 1 are

optimal, values from 0 to 1 are acceptable and values ≤ 0 representing an insufficient model

performance. In PBIAS values a perfect fit between model and observed data is represented by 0.

A positive value indicates a model underestimation, while a negative value indicates a model

overestimation. The RMSE labels a perfect model fit with 0. The RMSE value is the average error in

mm...... 33

viii

Introduction

Variabilities and changes in climate are expected to bring several changes to hydrological cycles and regimes in different parts of the world (Kolokytha et al., 2017). It is specifically in mountainous regions such as the Alps, where these variabilities and changes will first be recognized. While glaciers are retreating worldwide, periods of snow cover become shorter and precipitation events are expected to be more intense (Stocker, 2014).

The amount of water resources retained in mountainous areas as snow and ice, as well as the timing of meltwater production and the resulting runoff, is of high interest for glaciological and hydrological investigations (Hanzer et al., 2016). Ecological and economic processes such as winter tourism, hydropower production, and drinking water supply are also of great interest. Shifts in snow and ice melt inducted due to climate change, will alter the hydrological regimes in glacierized catchments in terms of both timing and magnitude of streamflow discharge (e.g. Jansson et al., 2003; Beniston, 2003; Collins, 2008; Bliss et al., 2014).

Apart from climate change impact studies, hydrological models are also applied in a wide variety of practical purposes. These include among others, flood forecasting, environmental impact assessments, and seasonal water availability estimations. For example, the estimation of the retained water from a catchment area during one winter is an important input variable for operation planning in the hydropower and energy sector (Barnett et al., 2005; Skaugen et al., 2012). For such applications, hydrological models can provide a realistic estimate of the contribution of snow, glacier, and rainfall runoff in mountain streams (Finger et al., 2015). One of the main challenges that hydrological modelling is facing is the uncertainty of observational data for calibration and validation functions (Grayson et al., 2002).

Enormous progress has been made over the last few decades to make observational precipitation data more accurate and reliable. Using remotely controlled rain gauges equipped with current profilers, the accuracy of discharge measurements was improved (Muste et al., 2004). Furthermore spatially distributed meteorological patterns are observed with weather radar and satellites (Borga, 2002; Xie and Arkin, 1996). For remote headwaters, data availability is often limited. In these remote areas, frequent estimations of snow, glacier and rainfall, model calculations have to be based on limited or less qualitative data availability (Finger et al., 2015).

In general, maps of precipitation distribution in mountainous areas are of limited value when detailed information about a particular location is required. The accuracy of available precipitation

1 measurements is affected by wind, frequent freezing and thawing, and heavy precipitation. In addition maps are usually based on interpolations between widely dispersed measurement stations.

The interpolations rely heavily on terrain height, but may not take into account terrain steepness, proximity to moisture sources or the measurement site’s exposure to prevailing winds (Whiteman, 2000).

To improve the detection of several meteorological parameters, especially in mountainous regions, the Integrated Nowcasting through Comprehensive Analysis (INCA) system has been developed by the Central Institute for Meteorology and Geodynamics (ZAMG), based in Vienna, Austria. The model uses a multi-sensor approach which includes surface station data, remote sensing data (radar, satellite), forecast fields of numerical weather prediction models and high-resolution topographic data (Haiden et al., 2011).

When the INCA system was released in 2004, the study area was partially integrated in the INCA precipitation calculations. The system used a small number of rain gauges along the border to Italy. The main precipitation fields were calculated via inverse distance weighting (IDW), including the Italian part of the study area. With this small number of rain gauges, which served as an input for the IDW, no reasonably reliable precipitation fields could be calculated. Furthermore no radar data on the Italian side of the study area had been integrated in the INCA system. Due to the lack of station data and precipitation radar data, the INCA precipitation fields were of poor quality. Moreover an elevation dependent precipitation correction did not exist before mid-2007, covering the complete INCA area. All these factors led to the decision that a reanalysis of the precipitation fields would be an advantage for further research projects of the alpine hydro climatology (AHC) workgroup.

This master thesis presents a method for the regionalization of precipitation estimation which is strongly orientated on the INCA algorithm developed by the ZAMG. The main aim is to optimize the precipitation estimation in the study area between the years of 2004 to 2009.

1. Background of precipitation analysis 1.1 Precipitation measurement techniques

"The recorded number of measurement value obtained from a measurement cannot be judged independently of that particular measurement technique or method. Peculiarities and limitations of the technique must be known. These comprise, e.g., the temporal and spatial resolution of the data

2 measured with a given instrument. Variations of the measured value smaller than the resolution of the instrument cannot be assessed" (Emeis, 2010).

In all sciences observation is a cornerstone for gaining cognition and understanding of natural processes. Therefore, measurements quantify observations. Quantifying data acquisition has been the precondition for all modern natural sciences and thus also for meteorology (Emeis, 2010).

In meteorology, we distinguish between point and spatial measurements. Predominantly, the point measurement of meteorological parameters is much easier than the spatial one. The quality of measurements, point and spatial, has increased significantly due to the expansion of the monitoring network in general, and the improvement of the measurement devices. The most common techniques for precipitation measurements and their characteristics are briefly explained below.

1.1.1 Precipitation Radar

Radar is the abbreviation for "Radio Detection and Ranging". It has its roots in radio. In 1934, Young and Taylor observed that an aircraft interrupted their radio signals. They had the idea of using pulses of energy for target detection. The development of radar-like instruments occurred in several countries simultaneously. By the end of World War II, radar had been thoroughly developed and successfully utilized. Much of the surplus military equipment became available for the civilian use, and this was the beginning of weather research using radar (Rinehart, 2010). The basis of radar is the echo- sounding principle. A radio wave, which is emitted by a transmitter, meets an object. Thereby part of the energy contained in the radiation is reflected, part is transmitted through the object, part is scattered in all directions, and part is absorbed by the material of the object. The properties of the object, and its position and orientation relative to the incident radiation, influences the behavior of the incoming energy. The reflected and scattered radiation is received with a suitable placed receiver. The position of the object is calculated due the time of travel of the radiation, and the direction from which it is received. Several characteristics of the target are estimated due to the measurement of the received energy, and the monitoring of various characteristics of the received radiation. There are two possible ways to receive the incoming signal. The more common method is the monostatic radar. The radar transmitter and receiver are at the same site and usually share a common antenna. With this arrangement the reflected or backscattered radiation towards the radar is received as an echo. The second method is the bistatic radar, where the transmitter and receiver are located in two different places (Raghavan, 2003).

The four main components of a radar are the transmitter, antenna, receiver and display. The transmitter generates the high-frequency signal, and the antenna sends the signal out into space and receives the echo from the target. The receiver detects and amplifies the signal so that it is strong

3 enough to be utilized, and the display system is used to view the detected targets. State of the art weather radars use a single antenna for both transmitting and receiving. They send out signals, short pulses of energy, which travel at the speed of light through the atmosphere until they hit their target and then return back to the antenna. After a certain period of time another pulse is sent out and the process is repeated continuously. Given the energy travels at the speed of light, it does not take long for the signal to travel hundreds of miles. Scanning the horizon in all directions around the radar site, the antenna of the radar usually rotates around a vertical axis. A radar signal only takes 2 milliseconds to travel 300 kilometers and back. The biggest advantage, and most valuable use of radar is the ability to detect storms and other weather phenomena (Rinehart, 2010). In radar meteorology, typically 9 length frequency bands are differentiated. The different bands are listed in Table 1. L, P and VHF devices are called windprofilers for wind estimation. S, C, X and K-band are used for measuring hydrometeors (Emeis, 2010). Due to the absorption of electromagnetic energy by water or ice drops, radars with shorter wavelength radiation (e.g., λ = 3 cm) would be more frequently used as their angular resolution is superior, however they suffer under echo power loss, which can be 100 times greater than the loss of radars which are operated with λ ≥ 10 cm (Doviak and Zrnić, 2006).

Table 1: Radar wave lengths and frequency bands. (Emeis, 2010)

Frequency Wave length Frequency bands Application 20-300 MHz 1-15 m VHF Windprofiler 400-900 MHZ 30-70 cm P-Band / UHF Windprofiler 1-2 GHz 15-30 cm L-Band / UHF Boundary layer windprofiler 2-4 GHz 7-15 cm S-Band / UHF Precipitation 4-8 GHz 4-7 cm C-Band Precipitation 8-16 GHz 2-4 cm X-Band Precipitation 16-20 GHz 1-2 cm Ku-Band Precipitation 35 GHz 8,5 mm Ka-Band Precipitation, Clouds 90-100 GHz 3 mm W-Band Clouds

It is very easy to detect rain using most radars. Rain occurs in a wide variety of forms, from light drizzle to near-blinding downpours and severe thunderstorms. The measurement of rain is one of the main applications of weather radars. Several properties of precipitation influence the detectability of precipitation by radar. Raindrop size distributions has been one of the elementary fields of research in meteorology for more than 50 years. In this time a number of techniques have been developed to sample raindrop size distributions. Using a highly-resolved camera, it is possible to record single raindrops within a volume of rain, and using this measurement it is possible to calculate rain rate (e.g. mm/h), liquid water

4 content (e.g. g/m³) and radar reflectivity (mm6/m³) (Rinehart, 2010)..

Figure 1: Marshall and Palmer drop-size distribution functions (dashed lines) compared with the results of Laws and Parsons (solid lines). (Rinehart, 2010 – based on Marshall and Palmer, 1948)

Figure 1 shows three raindrop size distributions, collected in the area of Ottawa. This distribution, known as the Marshall-Palmer distribution, is certainly the best known in meteorology, especially in radar meteorology. The Marshall-Palmer distribution is a convenient relationship which reflects an approximate size distribution for raindrops as a function of rain rate, it is useful for various analytical exercises and for deriving other relationships. The relationship is given by the formula

−휆퐷 푁푑 = 푁0푒

(1) where N0 = 8000/ (m³ mm) is the point where all of the lines converge. D is defined as rain drop diameter (mm) and λ is given by λ = 4.1푅−0.21

(2) where R is the rain rate in mm/h. The number of drops per unit volume, and per unit drop size interval, for any particular rain drop size or specific rain rate can be calculated using this relationship. Hence the radar reflectivity or liquid water content can be calculated out of the size distribution (Rinehart, 2010). A relationship between rain rate and radar reflectivity also exists. Radar reflectivity and rain rates have been calculated via extensively and experimentally measured drop-size distributions. By correlating rain rate against reflectivity statistically, a relationship between these parameters is determined. The empirical gathered Power-Law equation is the most commonly used mathematical expression for the relationship: 5

푧 = 퐴푅푏

(3)

Where R is the rainfall rate (mm/h), z is the radar reflectivity factor (mm6/m³), and A and b are empirical constants (Rinehart, 2010). It must be observed that the value of Z itself cannot provide a unique measurement of R. A real drop-size distribution requires an indefinite number of parameters in order to characterize it (Doviak and Zrnić, 2006). The formative source of error is the rain drop size distribution. Battan (1973) lists more than 69 different Z-R relationships for different climatic conditions around the globe, and since then many more have been determined. Even when considering rain conditions that were apparently the same (stratiform), considerable variability in the Z-R relationships have been detected (Atlas and Chmela, 1957). Based on Marshall et al. (1947) the most commonly used Z-R relationship is z = 200R1.6 which is called the Marshall and Palmer distribution. The Z-R relationship is the backbone of the precipitation measurement via meteorological radar (Rinehart, 2010). There are some important physical differences between rain and snow, and detecting snow using meteorological radar isn’t as easy as detecting liquid precipitation. The precipitation rate for snow is usually much lower than rain. The comparison between rain and snow is based on the water equivalent precipitation rate. It describes the rate of snow which is melted and then measured similarly to rainfall in mm/h of melted water. The maximum amount of moisture in the atmosphere is highly dependent on the temperature of the atmosphere. There is considerably more water vapor in the atmosphere at higher temperatures compared to cold temperatures. As a result, the heaviest precipitation, liquid and frozen, occurs at relatively warm temperatures. The heaviest snowfall often appears when the air temperature is just over the freezing point. When the air temperature is colder than the surface, otherwise precipitation would be in the form of rain instead of snow. Furthermore the dielectric constant of ice is less than that of water. If a radar is detecting snow or ice instead of liquid water, the backscattered receiving signal is around 7 decibel (dB) less. While cloud droplets are usually smaller than ice crystals, ice crystals appear to a radar although they are solid ice spheres of the same mass (Battan, 1973). Ice crystals are commonly larger due to their density being much lower than that of pure water or solid ice. The main reason why snow is not always detected by radar is the typical height of snow storms. The altitude of clouds in snow storms is usually much lower than that of most rain clouds, especially the very severe thunderstorms that produce heavy rain and hail. Often snow storms only extend a few thousand meters above the surface. In addition, especially in alpine regions, the topography covers the storm cells from the radar. All of these effects tend to make rain much easier to detect than snow (Rinehart, 2010). 6

It is a meteorological fact that much of the rain that occurs at the surface originates as snow. The transition from snow to rain, and the resulting changes on the radar signal have interesting consequences to the visibility of the radar. Another difference between snow and rain excepting the reflectivity from ice is less than that of water is the terminal velocity. The terminal velocity of a free falling object is the constant velocity that occurs when there is a balance between the force of gravity pulling it downward and the aerodynamic drag acting to slow it down (Raghavan, 2003). The terminal velocity of a particle is affected by its density and shape, as well as the density and viscosity of the surrounding atmosphere. Snow falls at a relatively low terminal velocity until it reaches its melting level where it begins to melt. The outer extremities of the falling snow crystals melt first. Once the snow crystal has melted to a certain point, a water coating is formed around the remaining moderately large and irregularly formed trunk. Hence the radar falsely recognizes a large, slow falling water droplet. The change from ice to water initially increases the reflectivity by as much as 7 dB. As the snow flake continues falling and melting, its size decreases, and its terminal velocity increases. Due to its decrease in size, its reflectivity decreases depending on the change in effective diameter between the water and snow drop. The transformation process of the snow crystals accelerates the falling particles, so the drops which are leaving the melting level move faster than those approaching it. As the density or concentration of snowflakes (number per cubic meter) decreases, this further decreases the reflectivity in this region. The snow crystals start with a given reflectivity above the melting level, there is on the order of 5 to 15 dB increase in reflectivity from the snow to the maximum received signal. Below this maximum signal the reflectivity will decrease 5 to 10 dB (Figure 2).

Figure 2: Schematic sketch showing the effects of particle coalescence, melting, and changes in terminal velocity on radar reflectivity through the bright band. Zero height is the melting level. The radar reflectivity Ƞ is given along the x-axis (Rinehart, 2010).

7

The reflectivity below the maximum of the curve is usually higher than it was in the snow above (Figure 2). This effect is known as bright band. Bright bands occur primarily during stratiform or stable situations. The same physics apply when strong convection is present. But the transition between snow and rain is often so chaotic that most of the time it is undetectable. The bright band occurs as a ring like region of enhanced reflectivity around the ground based radar position (Rinehart, 2010). Radars of a newer generation can produce 3-d images of convective cells. In general, weather radars detect precipitation echoes using multi-elevation volume scans (MEVS) and measure radar rainfall such as the plan position indicator (PPI), the constant altitude plan position indicator (CAPPI), and the column maximum (CMAX) (Lee and Ryu, 2009). There are two main indicators of radar estimated precipitation. Firstly, the CAPPI indicator has been widely employed. Its main application is in precipitation nowcasting and flood prediction. Second, the use of the CMAX indicator is also widespread. It has been used to inform meteorologists of severe thunderstorms, and its use in hydrologic flood forecasting has great potential. The CAPPI is detected from precipitation radar data received at a constant altitude plane from the ground (Figure 3). It is basically a horizontal cross-section through the radar precipitation estimation. The CAPI observes precipitation echoes on an equal footing at different distances from the weather radar. To calculate this cross- section it requires many observation angles, from near horizontal to near vertical inclination, to receive a cut that is as close as possible to the height needed at all distances (Atlas, 1990). The composite reflectivity is the maximum reflectivity among all available inclinations of the precipitation radar. The reflectivity at single inclinations of the precipitation radar indicates the precipitation intensity at a specific angle above the horizon. In combination, the highest Figure 3: Schema of CAPPI and CMAX. (Yoon et al., 2014) intensities available for the different inclinations above each point of the image are displayed (Figure 3). CMAX represents the maximum, physically possible precipitation in a column (Yoon et al., 2014).

8

1.1.2 Rain gauge

In addition to measuring rain using remote sensing, a much older method for direct capture exists. Most of the in-situ measurements of precipitation are done with rain gauges. The rain gauge is the hydrologist's fundamental tool for measuring rainfall. These instruments are relatively cheap to acquire, easy to maintain, and can supply a direct and accurate measurement of rainfall at a certain location. The disadvantage of rain gauges is their failure in capturing spatial variability of rainfall over time. However this is an important aspect for the credible modeling of a catchments response to rainfall (Sinclair and Pegram, 2005). Depending on the intended use of the measurements, several types of rain gauges exists. They vary in their precipitation collecting mechanism, temporal resolution of the measurement, and the automated broadcast of the collected data. The manually operating (non-recording) rain gauge typically collects the rain in a cylinder, and the rainfall depth is measured once a day, or less frequently, by an observer. These manually operating rain gauges are not integrated into fully automatic weather or flash flood models. Self-operational rain gauges have a wide range of techniques to collect precipitation (Sene, 2013). A commonly used automated rain gauge is the “tipping bucket rain gauge”. The water drips from the collecting vessel into a small tipping bucket. If the bucket on one side is filled with water, the tipping bucket turns over and the other bucket is filled. Each tipping of the bucket refers to a known amount of precipitation. The amount of turnings per time unit is recorded and is sent to the operator or saved on a data logger. Another automated and widely used technique is the mass weighing of the collected rain. The container which collects the precipitation is continuously weighed and even small discrepancies can be detected, however if the container is filled it must be emptied by hand or via a siphon (Emeis, 2010). The most commonly used methods for automated weather stations are the tipping bucket (approximately 83% of all responses) and the weighing rain gauge technique (16%) (Nitu and Wong, 2010). Although rain gauges provide accurate rainfall measurements, their weakness is the limited representation of the spatial extend (Villarini et al., 2008).

It is also possible to evaluate the spectrum of rain drop sizes using a disdrometer. The formation processes of rain can be inferred by utilizing the size spectrum of the measured rain drops. The size and falling speed of the rain drops can be measured using different approaches. Smaller drops are quite easily measured using laser-optical devices, while another drop spectrograph analyses the voltage pulses produced from the impact of drops on a piezo-electric sensor. Furthermore drop spectra can be obtained via the acoustic rain sensor on top of state of the art weather stations (Emeis, 2010). Disdrometers are often used to calibrate the drop size distribution function for operational running radar systems.

9

Instruments with a collecting surface have some limitations concerning their accuracy of observations. Wind has by far the largest influence on the measuring error. Rain and snow are blown away from strong winds, and as a consequence the precipitation does not reach the collecting surface. In some severe conditions the precipitation can be blown out of the collecting container, this mostly concerns snowfall measurements on account of its lower mass. Therefore wind shields, in the form of protection rings are installed around the rain gauges (Emeis, 2010). Due to the aerodynamic shape of instrument collecting surfaces, wind influence can be reduced (Strangeways, 2007). In alpine or colder regions, snowfall may affect readings or even block or cover the instrument’s aperture. Freezing can be prevented with the aid of an electrical heater placed around the opening and the bottom of the rain gauge.

Many countries have national standards for the installation of weather stations. Some examples of these are a minimum sensor height and certain distances to obstacles such as buildings. In areas where extensive snow cover is a regular occurrence, the sensor will be higher than in non-alpine or warmer regions. Nevertheless, after the measurements are received via a telemetry system, a quality check of the data is absolutely necessary. Outliers can be found by comparing the values with historical maximums, and readings from nearby gauges (Sene, 2013).

Additional uncertainties and systematic errors which can be considered in the case of precipitation can be due to splashing, evaporation, wetting and wind effects. These typically amount to 5% - 10% in summer, and 10% - 50% in snowy conditions (Pappenberger et al., 2009).

1.1.3 Multi-sensor precipitation estimates

When precipitation measurements are available from several observation sources, the next logical step is to combine the best features from each approach into a single estimate (Sene, 2013). Over recent years, rainfall measurements by radar have increased in importance. Rainfall estimates from radar have been improved via various methods, and the most obvious solution is to combine rain gauges and radar data. With rain gauges it is possible to take accurate rainfall measurements at specific points within the radar field of view. A calibration or adjustment to the radar data at those points is done with rain gauge point measurements. Due to this process, both systems give out the same values. To improve the radar rainfall measurement correction factors are applied to the surrounding radar values of the rain gauge. With the combined processing of radar and rain gauges it is possible to measure rainfall within about 10 to 15 percent of what a large rain gauge network might provide for the same event.

The precipitation rates which are measured with the rain gauges are automatically transmitted to the processing unit where it can be incorporated into the radar data in real-time. The temporal difference

10 between rain gauge and radar samples is significant. Operational rain gauges typically have a temporal resolution of 1 minute or longer. With radar it is possible to scan an entire storm in a matter of seconds. A comparison of these different temporal resolved data is only possible if a temporal averaging of both data is executed (Rinehart, 2010). The use of multiple sources of observations in the data assimilation process has been routine since the outset of Numerical Weather Prediction models. In recent years the increasing use of high resolution convective-scale and mesoscale models has led to the need to consider a wide range of observation systems and their interactions (Dabberdt et al., 2005).

1.2 INCA

The Integrated Nowcasting through Comprehensive Analysis (INCA) system developed by the Central Institute for Meteorology and Geodynamics (ZAMG) in Vienna, is an analysis and nowcasting system. It has primarily been developed as a means of providing improved numerical forecast products in nowcasting and very short range forecasting, especially in mountainous terrain. It integrates all available data sources as far as possible, and uses them to construct physically consistent analyses of atmospheric fields. The meteorological fields analyzed in INCA are:

• Temperature • Humidity • Wind • Gust speed • Precipitation • Precipitation type • Cloudiness • Global radiation • Snowfall line • Ground temperature

The update frequency depends on meteorological parameters. For precipitation, precipitation type and cloudiness, INCA is updated every 15 minutes. All other meteorological parameters are updated hourly. The Austrian operational domain of INCA, has a mesh size of 1 km and covers an area of 600 km x 350 km, centered over the eastern Alps (Haiden et al., 2011).

The INCA system provides frequently updated forecasts in the nowcasting range (up to +6 h) and improves numerical weather prediction (NWP) forecasts for up to +72 h through downscaling and bias correction (Haiden et al., 2011).

11

The input data source varies depending on the meteorological parameter. NWP fields are the basic information of the system. They are provided by the Austrian operational version of the Aire Limitée Adaption Dynamique Développement International (ALADIN) limited-area model described by Wang et al. (2005). It compromises 60 levels of vertical resolution, a horizontal resolution of 9.6 km and runs four times a day. The forecast range of the model is defined by 72 h (Haiden et al., 2011).

The most important input data source for the INCA system are surface meteorological stations. ZAMG operates a network of approximate 250 semi-automated weather stations (Teilautomatisches Wetterstationsnetz, TAWES) across Austria. The measured meteorological parameters include relative humidity, dewpoint, 10-m wind speed and direction, precipitation amount, sunshine duration and 2- m temperatures. In addition to the meteorological stations, run by the ZAMG, the hydrological service of Austria operates a network of real-time hydrometeorological stations, of which approximately 100 have already been integrated into the operational analysis system. Furthermore meteorological stations from the neighboring countries are permanently integrated in the running process. Even though the station network is growing, the station density at higher elevations is lower than it ideally should be, according to the area-height distribution (Haiden et al., 2011).

For precipitation, output of INCA precipitation radar data are integrated. For Austria, the radar network is operated by the civil aviation administration (Austrocontrol). It consists of five radar stations located at the Vienna airport, near the city of Salzburg, in northeastern Carinthia (on Zirbenkogel mountain), near the city of Innsbruck (on Patscherkofel mountain) and in western Austria (on Valluga mountain). ZAMG operationally obtains 2-D radar data synthesized from these five locations, containing column maximum values (Cmax) in 14 intensity categories, at a resolution of 5 minutes. Meanwhile, the dataset is supplemented with radar data from neighboring countries such as the radar on Gantkofel mountain near Bolzano (Trentino-Alto Adige, Italy). However, due to the mountainous Austrian terrain, radar data is of limited use in many areas in the western part of the country, especially during wintertime, when precipitation may originate from shallow cloud systems (Haiden et al., 2011).

The Meteosat 2nd Generation (MSG) satellite products used in INCA “Cloud Type” consist of 17 categories and the visible (VIS) image. The cloud types differentiate mainly between various degrees of opaqueness (low, medium, high). It can also diagnose whether clouds are more likely convective or stratiform in terms of character. The VIS image is used to downscale the infrared-based (and thus coarser resolution) cloud types during the day (Haiden et al., 2011).

The 1-km digital terrain model used by INCA is obtained through bilinear interpolation from a global 3” elevation dataset, provided by NASA (SRTM). For the extrapolation of 3-D ALADIN forecast fields in valleys, a “valley floor surface” is calculated from the elevation dataset. It represents the mesoscale

12 average height of valley floors and is computed by assigning to every grid point. The minimum elevation can be found within a boundary of 10 km. Afterwards the resulting image is smoothed with a running average 20 km x 20 km window (Haiden et al., 2011).

In the alpine area, the INCA system provides meteorological input for operational high-resolution flood forecasting and winter road maintenance. INCA employs a new weighting radar/rain gauge combination algorithm and includes elevation effects on precipitation, using an intensity-dependent parametrization as described in Haiden et al. (2011) and Haiden and Pistotnik (2009). The most important operational application of the system is flood forecasting.

2 Data and Methods 2.1 Study area

The study area (Figure 4) of this thesis is located in the central eastern Alps, mostly in the Italian federal state Trentino-Alto Adige. Some peripheral regions of the south-east are in the federal state of Venetia and in the south-west of the study area the border to Lombardy is nearby. Moreover, the study area covers a part of the Swiss Canton Grisons in the east. The northern part is completely in the Austrian federal state of Tyrol. The area of interest (AOI) has a north-south extend of 102.5 kilometers and an east-west extend of 143 kilometers, which covers an area of 14657 square kilometers. The array of the study area has an east-south extension of 286 pixels and a north-south extension of 205 pixels, with a spatial resolution of 500 metres. Furthermore, the central eastern Alps are subdivided in several parts. The north-west part of the AOI begins with the Sesvenna Alps. To the west, follows the Oetztal Alps. Further along connects the Stubai and Zillertal Alps. In the east, the Dolomites cover the main part of the study area. In the south-east, the study area ends with the Ortler Alps.

The mostly formative valley is the Adige valley. It runs from the Reschenpass, the border area between Italy, Switzerland and Austria, to the south-east over the Vinschgau to the capital city of Bozen. Eisack, the second biggest river of Trentino-alto Adige has his origin at the Brenner. It flows to the south and disembogues in the river Adige in the south of Bozen. Afterwards the Adige heads south till the end of the study area and leads into the Adriatic Sea. The continental watershed is running from west to east through the study area. The streams in the northern part of the study area are leading into the Black Sea.

The Oetztal Alps are the main glaciated regions of the study area. Their main glaciers are the Gepatschferner, Gurglerferner, Hintereisferner and Vernagtferner. Wildspitze has a height of 3768 m a.s.l. the highest peak in the Oetztal Alps (Förster et al., 2016). In addition, approximately 37 % of the 13

Oetztal Alps are covered with glaciers (Fischer et al., 2015). The highest summit in the study area is the Ortler (3905 m a.s.l), the Ortler region also contains some glaciated areas.

The topographical circumstances of the study area determine the local climatological conditions in a multifarious manner. In continental context, the Alps are influenced by the humid moderate zone of the northwest Atlantic. The impacts of continental air masses from the east are present, which leads to aridity, cold winters and warm summers. As well, the Mediterranean climate from the south determines humid winters and dry summers. The Alps are different distances from the sea, and function as a barrier for the moving air flows which are channeled in different directions. One of the best-known appearances of this, is the dry and warm katabatic wind called Föhn (foehn).

Precipitation increases from the Alpine foreland towards the northern and southern upslope and generally decreases towards the interior Alpine areas, despite the higher terrain (Frei and Schär, 1998). In the east of the area of interest begins the Vinschgau, completely surrounded by high mountain ranges. Hence, humid air is relatively uncommon in the Vinschgau. For this reason it is one of the arid regions in the Alps with an annual precipitation of 530 mm. Orographic precipitation is the main type of precipitation during winter and spring. In April, south streams lead to typical orographic rain fronts in the southern part of the study area. As the Brenner functions as an orographic border, areas located in the north are characterized by warm and dry weather. From May onwards, convective events cause the main precipitation in form of downpours and thunderstorms. July is the warmest month with the highest precipitation (Adler, 2015).

14

Figure 4: Terrain height with radar site in the study area. For better orientation the main sub regions of the central eastern Alps are labelled.

2.2 Data

Several data sources are required to calculate regionalized precipitation fields with a multi-sensor precipitation estimate. The main data components of the multi-sensor precipitation estimate are precipitation radar data and rain gauge observations. Furthermore a Digital Elevation Model (DEM) for Austria and Italy with a raster resolution of 500 metres was used and extracted for the area of interest. The data was derived by airborne laser scans and resampled to 500 metres. The DEM was made available by the AHC workgroup. To harmonize the future computations, the projected coordinate system WGS 84 / UTM zone 32N (EPSG: 32632) was used as target system for the study site.

2.2.1 Radar data

The radar data was provided by the weather service of South-Tyrol. The radar, which was manufactured by Enterprise Electronics Cooperation (EEC) in Alabama (USA), is located at the Gantkofel mountain next to Bolzano. It was installed in the year 2001 and is situated at an altitude of 1866 m a.s.l. Its type designation is DWSR-2500C. The radar operates in the C-Band frequency with 250 kW power and has a range of approximately 240 km. It is a Doppler radar and has a single polarization

15 mode. By measuring in 11 different angles, from 0° to 25°, a new measurement is completed every five minutes. When measuring precipitation the radar only uses the inclination from 0° to 4°. Measurements beyond this inclination are generally used for atmospheric wind estimations. After the five minute measuring cycle, an additional three minutes are required for post processing. The Marshall and Palmer Z-R relationship is used for the drop-size distribution. In addition ground clutter is removed from the data.

For this work, the radar data was received as a CMAX product with mm/h as an intensity unit. The precipitation map has a spatial resolution of 500 m and an extension of 480 x 480 pixels depicted in a stereographic projection. Each radar measurement file is saved as a 2-D file in an ASCII format.

The main use of the radar is observing convective thunderstorm activities in mountainous regions. Stratiform precipitation events are mostly undetectable by the radar due to the high altitude of the precipitation clouds. Due to topographic shielding, the radar coverage in the region of the Vinschgau and Puster valleys is poor. The radar of the South-Tyrol weather service plays an important role in the forecast of thunderstorms and hail for natural hazard warnings and for the protection of the agricultural industry in Trentino-Alto Adige.

In addition it has to be mentioned that the radar does not always function properly due to technical problems or routine maintenance. Therefore there are interruptions in the records of the radar data.

2.2.2 Rain gauge data

The rain gauge data used composes several different station network operators. The known operators are the ZAMG (TAWES network), hydrological service of Austria, the weather service of South-Tyrol, the hydrological service of South-Tyrol, the meteorological service of Trentino and the EURAC research center. All available precipitation recording station data in the study area has been used to obtain the most dense station network possible. In 2004, 102 automated stations measured local precipitation. In 2009, 122 automatically rain gauges had already been installed in the study area. Figure 5 shows the increase in the number of new installed stations from 2004 to 2009. All stations from 2004 are marked with a triangle, stations from 2009 are marked with a circle. The temporal resolution of the measurements is hourly.

For each station the altitude and location is known. With 212 m a.s.l Salurn (HD-ST-88820MS) is the lowest located station. Pitztaler Gletscher (ZAMG-17315) is the highest located station with an altitude of 2864 m a.s.l.

16

Due to the huge amount of station data the precipitation measurements could not be quality checked. Furthermore no statements can be made about the nature of the data measurement techniques. The rain gauge data were made available by the AHC workgroup.

Figure 5 shows the increase of rain gauges from 2004 to 2009

2.3 Approach of combined precipitation Analysis (CPF)

In the following chapter the method of the self-programmed algorithm for the combination of rain gauge and precipitation data will be explained. All programming work for the Combined Precipitation Field (CPF) was done in Python 2.7. The methods and procedures used are strongly inspired by the INCA algorithm described by Haiden et al. (2011). The algorithm is separated in two parts. Firstly the pre-processing part is preparing the meteorological input data. Afterwards the climatological scaling is performed. The main run part of the algorithm is the precipitation analysis. It computes a weighted combination of rain gauge and radar data, taking the effects of topographic shielding of radar into account. In addition topographic effects are accounted for due to the applied elevation correction.

17

2.3.1 Pre-Processing

The pre-processing of the station data includes several steps which are done separately for each year. Firstly all available stations for the study area were selected, and the array coordinates for each rain gauge were calculated. All precipitation values of the selected rain gauges, as well as the pixel coordinates were saved in a tabular fashion via comma-separated values (CSV) files.

For further computations the “valley-floor surface” is computed, which is a spatially slowly varying reference surface. It is smooth compared to the actual topography and connects significant valley floors (Haiden, 1998). The valley-floor surface is computed by assigning the minimum elevation found within a perimeter of 10 km to every pixel. The resulting raster is smoothed with a running-average window with a window with of 20 km x 20 km. The valley-floor surface file separates the topography into a base and a relative topography.

Although the radar data are available every 5 minutes, they must be aggregated to hourly values since rain gauges are only available in an hourly resolution. Therefore five-minute radar data is also accumulated to hourly precipitation amounts. Furthermore the aggregated radar files are clipped to the study area’s extension.

After the aggregation of the radar data the climatological scaling is the last step in pre-processing. The climatological scaling is necessary because the radar field is largely range dependent and biased by topographic shielding. This is a special characteristic of mountainous regions. Therefore the radar data must be scaled before it can be used in precipitation analysis. A climatological scaling factor RFCM(i,j) is computed monthly. A raster of the scaling factor has been calculated for each month (M) of the year as well. The following term describes the ratio of the multiyear (2004 – 2009), three-monthly (from M

-1 to M +1) accumulated precipitation, obtained from rain gauge precipitation interpolation PSTA(i,j) and the corresponding accumulated radar precipitation PRAD(i,j):

푀+1 ∑푀−1 푃푆푇퐴(푖, 푗) 푅퐹퐶푀(푖, 푗) = 푀+1 ∑푀−1 푃푅퐴퐷(푖, 푗)

(4)

The resulting raster is spatially smoothed with a 10 km x 10 km running average filter.

The climatological scaling procedure yields to two main problems. Firstly regions where the radar beam is shielded by the terrain scaling would yield arbitrarily high scaling factors. This leads to questionable precipitation values. By limiting the scaling factor with a threshold of a maximum value of 2, this undesirable effect is prevented. Secondly high precipitation rates, typically found within convective cells, are more visible and thus less underestimated by the radar. The scaling factor asymptotically

18 decreases toward one for radar values higher than a scaling threshold (PSC = 4 mm / 1 h), to prevent over scaling of such convective cells:

푃푆퐶 푅퐹퐶′(푖, 푗) = 1 + [푅퐹퐶(푖, 푗) − 1] 푃푅퐴퐷(푖, 푗)

(5)

2.3.2 Main precipitation analysis

From now on the algorithm iterates entirely automatically over the hourly ordered precipitation amounts from all years, and performs the following computations if a radar file exists for the selected time.

All of the following calculations, which are performed for a single raster, are given by grid point indices (i,j). The irregularly distributed 1 hourly rain gauge values are interpolated via IDW to the study extension area:

푃푘 ∑푘 2 푟푖푗푘 푃 = 푆푇퐴(푖,푗) 1 ∑푘 2 푟푖푗푘

(6)

2 Where r ijk is the squared distance between station k and grid point (i,j). Pk is the measured precipitation at the kth station.

2 2 푟푖푗푘 = (푥푘 − 푥푖) + (푦푘 − 푦푗)²

(7)

The climatologically adjusted radar field is calculated as follows. The previously calculated scaling factor field RFCM(i,j) is multiplied by the radar field.

∗ 푃푅퐴퐷퐴푅(푖, 푗) = 푅퐹퐶(푖, 푗)푃푅퐴퐷(푖, 푗)

(8)

In the next step the climatologically scaled radar field P*RADAR is re scaled on the basis of a comparison of measurements and radar values at the station locations.

A spatial shift of a maximum of 4 km in between the station and the corresponding radar pixel means we must take effects due to finite settling time of hydrometeors, effects of wind-drift etc. into account. 19

The algorithm identifies the radar pixel which fits best to the measured station value and uses this value for subsequent calculations. For each station location an optimal spatial shift vector is calculated. The radar precipitation field is shifted at each station location via a piecewise affine transformation in a slightly different direction and by a slightly different amount (Hartley and Zisserman, 2015).

The actual rescaling has the formula

∑ ∗∗ 푘 푤푖푗푘푅퐹퐴푖푗푘 ∗ 푃푅퐴퐷퐴푅(푖, 푗) = 푃푅퐴퐷퐴푅(푖, 푗). ∑푘 푤푖푗푘

(9)

The formula is nonlinear because the weights wijk and the scaling factor RFAijk are dependant on

P*RADAR(i,j). The weights are described as

∗ 푃푅퐴퐷퐴푅,푘 푚푖푛 (1, ∗ ) 푃푅퐴퐷퐴푅(푖, 푗) 푤푖푗푘 = 2 , 푟푖푗푘 + [푐(푅퐹퐶푘 − 푅퐹퐶(푖, 푗))]²

(10) where r²ijk is the geometric distance given by (7). In contrast to IDW, (10) contains two additional terms. In the denominator, the additional term increases the distance if the climatological scaling factor at a station and at the point in question are different. The coefficient has the value c = 10 km. As a result a difference in the scaling factor of 1 has the effect of increasing the distance by 10 km. This term is especially important for mountainous areas. There the RFC raster can vary considerably over short ranges due to topographic shielding. If the radar precipitation value at a station is lower than the one at the point in question, the term in the numerator reduces the weight.

The scaling factor RFAijk is given by

+ 푃푆푇퐴푇,푘 푅퐹퐴푖푗푘 = 푚푖푛 (푅퐹퐴 (푖, 푗), ∗ ), 푃푅퐴퐷퐴푅,푘

(11) where the parameter RFA+ is a function of the climatological scaling at the grid point in question

+ 푅퐹퐴 (푖, 푗) = 푚푖푛[푅퐹퐴푀퐴푋, 푚푎푥(푅퐹퐴푀퐼푁, 푅퐹퐶푃 − 푅퐹퐶(푖, 푗))],

(12) with RFAMIN = 1, RFAMAX = 3 and RFCP = 5.

Overall the scaling is defined by (9) – (11) for a grid point (i,j) and is the weighted average of the ratio between station and radar precipitation at the nearest stations. The weight decreases with increasing

20 distance, with increasing difference in climatological scaling, and with decreasing precipitation at the station (relative to the precipitation at the grid point).

Hereafter the final combination of the two precipitation fields PSTAT(i,j) and P**RADAR(i,j) to the final

PCPF(i,j) field is described. The final combined field gives a better estimation of the precipitation distribution than each individual field. The combination is implemented through a weighted relationship

∗∗ ∗∗ 푃퐹퐼푁퐶퐴(푖, 푗) = 푃푆푇퐴푇(푖, 푗) + 푣[푃푅퐴퐷퐴푅(푖, 푗) − 푃푅퐴퐷푆푇퐴푇(푖, 푗)],

(13) where the weight v is given by

1, 푅퐹퐶 < 푅퐹퐶0 푣(푖, 푗) = { 푅퐹퐶 − 푅퐹퐶0 . 푒푥푝 [−ln (2) ( ) ²] , 푅퐹퐶 ≥ 푅퐹퐶0 푅퐹퐶퐻 − 푅퐹퐶0

(14)

The ancillary field P**RADSTAT(i,j) is created by interpolating the scaled radar values at the stations’ locations onto the grid. There, P**RADAR(i,j)= P**RADSTAT(i,j), which means that v = 0. Within the limits of resolution, it is there the values which are observed at the stations can be reproduced. The better the radar is able to capture the precipitation climatologically, the larger the weight of the radar information becomes between the stations. This means that the smaller the RFC values are, the radar weight increases. The radar weight decreases if the threshold RFC0 is > 3. The value of RFCH, at which the radar weight has decreased to 50 percent, is 5.

In addition, topographic effects are accounted for the combined precipitation field PCPF due to the elevation correction described by Haiden and Pistotnik (2009). The implementation of the elevation parametrization works as follows. In the first step the station topography zST is computed. The topography is calculated by interpolating all station heights in the study area via IDW

푧퐻,푘 ∑푘 2 푟푖푗푘 푧 (푖, 푗) = , 푆푇 1 ∑푘 2 푟푖푗푘

(15) where r²ijk is described in formula (7). In addition a valley precipitation field is calculated

21

푃푘 ∑푘 2 푟푖푗푘 푃 (푖, 푗) = 푧 − 푧 ≤ ∆푧 , 푉퐴퐿 1 푘 푉,푘 푉퐴퐿 ∑푘 2 푟푖푗푘

(16) where the summation extends only over those stations which are located below ∆zVAL = 300 m above the valley floor surface. The reference precipitation at the valley floor level is represented by the field

PVAL, on which the elevation dependence is based. In the following, the (i,j) indices introduced above are extracted for better legibility. Each dependent variable is a gridded field depending on (i,j). The relative precipitation gradient between two elevations z1 and z2 is defined as follows:

1 푃2 − 푃1 1 푃2 퐺푃 ≡ = ( − 1). 푃1 푧2 − 푧1 푧2 − 푧1 푃1

(17)

Using the former calculated fields the precipitation increment, due to the elevation effect on the CPF grid, is computed from

∆푃퐸퐿퐸푉 = 퐺푃(푧퐻̂ − 푧푆푇)푃푉퐴퐿,

(18) where 푧퐻̂ is the modified topography height defined by

푧퐻 − 푍푀퐴푋 + ∆푍 푍푀퐴푋 − ∆푍 푒푥푝 (− ) , 푧퐻 > 푍푀퐴푋 − ∆푍 푧퐻̂ = { ∆푍 . 푧퐻, 푧퐻 ≤ 푍푀퐴푋 − ∆푍

(19)

The 푧퐻̂ has been introduced to reduce the elevation gradient at higher elevation. The parameters for

Austria have been set to ZMAX = 2800 m and ∆Z = 500 m. The parameter zH is the topography height taken from the DEM.

Hereinafter the final combination of the increments of the radar field and elevation dependence are done. The final combination formula from (13) can be written as

푃퐹퐼푁퐶퐴 = 푃푆푇퐴 + ∆푃푅퐴퐷퐴푅,

(20) where

∗∗ ∗∗ ∆푃푅퐴퐷퐴푅 ≡ 푣(푃푅퐴퐷퐴푅 − 푃푅퐴퐷푆푇퐴푇).

(21)

22

In addition to the radar increment ∆푃푅퐴퐷퐴푅, the elevation increment ∆푃퐸퐿퐸푉 is taken into account. To prevent double counting of the elevation effects, already present in the former calculated radar fields, the combination is created only if the increments have different signs:

∆P = ∆푃푅퐴퐷퐴푅 + ∆푃퐸퐿퐸푉 if ∆푃푅퐴퐷퐴푅×∆푃퐸퐿퐸푉 < 0,

(22) and if they have the same sign, only the larger value is used

∆P = max (∆푃푅퐴퐷퐴푅, ∆푃퐸퐿퐸푉) if ∆푃푅퐴퐷퐴푅×∆푃퐸퐿퐸푉 ≥ 0,

(23)

The formula of the final combination (20) is replaced by

푃퐹퐼푁퐶퐴 = 푃푆푇퐴 + ∆P,

(24) where ∆푃 now includes the radar and the elevation increments.

The elevation gradient is only applied to the interpolated precipitation field from the rain gauges. Also it is only on the extent which is not seen by the radar at the same time. Thus the precipitation from deep convective cells remain largely unaffected.

2.4 Evaluation methods

To demonstrate that a site-specific model is capable of making “sufficiently accurate” simulations, although “sufficiency accurate” can vary based on project objectives, a model validation is indispensable (Refsgaard, 1997). In order to be able to evaluate the huge number of outcomes, three main methods for the model evaluation have been selected.

2.4.1 Differences of CPF – INCA 2008

To assess the influence of the additional radar data from Gantkofel mountain and the additional rain gauges, a comparison between CPF and INCA for January and June 2008 was performed. Therefore the monthly accumulated precipitation fields of CPF and INCA is computed. Afterwards a difference image is calculated by subtracting the INCA precipitation field from the CPF field. Differences in the precipitation estimation become apparent, particularly in the manner of spatial distribution. A CPF overestimation would result in positive values in the difference image, whereas negative values characterizing underestimation of CPF.

23

2.4.2 Leave-one-out cross-validation

A conventional way of validating the quality of precipitation analysis is the leave-one-out cross- validation method.

Eleven stations have been selected for the leave-one-out cross-validation, so that all altitudes are covered. An altitude interval of approximately 265 meters was chosen. All stations used, including altitudes are listed in Table 2. The location of the stations are randomly distributed throughout the study area seen in Figure 10. Due to the mentioned intense computation time, the method is only applied for the months of February and August in 2008. February has been chosen because it is a good representive of a winter month, characterized by solid precipitation and low cloud altitude. Moreover the radar is not running continuously throughout the year. Maintenance is mostly done during the winter months, due to the shallow cloud altitude and the impeded registration of solid precipitation by the radar. August however, is influenced by convective cells and thunderstorms with heavy downpours. 2008 has been chosen due to the sufficient radar data availability compared to other years in the investigation period.

Table 2: All stations used for leave-one-out cross-validation

Station name Station ID Station altitude Station Salurn HD-ST-88820MS 212 m a.s.l Station Kollmann-Barbian HD-ST-74900MS 490 m a.s.l Station Obervintl HD-ST-65400MS 750 m a.s.l Station Glurns LabPhysChem-ST37 1010 m a.s.l Station St. Veit in Prags HD-ST-42700MS 1285 m a.s.l Station Radein HD-ST-87300MS 1562 m a.s.l Station Hochserfaus AHP-102988 1815 m a.s.l Station Campitello (Malga Do Col D'Aura) Trentino-T0229 2050 m a.s.l Station Dresdner Huette HD-T-103085 2290 m a.s.l Station Careser (Diga) Trentino-T0065 2600 m a.s.l Station Pitztaler Gletscher ZAMG-17315 2864 m a.s.l

For each station, one model run was performed for February and August, where the respective station was not included in the computation. Afterwards, the station values, and the computed CPF values at the grid point of the station which have been omitted, are compared.

A statistical analysis was carried out in addition to the graphical evaluation, however considering every statistical evaluation has its advantages and disadvantages, three different efficiency criteria were calculated. Only through the consideration of several efficiency criteria can a comprehensive certificate be given for the quality of the modelled data. These are Nash-Sutcliffe efficiency (NSE), Percent bias (PBIAS) and root mean square error (RMSE). All efficiency criteria are calculated on the hourly base from the CPF and station values. 24

Nash and Sutcliffe (1970), describe the NSE as a normalized statistic that determines the relative magnitude of the residual variance (“noise”) compared to the measured data variance (“information”). The NSE presents how well the plot of measured data fits the 1:1 line, compared to modelled data. NSE values ranges between -∞ and 1.0 (including 1), where 1 is the optimal NSE value. Acceptable levels of performance are between 0.0 and 1.0, whereas values ≤0.0 indicate that the mean observed values are a better predictor than the simulated ones, and indicate an unacceptable model performance (Moriasi et al., 2007).

The PBIAS measures the average tendency of the modelled data to be larger or smaller than their measured counterparts. The perfect PBIAS value is 0.0, with low-magnitude values indicating precise model simulation. A positive PBIAS value indicates a model underestimation bias whereas a negative PBIAS value indicates a model overestimation bias (Gupta et al., 1999).

The RMSE represents the sample standard deviation of the differences between modelled values and measured values. It indicates the error in the units of interest (precipitation in millimeter) which supports the analysis of results. The RMSE indicates how much the average estimate differs from the observation. An RMSE value of 0 indicates a perfect fit of the modelled data (Moriasi et al., 2007).

Like every method, this process also has its disadvantages. The method is computationally intense, moreover it requires a random distribution of station positions with respect to climatology and topography. The results also depend on the local conditions at the monitoring stations. Furthermore small-scale features are usually not captured due to the often inhomogeneous and sparse station networks (Kann et al., 2015).

2.4.3 Differences of CPF – INCA 2014

In order to be able to compare the CPF method with another precipitation estimation model which is at the same stage of development, a comparison of the CPF algorithm with INCA is carried out. The INCA precipitation fields from 2004 to 2009 do not have the elevation correction or the radar data from Gantkofel mountain. Therefore the 2014 radar data from Gantkofel mountain was made available for the evaluation. The CPF algorithm computed the precipitation fields for 2014 which were accumulated to monthly precipitation amounts. Furthermore the INCA precipitation fields for the year 2014 were also accumulated to monthly precipitation amounts. To detect differences between the models, a difference image of CPF and INCA was calculated. Therefore the INCA image of each month was subtracted from the monthly CPF precipitation field. This method allows a simple analysis of the differences in precipitation in a spatial context.

25

3 Results

In the following chapter the results of the study are presented. Firstly the monthly mean precipitation fields of CPF from 2004 to 2009 are demonstrated. Furthermore the results of the three evaluation methods are illustrated.

3.1 Precipitation results

Figure 6 shows the monthly accumulated precipitation field for January 2008. The maximal precipitation values in parts of the Zillertal Alps and Rieserferner group, which are located at the north- east of the study area, are clearly apparent. Furthermore impacts of the radar data can be seen, around the Oetztal Alps and the Dolomites they are characterized by the small cells with high precipitation amounts, whereas in January the valleys are characterized as dry.

Figure 6: Monthly accumulated precipitation for January 2008. The maximum precipitation amounts at the Rieserferner group (north-east) are clearly visible.

The accumulated precipitation for June 2008 can be seen Figure 7. As suspected, the precipitation amounts are much higher than in January.The highest precipitates occur in the higher altitudes, in the north-east of the study area. Furthermore the glaciated areas in the Oetztal Alps have received very

26 little rainfall. In the high altitudes of the Dolomites, as well as the Ortler Alps, higher levels of precipitation up to 300 mm were modelled. The valleys also show a rather intense precipitation of 70 mm. In August no clear influence of the radar can be detected.

Figure 7: Monthly accumulated precipitation for June 2008. The highest precipitation values in the Zillertal Alps and the Rieserferner group (north-east) are clearly visible.

3.2 Results of differences CPF – INCA in 2008

Hereinafter the results of the difference image computation between CPF and INCA in January and June 2008 are imagined. Figure 8 shows the difference image of January 2008. It is clear that the precipitation in the northern valleys was underestimated by CPF. Furthermore a great underestimation can also be seen around the Stubai Alps, in contrast to south of the city of Brixen, where an overestimation of CPF can be seen.

27

Figure 8: Difference image of CPF and INCA in January 2008.

In June (Figure 9), as in January (Figure 8), the northern valleys of CPF are strongly underestimated in comparison to INCA. In general the valleys are underestimated, but to a lesser degree in the south compared to the north. A strong overestimation of CPF is again found at the Zillertal Alps and the neighboring Rieserferner group. Moreover the values in the Dolomites and around the Ortler Alps are overestimated.

Figure 9: Difference image between CPF and INCA in June 2008.

28

3.3 Results of Leave-one-out cross-validation

Hereinafter the results of the leave-one-out cross-validation are represented. For a better overview, all stations and their altitude are shown in Figure 10.

Figure 10: Distribution and altitude of all in the leave-one-out cross-validation used stations.

In Figure 11, the monthly accumulated precipitation values of the measured stations and the modelled monthly values are shown. As it can be seen, the accumulated values are much lower in February than in August (Figure 12). The station Hochserfaus, does not show any records for most parts of February. The incomplete record of the station was removed from the evaluation. The lowest measured value in February was 0.4 mm at the station Campitello, where the CPF and INCA values were also very low at 4.2 mm. The top value for February is the station Salurn, with 25.0 mm. Here, however, CPF only shows 1.3 mm, and INCA 2.2 mm precipitation.

29

Figure 11: Accumulated precipitation for February 2008. Station Hochserfaus was out of order in February.

August is characterized by much stronger precipitation events which can be seen in Figure 12. The lowest measured value in August is significantly higher than the peak value in February. It is the station St. Veit in Prags with 36.6 mm measured, 156.2 mm with CPF, and 95.5 mm with INCA. The measured peak value for August was measured at the station Hochserfaus with 156.0 mm, 145.0 mm with CPF, and 166.5 with INCA.

Figure 12: Accumulated precipitation for August 2008. The accumulated precipitation is much stronger than in February.

30

Figure 13 and Figure 14 show the accumulated precipitation from February and August in temporal context. With both of these plots it is possible to identify individual precipitation events and the accordance of the measured and modelled CPF and INCA values over the month. In the case of the stations Salurn, St. Veit in Prags, and Radein, the CPF and INCA model values are underestimated, as illustrated in Figure 13Figure 13. On the contrary, the model overestimates the precipitation at the stations Obervintl, Dresdner Huette and Campitello. Whereby the overestimation of both models is extremely small at Campitello. At the stations Dresdner Huette and especially at Campitello, the two models are very close together. At the stations Kollmann-Barbian, Glurns, Careser and Pitztaler Gletscher the modelled CPF values are fit well with the observations. However at Kollmann-Barbian and Careser INCA is underestimated. Whereas Pitztaler Gletscher is significantly overestimated by INCA.

Figure 13: Station recorded and modelled accumulated precipitation for February. The x-axis marks the days in February and the y-axis represents the accumulated precipitation in millimeter.

Figure 14 visualizes the accumulated precipitation of the rain gauges, CPF, as well as the INCA values. As mentioned above, the precipitates in August are significantly higher than in February. Only at the station Campitello, the CPF model underestimates the magnitude of one precipitation event which leads to the super elevation of the station. INCA underestimated an event very early and thus deviates much earlier from the station values as CPF. An exaggeration of CPF can be recognized at the stations Obervintl, Glurns, St. Veit in Prags, Radein, Desdner Huette and Careser. At this point, it has to be considered that most of the precipitation events are registered by the model. When one precipitation event is overestimated, the graph is inflated. However, subsequent events are generally captured well, but due to the previous error the cumulative value is too high. INCA also has overestimated values at

31

St. Veit in Prags, Desdner Huette and Glurns. At the station Obervintl the INCA values are quite accurate. In Careser and Radein, however, they are less than the measured data. At the stations Salurn, Kollmann-Barbian, Hochserfaus and Pitztaler Gletscher the accuracy of capturing events and their magnitude correlates with the CPF data. However, the INCA values at Hochserfaus and Salurn are also very close to those measured by the stations.Variances are only seen at Pitztaler Gletscher, where the values are to high, as well as Kollmann-Barbian, where they are to small.

Figure 14: Station recorded and modelled accumulated precipitation for August. The x-axis marks the days in August and the y-axis represents the accumulated precipitation in millimeter.

Furthermore, efficiency criteria such as NSE, RMSE and PBIAS have been calculated for both model runs. As excepted the NSE values in Table 3 and Table 4 diverge clearly from one another, also when considering the month. In February, as displayed in Table , the stations Pitztaler Gletscher, Glurns, Careser and Kollmann-Barbian all have acceptable NSE values in CPF. In INCA only the station Obervintl is acceptable. All other values represent an unsatisfying model performance for either, CPF and INCA. The RMSE for CPF results in an error average that is satisfactory at the stations Pitztaler Gletscher, Glurns, Careser and Kollmann-Barbian. In INCA, however, it is the RMSE values of the Obervintl, Glurns and Campitello stations that are acceptable. The PBIAS for February is unsatisfactory for both models, with one exception. Only the station Careser had a minor underestimation compared to the measured values. The station Glurns had a value which was to high, and can hardly be seen in the optical analysis in Figure 13. All other PBIAS values point to a strong positive or negative deviation from the measured values. This circumstance can also be clearly seen in Figure 13.

32

Table 3 shows the computed efficiency criteria for CPF and INCA in February 2008. NSE values of 1 are optimal, values from 0 to 1 are acceptable and values ≤ 0 represent an insufficient model performance. In PBIAS values, a perfect fit between model and observed data is represented by 0, a positive value indicates a model underestimation, while a negative value indicates a model overestimation. The RMSE labels a perfect model fit with 0. The RMSE value is the average error in mm.

Station name Station NSE CPF NSE RMSE RMSE PBIAS PBIAS altitude INCA CPF INCA CPF INCA Station Salurn 212 m a.s.l -11.1 -10.1 22.4 21.5 95.7 92.0 Station Kollmann-Barbian 490 m a.s.l 0.1 -3.8 2.8 6.5 -19.4 64.8 Station Obervintl 750 m a.s.l -11.8 0.1 11.2 3.0 -82.5 22.9 Station Glurns 1010 m a.s.l 0.6 -1.1 0.8 1.9 -15.0 -35.2 Station St. Veit in Prags 1285 m a.s.l -0.9 -0.1 6.7 4.7 40.9 28.7 Station Radein 1562 m a.s.l -5.7 -4.8 11.4 10.6 83.8 78.2 Station Hochserfaus 1815 m a.s.l ------Station Campitello (Malga Do Col 2050 m a.s.l -4963.0 -4035.3 3.8 3.5 -3960.0 -3504.0 D'Aura) Station Dresdner Huette 2290 m a.s.l -34.6 -71.2 8.6 12.3 -273.8 -384.0 Station Careser (Diga) 2600 m a.s.l 0.8 -6.0 1.2 6.7 3.6 75.0 Station Pitztaler Gletscher 2864 m a.s.l 0.8 -25.0 0.9 11.1 -10.3 -178.0

The efficiency criteria for August 2008 are listed in Table . When looking at Table , very good NSE values are immediately noticeable. The stations Salurn, Kollmann-Barbian, Campitello and Pitztaler Gletscher are satisfactory in both models. At the stations Obervintl, Radein, Hochserfaus the INCA values are good, and even better than the CPF values. Only the RMSE for the station Hochserfaus is satisfactory for both models. The CPF PBIAS, at the station Pitztaler Gletscher stood out with only a slight underestimation of the measured values. The station Hochserfaus had only a very slight over- estimation in the model values in CPF, as well as in INCA.

Table 4 shows the computed efficiency criteria for CPF and INCA for August 2008. NSE values of 1 are optimal, values from 0 to 1 are acceptable and values ≤ 0 represent insufficient model performance. In PBIAS values, a perfect fit between model and observed data is represented by 0. A positive value indicates a model underestimation, while a negative value indicates a model overestimation. The RMSE labels a perfect model fit with 0. The RMSE value is the average error in mm.

Station name Station NSE NSE RMSE RMSE PBIAS PBIAS altitude CPF INCA CPF INCA CPF INCA Station Salurn 212 m a.s.l 0.8 0.8 13.6 9.5 -25.1 10.4 Station Kollmann-Barbian 490 m a.s.l 0.9 0.3 13.4 28.8 -11.4 33.8 Station Obervintl 750 m a.s.l -1.0 0.8 39.9 11.9 -70.0 17.5 Station Glurns 1010 m a.s.l -4.9 -2.4 27.7 21.1 -103.2 -77.9 Station St. Veit in Prags 1285 m a.s.l -32.0 -6.0 81.4 37.5 -247.5 -111.7 Station Radein 1562 m a.s.l -0.7 0.6 57.7 26.7 -67.6 19.9 Station Hochserfaus 1815 m a.s.l -1.0 0.9 9.6 5.5 -3.9 -4.4 Station Campitello (Malga Do Col 2050 m a.s.l 0.86 -0.2 19.4 57.8 11.6 52.1 D'Aura) Station Dresdner Huette 2290 m a.s.l -2.5 -16.4 34.2 75.9 -85.4 -208.2 Station Careser (Diga) 2600 m a.s.l -1.5 -0.5 33.5 25.8 -53.7 50.0 Station Pitztaler Gletscher 2864 m a.s.l 0.9 0.5 8.0 22.2 1.8 -34.5

33

3.4 Results of differences CPF – INCA in 2014

In the following chapter, the results from the comparison between CPF and INCA in the year 2014 are represented. Due to the large quantity of results, only one winter, and one summer month are represented. Figure 15 shows the difference image between CPF and INCA in January 2014. Positive values represent an overestimation of CPF towards INCA, and negative values represent an underestimation of CPF across INCA. The strong overestimation of CPF occurs mostly in the corners of the study area. However the underestimated areas are quite centrally located from north to south. The strongest negative deviations are found on slopes of steep valleys, this is particularly noticeable at Gantkofel mountain.

Figure 15: Difference image of CPF and INCA in January 2014.

From Figure 16 we can see that the greatest negative deviations prevail in valleys. An underestimation can be re cognized even in the narrow valleys of Ahrntal. In the northern part of the Dolomites and around the Ortler Alps, the CPF values are higher than the INCA values. However in the Zillertal Alps and Rieserferner group the values are quite excessive.

34

Figure 16: Difference image of CPF and INCA in June 2014.

4 Discussion

In the following chapter the results of the evaluation are discussed. For a better overview the discussion is divided into several parts.

4.1 Discussion differences of CPF – INCA in 2008

Figure 8 shows the difference image of January 2008 of CPF and INCA. The underestimation of precipitation in the valleys in the north of the study area may be due to the poor reliability of the Gantkofel radar. Indeed there has to be some influence of the radar in these areas, which can be deduced by regarding the small-scaled overestimations of CPF. Due to the great distance between the Oetztal Alps and the radar position, the cloud height was most likely at the upper end of the radar’s detectable capacity. The huge differences in the measured precipitation between CPF and INCA, with approximately 200 mm around the Stubai Alps, were triggered by a heavy snowfall event on January 13th caused by orographic precipitation on the south of the main Alpine ridge. The avalanche service of Tyrol measured an increase in snow depth of 60 to 80 cm around the Stubai Alps (Nairz, 2008). This event was recorded by the radar on Patscherkofel mountain which can be explained by the outline of the event. The radar on Patscherkofel mountain has the perfect topographic conditions to cover the Stubai valley. If a single rain gauge had generated these selective high values, it would likely be reflected in a different structure (bulls eye effect). Due to the same orographic precipitation event on 35 the south side of the Brenner, CPF recognized a huge amount of precipitation in the southern study area. The big differences can be attributed to the fact that INCA has no radar data from this area and inadequate rain gauge coverage.

The differences in precipitation for June 2008 are described in Figure 9. As in January, the CPF values in the north-east are less than the INCA values. This is repeatedly caused by the poor radar accessibility of the area. The high CPF precipitates in the south of the study area may be quite realistic as it rained more than the long-term average in June 2008 (Munari et al., 2008). The large differences between CPF and INCA in the south are once again due to poor radar accessibility and the scarcity of INCA stations. However the punctual underestimation of CPF in the south of the radar station cannot be explained.

In general the strong overestimation in the north-east of the study area around the Zillertal Alps, as well as around the Rieserferner group is most likely caused by the poor radar visibility of the Gantkofel radar. The Patscherkofel radar on the other hand, has a better coverage of this area which explains the lower values of INCA. Furthermore the high CPF precipitation amounts in the south of the study area can be explained by the good radar accessibility of the Gantkofel radar. The radar on Gantkofel mountain has no topographical obstructions towards the south of the study area.

4.2 Discussion of leave-one-out cross-validation

In this section the result of the leave-one-out cross-validation are discussed. In general it can be stated that this method gives a good overview of the reliability, or rather accuracy of the two models being used.

The rainfall in February 2008 was extremely low, as it can be seen in Figure 13. At the stations Salurn and Radein, both models were underestimated against the measured values. The models have only measured marginal precipitates instead of two precipitation events. These modelled events in INCA were most likely correct in the time but far too weak in terms of intensity. The modelled values at the Station St. Veit in Prags were also underestimated, however not to the same extent as with Salurn and Radein. Events before the 3rd of February were well recorded, however in both models the magnitude of events was underestimated. The stations Radein and Salurn are located close to the radar site, and due to this small distance, clouds with a low altitude were also detectable with low radar angles. Surprisingly, these precipitation events were not adequately covered. At the stations Obervintl and Dresdner Huette an overestimation of CPF can be seen. At Obervintl CPF and INCA recorded the first events quite well. However the INCA modelled values were weak. CPF shows an event which is not visible in the station values, and is far too high after this event. At the station Dresdner Huette, a very similar course can be seen. At the stations Kollmann-Barbian, Glurns, Careser and Pitztaler Gletscher

36 the modelled CPF values fit well with the observations. However at Kollmann-Barbian and Careser, INCA modelled values are underestimated, whereas Pitztaler Gletscher is significantly overestimated by INCA. Surprisingly the two highest stations, Careser and Pitztaler Gletscher have the best fit between stations and CPF values. Although both stations are located in areas which are difficult to grasp by radar due to topographic shielding. On the whole, it can be seen that in February the times of an event is often modelled well, however the magnitude of the event is often wrong, which leads to deviations in the accumulated precipitates. These deviations can be traced back to three sources. Firstly, the cloud level in winter is often very low which prevents detection by radar. Secondly, the detection of solid precipitation in general, is more difficult than that of liquid precipitation. And Thirdly, the measurement of solid precipitation is even more difficult in the presence of icing or strong winds.

In August the precipitation was much higher than in February, with a 156 mm maximum. Station Campitello is the only station where the CPF model underestimates the station’s value. It recognizes all events up until the 16th of August in good time and magnitude. But as in most cases an event with an extreme intensity is modelled, resulting in a strong positive deviation. This process can be observed at the Obervintl, St. Veit in Prags, Radein, Dresdner Huette and Careser stations. This strong overestimation of an event can result from a strong overestimation of radar data. The radar values are highly-weighted between the stations, and so the radar values are the dominating values between the stations. At the Salurn, Kollmann-Barbian, Hochserfaus and Pitztaler Gletscher stations, the CPF values fit surprisingly well to the measured values. The individual events are captured precisely in terms of timing and magnitude by CPF. INCA also fits well at the station Hochserfaus. As it can be seen at the PBIAS in Table , CPF is exaggerated in 10 out of 11 cases. This can be a result of a general overestimation of the radar data. Surprisingly the stations of Hochserfaus and Pitztaler Gletscher are very good, although they are quite far away from the radar site and are affected by topographic shielding. In general the radar records more weather events in the summer because the convective cloud formation is much higher than in the winter. Thus it is possible to detect small-scale precipitation events which would otherwise slip by unnoticed by the wide distributed network of stations. Moreover a direct correlation between efficiency criteria and station height is not apparent. The biggest limiting factor which can be seen with the leave-one-out cross-validation method is the wrong dimensioning of the intensities. The temporal accuracy is very good which signifies that at least during summer, a lot of the precipitation events are recognized by the radar.

37

4.3 Discussion differences of CPF – INCA in 2014

The results of the CPF - INCA difference image method from the year 2014 are discussed below. Figure 15 shows the difference image of January 2014. A massive undercut of the CPF values can be seen in large parts of the area except at the corners. The strongly negative deviating regions range from Brenner to south of Bozen until the end of the study area. In the regions of Brenner, Sterzing, Brixen, Meran and Bozen supernormal rainfall has occurred in January 2014. January 2014 had the highest rainfall rates since the beginning of the records. In addition the temperatures throughout the month were also above average. Several Mediterranean Lows led to these unusually high precipitations that mostly occurred in liquid precipitation form due to the higher temperatures (Munari et al., 2014). The underestimation of the rainfall in these areas is most likely due to an excessive scaling of the Gantkofel radar data. Seeing as the quality of the radar data is insufficient in this area in January, this results in a high climatological scaling factor which is described in chapter 2.3.1. The radar image is multiplied by the climatological scaling factor field. This results in the climatologically adapted radar image which usually contains reduced precipitation values. In addition there are very few stations in the Sarntal Alps. In this case the radar data should be strongly weighted between the few stations, however this is impossible due to the high climatological scaling factor as it prevents a higher weighting of the radar data. In addition the CPF values are strongly underestimated around the radar location. This most likely occurs due to the fact that the radar only measures precipitation to an angle of 4 degrees. This angle however, is too small to capture the clouds if they are located directly over the radar, or in close proximity. In addition it should be noted that in the INCA values, often influences of the Patscherkofel radar can be recognized as far as Bozen. This influence largely covers the areas underestimated by CPF in this case. The higher CPF values in the south-west and south-east corner of the study area can be explained by a lack of stations in INCA. This lack can be clearly recognized in the accumulated monthly precipitation field of INCA.

In June 2014, the discrepancies are mostly small in the narrow valleys, as illustrated in Figure 16. In the north-east, strong discrepancies can be seen, as well as in the south west, where medium-strong over estimations of precipitation occur. The huge differences in the north-east are caused by the lack of radar quality of the Gantkofel mountain radar. In large, flat valleys a strong underestimation of the CPF values can be seen. Since these underestimations are irregularly distributed, they can most likely be explained by imperfections in the elevation gradient computation.

38

5 Conclusion and Outlook

The aim of this master thesis was the optimization of the precipitation estimation in the study area between the years 2004 and 2009. The study area of this thesis is located in the central eastern Alps, mostly in the Italian federal state Trentino-Alto Adige. The area of interest has a north-south extend of 102.5 kilometers, and an east-west extend of 143 kilometers, which covers a total area of 14657 square kilometers.

The methodology and procedures of the algorithm which was developed in this thesis, is strongly inspired by the INCA system described by Haiden et al. (2011). The INCA model is designed as an analysis and nowcasting system which uses a multi-sensor approach that includes surface station data, remote sensing data (radar and satellite), forecast fields of numerical weather prediction models, and high-resolution topographic data. INCA analyzes several meteorological parameters within a 1 km spatial resolution, and at different temporal resolutions.

For the precipitation estimation process, rain gauge and ground based precipitation radar data are used as input values. The applied rain gauge data composes several different station network operators such as the ZAMG, and the meteorological service of Trentino. All available precipitation recording station data in the study area has been used to obtain a station network as dense as possible. In 2004, 102 automated stations measured the local precipitation. By 2009, 122 automatically rain gauges had been installed in the study area. The temporal resolution of the measurements is hourly.

The radar data was provided by the weather service of South-Tyrol. It is located on the Gantkofel mountain at 1866 m a.s.l. in the west of the city of Bozen. The radar operates in the C-Band frequency with 250 kW power and has a range of approximately 240 km. Due to the topographical conditions in the study area, the radar field is partially restricted. For this work the radar data was received as a CMAX product with mm/h as intensity value and 500 m spatial resolution.

The precipitation estimation algorithm, which was programmed in Python 2.7, is divided in two sections. The first part is the data preparation. The pre-processing aggregates the radar data into hourly values and computes the “valley-floor surface”. Moreover the climatological scaling field, and the selection of the rain gauges in the study area is computed. All rain gauge values are saved in a tabular format. The second, and main analysis of the algorithm is combining rain gauge with radar data. The algorithm consists of a nonlinear spatial interpolation of measured rain gauge values, in which the radar field is used as a spatial structure function. The interpolation duplicates the observed rain gauge values at a station location. In addition all elevation dependence effects of precipitation are accounted for by the elevation correction.

39

Three methods were used to evaluate the computed results. The difference image (Figure 8) between the CPF and INCA model in 2008, showed that even in winter, where precipitation estimation by radar is handicapped, heavy precipitation events are captured by radar. In this case the event would not have been measured by a rain gauges. Therefore the influence of the Patscherkofel and Valluga radars, whose measurements extend into the study area, can be clearly seen in INCA. Areas calculated by CPF with poor radar accessibility are characterized by excessively high rainfall rates during both months, in comparison to INCA. In the south of the study area the INCA values are lower than the CPF values. This is due to the low station density, and the lack of radar data in the model.

The leave-one-out cross-validation method was the second evaluation method used in this thesis. Eleven stations, which are located randomly and distributed over eleven altitudinal layers were selected for this analysis. The model ran eleven times, and each time a station was omitted. At the station’s location, the modelled value was compared with the measured one. This process was carried out for the months of February and August in 2008. A trend regarding an over or underestimation by CPF can only be seen in August. There, CPF values are exaggerated in 10 of the 11 cases. A correlation between model reliability and station height cannot be detected purely on the basis of the calculated data. Surprisingly the stations with the higher locations have had the best fit between CPF and rain gauge values. The biggest limiting factor, which can be revealed with the leave-one-out cross- validation method is the wrong dimensioning of the intensities. The temporal accuracy is very good, this signifies that, at least during summer, a lot of the precipitation events are recognized by the radar.

In the last evaluation method, a direct comparison via an image differencing of the two models was carried out. These should be on the same technical level. However CPF does not include radar data from Austria which is a disadvantage. In January 2014, CPF showed a massive underestimation of precipitation in central parts of the study area. This is caused by unusually high liquid precipitation values for January. The extremely high values from this event of the Gantkofel radar were underestimated as the quality of the radar data is insufficient in this area for the month of January. This results in a high climatological scaling factor. The radar image is multiplied by the climatological scaling factor field. This results in the climatologically adapted radar image, which usually contains reduced precipitation values. In addition it should be noted that in the INCA values, often influences of the Patscherkofel mountain radar can be recognized as far as Bozen. In this case, this influence largely affects the areas which are underestimated by CPF, and therefore, more reliable values were calculated in INCA. In June the discrepancies between CPF and INCA were relatively small.

The precipitation fields, improved by CPF, can be used as meteorological input values for future hydro- climatological modeling. Due to the high spatial resolution, the high station density, as well as an integration of the elevation dependence correction. The new modelled values offer a surplus value 40 compared to INCA, especially in the south of the study area. However the results could have been improved if the surrounding radars of Patscherkofel and Valluga mountain could have been integrated into the algorithm. This integration of all available radar data, as well as the expansion of the research area would be very exciting for future extensions of the existing methodology. It should also be mentioned that the ZAMG computes a correction of the radar data, which was not known at the time this thesis was prepared. The radar data which were used for this thesis were minimally corrected by the weather service of South-Tyrol. By using an equivalent correction of the radar data more accurate results could have been obtained. Perhaps the strong exaggerations of individual events, for example as seen in August 2008, would not have occurred with the corrected radar data. In order to be able to quantify the results in a more accurate way, an expansion of the leave-one-out cross-validation method is also needed. An increase in the number of stations and the computing of an entire year would mean that seasonal sensitivities could be understood more thoroughly. In the future, further evaluation methods could also be implemented. A comparison of the measured and modelled run-off, as described by Sikorska and Seibert (2016) would allow a quantification of the modelled precipitation fields in a catchment area. As a result, not only point-to-point evaluations between measured and modelled data could be carried out, but also the evaluation of the influence that topographical conditions have on the model, for example a valley basin.

41

References

Adler, S. (2015), Das Klima von Tirol - Südtirol - Belluno: [Vergangenheit - Gegenwart - Zukunft], Zentralanstalt für Meterologie und Geodynamik, Bozen. Atlas, D. (Ed.) (1990), Radar in Meteorology: Battan Memorial and 40th Anniversary Radar Meteorology Conference, American Meteorological Society, Boston, MA, s.l. Atlas, D. and Chmela, A.C. (1957), “Physical-synoptic variations of drop size parameters”, Proc. Weather Radar Conf., Vol. 1957 No. 65, pp. 21–30. Barnett, T.P., Adam, J.C. and Lettenmaier, D.P. (2005), “Potential impacts of a warming climate on water availability in snow-dominated regions”, Nature, Vol. 438 No. 7066, pp. 303–309. Beniston, M. (2003), “Climatic Change in Mountain Regions. A Review of Possible Impacts”, in Beniston, M. and Diaz, H.F. (Eds.), Climate Variability and Change in High Elevation Regions: Past, Present & Future, Advances in Global Change Research, Vol. 15, Springer Netherlands, Dordrecht, pp. 5–31. Bliss, A., Hock, R. and Radić, V. (2014), “Global response of glacier runoff to twenty-first century climate change”, Journal of Geophysical Research: Earth Surface, Vol. 119 No. 4, pp. 717–730. Borga, M. (2002), “Accuracy of radar rainfall estimates for streamflow simulation”, Journal of Hydrology, Vol. 267 No. 1-2, pp. 26–39. Collins, D.N. (2008), “Climatic warming, glacier recession and runoff from Alpine basins after the Little Ice Age maximum”, Annals of Glaciology, Vol. 48 No. 1, pp. 119–124. Dabberdt, W.F., Schlatter, T.W., Carr, F.H., Joe Friday, E.W., Jorgensen, D., Koch, S., Pirone, M., Ralph, F.M., Sun, J., Welsh, P., Wilson, J.W. and Zou, X. (2005), “Multifunctional Mesoscale Observing Networks”, Bulletin of the American Meteorological Society, Vol. 86 No. 7, pp. 961–982. Doviak, R.J. and Zrnić, D.S. (2006), Doppler radar and weather observations, 2nd ed., Dover Publications, Mineola, N.Y. Emeis, S. (2010), Measurement methods in atmospheric sciences: In situ and remote ; with 28 tables ; [quantifying the environment], Borntraeger, Stuttgart. Finger, D., Vis, M., Huss, M. and Seibert, J. (2015), “The value of multiple data set calibration versus model complexity for improving the performance of hydrological models in mountain catchments”, Water Resources Research, Vol. 51 No. 4, pp. 1939–1958. Fischer, A., Seiser, B., Stocker Waldhuber, M., Mitterer, C. and Abermann, J. (2015), “Tracing glacier changes in Austria from the Little Ice Age to the present using a lidar-based high-resolution glacier inventory in Austria”, The Cryosphere, Vol. 9 No. 2, pp. 753–766. Förster, K., Oesterle, F., Hanzer, F., Schöber, J., Huttenlau, M. and Strasser, U. (2016), “A snow and ice melt seasonal prediction modelling system for Alpine reservoirs”, Proceedings of the International Association of Hydrological Sciences, Vol. 374, pp. 143–150. 42

Frei, C. and Schär, C. (1998), “A precipitation climatology of the Alps from high-resolution rain-gauge observations”, International Journal of Climatology, Vol. 18 No. 8, pp. 873–900. Grayson, R.B., Blöschl, G., Western, A.W. and McMahon, T.A. (2002), “Advances in the use of observed spatial patterns of catchment hydrological response”, Advances in Water Resources, Vol. 25 No. 8-12, pp. 1313–1334. Gupta, H.V., Sorooshian, S. and Yapo, P.O. (1999), “Status of Automatic Calibration for Hydrologic Models. Comparison with Multilevel Expert Calibration”, Journal of Hydrologic Engineering, Vol. 4 No. 2, pp. 135–143. Haiden, T. (1998), Analytical aspects of mixed-layer growth in complex terrain, Eighth Conf. on Mountain Meteorology, Flagstaff, AZ. Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B. and Gruber, C. (2011), “The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region”, Weather and Forecasting, Vol. 26 No. 2, pp. 166–183. Haiden, T. and Pistotnik, G. (2009), “Intensity-dependent parameterization of elevation effects in precipitation analysis”, Advances in Geosciences, Vol. 20, pp. 33–38. Hanzer, F., Helfricht, K., Marke, T. and Strasser, U. (2016), “Multilevel spatiotemporal validation of snow/ice mass balance and runoff modeling in glacierized catchments”, The Cryosphere, Vol. 10 No. 4, pp. 1859–1881. Hartley, R. and Zisserman, A. (2015), Multiple view geometry in computer vision, 2. ed., 13.pr, Cambridge Univ. Press, Cambridge. Jansson, P., Hock, R. and Schneider, T. (2003), “The concept of glacier storage. A review”, Journal of Hydrology, Vol. 282 No. 1-4, pp. 116–129. Kann, A., Meirold-Mautner, I., Schmid, F., Kirchengast, G., Fuchsberger, J., Meyer, V., Tüchler, L. and Bica, B. (2015), “Evaluation of high-resolution precipitation analyses using a dense station network”, Hydrology and Earth System Sciences, Vol. 19 No. 3, pp. 1547–1559. Kolokytha, E., Oishi, S. and Teegavarapu, R.S.V. (Eds.) (2017), Sustainable Water Resources Planning and Management Under Climate Change, Springer Singapore, Singapore, s.l. Lee, J. and Ryu, C. (2009), Radar Meteorology, Sigma Press, Seoul. Marshall, J.S., Langille, R.C. and Palmer, W.M. (1947), “Measurement of rainfall by radar”, J. Meteorol, No. 4, pp. 186–192. Moriasi, D.N., Arnold, J.G., van Liew, M.W., Bingner, R.L., Harmel, R.D. and Veith, T.L. (2007), “Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations”, Transactions of the ASABE, Vol. 50 No. 3, pp. 885–900. Munari, M., Dinale, R., Maraldo, L., Mutinelli, C., Oberparleiter, C., Mair, E. and Stuefer, H. (2008), Hydroreport 06 2008: Südtirol - Alto Adige, Bozen.

43

Munari, M., Peterlin, D., Tollardo, M. and Geier, G., Verant, W. (2014), Climareport Jänner 2014: Südtirol - Alto Adige, Bozen. Muste, M., Yu, K. and Spasojevic, M. (2004), “Practical aspects of ADCP data use for quantification of mean river flow characteristics; Part I. Moving-vessel measurements”, Flow Measurement and Instrumentation, Vol. 15 No. 1, pp. 1–16. Nairz, P. (2008), Schnee und Lawinen 2007-2008, Innsbruck. Nash, J.E. and Sutcliffe, J.V. (1970), “River flow forecasting through conceptual models part I – A discussion of principles*”, Journal of Hydrology, No. 10, pp. 282–290. Nitu, R. and Wong, K. (2010), CIMO Survey on national summaries of methods and instruments for solid precipitation measurement at automatic weather stations, Instruments and observing methods Report No. 102, TD No.1544, Geneva. Pappenberger, F., Ghelli, A., Buizza, R. and Bódis, K. (2009), “The Skill of Probabilistic Precipitation Forecasts under Observational Uncertainties within the Generalized Likelihood Uncertainty Estimation Framework for Hydrological Applications”, Journal of Hydrometeorology, Vol. 10 No. 3, pp. 807–819. Raghavan, S. (2003), Radar meteorology, Atmospheric and oceanographic sciences library, Vol. 27, Kluwer Acad. Publ, Dordrecht. Refsgaard, J.C. (1997), “Parameterisation, calibration and validation of distributed hydrological models”, Journal of Hydrology, Vol. 198 No. 1-4, pp. 69–97. Rinehart, R.E. (2010), Radar for meteorologists: Or you, too, can be a radar meteorologist, part III, 5. ed., Rinehart Publ, Columbia, Mo. Sene, K. (2013), “Precipitation Measurement”, in Sene, K. (Ed.), Flash Floods, Springer Netherlands, Dordrecht, pp. 33–70. Sikorska, A.E. and Seibert, J. (2016), “Value of different precipitation data for flood prediction in an alpine catchment. A Bayesian approach”, Journal of Hydrology. Sinclair, S. and Pegram, G. (2005), “Combining radar and rain gauge rainfall estimates using conditional merging”, Atmospheric Science Letters, Vol. 6 No. 1, pp. 19–22. Skaugen, T., Stranden, H.B. and Saloranta, T. (2012), “Trends in snow water equivalent in Norway (1931–2009)”, Hydrology Research, Vol. 43 No. 4, p. 489. Stocker, T. (Ed.) (2014), Climate change 2013: The physical science basis Working Group I contribution to the Fifth assessment report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge. Strangeways, I. (2007), Precipitation: Theory, measurement and distribution, Cambridge University Press, Cambridge.

44

Villarini, G., Mandapaka, P.V., Krajewski, W.F. and Moore, R.J. (2008), “Rainfall and sampling uncertainties. A rain gauge perspective”, Journal of Geophysical Research, Vol. 113 No. D11, p. 385. Wang, Y., Williamson, C., Garvey, D., Chang, S. and Cogan, J. (2005), “Application of a Multigrid Method to a Mass-Consistent Diagnostic Wind Model”, Journal of Applied Meteorology, Vol. 44 No. 7, pp. 1078–1089. Whiteman, C.D. (2000), Mountain meteorology: Fundamentals and applications, Oxford University Press, New York. Xie, P. and Arkin, P.A. (1996), “Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions”, Journal of Climate, Vol. 9 No. 4, pp. 840– 858. Yoon, S., Jeong, C. and Lee, T. (2014), “Flood flow simulation using CMAX radar rainfall estimates in orographic basins”, Meteorological Applications, Vol. 21 No. 3, pp. 596–604.

45

Eidesstattliche Erklärung

Ich erkläre hiermit an Eides statt durch meine eigenhändige Unterschrift, dass ich die vorliegende Arbeit selbständig verfasst und keine anderen als die angegebenen Quellen und Hilfsmittel verwendet habe. Alle Stellen, die wörtlich oder inhaltlich den angegebenen Quellen entnommen wurden, sind als solche kenntlich gemacht.

Die vorliegende Arbeit wurde bisher in gleicher oder ähnlicher Form noch nicht als Magister-/Master-/Diplomarbeit/Dissertation eingereicht.

______Datum Unterschrift

46