Weather Forecasting for a large Sporting Event: World Sailing Games 2006

a diploma thesis submitted to the INSTITUTE OF METEOROLOGY AND GEOPHYSICS

UNIVERSITY OF INNSBRUCK

for the degree of MASTER OF NATURAL SCIENCE

presented by MICHAEL BURGSTALLER

OCTOBER 2008

‘Wir können den Wind nicht wehen lassen. Aber jeder von uns kann ein Segel setzen, sodass wir ihn einfangen können, wenn er kommt.’ E. T. Schumacher

Abstract

Abstract

In May 2006 the ISAF World Sailing Games took place on lake Neusiedl, Austria. For this event, a forecasting team of the ‘Central Institute for Meteorology and Geodynamics’ (ZAMG) made daily predictions for the organization committee, the sailors, their managers and the public. The aims of this thesis were to review weather prediction for large sporting events and to evaluate the forecast quality for the World Sailing Games. Meteorological support for the Winter Olympics in Innsbruck 1964 and 1976, the Summer Olympics in Sydney 2000 and the Winter Olympics in Salt Lake City 2002 is reviewed and provided the basis of the following investigation. The remarkable is quality and quantity of the meteorological preparations for the events has increased strongly and the prediction quality has improved as well. For the World Sailing Games 2006 a climatology is developed by means of a 10- minute-mean data set of the automatic weather station Neusiedl to find the best time of the year for the sailing event and to identify wind regimes on the lake Neusiedl. Statistical investigations are made for all used prediction tools and models, for example model output statistics (MOSMix and AustroMOS), direct model output (Aladin and ECMWF), and persistence by means of common methods like bias, mean error, correlation and one skill score for mean temperatures, minima and maxima, as well as sailing specific predictors like wind speed, wind direction and gusts in the period between May 7 and 22, 2006 to find out the best model for each parameter. The calculations of all indices returned different results for each parameter. For the ISAF World Sailing Games 2006, MOSMix and AustroMOS were best in wind speed and gust predictions. AustroMOS and direct output from the global prediction model from ECMWF provided good forecasts for wind direction. Minimum, maximum and mean temperature forecasts were best from MOSMix, AustroMOS and the mesoscale model Aladin. As these results show, the best forecasts for different small local areas and predictors can be made by using different models and tools. These should be evaluated by climatologies, a testing phase and verifications based on (if necessary additional) weather stations in the run-up time of an event.

i Zusammenfassung

Zusammenfassung

Im Mai 2006 fanden die ISAF World Sailing Games am Neusiedlersee, Österreich statt. Für diese Veranstaltung erstellte ein Team der Zentralanstalt für Meteorologie und Geodynamik (ZAMG) täglich aktuelle Wetterprognosen für das Organisationskomitee, die Segler und die Zuschauer. Die Ziele dieser Arbeit waren es, sowohl einen Überblick über verschiedenste Vorgehensweisen bei Wettervorhersagen für große Sportveranstaltungen zu geben, als auch die Vorhersagequalität für die World Sailing Games 2006 zu evaluieren. Dazu wurden das unterschiedliche Vorgehen der Wetterdienste, die die Vorhersagen für die Olympischen Spiele in Innsbruck 1964 und 1976, Sydney 2000, sowie Salt Lake City 2002 lieferten, analysiert und als Grundlage für die folgenden Untersuchungen herangezogen. Bemerkenswert ist, wie sich in der Meteorologie die Vorbereitungen auf solche Veranstaltungen und die Wettervorhersage durch eine Vielzahl von Modellen verbessert haben. Eine der Vorbereitungsmaßnahmen ist die Ausarbeitung einer Klimatologie, welche für den Neusiedlersee anhand eines Datensatzes bestehend aus 10-Minuten-Mittel diverser Messparameter der teilautomatischen Wetterstation Neusiedl, erstellt wurde, um den besten Zeitpunkt für die Veranstaltung der World Sailing Games 2006 zu evaluieren und um allenfalls bestehende Windsysteme ausfindig zu machen. Statistische Untersuchungen wurden für sämtliche verwendeten Vorhersagemethoden, wie Model Output Statistic (MOSMix und AustroMOS), Direct Model Output (Aladin und ECMWF) und Persistenzprognosen ausgewertet. Dazu wurden statistische Methoden wie Bias, Mean Errors, Korrelationen und ein Skill Score verwendet, um mittlere Temperaturen, Minima und Maxima, sowie segelspezifische Parameter wie Windgeschwindigkeit, Windrichtung und Böen für den Zeitraum der Veranstaltung von 7. bis 22. Mai 2006 zu analysieren, und dafür die besten Modelle ausfindig zu machen. Die Berechnungen aller Parameter ergaben im Modellvergleich unterschiedlichste Ergebnisse. Für die Dauer der Veranstaltung der ISAF WSG 2006 lieferten MOSMix und AustroMOS die besten Vorhersagen bezüglich Böen und Windgeschwindigkeit. Die besten Modelle für die Vorhersage der Windrichtung waren AustroMOS und das

ii Zusammenfassung

Globalmodel des ECMWF. Temperaturvorhersagen wurden am genauesten durch MOSMix, AustroMOS und Aladin wiedergegeben. Wie diese Untersuchungen zeigen, benötigt man für genaue Vorhersagen unterschiedlicher Pädiktoren für kleinräumige Gebiete verschiedene Modelle. Diese sollten anhand von Klimatologien, Probevorhersagen und Verifikationen an (gegebenenfalls zusätzlich installierter) Wetterstationen im Vorfeld der Veranstaltung ermittelt werden.

iii Table of Content

Table of Content

Weather Forecasting for a Large Sporting Event: World Sailing Games 2006

ABSTRACT...... I

ZUSAMMENFASSUNG ...... II

TABLE OF CONTENT...... IV

1 INTRODUCTION...... - 1 -

2 LITERATURE REVIEW...... - 3 -

2.1 OLYMPIC SUMMER GAMES SYDNEY 2000...... - 3 -

2.2 OLYMPIC WINTER GAMES SALT LAKE CITY 2002 ...... - 11 -

2.3 OLYMPIC WINTER GAMES INNSBRUCK 1964 / 1976 ...... - 17 -

3 FORECAST VERIFICATION ...... - 19 -

3.1 BIAS ...... - 21 -

3.2 MEAN ABSOLUTE ERROR ...... - 22 -

3.3 MEAN SQUARE ERROR ...... - 23 -

3.4 ROOT MEAN SQUARE ERROR ...... - 24 -

3.5 CORRELATION COEFFICIENT ...... - 25 -

3.6 COEFFICIENT OF DETERMINATION ...... - 27 -

3.7 RANK CORRELATION COEFFICIENT ...... - 28 -

3.8 MEAN SQUARE ERROR SKILL SCORE ...... - 30 -

3.9 SYNOPSIS ...... - 31 -

iv Table of Content

4 WORLD SAILING GAMES 2006...... - 32 -

4.1 GENERAL INFORMATION ...... - 32 -

4.2 CLIMATOLOGY ...... - 33 -

4.2.1 Temperature...... - 37 -

4.2.2 Precipitation ...... - 40 -

4.2.3 Wind Speed ...... - 42 -

4.2.4 Wind Direction...... - 43 -

4.2.5 Weather Type Classification ...... - 46 -

4.2.6 Wind Gusts...... - 53 -

4.3 THE FORECASTING PERIOD , MODELS AND TOOLS USED ...... - 55 -

4.3.1 Daily Routine of a Forecaster...... - 55 -

4.3.2 Models and Tools Used...... - 58 -

4.3.2.1 Instrumentation and Observation ...... - 58 -

4.3.2.2 Global Models...... - 61 -

4.3.2.3 Limited Area (Mesoscale) Models...... - 63 -

4.3.2.4 Model Output Statistic ...... - 68 -

4.3.3 Synoptic Situation during the World Sailing Games 2006...... - 70 -

4.4 FORECAST VERIFICATION ...... - 79 -

4.4.1 Comparison of Station Data ...... - 79 -

4.4.2 Wind Direction...... - 86 -

4.4.3 Wind Speed ...... - 93 -

4.4.4 Speed of Gusts...... - 98 -

4.4.5 Air Temperature...... - 100 -

4.4.6 Minimum / Maximum Temperature...... - 107 -

5 CONCLUSIONS AND OUTLOOK ...... - 111 -

APPENDIX...... - 115 -

BIBLIOGRAPHY...... - 118 -

ACKNOWLEDGEMENTS ...... - 124 -

v Introduction

1 Introduction

In a modern and fragmented society, meteorology has to meet various and often contrasting demands: Big business managers have their main focus in weather concerns on other meteorological parameters than athletes climbing up a mountain or scientists making field experiments. But forecasts are not only made for individuals. For human safety purposes special forecasts deal with the probability of severe thunderstorms, tornados and hurricanes. Today a good and successful forecaster knows exactly about the demands of his clients and is able to make predictions, which satisfy the costumers (with the exception of weather conditions). The demands on weather predictions increase with changing economic businesses and ascending risks depending on current weather conditions. Especially outdoor events are focused on an exact knowledge of the upcoming weather for planning their time schedules for competitions, ceremonies as well as outdoor caterings and parties. From the beginning of modern meteorology, weather forecasts and climatologies are made for Olympic Games. Today, these large sporting events occupy many special subjects of the meteorology. Climatological investigations and long-term weather predictions are made for the organization committee, medium-range weather forecasts and nowcasts for competition-depending parameters are computed for athletes, venue- and competition manager and of course the public. The following chapter gives an overview about weather forecasts for already held Olympic Games. Due to their extended meteorological support, they consisted of a sample of used weather prediction models, tools and instrumentation. Described are the Olympic Summer Games in Sydney in the year 2000 and the Winter Olympics in Salt Lake City. Additional to these ‘modern’ Games, the Olympics of Austria of the years 1964 and 1976 are reviewed to demonstrate the increasing meteorological demands in the course of time. During the Olympic Games in Innsbruck in the years 1964 and 1976 the meteorological support contained just temperature and wind climatologies and did not analyze event specific parameters. To the end of the century, demands changed completely. The meteorological support for the Olympic Games in Sydney 2000 and

- 1 - Introduction

Salt Lake City 2002 investigated all requirements on meteorological variables and found out best forecast models and strategies for each parameter. The third chapter reviews statistical methods and scores for comparing and evaluating forecasts. It reveals advantages and drawbacks of statistical analyses and summarizes the methods used during and after the World Sailing Games, which were taken as a case study. The fourth chapter discusses the World Sailing Games 2006 in detail. A short review about the event is followed by the description of the used models and tools. The synoptic situation during the event is presented as well as the climatology, which was prepared before the competitions took place on the lake Neusiedl. Finally, the meteorological models used for the event are verified. The last chapter gives a scientific summary as well as the authors’ personal impressions of the event.

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2 Literature Review

2.1 Olympic Summer Games Sydney 2000

The forecasting period of the Olympic Summer Games in Sydney 2000 was set from September to the end of November. For this special service, the World Weather Research Program (WWRP) instigated the Sydney 2000 Forecast Demonstration Project (FDP) and therefore the ‘Sydney 2000 Olympic Games World Weather Research Program Forecast Demonstration Project (WWRP FDP)’ was founded. It should reveal how nowcasts (including severe weather) had become more accurate with the latest Numerical Weather Prediction (NWP) models and radar technologies and processing. In cooperation between the WWRP FDP and the Australian Bureau of Meteorology, problems with the short forecast-timescale of just some hours were discussed and described (May et al, 2004) . The observing system was enhanced and prepared to support the WWRP FDP. Before the forecasting period for the Olympic Summer Games in Sydney started, an extensive climatology had been established (May et al, 2004) . Considering the regions in which the Olympics took place (Figure 2.1.1) , characteristics for this climate and extreme weather conditions like thunderstorms and bushfires were especially discussed. Caused by the rapid change from dry to continental to moderate climate, this region (Figure 2.1.2) is extremely susceptible to severe convective storms with hail. An example is the 14 April 1999 hailstorm, which struck the eastern part of Sydney. Hailstones with a diameter greater than 9cm caused storm losses of more than 1 billion Australian $ (May et al, 2004) . More than 300mm rain fell on 6 August 1986 in 1 day and gusts up to 230kmh -1 were caused by severe thunderstorms on 21 January 1991. A different risk is posed by bushfires. In the year 1994 for example, bushfires destroyed more than 200 urban houses in the vicinity of Sydney (May et al, 2004) . The Organization Committee of the Olympic Games chose a date in summer with a minimum of severe weather conditions (May et al, 2004) .

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Figure 2.1.1: Topography in the surrounding of the Olympic venues. All three sides of the basin of Sydney are covered by eucalyptus forests (Spark and Connor, 2003).

Figure 2.1.2: Köppen climate classification for Australia. In the surrounding of Sydney, climate rapidly changes from dry to continental to moderate. This causes strong temperature gradients and leads to severe weather phenomena (http://www.worldbook.com/).

The main focus of the prediction was on precipitation, wind shifts and severe weather events like tornados, thunderstorms and bushfires occurring sometimes in this time of the year. Due to the complexity of the provided weather service, the forecasting team was split into several teams working closely together in the Bureau of Meteorology’s New South Wales (NSW) regional forecasting centre in Sydney. Parts of this forecasting team were

- 4 - Literature Review formed by aviation forecasters, public weather forecasters, hydrologists and severe weather forecasters (May et al, 2004) .

Figure 2.1.3: The topography of the surrounding of Sydney and the used observation network with instrumentation used is shown in this figure. A fill of instruments had worked together providing the necessary data for the Olympic weather predictions (May et al., 2004).

To calculate all predictions, a large network of atmospheric observations and measurement systems was needed (Figure 2.1.3) . In the whole area, thousand volunteers observed the weather conditions and reported it to the forecast centre in Sydney. Wind and thermodynamic soundings were taken twice a day at the airport centre in Sydney and additional to these, a radar tracked pilot-balloon system with an accuracy better than 1ms -1 was installed. The pilot balloons were launched on request, when severe weather events were threatening (May et al, 2004) . A variety of satellite data were available including National Oceanic and Atmospheric Administration (NOAA) polar orbiters and hourly visible (1km-resolution), infra-red (4km-resolution) and 6.7 m water vapor (4km-resolution) images from the Japanese Geostationary Meteorological Satellite 5 (GMS-5). These data were used for subjective interpretation of the forecasters on one hand, and for assimilations in NWP models on the other hand. Additional to all this data, a surface network consisting of 33 AWSs (one offshore) and

- 5 - Literature Review

121 rain gauges was established. Further data about wind and temperature for verifying and computing were taken by commercial aircraft and wind profilers (May et al, 2004) . Several objective nowcasting systems were tested. To predict tracks of thunderstorms, tornados, wind fields and areas of heavy precipitation, forecasters used conventional and Doppler radar system combined with wind and temperature data from commercial aircraft, atmospheric profilers and radiosonde flights. Data from all conventional and Doppler radars were collected and fed to the bureau’s 3D-Rapic radar communication and display system, which included a 3 dimensional visualization system and pseudo-range-height indicator scans and displayed reflectivity and Doppler velocity. It was used for observing developments of cells and tracking storms. Integrated in this system was the TITAN (Dixon and Wiener, 1993) cell identification and tracking, providing objective guidance. The Canadian Radar Decision Support System (CARDS) was used for severe weather detection and put out rain-meteograms for specific regions. This system used Doppler and conventional radar data as well as sounding data to determine, for example decide the freezing level (Lapczak et al, 1999) .

Forecast System System outputs System inputs Reference Radar visualization TITAN Conventional or Mueller et 3D-Rapic storm tracks Doppler Radar al., 2003 Forecast storm position, Conventional and size, intensity, convergence Doppler Radar, line position, regions of Mueller et Auto-nowcaster soundings, AWS, likely convective initiation, al., 2003 profiler and satellite mesoscale wind- and rain data fields Canadian Radar Conventional and Severe weather detection, Decision Doppler Radar, Lapczak et meteograms of rain at Support System soundings (for freezing al., 1999 specific locations (CARDS) levels) Dixon and TITAN Forecast storm position Conventional Radar Wiener, 1993 Large hail detection, Polarimetric radar, Polarimetric Keenan, quantitative rain fall soundings (for freezing classifier 1999 measurements levels) Table 2.1.1: A summery of the used Forecast Systems, System outputs and System inputs.

Also, two experimental systems were used. The polarimetric hydrometeor classification scheme (Vivekanandan et al, 1999; Keenan, 1999) , being at the leading edge of radar

- 6 - Literature Review technologies just entering operations, and a 4-dimensional data assimilation system, which was provided by the National Center for Atmospheric Research (NCAR) Auto- nowcaster (Mueller et al, 2003; Crook and Sun, 2002) . This system assimilates multiple-Doppler radar data, surface and sounding data on a very fine resolution grid for analysis and short-term forecasts. It predicted storm position, size and intensity as well as lines of convergence, regions of likely convective initiation, mesoscale wind fields and forecasted rain fields. These forecast systems are summed up in Table 2.1.1 . While the Games lasted from 15 September to 1 October 2000, the Paralympic Games took place from 18 to 29 October 2000 and consequently, the installed network was kept on running in the storm season to enhance the prediction of severe weather events of the BoM. With the obtained dataset of all measured parameters, the 21 forecasters made their predictions for the different sport venues and the different needs of sports. The most sensitive venue was the sailing sport of course. The forecasts had to be more precise and more detailed than for other sports. Therefore two high-resolution weather prediction models and statistically derived models for wind field prediction were used. The first one was the LAPS 05 model, which was an internal system of the BoM. It had been available in test mode during the 1999 Olympic test events. Forecasters therefore were familiar with model biases, allowing interpretations of the model outputs (Spark and Connor, 2003) . It was available at a 0.125° resolution (about 14km) in graphical and tabular form over the Sydney Harbor domain. The second NWP model was provided by the University of New South Wales Centre for Environmental Modelling and Prediction (CEMAP) and had a 5km and a 1km resolution. Unfortunately, this model was only finalized just before the beginning of the Games and left the forecasters no chance to assess its reliability (Spark and Connor, 2003) . Statistical guidance was given by methodologies relying directly or indirectly to NWP model outputs. Predictors included wind parameters, sea surface temperatures, inland maximum temperatures and a number of combined parameters, which were important for upcoming land-sea breeze circulations (Spark and Connor, 2003) . The five most important statistical methods used were the Sydney Harbor Climatology (Harbclim), the Stratified Model Output Statistic Sydney Harbor scheme (SMOSSH), the Tree Breeze, the Regression Breeze and the John Townsend Model (JT model).

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The ‘Harbclim’ was developed for the coastline-oriented gradient wind directional quadrants (Houghton, 1992) . The development dataset contained wind measurements of Fort Denison, the offshore reference station and Wedding Cake (Figure 2.1.4) for the period August to October of the years 1991-1999. The data were partitioned into directional quadrants and wind speed intervals of 5kt. Results of these data were the mean surface wind and modal direction for each hour and each location. The ‘SMOSSH’ (Spark and Connor, 2003) was derived from a similar data set contains the years 1993-1999. Results of the statistical prediction were hourly wind vectors for offshore and inshore locations. ‘Tree Breeze’ is a ‘perfect prog’ based scheme (Klein at al., 1959) using the Classification and Regression Tree algorithm (Breiman et al. 1984) with a historical data set of surface wind (August-October) from Fort Denison and the offshore reference station matched with observed gradient wind (900hPa) data from the years 1991-1997.

Figure 2.1.4: The topography Sydney Harbor contains circles labeled A-F showing the approximate location of sailing courses, the offshore reference station and buoys and AWS from BoM (Spark and Connor, 2003).

The ‘Regression Breeze’ was especially developed by Kim, 1999 , and is a statistical, linear model approach for wind prediction of Sydney Harbor. Surface data from Fort Denison and the offshore reference station for the period August-October in the years 1991-1997 were matched with surface temperature data and observed upper wind data

- 8 - Literature Review from Sydney Airport. Kim partitioned this dataset in three distinct sea breeze regimes and developed prediction equations for wind speed and direction using multiple regression. The JT Model is named after its author John Townsend, who developed a very simple one-dimensional sea breeze model for the sailing forecasters during the Olympics in Atlanta in the year 1996. In Sydney it was used for quick spot checks and updates when predicted values by NWP deviated from observed synoptic gradient wind conditions. Input was the expected synoptic gradient wind variations throughout the day, cloudiness, maximum temperature at an inland location, sea surface temperature and the expected convective available potential energy (CAPE). Due to the local wind variations within the harbor, the JT model returned poorly results at Wedding Cake and was not used or verified at this location. The limitation was, that it was only used when gradient wind was offshore. For the daily forecasting routine, a separate office onshore the race venue was installed. The challenges of the forecasters were face to face briefings with the race management, the sailing team leaders and the athletes which were held twice a day. Additional to these, the team had to update an internet information site which contained observations, real-time data, historical, present and forecasted wind data (mean wind direction and speed as well as gust strength and direction in knots and other common units) and corrections of wind directions relative to magnetic north (it deviates from geographic north by about 12.5 degrees). Also they had to work out graphical presentations with one minute wind data, a daily info package including an overview about observations from the last day, the current synoptic situation and forecasts for the following day and finally the updated charts from harbor (May et al, 2004) . The Olympic Sailing Weather Office was staffed from 0430 to 1900 local time (LT). In the office, the forecasters made hourly wind and weather predictions issued at 0730 and 1630 LT. In the briefing, which was set later in the afternoon, wind and weather conditions were presented for the 12 to 24 hour lead time and a 3 to 4-day overview was given about the synoptic situation for race planning. When the races were going on, the forecasters made an hourly wind update for the managers. This weather data information was necessary for changes in the racing program, as, for example, the ISAF regulation claims for upcoming changes in wind shift of 20 degrees or more, as it is mentioned in text below (Spark and Connor, 2003) .

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After the short period of prearrangement and duration of the events, output and nowcasts were evaluated throughout a relatively short period. Due to the fewer severe weather events during the trial periods in the years 1998 and 1999 than on climatological average, the exploration of the full potential of the products was limited. The prediction of such weather phenomena and the development of 3-hourly predictions out to 24 hours occupied much of the time and energy of the forecasters (Anderson- Berry et al, 2004). The evaluation of the Sydney Olympic Sailing Weather Office’s (SOSWO), various numerical weather prediction and statistical models was made by means of the ‘Index of Agreement’, the ‘bias’, the ‘root mean square error’, the ‘wind bearing wrror (mean absolute error)’, the ‘magnitude of vector error’ and the ‘brier skill score’ (Spark and Connor, 2003) . Most of them are explained in the upcoming chapter. Investigated were the 15 days of the Olympic period (16-30 September 2000) and the full 43-day period when the Olympic forecasting was operational. The amount of data available for verification was very low; nevertheless, statistics were made for each location. Official SOSWO forecasts which were made by humans and LAPS 05 predictions had the greatest overall skill for Fort Denison. LAPS 05 presented the smallest magnitude of the wind vector error of 3.2ms -1 and provided with 56% of wind bearing forecasts within 20° of observations. ‘Tree Breeze’ also showed good results. In Wedding Cake, wind speeds are generally higher and therefore the errors over all forecasts and periods are larger, than for Fort Denison. The best predictions were provided by SOSWO, LAPS 05 and ‘SMOSSH’. Best prediction models for the offshore reference station were SOSWO, LAPS 05, ‘SMOSSH’ and ‘Tree Breeze’. The LAPS 05 wind speed bias was still negative with a value of -0.7ms -1 but this was much less than for land results (-2.1ms -1 for Fort Denison and -2.6ms -1 for Wedding Cake for all forecasts). For the Olympic period, quality of forecasts was verified with Brier Skill Score with respect to climatology and persistence. Results for all models were good (most results between 0.2 and 0.7) for wind direction, both inshore and offshore. Generally poor for the period of the Olympics were the predictions of wind speed. Only SMOSSH and ‘Harbclim’ showed slight positive skill for Fort Denison and only SOSWO showed slight positive skill for offshore.

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2.2 Olympic Winter Games Salt Lake City 2002

Another large sporting event, where a professional weather service was provided, was the Olympic Winter Games in Salt Lake City (SLC) two years later. These Olympic Games were carried out from 8 February to 24 February 2002, followed by the Paralympic Games from 7 March to 16 March 2002.

Figure 2.2.1: Locations of Olympic venues (number 1 – 10) and the sports (icons) of the Olympic Winter Games in Salt Lake City 2002. Opening and closing ceremonies (11), medals plaza (12) and the Salt Lake City International Airport (13) are shown as well as the outdoor venues like Snowbasin Ski Area (2), Utah Olympic Park (6), Park City Mountain Resort (7), Deer Valley Resort (8) and Soldier Hollow (9) (Horel, 2002).

The planning for the weather service started in the year 1995. Due to the dimension of this project and the complex weather-related needs of the Olympics, many different suppliers had to work together. The weather service for the Olympic Winter Games in Salt Lake City was composed of private weather firms, the University of Utah and governmental agencies. In the prearrangement time, extensive climatologies were developed, to find out more about existing microclimates, which are often found in mountain environments. The length of records varied from more than 30 years at the valley to a few years for some mountain locations. The mountain range named ‘Wasatch Front’ (Figure 2.2.2) , which has an elevation of about 1300m and covers a North-South distance of 110km to the West of the Wasatch Mountains along the Interstate 15 (I-15), has average minimum

- 11 - Literature Review temperatures of around -4°C in February and -1°C in March. The calculated afternoon maximum temperature varied from 4°C in February to more than 10°C in March. Further, investigations of typical weather conditions were made, like stable episodes coupled with heavy fog in the valleys and the relative occurrence of severe weather phenomena like snowstorms with averaged snowfall of about 24cm. Also an 8% chance of fog development during a particular day in February at the Salt Lake City airport was evaluated. Temperatures at higher elevations like Herber Valley (~1900m) (Figure 2.2.1) tend to be significantly lower in the morning, while afternoon temperature are mostly similar to temperatures of the Wasatch Front (Horel, 2002) . Large differences for snowfall in February were evaluated between the top of men’s downhill course (2826m) with 193cm in the mean and the Soldier Hollow (1670m) (Figure 2.2.1) with 48cm in the mean. Wind exceeds 30ms -1 at the ridge tops and remains below 5ms -1 at the base of the mountains. Severe weather conditions including low temperatures, heavy snowfall and high winds have been observed and recorded at Salt Lake City, the Hill Air Force Base and the Provo Airports over the last 30 years (Horel, 2002) . The NOAA Cooperative Institute for Regional Prediction (CIRP) supported the weather operations by the Salt Lake Olympic Committee Weather Support Group (SLOC) and the various forecast groups. Faculty and students monitored weather conditions in northern Utah and provided venue observations and numerical guidance from the Intermountain Weather Forecast System (Horel, 2002) . All data were transmitted to the SLOC. During February to March a network of 278 weather observations was available for the northern part of Utah. The period of measurement was from 5 to 60 minutes. All these automatic weather stations reported wind, temperature and relative humidity and one station at each sports venue added liquid equivalent precipitation, snow depth and air pressure. Volunteers reported their data to verify the station measurements, for example snowfall and manual observations like cloud cover. This fill of data was used for numerical weather prediction like two years before in Sydney. In the run up time, CIRP provided real time model guidance twice a day. So, the WFO and the KSL forecasters could develop a Model Output Statistic (MOS), which is described in further detail in this thesis.

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Figure 2.2.2: Local weather phenomena, which often occur in the environment of Salt Lake City. Preferred areas for ice fog in the lowest elevations of Salt Lake Valley, which affects the airport (green dot) and the surface travel near the intersection of Interstate I – 215 and the Interstate I – 15 (green dot), are shaded green. Snowbands tending to form downstreams parallel to the direction of the prevailing wind flow (pink arrows) are sometimes caused by the Great Salt Lake. Areas, where wind storms often occur, are shaded in pink and can be found downslope to the West of the Wasatch Mountains including the Olympic Stadium (red dot). Blizzard conditions can be found over mountain passes (double red lines) and higher avalanche risks along the major roadways (double blue lines). Shaded in grey is the terrain with elevation above 2000m (Horel, 2002).

The NWP model used is known under ‘Intermountain Weather Forecast System (IWFS)’ (Horel, 2002) and was provided for the years 1999 to 2001. It is based on the nonhydrostatic Pennsylvania State University – National Center for Atmospheric Research fifth-generation Mesoscale Model (MM5) version 3 and had nested grids with meshes of 36.12 and 4km, respectively. It took about 70 minutes to complete an hourly model output. Forecasts were available twice a day by 03LST (15UTC) and 0445LST (1645UTC) (Horel, 2002) . An MM5 based MOS was tested in the winter season 2000 / 2001 and upgraded in the summer 2001 by using a three-year period of observations and forecasts (Horel, 2002) . This MOS provided hourly forecasts of wind speed and direction, temperature and dewpoint at the outdoor venues and weather sensitive locations in the environment (Horel, 2002) .

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The forecasters provided weather information to the Olympic committee, the venue and sport managers, the staff and the public. The National Weather Service of the United States (NWS) provided and coordinated the weather service for northern Utah by issuing routine weather forecasts, warnings and special Olympic-related forecasts. To protect lives and property, the NWS had to work closely together with the KSL Weather Team (from KSL Television and Radio, SLC, UT) as well as forecasters at the Aviation Security Operation Center (ASOC) at Hill Air Force Base (Horel, 2002) . ASOC forecasters provided forecasts and meteorological briefings for pilots on their way to the venues. To support aviation traffic landing at outdoor venues, a tactical meteorological observing system with portable weather stations near the Snowbasin, Olympic Park, Park City and Soldier Hollow as well as at one location west of the mountain pass between SLC and Park City was installed. It provided meteorological parameters like temperature, moisture, cloud cover, visibility, liquid equivalent precipitation and present weather (Horel, 2002) . The KSL Weather Team was a team of 13 private meteorologists, who predicted detailed microscale forecasts for all different 5 outdoor venues. These forecasts were primary used by the athletes, sports and venue managers, team captains and on-site spectators. Further jobs were briefings to the SLOC and the contact to the Officials. Four members of this KSL Weather Team worked in the Weather Operations Center (WOC), which is normally known as the Salt Lake City Weather Forecast Office (SLC WFO), together with forecasters of the NWS and staff from the National Severe Storms Laboratory (Horel, 2002) . The daily routine of the Olympics in SLC was similar to the Olympic Summer Games 2000 in Sydney. Briefings at each venue were given when it was needed, but normally two or three times a day. The venue forecasters had real time access to the latest weather observations, graphics and model data and updated the weather predictions three times a day at 0600, 1200 and 1800 LT. The first 13 hours of each prediction provided 1-hourly, followed by 3-hourly forecasts out to 60 hours. The forecasts included air and snow temperature, wind direction, speed and chill, wind gusts, visibility, humidity and precipitation (Horel, 2002) . The forecast verification was made for the time between 23 January and 25 March 2002 including the area of the Great Salt Lake, the Stansbury, Oquirrh and Wasatch Mountains. Observations within 15 minutes of and nearest to the verification times were

- 14 - Literature Review used for the evaluation. Nearly 200 stations measured temperatures, wind speeds and directions for the examination. The precipitation forecasts were verified by about 100 stations and additionally with observations reported by the National Climatic Data Center (NCDC) Cooperative Observing Network (COOP) (Hart et al, 2005) . Locations were classified as mountain, mountain valley or Wasatch Front sites depending on their terrain characteristics. ‘Mountain sites’ were high-elevation locations in the ‘Wasatch-‘ and neighbor mountains, ‘mountain valley sites’ were located in valleys east of the ‘Wasatch Crest’, and ‘Wasatch Front sites’ were located in the lowland region in the immediately west to the Wasatch Mountains (Hart et al, 2005) . The verification results which are based on a bilinear interpolation of lowest half- sigma-level (about 40m) gridpoint forecasts to the weather stations, are presented for the 12km and 4km domains for all four initialization times and can be summed up as followed: The differences of mean absolute error (MAE) between the temperature forecasts of the abovementioned bilinear interpolations and all weather stations were small with values of 3.4°C and 3.3°C. On closer inspection, the temperature MAEs for mountain locations show, that the 4km MAE difference was much smaller than that of the 12km domain (3.0°C and 3.8°C). The bias errors also where lower for the 4km domain, than for the domain with 12km. In contrast, at mountain valley and Wasatch Front locations, the bias of the 4km domain were equal to or higher than those of the 12km domain. Performing topographic and sensor height corrections did not improve the forecast. At mountain valley and Wasatch Front sites this resulted in no improvement or a degraded forecast due to the higher frequency of stable boundary layer conditions (Hart et al, 2005) . For all stations, overall wind speed MAEs were 2.3ms -1 for the 12km and 2.1ms -1 for the 4km domains. Wind speed forecasts improved as grid spacing was decreased from 12km to 4km. By site-type, the 4km MM5 model produced lower MAEs and bias errors (BEs) at valley sites (mountain valley and Wasatch Front combined), only little improvements were found for mountain sites. Further investigations revealed that MAEs and BEs depended on the time of day. Due to changing wind conditions between night and day, MAEs are little higher during night- than during daytime (Hart et al, 2005) . The 12km wind direction MAEs were 55° whereas the 4km wind direction MAEs were 50° for all stations. Wind direction forecasts also improved with decreasing

- 15 - Literature Review grid space. They were largest at the mountain sites. In fact, MAEs of 4km grid spacing were 23% smaller than wind direction MAEs of the 12km domain. The best results presented the 4km MM5 for mountain sites with an average MEA of nearly 45° (Hart et al, 2005) .

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2.3 Olympic Winter Games Innsbruck 1964 / 1976

In this chapter it should be recalled that the city of Innsbruck was scene of the Olympic Winter Games for two times. In the year 1964, the IX Olympic Winter Games took place in Innsbruck (Austria) for the first time. Calgary (Canada), Lahti (Finland) and Åre (Sweden) applied for the event, but Innsbruck became accepted just after one ballot. 36 nations from all continents took part in these Games between 29 January and 9 February 1964 (http://www.olympic.org/uk/games/) . In the run-up to these Games, namely as part of the application to the International Olympic Committee (IOC), Dr. Elmar R. Reiter from the Institute of Meteorology and Geophysics in Innsbruck made a climatological expertise coming to the conclusion, that there is no better time during the winter months, to organize such an event than the last week in January and the first week in February. He looked at temperature, especially the vertical structure of the temperature, the snow cover, the relative frequency of precipitation and something typical for a city in the Alps, the Foehn winds (Reiter, 1958) . Evidently, his results were so convincing that the IOC accepted it and gave the Olympic Games to Innsbruck. The second task for the meteorologists was forecasting the weather for the upcoming event. The basic situation was defined by the weather of the two months December 1963 and January 1964. Compared to the mean, this period was not normal at all. There was almost no snow and no change in the extraordinary circulation system to be expected. The winter of 1963/64 was dominated by the strongest blocking in the history of weather observations in Innsbruck. For many weeks the general circulation system was characterized by a ‘low-index’ phase, a so called ‘Omega situation’. In these cases, the blocking is typically situated between the Eastern Atlantic and Middle Europe with cold air aloft from Spain to the Mediterranean Sea and a warm surface high between Iceland and Middle Europe, which causes very strong temperature inversions. In the winter of 1963/64 this configuration was dominant from 28 November until 30 January with one short interruption around 15 December and a short Foehn period around 13 January lasting for about five hours. Considering the regional scale it has to be stated that the air in the Inn valley is quite frequently blocked by inversion during the winter month (Vergeiner et al, 1978) . Consequently, there was quite a pressure on the meteorologist to forecast the end of this period. Precipitation normally does not occur during times of inversions in strong

- 17 - Literature Review blocking situations when the disturbances from the Atlantic pass the Alpine area on a path either far to the North over Scandinavia or on a path far South over the Mediterranean Sea. During the whole January, precipitation of 6mm was measured in Innsbruck together with the precipitation of 17mm during the month of December. This was not a sufficient amount for a reasonable snow cover.

In this year, 1091 athletes competed in 34 competitions in 6 sports, despite this lack of snow cover. Due to this fatal weather conditions, the Austrian army had to quickly dig off 20.000 blocks of ice to prepare the bobsled and luge runs. Another 40.000m³ of snow had to be carried to the alpine skiing courses. Actually the beginning was adumbrating from havoc (http://www.olympic.org/uk/games/) .

Because of existing infrastructure, Innsbruck carried out the XII Olympic Winter Games instead of Denver (USA) twelve years later. Therefore the Institute of Meteorology and Geophysics was consulted again to examine the climatological conditions. Dr. Ekkehard Dreiseitl had to work out a new expertise with current data (Dreiseitl, 1975) and came to the same results as Dr. Elmar Reiter twelve years before. The predictions for that event were made by the Austrian Weather Service.

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3 Forecast Verification

‘The usefulness of forecasts to support decision making clearly depends on their error characteristics, which are elucidated through forecast verification methods.’ (Wilks, 1995) In the daily routine of forecasting weather services, forecasters and numerical computer models predict different atmospherical parameters during their work. Lots of indices and skill scores were developed over the time to identify the best forecasters and models for prediction. One of the oldest and most cited papers about this topic is from Finley, 1884 . In this case study, he made a simple but significant experiment about tornado events. The data set contained records, whether a tornado did or did not occur. One of the first things Finley found out was that it would be better for his ‘statistical prestige’ to negate tornado events due to the rareness of its appearance. He developed different scores in order to encourage other forecasters predicting severe weather events (Jolliffe, 2003) . Forecast verification can be done with statistical methods. These indices and scores try to make different models, tools and even humans comparable to the other. But interpretations of the results should be done very carefully with a good basic knowledge about the mathematical and statistical calculations. In everyday life, however, everyone judges forecasts, which are made by humans or computer models, by his own subjective impressions. If the national weather service predicts ‘almost sunny’ and a little rain shower occurs, people are often devaluing this service due to their preference to sunshine. When an ‘almost rainy’-day was predicted and the sun shines for some minutes, the forecaster’s prestige seems to be much better than in the first scenario. Especially when severe weather conditions occur, forecasters who overstate the impacts have subjectively done better work than forecasters who understate, although the skill score may be the same. The perceived skill of prediction of higher wind speed during a storm seems to be better than the skill which underrates the wind speed, although both may have the same deviation. On this account, outliers from Numerical Weather Prediction (NWP) models will not be totally abraded by such skill scores.

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In this case of prediction for a sailing event, forecast verification is done by verifying the NWP models. The short duration of such events, a regularly changing forecasting team and varying degrees of forecasters’ knowledge about sports lead to a small sample size for serious verifications. Even the comparison of computer models is difficult, because of the mostly limited and different areas, for which the predictands are made. Such territories may differ sometimes from the calculated grid points of NWP models, due to the orography, for example. Another difficult point is the accuracy and the representatively of measurements, like temperature, rain rate, wind speed and direction. Where are these instruments positioned and how accurately they measure? In this chapter, some of common skill scores and indices are described, which are used for verification of the forecasts made during the World Sailing Games 2006 on the Lake Neusiedl.

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3.1 Bias

A common index in economics and science is the bias (b) (unconditional or systemic bias). It is a measurement for the correspondence between a forecasted and an observed value. Finley, 1884 , for example, used it to analyze forecasted tornado events. The bias is a systematical deviation of forecast from the observed values. Applied to a temperature forecast the result shows if a model goes systematically too warm or too cold. On a precipitation forecast the result reveals if the model goes too wet or too dry. As the formula (Equation 3.1) shows, warmer and colder or too wet and too dry forecasts can compensate themselves. If positive and negative values (deviations from the observed values) cancel, the investigated forecast has no systematical error. Due to this, this score is very sensitive to outliers when there are no values in the opposite direction erase the others. Therefore large results in bias can be caused by only a few outliers in one direction, especially with small sample sizes. Numerical Weather Prediction models sometimes exhibit such systematical errors (they tend to be warmer than the measured values are, for example), which will be corrected with a calculated bias and other scores. Human forecasters do not produce any systematical biases, because of non systematical errors (Wilks, 1995; Jolliffe, 2003; Schoenwiese, 2000) . Another name for bias, especially with continuous variables, is mean error (ME). The formula for a bias for continuous variables (which is the same as the ME) can be written as:

i = 1 − b ∑ ( fi oi ) (3.1) n n=1

The variable f indicates forecasted values and o observed values. If the result (units of the variable persist) is positive, the forecasts, for example, were too warm and vice versa. If the result of the equation is zero, this may have two reasons. On the one side it can be a perfect forecast, on the other deviations can cancel out themselves. To find out more about the result, other indices or skill scores can be investigated.

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3.2 Mean Absolute Error

The difference between the bias and the mean absolute error is the usage of the absolute value function, which averts the mutual erasing of positive and negative forecast and observation pairs and causes only positive deviations. The results are the arithmetic average for absolute values and are therefore a comparable size for continuous predictands. The mathematical formula for the MAE can be written as:

n = 1 − MAE ∑| fi oi | (3.2) n i=1

If the result of MAE (Equation 3.2) is zero, the forecast is perfect and has therefore neither a systematic nor a nonsystematic error. In this score, as well as in the bias, units of the variables persist. The more numbers of pairs from forecasts and observations exist, the more the line of deviation is approaching the bias line (Wilks, 1995) . During the Olympic Summer Games in Sydney in the year 2000, this score was used for decision making in changes of courses. The International Sailing Federation manual for race officials mentions: ‘… start thinking about a change of course when the wind shift turns out to be of 20 degrees or more.’ (ISAF, 1997) This score was named the ‘wind bearing error’. The formula is the same as the MAE (Spark and Connor 2003) .

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3.3 Mean Square Error

Another measurement for accuracy of a scalar of continuous predictands is the mean square error (MSE). This score is similar to the MAE with the exception of the used squaring function instead of the absolute value function. The dimension of the result is the squared values of the primary variables. The formula for mean square error is defined as:

n = 1 − 2 MSE ∑ ( fi oi ) (3.3) n i=1

If the result of Equation (3.3) assumes the value zero, a perfect forecast was made. The deviation from the line of zero of MSE is normally larger than the deviation from MAE due to the square function. Therefore the MSE is more sensitive to outliers than the MAE but small deviations can be neglected. Two deviations with each 1°C produce the same MAE score, as one deviation with 2°C. In the MSE score, the deviation of 2°C is more weighted than two errors with only 1°C. Bias and variance are small when the MSE is small (Wilks, 1995; Jolliffe 2003) .

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3.4 Root Mean Square Error

A variant of the MSE is the root mean square error (RMSE), where the root function is added. This little change returns the physical dimensions of the used values and can be written as:

n = 1 − 2 RMSE ∑ ( fi oi ) (3.4) n i=1

The root mean square error is a typical magnitude for forecast errors. If the result is zero, a perfect prediction without any bias was done. Due to the square and the square- root function, the value range contains only positive values and spans from zero to infinity. Outliers affect the RMSE not as strongly as the MSE but more than the MAE (Wilks, 1995) .

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3.5 Correlation Coefficient

A single-valued measurement of associations between two variables x and y is often needed. In that case, many data analysts calculate a correlation coefficient. Correlation is a dimensionless objective score. This advantage makes variables with different units comparable. Its square is equal to the mean square error skill score (described in the text below) for forecasts without any bias. It should be noted, that two uncorrelated variables are not necessary independent. They can be linearly and nonlinearly related to the other. The most commonly used correlation coefficient is the ‘Pearson product-moment coefficient of linear correlation’:

1 n [( o − o)( f − f )] − ∑ i i = (n )1 i=1 rof 1 n 1 n (3.5) (o − o)2 ( f − f )2 − ∑ i − ∑ i (n )1 i=1 (n )1 i=1

In this formula (Equation 3.5), the numerator is the covariance between the forecasted and the observed values (Wilks, 1995) . To make this formula computable, it can also be written as:

n n n − 1 ∑oi fi ∑oi ∑ fi =n = = r = i 1i 1i 1 of n n n n (3.6) 2 − 1 2 2 − 1 2 [∑oi ( ∑ oi ) ] [∑fi ( ∑ fi ) ] i=1n i = 1 i=1n i = 1

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Two characteristics of the Pearson’s ordinary correlation coefficient should be pointed out. It is not robust. That means that strong nonlinear relationships between two variables may not be recognized. Nor it is resistant, because it is extremely sensitive to outliers. The result is in the domain [-1; 1]. If the equation results -1, a perfect negative linear association exists. A scatter plot with this result contains points on a line with a negative slope. If rof is zero, there is no linear association between the two variables. The correlation coefficient is symmetrical: It is equal, whether f is the dependent variable and o the independent or vice versa (Wilks, 1995) .

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3.6 Coefficient of Determination

In this thesis, the square of Pearson’s ordinary correlation coefficient, which is called the coefficient of determination, is used. It describes the proportion of the variability of one of the two variables, which shows a linear association.

2 R = r of (3.7)

The result of the Equation 3.7 is dimensionless and expressible in percent. The correlation coefficient is no statement about the causal relationship between the variables in any physical sense. It may be that one variable depends in a physical way on the other, but in the most cases, they depend on other processes and quantities (Schoenwiese, 2000; Wilks, 1995) .

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3.7 Rank Correlation Coefficient

Rank correlation coefficients are enhancements of correlation coefficients. By and by different types of rank correlations were founded by different persons like Pearson, Spearman or Kendall. In this thesis, the Spearman rank correlations coefficient is used. This is a robust and resistant alternative to Pearson’s correlation coefficient. The Spearman’s rank correlation is in principal the same as the Pearson’s correlation coefficient (Equation 3.5) but using the ranks of the data.

x y x y x y 2 5 1 3 1 3 8 7 4 4 2 5 9 2 5 1 3 2 3 9 2 5 4 4 5 4 3 2 5 1 Table 3.7.1: This data set contains 5 pairs of values. In the left table neither values of the variable x nor values of the variable y are sorted. In the middle table each values is reassigned to the rank compared with the others. In the right table all pairs of values are arranged.

Both variables will be reassigned separately to its rank compared to the other (Table 3.7.1) . Pairwise all values will be arranged in ascending order. For example: x = 8 is the fourth largest value and gets the order 4. Its corresponding y-value is 7, which is also the fourth largest value. The table in the middle (Table 3.7.1) shows the result, when it is done for all pairs. The table to the right shows the ascending order of the new values. This has to be done before computing the correlation. Since of the data set consists of integers from 1 to the sample size n, the Equation (3.6) can be simplified to:

n 2 6∑ Di i=1 R = 1− (3.8) rank n(n 2 − )1

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In this formula, D is the difference in ranks between the ith pair of the data set. If this difference appears more than one time, all these ‘ties’ are assigned their rank before computing the Di values. The results of Spearman are better than the results of Pearson because the Spearman’s Rank correlation expresses the strength of linear relationship, whereas the Pearson’s correlation coefficient reflects the strength of monotonic relationship (Wilks, 1995) .

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3.8 Mean Square Error Skill Score

The skill of forecasters is measure relative to a set of standard control forecasts like climatology or persistence. These skills are normally presented as a percentage improvement over the reference forecasts. In literature, these results are called skill scores (SS) (Wilks, 1995) . In the course of time, different skill scores were developed. The mean square error skill score (MSESS), also called the Brier skill score, was established by Murphy and Epstein, 1989 .

= − MSE MSESS 1 (3.9) MSE c lim

As it is shown in the Equation (3.9), the computed results have no dimension. If the mean bias of the forecasts is zero, the MSESS is the same as the square of the Pearson’s correlation coefficient – the coefficient of determination R. If the result of the Equation (3.9) reaches the maximum of one, a perfect forecast was made. If the result is zero, the forecast is as good as the reference forecast. Getting a negative result, the compared forecast (in this case the climatological) would be better but this does not necessary mean that the model has no skill at all (Jolliffe, 2003) . In this thesis, the forecasted values of each numerical weather prediction model are compared with values of persistence and climatology.

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3.9 Synopsis

In the following Table (3.9.1) , the different indices and skill scores are summarized. The results for the World Sailing Games 2006 are presented and discussed in the chapter 4 – ‘World Sailing Games 2006’.

Name: Formula: Co-domain: Units:

i = 1 − bias (b) b ∑ ( fi oi ) [-∞;∞] of variables n n=1

n mean absolute = 1 − MAE ∑| fi oi | [0; ∞] of variables error (MAE) n i=1

n mean square = 1 − 2 squares of MSE ∑ ( fi oi ) [0; ∞] error (MSE) n i=1 variables root mean 1 n square error = − 2 RMSE ∑ ( fi oi ) [0; ∞] of variables n = (RMSE) i 1 Pearson’s = cov( o, f ) correlation rof [-1;1] dimensionless var( o) var( f ) coefficient coefficient of = 2 R rof [0;1] Percent / 100 determination

n 2 rank 6∑ Di = − i=1 [-1;1] dimensionless correlation Rrank 1 n(n 2 − )1

mean square error skill = − MSE MSESS 1 [-1;1] Percent / 100 score MSE c lim (MSESS) Tab. 3.9.1: The most commonly used indices and skill scores are summed up. For each score, the name, which can be found in literature, the mathematical formula, the co-domain and the units of the result are stated.

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4 World Sailing Games 2006

4.1 General Information

Established in Paris in October 1907, the International Sailing Federation (ISAF) founded several sailing events to promote the development of the sport. One of them are the ‘World Sailing Games’ (WSG). The basic idea is to crown world champions in different sailing boat classes using the same materials for sailing provided by the organization committee. The athletes represent their home country, while the organization committee allocates the materials and sailing equipment. The first ISAF World Sailing Games took place in La Rochelle (France) in 1994. Like the Olympic Games, this event is carried out every four years. In 1998 the competitions took place in Dubai followed by Marseille (France) in the year 2002 (http://www.wikipedia.at) and finally in Neusiedl (Austria) in the year 2006. Between 10 and 20 May 2006, the World Sailing Games took place on the lake Neusiedl (Austria). More than 700 sailors from well over 60 nations from all continents took part in this world championship in sailing. A large Organization Committee and hundreds of volunteers from all over Europe kept the event running. The sailing classes were: Men's and Women's Windsurfing in Neil Pryde RS:X, Men's One Person Dinghy in Laser, Women's One Person Dinghy in Laser Radial, Men's and Women's Two Person Dinghy in 470, Skiff in 49er, Men’s Multihull in Hobie Tiger WSG Edition, Women’s Multihull in Hobie 16 with spinnaker and the team sailing in the 420 class.

The meteorological service was provided by a forecasting team of the Austrian national weather service, the ‘Central Institute for Meteorology and Geodynamics (ZAMG)’. Additional consulting was provided by the ‘University of Natural Resources and Applied Life Sciences Vienna’ and the ‘University of Veterinary Medicine Vienna’.

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4.2 Climatology

Before prearranging an event, developing a climatology of the available venues can help choosing the best location in the face of relevant meteorological parameters for any sports. In most of cases, the time in year with a statistical minimum of severe weather events is chosen. Climatologies shed also light on frequent weather conditions, which are typical for a region or a limited area. The importance of meteorological parameters depends on the kind of event and its sensitivity to these factors. Normally temperature, wind conditions, rain probability, probable tracks of thunderstorms and the relative frequency of severe weather events are precise examined in climatologies. Many meteorologists apply climatologies of relevant predictands, to compensate for missing local experience. Precipitation rate, temperature and pressure differ from Direct Model Outputs (DMO) of NWP models. These variables exhibit often a bias, which is typical for small scale areas. The sum of all investigations leads to a better comprehension of the environment and the predominating mesoscale weather conditions and therefore to better predictions. Similar corrections are done by Model Output Statistics (MOS). Typically, MOS have two operating modes. The first is the dependency of special weather phenomenas like fog, thunderstorms and sky cover on different predictands, the second is the statistically correction like the bias of NWP predictors. With NWP models only, it is often very difficult to predict exact wind forecasts due to the used model topographies. MOS predictions on the other hand provide more precise forecasts because these forecasts are computed and developed on the basis of data from local weather stations. If an event takes place, a network composed of several automatic weather stations (AWS), has to be installed at any venues some years before in order to develop MOS equations for the predictions. In the chapters ‘wind speed’ and ‘wind direction’, investigations are made for wind conditions and the dependency on seasonal and daily changes. Further, the proof of an existing land – sea breeze circulation was made as well as an investigation, whether the surface wind conditions are directly connected with oncoming jet flows. Similar investigations were made by Lotteraner (2001) , who came to the same results.

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In the case of the World Sailing Games, the data set was taken from semi-automatic weather station of the ZAMG in the town of Neusiedl at the Northern end of the lake. This weather station has the international code 11194 and is positioned at 47°56’34.80’ North (47.943°) latitude and 16°51’28.80’ East (16.858°) longitude at 135 meters above mean sea level. Data are available back to the year 1993 at a resolution of 10 minutes. In the year 2004, the garden where the station was mounted was redone and consequently, the weather station had to be relocated to another place in the city of Neusiedl. Since 2 February 2004, the station has been positioned at the new coordinates 47°57'3.60’ North (47.951°) latitude and 16°50'34.80’ East (16.843°) longitude (Picture 4.2.1; Map 4.2.1) at 154 meters above mean sea level.

measued parameter: Algorithm: Cycle: Resolution: 2m-temperature mean 10 minutes 10 -1 K 2m-maximum maximum 10 minutes 10 -1 K temperature 2m-minimum minimum 10 minutes 10 -1 K temperature wind direction mean 10 minutes 1 degree wind speed mean 10 minutes 10 -1 ms -1 gust strength maximum (mean) 10 minutes (3 seconds) 10 -1 ms -1 Table 4.2.1: Measured values of the ‘AWS Town’ in Neusiedl, their algorithm, cycle and resolution.

In Table 4.2.1 all measured values of the AWS Town are listed. These parameters were the basis for climatologies and MOS development and where presented in real-time for organization committee, athletes and visitors. The gust strength is the mean of three seconds, and the 10 minute-maximum of those ‘blocks’.

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Picture 4.2.1: The new location of the AWS Town 11194 in the city Neusiedl. The wind and radiation measure instruments are installed on a roof (Photo: Archive of the ZAMG).

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Map 4.2.1: The two colored points mark the old and the new position of the ‘semi-automatic weather station (AWS Town)’ in Neusiedl. The red point marks the old position. On 2 February 2004, the station moved to a new location, which is marked with the blue point. The horizontal distance between the two locations is about 1.43km (Map: © BEV).

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4.2.1 Temperature

The first investigation in climatology, which was developed for the World Sailing Games, dealt with temperature. Figure 4.2.1.1 shows the comparison between the different kinds of maxima. Compared to the maximum temperatures of the long time period (1993-2005), May 2006 was warmer except for the last three days. Compared to the absolute maximum temperatures of May, no new records were established in this year. On 22 May, the absolute maximum temperature of 25.4°C coincided with the previous maximum reached in 2005, which was measured exactly one year before. The increasing trend lines of the maximum temperatures of the long time period show the increasing temperatures from the beginning to the end of May. In the year 2006, a synoptical interruption caused decreasing temperatures. Figure 4.2.1.2 presents the equal comparison for the minimum temperatures. The 1 st May 2006 was 3.5°C colder than the 1 st May of the past 13 years. All other days were warmer compared to the absolute minima and colder than the average minimum temperature, except 23 May.

35

30

25

20 Temperature / °C

15

10 1 6 11 16 21 26 31 May

Figure 4.2.1.1: The absolute values of daily maximum temperature of the period from 1993 to 2005 (red solid line), the measured absolute maximum temperature of the year 2006 (green solid line) and the mean of the maximum temperatures in the long time period (1993 through 2005) (blue solid line).

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20

18

16

14

12

10

8 Temperature / °C 6

4

2

0 1 6 11 16 21 26 31 May

Figure 4.2.1.2: The mean of the minimum temperatures in the period from 1993 to 2005 (blue solid line), the absolute minimum temperature in the month of the WSG (green solid line) and the absolute minimum temperatures in the long term period (1993 to 2005) (red solid line).

10

8

6

4

2

0

−2 Aberration / °C

−4

−6

−8

−10 1 6 11 16 21 26 31 May

Figure 4.2.1.3: Difference between the 2m-air-temperature of May 2006 in the period from 1993-2005.

The difference between the average of the daily mean temperature of the long term period (1993 – 2005) and the daily mean of the measured values of May 2006 is

- 38 - World Sailing Games 2006 depicted in Figure 4.2.1.3. The first few days in May 2006 were definitely colder than the mean of the last 13 years. During the World Sailing Games (10 May – 20 May 2006) the temperatures were oscillating around the long term values, but to the end of May the temperatures fell again in comparison with the long term mean. In Figure 4.2.1.4 the temperatures for the city of Neusiedl are pictured as box plots. As it is shown, in January, the median of the daily mean temperature is about 0°C and therefore the coldest month in the mean. Temperatures are rising until August, which is the warmest month in the North of the lake. The month May is the first month without any outliers under the 0°C–line and therefore statistically the first suitable month for sailing. This is important because of the limited depth of the lake. If the temperature is little lower than 0°C over several consecutive days, the lake may freeze up. The temperature spread in May ranged from 2.2°C to 33.6°C over the last 13 years and the month mean temperature was about 16.4°C.

40

30

20

10 Temperature / °C 0

−10

−20 1 2 3 4 5 6 7 8 9 10 11 12 Month

Figure 4.2.1.4: Distribution of daily mean temperature of the city of Neusiedl. The blue boxes cover the interquartile ranges (IQR), which contains 50% of the data. The red lines in those boxes are the medians, the blue vertical dashed lines are called whiskers, extending to 1.5 times of the length of the IQR. Data further outside are individually marked with red crosses (Jolliffe, 2003).

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4.2.2 Precipitation

A climatology of precipitation is not required for the sailing races themselves but rather for planning of the logistics. If the probability of precipitation is low, more open air areas for visitors, catering areas and side events like parties can be provided outside. The area around the lake Neusiedl is usually drier than regions north or south of the Alps. The comparison between the averaged monthly means of accumulated rain for the month May in Austria’s state capitals of 86,2mm 1 shows, that it is rather low due to the averaged monthly mean of accumulated rain for the city of Neusiedl with 61.4mm in the period between 1971 and 2000. Figure 4.2.2.1 shows the accumulated precipitation for Neusiedl from 1993 to 2005 as a boxplot. The two outliers with 207mm mark the months July in the year 1997 and October in the year 1996, where the sum of month were about 3 times higher (July: 2.8 times; October: 3.7 times), than the average in the other 12 years. Figure 4.2.2.2 shows bar plots for the averaged daily mean of accumulated precipitation (from 00:10 to 24:00UTC) in May. A very slight trend line is visible, which rises to the end of May and is caused by the rising precipitation in year until July. Together with a climatology for temperature, the month May proves to be a good month for sailing because of less precipitation compared with the other months of the year with minimum temperatures higher than 0°C.

1 Mean of the averaged accumulated rain for the month May 1971-2000 of all state capital (Hörsching (Linz) 69.1mm, St. Pölten 73.5mm, Hohe Warte (Wien) 61.8mm, Eisenstadt 62.6mm, Graz (airport) 82.0mm, Klagenfurt 78.5mm, Salzburg 114.5mm, Innbruck (airport) 87.1mm, Bregenz 46.7mm) (http://www.zamg.ac.at/fix/klima/oe71-00/)

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200

180

160

140

120

100 RR / mm 80

60

40

20

0 1 2 3 4 5 6 7 8 9 10 11 12 Month

Figure 4.2.2.1: Average monthly accumulated participation of Neusiedl for the long time period between 1993 and 2005.

4.5

4

3.5

3

2.5

RR / mm 2

1.5

1

0.5

0 1 6 11 16 21 26 31 May

Figure 4.2.2.2: Average 24-hour accumulated precipitation from 00:10 to 24:00UTC at Neusiedl for 1993-2005.

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4.2.3 Wind Speed

For sailing events, wind is the most important variables. Therefore, a couple of investigations were made to analyze the predominant wind regimes like land–sea breeze circulations and the wind speeds. Investigations of historical wind data sets mostly point out existing circulations on a certain time (of day or in year) which may help managers of wind depending sport to fix their time schedules to optimal dates for their needs. In the case of the World Sailing Games, the climatological analysis for wind speed (Figure 4.2.3.1) shows that the maximum of relative probability is between 2 and 3ms -1 for mean values during the day. During the night, the average speeds are low. With dawn, the speed increases till afternoon. At 18UTC (20LT) wind speed decreases rapidly. With this knowledge races of sailing classes with lower wind requirements can be started prior to sailing boats with a higher demand of wind speed. The different boat types have different wind speed ranges to sail. These ranges depend on the canvas and the hull of the sailing boat. To minimize the risk of canceling the sailing challenge, the different boat types with their different requirements on wind speed can be scheduled for climatologically best time of day.

12

9 −1 5 5 10 6 5 10 15 5 10 15 20 10 2025 25 15 30 30 15 20 35 20 Wind speed / ms 40 4035 25 45 50 45 25 3 30 35 50 30 40 45 45 50 50 55 55 60 40 60 0 40 35 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time of day / UTC Figure 4.2.3.1: Relative frequency of wind speeds during the day measured on the AWS Town in the years 1993 – 2005.

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4.2.4 Wind Direction

For sailing competitions, the expected wind directions are of huge importance. With an exact knowledge about changing conditions during a day, sailors can work out clever tactics to beat their opponents. Therefore, the first investigation of wind direction for the city of Neusiedl was made for the diurnal distribution of the wind directions. To compensate for the usual errors in measurements and to simplify the computing, the eight main wind directions were used. Figure 4.2.4.1 shows the relative frequency of the occurred wind direction of one group in the month May over 13 years. The predominant main wind direction in the city of Neusiedl, which was chosen to be representative for the North of the lake, is from North West due to the combination of topographical influences and the predominating west wind above Europe. During the day, this main direction is diminished by a slight land-sea breeze circulation from South East to South. Lotteraner (2001) arrived at the conclusion the same in his work about dominant wind regimes on the lake Neusiedl using different methods. When the sun rises and gets strong enough to warm the surface at about 07UTC (09LT), the relative frequency for wind from South East to South is rising. This land-sea breeze circulation mostly continues until the land gets colder than the surface of the lake. This happens usually in the evening, some hours after sunset. Next investigations pay their attention to the question, whether wind directions depend on occurring wind speeds. The data set was classified in three categories of wind speed which were discretionary chosen for weak, moderate and strong winds, and 12 classes of wind direction. The first category of wind speed stands for weak to average wind speeds with 1 to 7ms -1. The second category contain wind direction data for average to strong wind speeds with 7 to 13ms -1 and the last of the three categories were created only for strong winds with speeds more than 13ms -1 (Figure 4.2.4.4) .

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20 15 25 2025 20 30 35 30 3025 NW 40 30 40 35 3025 35 35 30 25 20 3025 2520 15 10 2015 15 W 5 10 10 5 5

SW 10 15 10 20 S 5 5 10 10 15 15 15 SE 20 20 15 15 10 20 15 10 E 10 10 Winddirection / degree 5 10

5 5 NE 20 15 10

15 N 15 10 5 20 15 10 15 2520 20 251 2 3 4 5 6 7 8 9 103011 12 13 14 15 162517 18 19 20 21 22 23 24 Time of day / UTC Figure 4.2.4.1: Colors mark the relative frequency of occurring wind directions during a day in May measured on the AWS Town in the period between 1993 and 2005.

For low to average wind speeds, the main wind direction is northwest, as it is illustrated in Figure 4.2.4.2 . The comparison between the plot of wind speed (Figure 4.2.3.1) and the plot of wind direction (Figure 4.2.4.1), shows that the existing land-sea breeze circulation is coming from South East to South especially with wind speeds between 1 and 7ms -1. Other wind directions in this category are below the 5 percent level and are therefore not representative for the Northern part of the lake. For wind speeds over 7ms -1 pictured in Figure 4.2.4.3 , the wind direction is from North West around 75 percent. A small chance with nearly 15 percent exists for wind from South East to South. It can be assumed, that this category is hardly influenced by the existing land – sea breeze circulation, but more from synoptical flows and disturbances.

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0 330 30

300 60

25% 15% 20% 5% 10% 270 90

240 120

210 150 180

Figure 4.2.4.2: Category I: 1 – 7ms -1. Relative frequency of the mean wind direction of 10 minutes in May measured at the AWS Town in the period between 1993 and 2005.

0

330 30

300 60

60% 40% 20% 270 90

240 120

210 150

180

Figure 4.2.4.3: Category II: 7 – 13ms -1. Relative frequency of the mean wind direction of 10 minutes in May measured at the AWS Town in the period between 1993 and 2005.

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4.2.5 Weather Type Classification

To find out, if different weather conditions have impacts on wind direction, the whole climatology data set has to be filtered by different synoptical weather conditions. A useful help for this task is the ‘Ostalpine Stroemungslagenklassifikation’ (Steinacker, 1991) . This classification for synoptical weather types can be done for places located in or in the surrounding of the Alps. Details of this classification are given in the APPENDIX: Definition of Steinacker’s Stroemungslagenklassifikation . In this classification, synoptical weather situations are classified in ten groups for eight wind directions, a weak-wind class and a variable class for days when wind direction changes, for example after the passage of a frontal system. Synoptical wind from North or North East ( Figure 4.2.5.3 ) causes more North Westerly wind flows in Neusiedl. There, the predominant wind direction from North West is clearly marked out but with a tiny but identifiable rising of relative frequency from North East. Figure 4.2.5.1 shows group 7, which stands for a synoptical wind from North West. This picture presents something unexpected. If the synoptical flow is approaching from North West, it causes a form of lee effect, by which the frequencies for wind directions from West over South to South East are rising. Compared with the wind speeds for these synoptical conditions (Figure 4.2.5.2) , the result shows a maximum of relative probability for average wind speeds during the day with 5 to 6ms -1.

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50

55

40 45

20 50 25 25 3025 70 30 30 55

35 35 65 70 35 4540 4540 60 5550 5550 NW 65 60 65 65 60 60 55 55 60 55 60 65 4550 4550 50 40 30 35 40 4045 35 40 25 20 2530 35 30 W 15 20 25 20 15 10 10 15 5 10 SW 5 5 5

5 S 5 5 5 5 SE 5

wind direction / degree E

30 NE 5570 50 10 70 6070 5 1545 5 20 25 65 5 35 20 10 N 40 10 5 35 40 15 10 15

15 55 60 20 20 25 25 65 30 35 25 30 35 30 1 2 3 4 455 6 7 8 94010 11 12 134514 15 16 174018 19 20 214522 5023 24 daytime / UTC

Figure 4.2.5.1: Relative frequency of wind direction during a day for synoptic flow from North West measured at the AWS Town. The red area in each corner is caused by lack of data. The classification is made with the data set for the years 1993 – 2005, caused by less data, which remains by filtering only May values out.

8−10

5 7−8 10 5 5 5 6−7 15 −1 10 10 10 5−6 20 15

15 20 4−5 15 15 20 15 wind speed / ms

15 15

15 20 3−4 20 20 20 20

2−3 10 10

15

20 20 1−2 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 daytime / UTC

Figure 4.2.5.2: Relative frequency of wind speed during a day for synoptic flow from North West measured at the AWS Town. The classification is made with the data set for the years 1993 – 2005, caused by less data, which remains by filtering only May values out.

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55 60

50 65 3070 35 3035 3035 45 40 555045 40 555045 40 656070 656070 NW 70 556065 55606570 55606570 40 4550 40 4550 40 354550 20253530 20253530 202530 W5 1510 5 1510 5 1510 5

SW

S

SE

wind direction / degree E

5 NE 15 5 4020 1035 30 50 7065 5 45 5 10 6025 N 55 10 201015 2015 201555 30 25 30 2535 30 2535 45 3540 50 45 40 50 45 40 1 2503 604 5 6 7 8 559 10 11 12606513 14 15 16 175518 19 206021 22 23 24 daytime / UTC

Figure 4.2.5.3: As Figure 4.2.5.1 but for s synoptic flow from North East.

An approaching flow from East causes rising probability from East, of course. An approaching flow from South ( Figure 4.2.5.4 ) causes a main wind direction and the highest probability from South East. How an approaching flow from East causes high probability from East of course, but a marked high probability from North East and East in the second half on night is remarkable. This can also be seen in Figure 4.2.4.1 in the period from midnight to 06UTC. A reason for this phenomenon is the land-sea breeze circulation, because of convection over the warm lake during the night hours. Therefore weighty cold air is necessary to compensate the lack of air mass. This air is pulled up from the environment. If the flow streams from West (Figure 4.2.5.5) , the relative probability is distribute equably more or less.

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15 4510 25 303565 10 2060 4035 55 554050 45 2570 6065 5 7050 NW 201530 5 5 W

SW 5 10 5 5 15 S 1015 5 10 20 10 25 15 20 25 30 25 3020 25 30 35 45 35 35 40 45 50 40 45 SE 40 50 60 60 55 60 5550 40 45 55 5055 55 40 4045 5045 35 35 3035 4035 wind direction / degree 30 2025 20 20 25 30 E 30 25 15 20 15 10 15 20 10 5 NE 4065 5 3045 35705525206050 15 35705525206050 653045 1060 40 20 1535 30 10 10 55 15 N 5 65 25 45 407050 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 daytime / UTC

Figure 4.2.5.4: As Figure 4.2.5.1 but for s synoptic flow from South.

5540 25 3035 50 455060 60 65 15 10 657025 70 20 15 15 5540 4535 30 20 20 NW25 25 25 20 20 20 15 15 15 W 10 20 10 10 15 5 SW 5 10 5 10 5 15 20 S 10 15 10 15 15 20 25 20 20 SE 25 30 20 25 20 15 15 20 15 wind direction / degree 10 E 10 5

10

5 25 705040 NE 65106055 153545

10 3545 25 30656055 30 504070 30 45 5055 60 6570 N 403520 20 5 15 10 10 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 151516 17 18 19 20 21 22 23 24 daytime / UTC

Figure 4.2.5.5: As Figure 4.2.5.1 but for s synoptic flow from West.

Also the wind speed has its dependence on the approaching flow. The highest speeds are reached by a stream from North (Figure 4.2.5.6) . In this case, wind speeds from 5 to 6ms -1 have its maximum of relative frequency by 25 to 30 percent. Even wind speeds between 8 and 10ms -1 have relative frequencies up to 10 percent. From this direction,

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8−10 10

5 7−8 5 5 15

15 10 10 6−7 10

15 20 10

−1 15 25 5−6 10 30 20 25 15 20

20 20 15 15 15 4−5 20 20 15 wind speed / ms 20 20 20 10 15 10 3−4 15 20 15 5 15 2−3

15 10 5 5 1−2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 daytime / UTC

Figure 4.2.5.6: As figure 4.2.5.2 but for a synoptic flow from North.

8−10

7−8

5 5 6−7 5 10 10 5 −1 5−6 15 10 10 15 20 15 4−5 20

15 wind speed / ms 20 15 25 3−4 25 25 20 20

20 20 20 20 20 20 2−3 25 15 15

20 25 25 1−2 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 daytime / UTC

Figure 4.2.5.7: As figure 4.2.5.2 but for a synoptic flow from East.

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The last two classes in the classification of Steinacker were the class of variable wind conditions, which explain the wind conditions after the passage of a frontal system for example, where the wind field (direction and speed) changes completely during the day and the class of gradient weak weather condition, where no significant synoptical flow exists. Due to changing conditions, the class of variable does not suit for investigations of constant wind systems. The class of gradient weak conditions (Figure 4.2.5.8) confirms the results of the investigation of the wind direction, which was described above. Apart from the predominating wind direction of North West, a day curve is noticeable. During the night, wind comes from North to North East. After sunset, wind direction turns into South East to South and in the evening these changes turn around again. During noon, wind from North East to East is very rare, with a relative frequency under 5 percent. Also wind from South to West does not occur from dusk till dawn. Caused by low gradient, the wind speed (Figure 4.2.5.9) is slow during the time of these conditions. At noon, there is a chance of 5 percent, to have wind speeds about 5 to 6ms -1 for sailing.

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20 30 2530 20 35 40 25 NW20 35 45 35 20 30 40 35 30 25 30 25 20 10 15 20 15 15 W5 5 10 5 10 10 5 SW

S 10 15 5 10 15 20 SE 25 20 10 10 20 wind direction / degree E 10 10 5 15

15 15 NE 5 10 20 20 N 15 10 25 20 15 30 30 25 1 252 3 4 5 6 7 8 9 10 3511 12 13 14 15 16 17 18 19 20 212022 23 24 daytime / UTC

Figure4.2.5.8: Relative frequency of wind direction weak-wind conditions measured at the AWS Town. The classification is made with the data set for the years 1993 – 2005, caused by less data, which remains by filtering only May values out.

Figure 4.2.5.9: As figure 4.2.5.2 but for weak-wind conditions.

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4.2.6 Wind Gusts

The most difficult variable for sports and forecasting are the gusts. They appear mostly on inland waters. As opposed to this, offshore conditions exhibit continual wind fields. But on inland waters gradients and mesoscale turbulences caused by the environment and orography causing gusts. Compared to the mean of 10 minutes (Figure 4.2.3.1) , the relative frequency of gusts (Figure 4.2.6.1) rises with the break of dawn at 02UTC (04LT), reaches its maximum at 14UTC (16LT) and falls down rapidly during sunset at 18UTC (20LT). The relative frequency of the maximum is about 5 percent for 13ms -1. More interesting than the mean of gusts are the absolute maxima. Figure 4.2.6.2 shows the absolute maxima for each day (red crosses). Absolute maximum of gust speed for May is 21.5ms -1, which was reached on 26 May 1994 at 2030UTC. The green solid line in this plot shows the mean of maximum gusts for this month. In this figure, no trend line is visible. The strength stays around 17ms -1. Even the 10 minutes mean wind speed (blue dashed line) has no trend line and oscillate around 3ms -1.

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21

18

15

−1 5 12 5 10 10 5 15 5 9 20 15 10 wind speed / ms 25 10 15 20 20 25 35 6 30 30 15 25 3025 30 35 35 3 35 40 253035 40 35 30 2015 20 35 0 25 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 daytime / UTC

Figure 4.2.6.1: As figure 4.2.5.2 but for gusts.

25

20 −1 15

10 Speed of Gusts/ ms

5

0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 May

Figure 4.2.6.2: Absolute maxima of gusts (red crosses), mean speed of gusts (green solid line) and the mean of 10 minutes of wind speed (blue dashed line) for the month May measured at the AWS Town for the period between 1993 – 2005.

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4.3 The Forecasting Period, Models and Tools Used

4.3.1 Daily Routine of a Forecaster

After many preliminary discussions, the forecasting team of the ZAMG started its forecasts on Monday, the 8 May 2006. Before making the predictions, it was decisive that the forecasting team and the Organization Committee had come to an agreement about the general framework of the forecasts. Job of the forecasting team was to provide most accurate predictions for air temperature, precipitation, wind speed and directions. Two briefings a day were held to the officials, the spectators and their coaches in the morning at 08:00 MESZ, before the competitions started.

The ‘Headquarter’ of the ZAMG forecasting team was a large tent (Picture 4.3.1.1) on the bank of lake Neusiedl. Here, the predictions and information were prepared and forwarded to the officials and sailors. The tent was packed with the necessary equipment for the daily forecasts and presentations. Computers, laptops, flat screen TVs and other technical devices were mounted in this tent, which was open to the organization committee, sailors, coaches and even to the public.

Picture 4.3.1.1: The ‘weather tent’ with installed equipment (Photo: Mag. Harald Seidl).

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Work started for the two meteorologists on duty about 05LT (03UTC), when the results of the first numerical weather models arrived. The predictions were prepared for the first briefing of the day at 08LT (06UTC) (Picture 4.3.1.2) , where the organization committee, the competition and venue managers took part.

Picture 4.3.1.2: The two forecasters on duty holding the briefing for the officials and presenting the daily handout with the updated weather information (Photo: Michael Burgstaller).

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Picture 4.3.1.3: Dr. Albert Sudy is holding the weather briefing for the sailors and their coaches. They get informed about the changing conditions in wind field during the upcoming day

(Photo: Michael Burgstaller).

A short outlook and the wind regime with changes in wind speed, gusts and direction were discussed for the oncoming day. At 0830LT (0630UTC), a similar briefing for the sailors and their coaches was held in the sailing tent (Picture 4.3.1.3) . In the meantime, the organization committee and the competition and venue managers made ready for their day. At about 09LT (07UTC) the two forecasters returned to the ‘Weather Tent’ to update the predictions with new data of numerical models, current data from AWS and the local observations, which were made by the forecasters themselves. This work was of great importance, because of rapidly changing weather situations by changing wind shifts or during the passage of a front which happened on Wednesday, the 17 May. Main focus was given by the wind regime as direction, speed and gusts. End of work for the forecasters was after the last competition, at about 19LT (17UTC). The forecasts for the next day were prepared and the final look at the updated models was taken.

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4.3.2 Models and Tools Used

To make precise predictions lots of models, instrumentation and tools had to be used. A fill of Numerical Weather Prediction (NWP) model data, satellite data, data from AWS and MOS data had to be assembled. The following chapters describe the various models and tools in use during the World Sailing Games.

4.3.2.1 Instrumentation and Observation

Getting an idea of the current condition of the atmosphere and verifying the direct model outputs (DMO), a network consisting of human observers, automatic or semi- automatic weather stations with special measure instruments and satellite- and radar data was installed and applied. For observations at the venue, the location of the ‘headquarters’ – the ‘Weather Tent’ – was set at the bank of lake Neusiedl. There, the forecasters could constantly take a look at the current weather condition. This was very important for verifying the model data and affirming the own predictions. For verifying the output of the numerical weather prediction models with the current temperatures, wind conditions and cloud cover, a visualized computer program called ‘MAVIS’ were used. This tool provides meteorological data from any existing AWS. The update interval for surface station data is 10 minutes, the satellite data are refreshed every 15 minutes. To verifying the output of the used mesoscale model INCA (described in the text below), three additive AWS were prepared on the event venues Breitenbrunn, Podersdorf and Neusiedl (Figure 4.3.2.1.1) . These weather stations recorded wind speed and direction, temperature and relative humidity. The data set was available in the internet and updated every minute. To observe fronts and convective cells, the common infrared satellite loop of the ZAMG were used and represented on a large screen (Picture 4.3.1.1) . It was updated every 30 minutes and showed the cloud cover over Europe (from 45°W latitude and 60°N longitude to 29°E latitude and 30°N longitude).

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Figure 4.3.2.1.1: Three automatic weather stations were established to provide real time data from the event venues Breitenbrunn, Neusiedl and Podersdorf (Exposé ISAF World Sailing Games, 2006).

Figure 4.3.2.1.2: The four Austrian EEC Doppler Weather Radar Devices with are currently in operation are placed on the Kolomannsberg near Feldkirchen (Salzburg), in Schwechat near Vienna (Vienna), on the Patscherkofel near Innsbruck (Tyrol) and on the Zirbitzkogel (Styria).

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Another very useful instrumentation for nowcasting is radar technology. Therefore a visualization for the current reflectivity in the atmosphere above Austria was used. Combined pictures of the four weather radars (Figure 4.3.2.1.2 ) show the reflectivity- classes (in decibel) in different colors. Boundary values for drizzle (the smallest category) has a reflectivity of 12 - 15dBZ, thunderstorms has more than 50dBZ and hail more than 58dBZ. This process in rain and thunderstorm detection is experimental, but shows good correlations between measured values of the radar network and the observed weather conditions. Advantages of this technology are the high spatial resolution of 1 km² and the fast update interval of 5 minutes. Drawbacks are the natural physical boundaries, an inconvenient orography and the high costs of operation.

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4.3.2.2 Global Models

Global circulation models are basics of modern weather forecasting. These models acquire the actual condition of the atmosphere by a large data set of observed and measured values and compute by given model physics and equation the conditions of the atmosphere in future. Lots of global circulation models are world wide in use. The well known and mostly used global circulation models are the ‘Global Forecast System’ (GFS) developed in the United States, the ‘European Centre for Medium-Range Weather Forecasts’ (ECMWF) with its headquarters in Reading near London (UK), the global circulation model of the ‘United Kingdom Meteorological Office’ (UKMO) and the Global Model of the ‘Deutscher Wetterdienst’ (GME). During the World Sailing Games the ECMWF model provided one of the used data sets for the sailing predictions. This model was computed with a horizontal grid point distance of nearly 25 kilometers (since 1 February 2006) and 90 vertical levels (http://www.ecmwf.int) . The meteorologists computed the focused city Neusiedl by using a bilinear interpolation, to get usable predictands for wind parameters, temperatures, cloud cover and type, lability indices, rain parameters and relative humidity in different vertical levels. This DMO was available twice a day, in the early morning and evening. The forecast scale ranged from the current day 00UTC to the day after next day 18UTC and was therefore made for short range weather prediction. Figure (4.3.2.2.1) shows the horizontal resolution of 0.5 degrees (nearly 25 kilometers) of the numerical weather prediction model ECMWF in the Alps.

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Figure 4.3.2.2.1: Grid points of the global circulation model ECMWF with a horizontal resolution with 0.25°. The red circle marks the lake Neusiedl.

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4.3.2.3 Limited Area (Mesoscale) Models

In recent years, high resolution limited area models became more and more important for weather prediction in small scaled areas. These models are called mesoscale or limited area models and have the function to predict changes in weather conditions in the area between 10 and 1000 kilometers. These changes are often caused by topographic or thermal effects, which lead, for example, to the development of vertical clouds and in further succession to precipitation. Mesoscale models have similar physics than global circulation models using physical parameterizations for processes caused by soil, vegetation, water surface and turbulent mixture of heat, humidity and impulse in the boundary layer. Well established limited area models are LM, Aladin, MM5 and RAMS. For the World Sailing Games 2006, the forecasting team of the ZAMG used the mesoscale model ALADIN for the region Austria. This model covers an area from 2770 x 2480 squarekilometers with a horizontal grid spacing of 9.6 kilometers and 45 vertical levels. It was calculated twice a day at 00 and 12UTC and was available for predications in the morning and the evening. In Figure 4.3.2.3.1 the high resolution of the model orography of the ALADIN-AUSTRIA is shown. The colors reach from blue (lower elevations) to red (very high elevations). Among visualizations of all variables (Picture 4.3.2.3.1) , the forecasters had similar bilinear interpolations in text formatted outputs as it was available for the DMO of the ECMWF model.

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Figure 4.3.2.3.1: The model orography of ALADIN-AUSTRIA shows lower elevations in blue colors. Elevations of 1000m.a.s.l. are dyed green, elevations from 1800ma.s.l. to 3600m.a.s.l. are dyed yellow to red (Zwatz-Meise, 2006).

Picture 4.3.2.3.1: The ALADIN forecast for mean sea level pressure and wind field for 14LT (12UTC). The red circle marks the position of Neusiedl, the red square the position of Rust (Photo: Mag. Harald Seidl).

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A giant leap for nowcasting was the development of the numerical weather prediction model INCA (Integrated Nowcasting through Comprehensive Analysis). This model covers Austria within a region of 350x600 squarekilometers and has a horizontal grid spacing of 1 kilometer. The 20 vertical levels are equidistant with a spacing of 200m. The mesoscale model ALADIN provides input data for temperature, humidity, wind field, precipitation, cloudiness, global radiation and ground temperature. Observation devices allocate satellite and radar imageries and surface station data. A high resolution (930m latitudinal and 630m longitudinal) topography (Figure 4.3.2.3.2) which was developed by the US Geological Survey provides the model surface for computing (Csekits, 2005) . All these data are used to predict precipitation and type, cloudiness, global radiation, temperature, humidity and the wind field. The INCA model provides hourly analyses, which is a combination of weather radar and -station data, and forecasts for the wind field within the next 8 hours. For example, the weighting for general forecasts is shown in Figure 4.3.2.3.3 . In the first 6 hours of the forecast, an extrapolation of analyses based on motion vectors provides the nowcasting data. After 6 hours, the mesoscale weather prediction model ALADIN is maximized weighted. The last hours of each forecast is dominated by the ECMWF model. For the World Sailing Games, the INCA model computed especially the wind field for exact sailing predictions. One of these images is shown in Figure 4.3.2.3.4 . The colors mark areas with different wind speeds and little black arrows show the expected wind directions.

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Figure 4.3.2.3.2: The INCA-AUSTRIA orography provided by the US Geological Survey in a resolution of 930m latitudinal and 630m longitudinal. Blue colors marks low elevations, yellow colors are elevations about 2000m and red dyed regions are elevations higher than 3000m (Csekits, 2005).

Figure 4.3.2.3.3: INCA is a combination of 3 weighted prediction models. The first 2 hours of a current model run are dominated by a Nowcasting System which proceeds to the mesoscale model ALADIN till the upcoming 6 hours. Finally, the medium range prediction model ECMWF takes over ALADIN at least at 43 hours (Haiden, 2006).

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Figure 4.3.2.3.4: The INCA-AUSTRIA model of the ZAMG provided hourly wind forecasts for the upcoming 8 hours. Wind speed is illustrated with colors and wind direction with little black arrows. The light-blue solid line in the middle marks the lake Neusiedl, the dark-red solid line the national border to Hungary, the red circle the position of Neusiedl, the red square the position of Rust.

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4.3.2.4 Model Output Statistic

In modern synoptical meteorology, statistical predictions have become more and more important for medium range weather forecasting. One of the preferred statistical methods are Model Output Statistics (MOS). Numerical weather prediction models deliver different forecast parameters which are difficult to measure in free atmosphere. The idea of MOS is to use statistical relations between measured values of weather stations on surface which relate to DMO forecast parameters. For example the 850hPa temperature and the layer thickness (examples of tow predictors) may exhibit good correlations with the air temperature measured two meters above ground (an example of a predictand). In most cases the best results for a certain predictand are provided by a combination of many different predictors and coefficients. To develop statistical relationships, a historical data set of observed values and DMOs are required (Wilks, 1995) . The used correlation equations will be modified until the correlations between the predictor and the predictand are as high as possible. Now these equations can be applied on ‘today’s’ predictors of NWP models, to forecast the predictands of future.

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In Figure 4.3.2.4.1 a schematic diagram describes an example of the relationship between predictor and predictand.

Model Output Statistic (MOS)

DMO: predictor: predictor:

layer thickness layer thickness layer thickness 850hPa temp 850hPa temp 850hPa temp

observed values: observed values: predictand:

air temperature air temperature air temperature

historical data set correlations MOS forecast

Figure 4.3.2.4.1: To develop a MOS, historical data sets of numerical DMO (later predictor) and observed values are necessary. In this case, finding correlations between the predictors layer thickness and 850hPa temperature, and the observe air temperature in historical data sets enables better forecasts on the basis of current NWP forecasts.

In contrast to other statistical weather prediction methods, MOS uses the same correlation equation for development and implementation, the mathematical formula is written as followed:

= yˆt f MOS (xt ) (Equation 4.3.2.4.1)

In the Equation 4.3.2.4.1 , the variables xt are predictors of a NWP model, t describes the time in the future, for which the forecast is made and the function fMOS characterizes the implementation and development equations. This statistical forecasting system can not exhibit any systematical errors like some NWP models do. These errors will be eliminated through the used correlation equations. Faults of this statistical prediction type may be the result of inaccurate data sets. If the precipitation or temperature, for example, is recorded wrongly due to the fault of an observer or instrumentation, the used data set of records will permanently degrade the performance of a MOS (Glahn and Lowry, 1972) .

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4.3.3 Synoptic Situation during the World Sailing Games 2006

In this chapter, an overview about the predominating weather conditions during the World Sailing Games is resumed. The visualized records of the AWS Town in Figure 4.3.3.1 may help the reader to understand the changing situations for sailors and the challenge for the forecasting team.

09 10 11 12 13 14 15 16 17 18 19 20 21 22 30 5

4.5

25 4

−1 3.5 20

3

15 2.5 rainrate / mm 2

10 1.5 temperature / °C, windspeed ms

1 5

0.5

0 0 09 10 11 12 13 14 15 16 17 18 19 20 21 22

Figure 4.3.3.1: 10 minutes means of air temperature (red solid line), wind speed (green solid line) and rain rate (blue solid line) are visualized together with the maximum gusts (mean of 3 seconds) of 10 minutes (pink dashed line). Temperatures, wind speeds and gusts refer to the left ordinate, rain rates refer to the right y-axes.

On Wednesday 10 May, the day of the opening ceremony of the fourth ISAF World Sailing Games, a low above Russia led to a North to North East wind regime with average wind speeds of 3.8ms -1 from 07 to 16UTC. On lake Neusiedl, the wind field was strong enough for sailing the first qualifying day in all classes (Picture 4.3.3.1) . On Thursday 11 May, the low above Russia moved eastwards and a slight ridge (Figure 4.3.3.2) formed over western and middle Europe. High pressure influence in the north of the Alps entailed wind from northwest to north with low wind speeds with a mean value of 3.3ms -1 from 07 – 14UTC.

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Picture 4.3.3.1: Stratocumulo translucidus virga on the day of opening ceremony, May 10, 2006. The line of sight is southwest (Photo: Michael Burgstaller).

Figure 4.3.3.2: On 12 May at 12UTC a slight ridge is laying above Middle Europe. The solid lines mark the geopotential high in ten meters, the blue color points out areas with average wind speeds between 30 and 40ms -1 at the pressure level of 300hPa. The red point marks the position of lake Neusiedl.

On Friday 12 May, the ridge moved further eastwards and a high pressure above northern Italy caused weak winds (mean of 2.3ms -1 on 12 May and of 2.6ms -1 on 13

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May) till Saturday 13 May from south to southeast at the surface, fair sky and temperatures about 22 degree Celsius during the day. Due to this weak wind conditions, some competitions had to be displaced. On Sunday 14 May, a depression over the Atlantic Ocean moved slowly northwards, and a deep ridge formed west of France (Figure 4.3.3.3) . This ridge was approaching Austria and caused a northwestern flow above lake Neusiedl, where the World Sailing Games held their last qualifying day. This synoptical flow interspersed through all levels down to the surface. On that day, a rather weak cold front followed by a short- wave-trough passed the venues in the afternoon, which was embedded in front of the ridge pattern. Due to this fact, the wind field refreshed from west to northwest and the sky became overcast at lake Neusiedl. Consequences were colder temperatures but good wind conditions (about 4ms -1 with the strongest gust of 12.9ms -1 during the day) so that all qualifying races could be carried out.

Figure 4.3.3.3: On Sunday 14 May at 12UTC, Austria was at the front of a deep ridge, which was approaching quickly from West. The solid lines mark the geopotential high in ten meters, the different colors mark mean wind speeds from 0 to 60ms -1 at the pressure level of 300hPa. The red point flag lake Neusiedl.

On Monday 15 May a huge ridge crossed Middle Europe and introduced warm air from North Africa and Spain. The temperatures were rising up to 23.8°C and the wind field on lake Neusiedl turned from northwest to southeast.

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On Tuesday 16 May, the first of the five ‘final days’, the leaving ridge which caused a synoptical flow from nearby west, entailed increasing high pressure, temporarily sunshine and temperatures about 21°C. Wind conditions were changing from weak local winds from northeast in the morning to refreshing wind from southwest. In the afternoon rain cells were moving in close vicinity to the venues which stayed almost dry. On Wednesday 17 May weather conditions changed completely. A cold front (Figure 4.3.3.4, Figure 4.3.3.6, Picture 4.3.3.2 and Picture 4.3.3.3) followed by a short wave trough (Figure 4.3.3.5) passed the eastern part of Austria in the late forenoon, which was responsible for high wind speeds (mean of 4.1ms-1 between 07 and 14UTC), strong gusts (highest gust at 1250UTC with 13.9ms -1) from northwest and heavy precipitation (Figure 4.3.3.1) . The temperatures did not change significantly due to the returning warm air after the passage of the front (Figure 4.3.3.7) . In the following, the sky cleared off quickly and the sunset with afterglow was apparitional (Picture 4.3.3.4) .

Figure 4.3.3.4: The different equivalent potential temperatures are pointed out with different colors in the level of 850hPa on 17 May at 06UTC. Lake Neusiedl is highlighted with a white point.

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Figure 4.3.3.5: On 17 May just before noon, a short wave tough passed the eastern part of Austria. Black solid lines mark the geopotential high in ten meters, different colors present different wind speeds at the level of 300hPa.

Figure 4.3.3.6: Compared with Figure 4.3.3.4, the changing equivalent potential temperature field is illustrated. The colors in the environment of the venues (white point) were changing from red (42 – 46°C) into green (34 – 38°C), marking the cold front moving over the venues. The different equivalent potential temperatures are pointed out with different colors in the level of 850hPa on 17 May at 12UTC.

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Figure 4.3.3.7: After passage of the cold front, the warm air streamed back northeastwards. Due to this, the temperatures did not drop down distinctively. The different equivalent potential temperatures are pointed out with different colors in the level of 850hPa on 17 May at 18UTC. The venue is highlighted with a white point.

Picture 4.3.3.2: A line of cumulus congestus clouds was established in the southwest over the lake Neusiedl in the morning of the 17 May (Photo: Michael Burgstaller).

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Picture 4.3.3.3: The approaching cold front from northwest can be identified easily, which arrived on the lake at 1030UTC. Heavy precipitation and strong gusts were entailed (Photo: Michael Burgstaller).

Picture 4.3.3.4: After the passage of the front, the cloud cover was removed and the sunset could be watched by cloudless sky (Photo: Michael Burgstaller).

On 18 May a zonal flow pattern expanded over the Atlantic Ocean which pushed a ridge over Western Europe (Figure 4.3.3.5) further ahead to Austria. This ridge moved quickly, upcoming low pressure above northern Europe entailed wind from South with

- 76 - World Sailing Games 2006 average wind speeds of 4.0ms -1, fair sky and temperatures up to 26.1°C – perfect weather conditions for sailing.

Picture 4.3.3.5: Stormy wind and strong gusts caused difficulties for the sailors during the finals on 20 May (Photo: Michael Burgstaller).

Picture 4.3.3.6: Sunset on the bank of Podersdorf during the closing ceremony on 20 May 2006 (Photo: Michael Burgstaller).

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Because of the quick movement of the ridge, the synoptic situation changed in a mixture of short wave troughs and zonal flow for the last two days of the event. Quickly changing wind conditions, a temperature drop and a mixture of sun and clouds made the last two final days difficult for sailors, the officials and the forecasters. In the night of the 18th May a further quickly moving cold front from northwest caused precipitation and as its name implies cold air advection which leaded to a cloudy sky, lower temperatures and increasing winds from northwest on 19 May. In the afternoon, wind direction was changing to south and wind speed was decreasing due to the formation of a low pressure area above Geneva. On 20 May the weather conditions were not perfect for sailing. Strong winds from south with lots of gust (Picture 4.3.3.5) caused by the low pressure area above northern Italy leads to difficulties for the sailors on the final day. But not only the thrilling competition but also the final weather conditions found a happy end of the World Sailing Games on Saturday the 20 May. The closing ceremony in Podersdorf was accompanied by sunset with afterglow on the bank of lake Neusiedl (Picture 4.3.3.6) .

The sailing Media-Team described the weather conditions as followed: ‘Changing Conditions: The weather through the qualifying series had provided a mixture of big breeze and glassy conditions, with days three and four virtually windless until the late afternoon. The up and down trend continued through the final series, with the first day of the gold fleet racing severely restricted due to very light breezes. However, from then on there was wind, and in varying strengths and directions. Day eight brought storms and gusts over 30 knots, light winds followed on day nine, with day ten and the decisive races taking place in a strong 15-20 breeze which dropped through the day. The changeable weather and the tricky, shifty conditions on the lake meant the sailors really had to show their versatility to avoid high scores (ISAF World Sailing Games 2006 - Looking Back On Austrian Adventure) .’

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4.4 Forecast Verification

To verify all forecasting models which were used, a statistical routine had to be developed. Only the important predictands for sailing are verified. The following figures are comparisons between all used models for the runs. The models start at 00UTC and 12UTC every day. These runs are started each day a year and therefore there are +3 hours, +6 hours, +9 hours, etc. forecasts for each day. These are averaged and compared against each other to find out the best numerical or statistical weather prediction model for each parameter. But before verifying the different models, the two used semi automatic weather stations have to be quality controlled.

4.4.1 Comparison of Station Data

Before verifying the forecasts, which were made for the World Sailing Games 2006, the measurements of the two different weather stations in the city of Neusiedl (called ‘AWS City’) and at the venue at the lake (‘AWS Lake’) had to be compared. The weather station in the city is part of the operational network of the Austrian weather service. Its data are mostly used for developing MOS and feeding numerical weather prediction models. Due to its location, the data of this semi automatic weather station are not directly representative for conditions on or above the lake. Therefore three additional stations were installed for the duration of the Games. One of them was mounted at the headquarter of the games (it will be referred to as ‘AWS lake’ in the following), the two others in Breitenbrunn and Podersdorf. These stations measured the conditions at the different venues and provided the data for verifying the forecasts and computing the mesoscale model INCA. The predictands (forecasted meteorological parameters) of the other numerical models and MOS were calculated for the AWS town station in Neusiedl. Map 4.4.1.1 shows the locations of the two weather stations ‘town’ (blue) (Picture 4.2.1) and ‘lake’ (green). Picture 4.4.1.1 shows the used instrument type ‘Vaisala Multisensor WXT510’. The exact position of this instrument at the venue is 16°50'6.14’ East latitude and 47°55'42.92’ North longitude at an altitude of 115 meter above mean sea level.

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Map 4.4.1.1: The blue point on this map marks the position of the semi automatic climate station in the town of Neusiedl (‘AWS Town’). The green point locates the additional weather station on the waterside, which was installed by the ‘University of Natural Resources and Applied Life Sciences Vienna’ for the World Sailing Games 2006 (‘AWS Lake’) (Map: © BEV).

Picture 4.4.1.1: A part of an automatic weather station ‘Lake’ on the venue in Neusiedl (Photo: Michael Burgstaller)

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The data set of the automatic weather station, placed waterside, is regrettable incomplete. Most data are missing during the night hours when no technicians were on duty. During the daytime there where lots of problems with this station as well, especially with recording the data. The values were recorded at a time interval of 1 minute, so these data had to be averaged to 10 minute means for comparing this set with the ‘AWS Town’. Data with wind speeds below 1ms -1 were not compared. The remaining data set contains only 32.8 percent of all data. In Figure 4.4.1.1 , the two curves of temperature in the town and at the lake are plotted against each other. On some days, differences of more than 2°C are noticeable. In Figure 4.4.1.2 these differences are plotted separately. The temperature near the lake was colder than the temperature in the city. The water temperatures measured in the town Rust were librating between 17 degrees at night hours and 21 degrees in the afternoon. As expected, the negative differences are especially during daytime, when the air above or in the direct environment of the lake does not heat as quickly as the air mass in and above the town of Neusiedl. The bias between the two stations is -1.3°C, the mean absolute error is 1.3°C.

28

26

24

22

20

18

16 temperature / °C

14

12

10

8 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 May

Figure 4.4.1.1: 2 meter air temperature at AWS Town (green solid) and AWS Lake (blue dashed), respectively.

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4

3

2

1

0

−1 temperature difference / °C −2

−3

−4 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 May

Figure 4.4.1.2: Temperature difference between AWS Lake and AWS Town. Negative values mean that temperatures above the lake are colder than above the town.

The first directly important variable for sailing is the wind direction . To investigate how much the different wind directions depart from each other, the difference between the individual pairs are calculated. Figure 4.4.1.3 shows the wind directions for the period of the World Sailing Games, Figure 4.4.1.4 the difference between them. On closer inspection, the daytime dependence of the differences is noticeable. Differences of more than 45° appear especially in the night hours, when wind speed was low and when wind was coming from North to North East during an existing land–sea breeze circulation. The bias for wind directions is 3.1°. This small bias means that there was no systematical error and therefore no specific direction of deflection. The mean absolute error MAE was much larger: 43.5°

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360

315

270

225

180

135 wind direction / degree 90

45

0 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 May

Figure 4.4.1.3: Wind directions in the city (green solid) and at the lake (blue dashed)

180

135

90

45

0

−45

−90 wind direction difference / degree

−135

−180 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 May

Figure 4.4.1.4: Differences between wind direction in the city and at the lake. Negative values mean a clockwise wind shift from AWS Lake.

Generally, wind speed differences depended on wind directions (Figure 4.4.1.5) . The reason is lower surface roughness, especially when the upwind fetch is over water, for example when the wind blows from south or southeast. Comparing the wind speeds of the two stations returns a bias of 0.3ms -1 and a MAE of 0.7ms -1. Values were similarly

- 83 - World Sailing Games 2006 low for wind gust: a bias of 0.1ms -1 and a MAE of 1.2ms -1, respectively. With proper correction the long term record of the AWS City can be used as a proxy climatology for the AWS Lake location.

NW

W

SW

S

SE wind direction

E

NE

N

−4 −3 −2 −1 0 1 2 3 4 −1 difference / ms Figure 4.4.1.5: Difference of wind speed (AWS Lake minus AWS City) as a function of wind direction at the lake for the period between 7 May and 23 May 2006. The lake is situated S and SW of AWS Town and AWS Lake.

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The correspondence between symbol and model is shown in Table 4.4.1.1 and next to the figure three forecasts are compared. One class uses only observations (climatology, persistence), another class uses direct model output interpolated to the city of Neusiedl, and the last class uses statistically postprocessed model output (AustroMOS, MOSMIX). The persistence, marked by ‘◄’-symbols, is arranged in two classes, one for verifying the 00UTC runs, another for 12UTC runs. Its values are measured 24 hours before.

Symbol: Valid for (filled – 00 run; open – 12 run): + Measured values from AWS Town ▲ Global Circulation Model ECMWF, DMO, interpolated to the city of Neusiedl ♦ Limited Area Model ALADIN, DMO, interpolated to the city of Neusiedl ● AustroMOS ▼ MOSMix ► Climatology ◄ Persistence Table 4.4.1.1: This table sums up the different symbols and the models for which they are valid. Filled symbols are for 00UTC runs of the numerical models, whereas open symbols are for 12UTC runs.

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4.4.2 Wind Direction

The wind direction is one of the three most important parameter for sailing, (among the wind direction and gusts) but at the same times the most difficult to measure and forecast. The big problem for forecasting and verification of the wind direction are the rapidly changing wind conditions and the sensitivity to anomalies of the environment and changes in wind speed. The current wind direction can be completely different from the mean within one minute and this may differ from the mean of ten minutes. Further the measurement method can cause changes of wind direction due to the internal friction of mechanical measure instruments, if those are in use. How it is shown in this chapter, the wind direction measured during low wind speeds departs from forecasted wind directions more than wind directions measured with average and strong winds. For that reason, the wind direction data are divided in 3 classes of wind speed. The first class contains wind data with speeds below 1.5ms -1 (3 knots). Wind direction data for wind speeds between 1.5 and 3.6ms -1 (3-7 knots) are collected in the second class and wind speeds over 3.6ms -1 are covered by the third and last class. In Figure 4.4.2.1 forecasted wind directions of all models and observed values of the AWS Lake are plotted for the next 6 hours. The size of symbols shows the class of wind speed. The closed symbols stand for the expected wind directions and the reached wind speeds for each day at 06UTC (08LT) whereas the open symbols are valid for the expected conditions daily at 18UTC (20LT). Figure 4.4.2.2 shows the differences between these forecasts and the measurements. As expected, forecasts of wind directions with lower wind speeds depart more from the measured values than wind directions with higher wind speeds The bias of this data set is illustrated in Figure 4.4.2.3 , which shows good results for the ECMWF and the Aladin model, even for convective weather conditions during the afternoon. MOSMix exhibits a systematical difference counter clockwise for the 12UTC run. The mean absolute error or wind bearing index, how it is called from the ISAF Committee, is illustrated in Figure 4.4.2.4 . In this case the wind shift is more than 20°, which is caused by verifying the direct model output instead of the forecasted range of wind direction and therefore not worrying. In contrast to investigations of other meteorological parameters, the numerical weather prediction models are better in

- 86 - World Sailing Games 2006 forecasting wind directions than the two MOS prediction models. This can also be seen in the indices of the RMSE (Figure 4.4.2.5) and the MSESS (Figure 4.4.2.6) , where the numerical weather prediction models from ECMWF and Aladin replay good results. Figure 4.4.2.7 shows a larger spread of values of the coefficient of determination than other scores and indices due to the squaring function. Good correlations are reached by MOSMix and ECMWF. For the sorted rank correlation coefficient, good results are achieved by Aladin and ECMWF, although the values compared to other scores do not change ostentatiously. Table 4.4.2.1 summarizes the mean of all scores over the first 24 hours. The run of the ECMWF model, which was initialized at 12UTC, provides very good wind direction forecasts. It achieves top three positioning in all scores and indices. But these scores have to be treated carefully. As above in this chapter, the values in this table are the mean of all times of forecasts, which is a mean of all values presented in the other figures. The good bias of the 12UTC run of the ECMWF is caused by the mathematical kind of summarizing. If positive and negative values occur, while calculating the mean, they can extinguish themselves, which happened in this case. Same is valid for the MSESS and the rank correlation coefficient (Figure 4.4.2.8) . In the case of MSESS, only the persistence has inferior results due to larger deviations to the compared climatologically values. The results of the coefficient of determination do not change due to positive values only.

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360

315

270

225

180

135 wind direction / degree 90

45

0 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 May

Figure 4.4.2.1: Different symbols mark the models and runs (open symbols: 12UTC; closed symbols: 00UTC) for all +6 hour forecasts made during the WSG. The size of symbols distinguishes wind directions into three classes, small symbols show wind speed with less than 3 knots, big symbols shows wind speed more than 7 knots. ‘+’-symbols stand for the measurements of the AWS Town.

180

135

90

45

0

−45 difference / degree

−90

−135

−180 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 May

Figure 4.4.2.2: Difference between AWS Town measurements and the + 6 hour forecasts started at 00UTC verifying at 06UTC. Different colors and symbols mark the used weather prediction models, same as in Figure 4.4.2.1. Positive values distinguish differences clockwise from the AWS Lake.

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30

20

10

0

−10

−20 bias / degree −30

−40

−50

−60 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.2.3: Wind direction bias for all models and forecast steps for the period between 7 and 22 May 2006. Different colors mark different models.

120

100

80

60

40 mean absolute error / degree

20

0 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.2.4: As figure 4.4.2.3 but for mean absolute error of wind direction.

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140

120

100

80

60

40 root mean squared error / degree

20

0 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.2.5: As figure 4.4.2.3 but for root mean square error of wind direction.

1

0.5

0 mean square error skill score clima

−0.5 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.2.6: As figure 4.4.2.3 but for mean square error skill score of wind direction.

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1

0.9

0.8

0.7

0.6

2 0.5 R

0.4

0.3

0.2

0.1

0 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.2.7: As figure 4.4.2.3 but for the coefficient of determination of wind direction.

1

0.8

0.6

0.4

0.2

0 rank correlation coefficients

−0.2

−0.4 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.2.8: As figure 4.4.2.3 but for the rank correlation coefficient of wind direction.

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Table of averaged means coef. rank for forecasted bias MAE RMSE MSESS of wind directions det. corr. ECMWF Run: 00 UTC 5.44 40.46 56.47 0.73 0.34 0.33 ECMWF Run: 12 UTC -0.03 38.23 49.83 0.79 0.38 0.43 Aladin Run: 00 UTC 4.66 42.03 57.00 0.73 0.33 0.39 Aladin Run: 12 UTC 1.65 44.33 59.03 0.71 0.53 0.53 AustroMOS: Run 00 UTC -8.47 34.93 47.81 0.80 0.30 0.26 AustroMOS: Run 12 UTC -8.56 32.24 45.25 0.83 0.29 0.33 MOSMix: Run 00 UTC -10.65 40.35 53.74 0.79 0.50 0.45 MOSMix: Run 12 UTC -9.16 38.32 53.02 0.77 0.35 0.40 Persistence: 00 UTC -3.23 86.70 102.65 0.05 -0.18 -0.21 Persistence: 12 UTC -4.13 87.49 102.89 0.05 -0.18 -0.23 Climatology: 00 UTC - - 108.98 - - - Climatology: 12 UTC - - 108.62 - - - Table 4.4.2.1: Scores and indices of all models are averaged over first 24 hours. The best three results are marked bold and blue.

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4.4.3 Wind Speed

The wind speed is another of the three most important parameters for sailing. Exact information about the wind speed is similarly important for the Organization Committee, which is the basis of decision making for the challenges and races, as for the sailors and managers for fixing their tactics for the coming up competitions. The bias of the forecasted wind speeds is shown in Figure 4.4.3.1 and presents values of all used models, which exhibit mostly a slight overrating. For numerical prediction models the results for bias are not so bad due to the measured decelerated wind speed which is caused by the roughness of the environment of the weather station. The computed differences are mostly positive and are oscillating from 0 to 1.5ms -1. During steady weather conditions and a stable land-sea breeze circulation, the persistence is therefore a useful tool to verify and enhance the model output of other weather prediction models. The lowest systematic error exhibit among the persistence the MOSMix and the model output statistic of the ZAMG (AustroMOS). The values of the data set of the AWS Town contain the mean of the last ten minutes. Differences of 0.2ms -1 for example can be caused by a slightly increasing of most of the values or it can be entailed by just a few outliers. Although the raw data of MOSMix for wind speed had to be converted from knots in ms -1. Anyway the MOSMix, especially the 12UTC run has a lower MAE than the other models (Figure 4.4.3.2) . Other models like the Aladin or the ECMWF reaches an error of 1 or more meter(s) per second. Same is valid for the other scores like the RMSE (Figure 4.4.3.3) and the MSESS (Figure 4.4.3.4) . The coefficient of determination (Figure 4.4.3.5) shows good results for the mesoscale model Aladin and the daytime forecasts of the global model from ECMWF. Figure 4.4.3.6 shows good results for rank correlation coefficient for MOSMix and the morning and night hour forecasts of Aladin. As it is summed up in Table 4.4.3.1 , both runs of the MOSMix model have better scores than all others. Nearly as good as the MOSMix is the 12UTC run of the Aladin model, which is therefore also a good prediction model for wind speed forecasts. In the case of wind speed, persistence forecasts are inferior to other parameters due to the rapidly changing conditions.

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1.6

1.4

1.2

1

0.8 −1

0.6

bias / ms 0.4

0.2

0

−0.2

−0.4 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.3.1: Wind Speed bias of all models and runs for the time from +3 hour through +24 hour forecasts.

2

1.8

1.6

−1 1.4

1.2

1

0.8

0.6 mean absolute error / ms

0.4

0.2

0 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.3.2: As Figure 4.4.3.1 but for mean absolute error of wind speed.

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2.5

2 −1

1.5

1 root mean square error / ms 0.5

0 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.3.3: Root mean square error of wind speed as a function of forecast, the lead time for 00UTC and 12UTC forecast runs from +3 hour through +24 hour.

1

0.5

0

−0.5

−1 mean square error skill score clima −1.5

−2 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.3.4: As Figure 4.4.3.3 but for the mean square error skill score.

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1

0.9

0.8

0.7

0.6

2 0.5 R

0.4

0.3

0.2

0.1

0 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.3.5: As Figure 4.4.3.3 but for the coefficient of determination.

1

0.8

0.6

0.4

0.2

0 rank correlations coefficient

−0.2

−0.4 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.3.6: As Figure 4.4.3.3 but for the rank correlation.

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Table of averaged means for coef. rank forecasted bias MAE RMSE MSESS of wind speeds det. corr. ECMWF Run: 00 UTC 0.46 1.04 1.26 0.19 0.48 0.44 ECMWF Run: 12 UTC 0.38 0.94 1.20 0.30 0.50 0.47 Aladin Run: 00 UTC 0.80 1.23 1.58 -0.37 0.51 0.52 Aladin Run: 12 UTC 0.70 1.20 1.50 -0.17 0.57 0.57 AustroMOS: Run 00 UTC 0.06 0.91 1.16 0.34 0.52 0.53 AustroMOS: Run 12 UTC 0.04 0.78 1.00 0.53 0.52 0.55 MOSMix: Run 00 UTC 0.19 0.87 1.05 0.52 0.64 0.65 MOSMix: Run 12 UTC 0.17 0.73 0.89 0.61 0.69 0.65 Persistence: 00 UTC -0.01 1.43 1.72 -0.53 -0.07 -0.08 Persistence: 12 UTC -0.05 1.43 1.73 -0.48 -0.05 -0.07 Climatology: 00 UTC - - 1.41 - - - Climatology: 12 UTC - - 1.44 - - - Table 4.4.3.1: Scores and indices of all models are averaged over the first 24 hours. The best three results are marked bold and blue.

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4.4.4 Speed of Gusts

For this event, the comparison of gusts is difficult due to lack of data sets for ECMWF and AustroMOS models. Noticeable is, that the Aladin model overrated whereas the MOSMix underrated systematically the speed of gusts, which is shown by the bias score (Figure 4.4.4.1) . Figure 4.4.4.2 presents the mean square error skill score of both models. The values of Aladin are slightly positive, which means that its forecasts are slightly better than forecasts made by the climatology. The summarized scores of the two models are shown in Table 4.4.4.1 . As expected, predicted speeds of gusts are very difficult for models and forecasters, because of its mesoscale parameters, which are hardly ascertainable and computable for humans and computers. Nearly same good results are achieved by MOSMix and the mesoscale model Aladin.

3

2

1 −1

0 bias / ms

−1

−2

−3 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.4.1: Bias for persistence, Aladin and MOSMix for all forecast steps and runs for gusts for the next +24 hours.

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1

0.5

0

−0.5

−1 mean square error skill score clima −1.5

−2 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.4.2: As figure 4.4.4.1 but for mean square error skill score.

Table of averaged means for coef. rank forecasted bias MAE RMSE MSESS of speed of gusts det. corr. Aladin Run: 00 UTC 1.22 2.30 2.81 0.00 0.62 0.64 Aladin Run: 12 UTC 0.98 2.04 2.64 0.01 0.64 0.64 MOSMix: Run 00 UTC -1.31 1.82 2.36 0.50 0.59 0.58 MOSMix: Run 12 UTC -1.21 1.81 2.32 0.34 0.67 0.65 Persistence: 00 UTC 0.08 2.84 3.52 -0.55 -0.11 -0.10 Persistence: 12 UTC -0.04 2.90 3.57 -0.53 -0.12 -0.11 Climatology: 00 UTC - - 2.94 - - - Climatology: 12 UTC - - 3.02 - - - Table 4.4.4.1: Scores and indices of all models for gusts are averaged over first 24 hours. The best three results are marked bold and blue.

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4.4.5 Air Temperature

As was discussed in the last chapter, forecasting temperatures with more than 3°C differences exits over a horizontal distance of just a few hundred meters and a vertical difference of 39 meters. Since MOS was computed for the AWS Town, temperature forecast verification will be made relative to these measurements. In Figure 4.4.5.1 all +9 hours forecasts made by different weather prediction models are visualized. To work out the differences to the measured values more clearly, the absolute difference of the run from 00UTC is illustrated in Figure 4.4.5.2 and for the run from 12UTC in Figure 4.4.5.3 . A +9 hour forecast for the 00UTC run verifies in mid-morning with relatively higher temperatures than at 21UTC, the verification time of the +9 hour forecast from the 12UTC run. The spread of differences between the 00UTC run shown in Figure 4.4.5.2 and the 12UTC run shown in Figure 4.4.5.3 is from today’s point of view not explainable. During gradient weak weather conditions persistence forecasts are useable due to similar values of the meteorological parameters over a long period. In Figure 4.4.5.4 large failings are identifiable for the ECMWF for both runs. The limited area model Aladin and the MOSMix presents good results with no or only slight systematical errors for each weather prediction model in 3 hour by time steps for the first 24 hours. For the short verification period, persistence was one of the forecasting methods with the lowest systematic deviation. Direct model output from ECMWF 00UTC run also had little bias for morning and early afternoon predictions. But as Table 4.4.5.2 shows, the mean values of the bias for all forecasts together downgrade the ECMWF forecast due to the forecasting period during the evening and night hours, where this model has its highest failings, even higher than all others.

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24 23 22 21 20 19 18 17 16 15 14 temperatures / °C 13 12 11 10 9 8 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 May

Figure 4.4.5.1: The different symbols mark the models and the runs (open symbols: 12UTC; closed symbols: 00UTC) for all +9 hours forecasts made during the WSG. ‘+’-symbols stand for the measurements of the AWS Town.

6

5

4

3

2

1

0

−1 difference / °C −2

−3

−4

−5

−6 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 May

Figure 4.4.5.2: Difference between AWS Town measurements and the +9hour forecasts started at 00UTC verifying at 09UTC. Different colors and symbols mark the used weather prediction models.

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6

5

4

3

2

1

0

−1 difference / °C −2

−3

−4

−5

−6 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 May

Figure 4.4.5.3: As Figure 4.4.5.2 but for the 9hour forecast of the 12UTC run, verifying at 21UTC.

The best forecast in the mean is the Aladin run for 12UTC, whereas the worst model in the mean is the ECMWF run for 12UTC. Generally, model output statistics are better than numerical models for temperature forecasts. The largest mean absolute error is calculated for the ECMWF due to the same reasons as the failing for the bias. Continuous negative differences lead to such high differences. Especially for MOSMIX and AustroMOS variables, differences have a diurnal cycle (Figure 4.4.5.5) . The best results for the score of MAE achieve the MOSMix and the AustroMOS (Table 4.4.5.2) . The root mean square error is illustrated in Figure 4.4.5.6 . Best in the mean are the models AustroMOS, MOSMix and Aladin (Table 4.4.5.2) , due to small outliers. In this investigation, the values of climatology were added to compare it with the other models. It is marked with ‘►’-symbols. The mean square error skill score, which has its best results by reaching the value of 1, is shown in Figure 4.4.5.7 . In this case, the skill score was computed by using the values of climatology as reference. As discussed in Chapter III, variables with the value of 0 are as good as a climatological forecast and in the case of a computed result below 0, the climatological forecast would have done better. This is the case for the persistence, all +9 hours forecasts of the ECMWF 12UTC run and all +21 hours

- 102 - World Sailing Games 2006 forecast of the ECMWF 00UTC run. The best averaged values achieved again AustroMOS and MOSMix (Table 4.4.5.1). In the figure for the coefficient of determination (Figure 4.4.5.8) , no clear favorite is visible. The MOSMix and the AustroMOS predictands agree with the observed values over 80 percent, which means a correlation with the observed values in this magnitude. The average of all means over the forecast time-steps shows, that all models are nearly equal in this index. MOSMix and AustroMOS are slightly better than all others. For the rank correlation score, all models are closer together than the coefficient of determination (Figure 4.4.5.9) . The means for the first 24 hours reach nearly equal results for MOSMix, AustroMOS and the mesoscale model Aladin. Persistence forecasts for temperatures are generally good during gradient weak and steady conditions. For changing conditions, a mix of persistence and numerical predictions provide good forecasts, like MOS do.

1

0.5

0

−0.5

bias / °C −1

−1.5

−2

−2.5 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.5.4: Temperature bias of all models and runs for the time from +3hour through +24hour forecasts.

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3.5

3

2.5

2

1.5

mean absolute error / °C 1

0.5

0 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.5.5: As Figure 4.4.5.4 but for mean absolute error of temperature.

6

5

4

3

2 root mean square error / °C

1

0 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.5.6: Root mean square error of temperatures as a function of forecast, the lead time for 00UTC and 12UTC forecast runs from +3hour through +24hour.

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1

0.5

0

−0.5

−1 mean square error skill score clima −1.5

−2 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.5.7: As Figure 4.4.5.6 but for the mean square error skill score.

1

0.9

0.8

0.7

0.6

2 0.5 R

0.4

0.3

0.2

0.1

0 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.5.8: As Figure 4.4.5.6 but for the coefficient of determination.

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1

0.8

0.6

0.4

0.2

0 rank correlations coefficient

−0.2

−0.4 3 6 9 12 15 18 21 24 forecast−time / h

Figure 4.4.5.9: As Figure 4.4.5.6 but for the rank correlation coefficient.

Table of averaged means for coef. rank forecasted bias MAE RMSE MSESS of air temperatures det. corr. ECMWF Run: 00 UTC -0.77 1.38 1.73 0.59 0.80 0.78 ECMWF Run: 12 UTC -1.23 1.60 1.93 0.48 0.81 0.81 Aladin Run: 00 UTC -0.54 1.23 1.49 0.78 0.83 0.84 Aladin Run: 12 UTC 0.03 1.12 1.40 0.80 0.79 0.83 AustroMOS: Run 00 UTC -0.25 0.97 1.19 0.85 0.86 0.83 AustroMOS: Run 12 UTC -0.48 1.06 1.31 0.86 0.83 0.84 MOSMix: Run 00 UTC -0.15 1.05 1.34 0.84 0.84 0.83 MOSMix: Run 12 UTC -0.32 0.97 1.19 0.83 0.84 0.86 Persistence: 00 UTC -0.09 2.31 2.72 0.15 0.25 0.29 Persistence: 12 UTC -0.22 2.36 2.78 0.14 0.21 0.25 Climatology: 00 UTC - - 3.52 - - - Climatology: 12 UTC - - 3.54 - - - Table 4.4.5.1: Scores and indices of all models are averaged over the first 24 hours. The best three results are marked bold and blue.

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4.4.6 Minimum / Maximum Temperature

Because minimum and maximum temperatures play a less important role for sailing than wind parameters, only the most important facts are summarized in this chapter. In Figure 4.4.6.1 , the biases of all used models are shown for 8 days forecasts. Differences of the minimum temperatures are swapping around 1.5°C, which results in usable values. Minimum temperatures of both runs of the AustroMOS exhibit a negative bias. The same is valid for the 00UTC run of the ECMWF model whereas the 12UTC run perform mostly positive differences. Best is the 00UTC run of MOSMix. The mesoscale model Aladin provides minimum temperatures for only 2 days. The differences for the maximum temperatures (Figure 4.4.6.2) are up to 2°C higher in the afternoon. The curve of the ECMWF model turns from a negative bias after the fourth day in a positive bias, whereas most of the other models have constantly too high maximum temperatures. Figure 4.4.6.3 and Figure 4.4.6.4 illustrates the mean absolute errors for minimum and maximum temperatures. The steady increasing differences are showing the continuous increasing inaccuracy from day to day. The 12UTC runs of the most models provide better predictions than the 00UTC runs. Further, it is noticeable that the MAE increases linearly, not exponentially. This is also shown in Figure 4.4.6.5 and Figure 4.4.6.6 for the coefficient of determination for both parameters. In this case, extreme values over several days are only useable from 12UTC runs, especially for Aladin, which predict the upcoming two day, and the two used model output statistics.

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2

1.5

1

0.5

bias / °C 0

−0.5

−1

−1.5 1 2 3 4 5 6 7 8 9 forecast−time / day(s)

Figure 4.4.6.1: Bias as a function of forecast time for minimum temperatures for the upcoming 8 days.

2.5

2

1.5

1

0.5

bias / °C 0

−0.5

−1

−1.5

−2 1 2 3 4 5 6 7 8 9 forecast−time / day(s)

Figure 4.4.6.2: As Figure 4.4.6.1 but for the bias of maximum temperatures.

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3.5

3

2.5

2

1.5

mean absolute error / °C 1

0.5

0 1 2 3 4 5 6 7 8 forecast−time / day(s)

Figure 4.4.6.3: As Figure 4.4.6.1 but for the MAE of minimum temperatures.

4

3.5

3

2.5

2

1.5 mean absolute error / °C 1

0.5

0 1 2 3 4 5 6 7 8 forecast−time / day(s)

Figure 4.4.6.4: As Figure 4.4.6.1 but for the MAE of maximum temperatures.

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1

0.9

0.8

0.7

0.6

2 0.5 R

0.4

0.3

0.2

0.1

0 1 2 3 4 5 6 7 8 forecast−time / day(s)

Figure 4.4.6.5: As Figure 4.4.6.1 but for the coefficient of determination for minimum temperatures.

1

0.9

0.8

0.7

0.6

2 0.5 R

0.4

0.3

0.2

0.1

0 1 2 3 4 5 6 7 8 forecast−time / day(s)

Figure 4.4.6.6: As Figure 4.4.6.1 but for the coefficient of determination for maximum temperatures.

- 110 - Conclusions and Outlook

5 Conclusions and Outlook

Finally, all these facts, investigations and experiences had to be summed up. Before starting a professional weather support for an economic event, the expectations and the financial budget for such a support has to be clarified with the organization committee. As anywhere in economy, an exact cost-benefit calculation is the basis of a good and successful service. Depending on these funds, the extension, the possibilities and the limits of such a meteorological support should be clearly worked out, as it was done for the World Sailing Games. In most cases, cooperations between different departments and firms lead to the best results. Depending on the meteorological demands, different NWP models, instrumentations and tools have to be used. A comparison between the used equipment for the Olympics in Sydney or in Salt Lake City and the equipment used during the World Sailing Games shows the different extents of both sporting events, while the structure of these services was almost the same. If an agreement between the weather service team and the organization committee is found, there are lots of preparations, which had to be done for a successful support. As it is written above, developments of different climatologies helps to determine the best time in year for an event. Depending on the demands of the event, different meteorological parameters have to be investigated carefully. The evaluation of a climatology turns the forecaster’s attentions to place-dependent weather condition. These long-time investigations compensate missing local experiences and are very practical for verifying the usefulness of direct model outputs. Particularly during constant and gradient weak weather conditions, climatological and persistence forecasts are sometimes necessary because of recurrent wind systems like land-sea circulation systems or valley-wind circulations. Such existing systems are hardly covered by NWP models, due to its mesoscaled appearance. Adverted to the case study of the World Sailing Games, periods with constant high wind speeds can be clearly marked out. If events take place at locations with existing high risks of severe weather events like hurricanes, bush fires, tornados and heavy thunderstorms, the organization committee has to think about another time in year with a smaller climatological probability regarding the safety of all athletes, managers and visitors. Sometimes, professional meteorological forecasts supported by very high resolution models are inferior in

- 111 - Conclusions and Outlook predictions to humans, who are living in the surrounding of the venues and observing weather phenomenas almost every day. Therefore, discussions with such ‘long-time observers’ are very helpful to understand and know more about such mesoscaled weather events. These meteorological backgrounds like climatologies and long-term observations help forecasters together with NWP models and MOSs making good predictions during an event. Which NWP model or MOS is the best, depends mostly on the weather situation, the place of the event and the predictands, as it is shown in the case study of the WSG. A first decision guidance for choosing a prediction model can be given as followed: In the case that a distinctive orography has impact on mesoscaled weather conditions, a limited area model is mostly usable. For nearly even environments, a global circulation model is satisfactory most of the time. To get quickly an idea of the synoptic situation, which is often important due to lack of time during an event, optical visualizations are advantageous. Direct model outputs in text formatted forms (APPENDIX: ECMWF QFA) are usable for direct readable predictands like temperature, wind direction and wind speed. By means of the usage of a combination of text formatted and visualized model outputs, synoptic distributions can be detected easily. Other forecasting models like MOS provide precise predictands, as it is shown in this thesis as a result of the forecast verification in chapter IV for the two used model output statistics MOSMix and AustroMOS. The advantage of these models is the combination of weighted predictands of numerous weather prediction models and a long-term weather station data set, for which the MOS is calculated. The usage of skill scores and indices helps the forecasters finding out the best combination of prediction models for the venues and to enhance their own predictions by correcting systematical errors of the DMOs for each predictand. Because each meteorological job definition has its specific demands on atmospheric parameters, different skill scores and indices should be investigated. If models result in unsatisfactory scores, the forecasting team has to analyze the reason for the failings. In the case that no reasons can be found or no corrections of the forecasted predictands can be done, other prediction models should be considered. Independent from the correctness of weather prediction model outputs and the skills of the forecasters, another important point for making good predictions and decisions, is a good communication between the meteorologists, the organization committee and athletes, in face of lack of time during an event.

- 112 - Conclusions and Outlook

As the case study of the WSG has shown, a mixture of different NWP models, MOSs, local experience and forecasters, who are able to understand and interpret model outputs leads to good results in weather prediction for sporting events. A fill of different experiences, models and tools is the way to success. The predictions, which were made during the WSG where not only useful for the organization committee, they were even valuable for economy. For example, caterers used precipitation forecasts to arrange manpower saving costs. For me, this project was of large interest in all respects, although it was not the first economic sporting event which I accomplished. The first time in my life, when I supported a sporting event with meteorological services, was the meteorological support for the World University Winter Games (Winteruniversiade) in Innsbruck, Seefeld and Hochfilzen in the year 2005. Every time I was taking part in such an event, I gained lots of experiences and knowledge, which can not be found in any textbook. But for the event all knowledge, which I acquired during my studies of Meteorology and Geophysics, could usefully be applied for such high-end weather forecasts and nowcasts. The weather service for the World Sailing Games was a great success. The forecasting team of the ZAMG demonstrated together with its partner of the ‘University of Natural Resources and Applied Life Sciences Vienna’ and the ‘University of Veterinary Medicine Vienna’ that even for smaller events than the Olympics, a professional weather service can be provided. The organization committee, managers, sailors and even the public used this service for information about the current and the upcoming weather situation and general information in meteorology. Many persons, who visited the meteorologists in their weather tent, were very interested in discussions with the forecasters. Because everybody is concerned with weather in someway, all people were glad to meet meteorologists for discussions about their own experiences and observations of meteorological parameters. In my opinion, meteorological services and supports, especially nowcasts, become more important for future events due to steadily increasing forecast accuracy and quality and therefore the possibility of better planning and accomplishment of events. This precise knowledge about the upcoming weather conditions helps maximizing profits of financial by support decision making and the potential of exacter planning of human and material resources. Even public and spectators’ safety is rising due to precise storm

- 113 - Conclusions and Outlook and severe weather warnings, when emergency authorities, like ambulances, firefighters and disaster control are better prepared for service operations. These weather supports make events safer and more pleasant for all participants, and easier for the organization committee in decision making.

- 114 - Appendix

Appendix

Table of Wind Speed:

Table of Wind Speeds beaufort kmh -1 ms -1 knots 0 < 1 <0.2 < 1 1 5 0.3 - 1.5 1 - 3 2 11 1.6 - 3.3 4 - 6 3 19 3.4 - 5.4 7 - 10 4 28 5.5 - 7.9 11 - 16 5 38 8.0 - 10.7 17 - 21 6 49 10.8 - 13.8 22 - 27 7 61 13.9 - 17.1 28 - 33 8 74 17.2 - 20.7 34 - 40 9 88 20.8 - 24.4 41 - 47 10 102 24.5 - 28.4 48 - 55 11 117 28.5 - 32.6 56 - 63 12 > 117 > 32.6 > 63

Definition of Steinacker’s Stroemungslagenklassifikation:

‚Die lokalen Windverhältnisse im Alpenraum sind in extremem Maße von der Topographie geprägt. Während thermisch angeregte Zirkulationssysteme - Hangwind, Talwind, gesamtalpine thermische Zirkulation - von der großräumigen Strömung moduliert werden, sind Paßwinde, Föhn, Bora, Bise, Mistral, etc. direkt von der großräumigen Druckverteilung und Strömung in der Atmosphäre verursacht. Eine synoptisch-klimatologische Erfassung von Lokalwindsystemen, z.B. in Hinblick auf lufthygienische Auswirkungen, sollte daher durch eine Strömungslagenklassifikation der untersten Troposphäre besser gewährleistet sein, als durch die alleinige Beschreibung von Hoch- und Tiefdruckgebieten. Die Lage der Druckzentren ist nur recht lose mit der lokalen Strömungsrichtung bzw. -intensität verknüpft. Die Schüepp'sche Klassifikation, die zwar eine Strömungslagenklassifikation darstellt, ist aufgrund der verwendeten 500 hPa-Analyse dafür nicht optimal geeignet. Die Windrichtung unterscheidet sich nämlich zwischen der 500 und z.B. 850 hPa-Fläche z.T. beträchtlich. So ist etwa im Zeitraum 1946-1979 (Fliri und Schüepp, 1984) Südföhn in Innsbruck bei 30 von den 32 Schüepp'schen Witterungslagen aufgetreten nur bei antizyklonaler Nord-Lage und interessanterweise bei zyklonaler Südostlage nie),

- 115 - Appendix obwohl man bei einer Strömung aus dem nördlichen Sektor Südföhn für schwer möglich halten würde. Der Vorteil einer reinen Strömungslagenklassifikation liegt in der geringen Anzahl von Lagen, nämlich 10, entsprechend den 8 Haupt- und Nebenwindrichtungen, einer gradientschwachen Lage und einer Lage, wo sich z.B. durch eine Frontpassage die Strömungsrichtung im Tagesverlauf markant ändert (‘variabel’). An ‘gradientschwachen’ Tagen sollten die thermisch induzierten tagesperiodischen Windsysteme im Alpenraum dominieren. Die Klasse ‘variabel’ ist wohl der größte Nachteil einer Strömungslagenklassifikation, vor allem, weil diese verhältnismäßig oft auftritt. Allerdings ist zu berücksichtigen, daß sich an einem Tag mit Frontdurchgang die Strömungsverhältnisse in Alpentälern meist dramatisch ändern (z. B. Föhnzusammenbruch) und es daher eben nicht möglich ist, nur ein charakteristisches Muster für diesen Tag anzugeben. Hier wird die Grenze von Wetterlagenklassifikationen deutlich vor Augen geführt (Steinacker, 1991) .

- 116 - Appendix

ECMWF QFA - example for the WSG 2006 (May 16):

11194 OS Neusiedl am See 16.86 47.94 129 Orog : 166

AGL : Dienstag, 16.Mai 2006 00 UTC ZAMG/ECMWF (0.5 Grad) Tage 0-2

======Termin | DI,16.Mai 2006 | MI,17.Mai 2006 | DO,18.Mai 2006 | Zeit UT | 00 06 12 18 | 00 06 12 18 | 00 06 12 18 | ======|======|======|======| N | 7 8 5 8 | 8 8 3 0 | 7 0 8 8 | Nh | 6 8 4 8 | 8 8 1 0 | 7 0 8 8 | Nm | 3 1 2 1 | 1 3 2 0 | 1 0 7 3 | Nl | 0 0 0 0 | 0 2 1 0 | 0 0 0 0 | N-CU | 5 6 3 4 | 3 1 2 2 | 2 2 5 4 | ------|------|------|------| SHW-IX | 4.0 5.2 1.4 0.8 | 0.4 2.0 2.4 2.7 | 4.1 6.6 3.6 2.5 | AUSL.T | 26 27 24 26 | 23 16 19 22 | 23 25 27 27 | WX -CUF | NIL NIL RASH TS | TS RASH RASH RASH | NIL NIL NIL RASH | ------|------|------|------| RR | --- 0.5 0.4 | --- 4.8 4.8 0.0 | ------0.6 | RR-str | ------| --- 0.7 0.4 --- | ------| CONV | --- 0.5 0.4 | --- 4.1 4.4 0.0 | ------0.6 | SN | ------| ------| ------| ------|------|------|------| SN--RA | 2700 2700 2800 2900 | 2800 2600 2600 2700 | 2700 2900 3100 3100 | ------|------|------|------| QAO 300 | 32019 30021 29023 29019 | 27019 25022 31028 33045 | 33037 31027 29026 27028 | QAO 500 | 30014 30015 28017 30014 | 27013 28019 30015 32021 | 31019 32019 29017 27019 | QAO 700 | 28009 29011 27012 28014 | 27011 29012 33010 32011 | 30012 31014 29014 28014 | QAO 850 | 25007 28007 26009 28009 | 29008 31008 34009 33010 | 31007 26004 25008 27011 | QAO 925 | 22012 26005 26008 27006 | 31010 32009 33008 33009 | 32004 20005 20007 27008 | QAN | 19004 20002 25004 22002 | 30003 32004 32005 30002 | 30002 17003 19004 29002 | QANmax | 5 8 4 | 5 8 9 9 | 4 5 10 4 | ------|------|------|------| T 300 | -45 -44 -44 -45 | -46 -47 -46 -43 | -42 -41 -40 -41 | T 500 | -17 -16 -16 -16 | -16 -17 -19 -17 | -16 -14 -14 -13 | ------|------|------|------| T 3000m | 0 -1 1 1 | 1 0 0 0 | 0 1 3 3 | T 2000m | 7 7 7 8 | 7 5 5 6 | 6 7 10 9 | T 1500m | 10 11 10 12 | 11 8 8 9 | 9 10 13 13 | T 1000m | 14 15 14 16 | 14 11 11 13 | 13 13 17 16 | T 800m | 16 16 16 18 | 16 12 13 15 | 14 14 18 18 | T 700m | 16 16 17 19 | 16 12 14 16 | 15 15 19 18 | T 500m | 16 17 19 20 | 17 13 15 17 | 15 15 21 20 | ------|------|------|------| T 2m | 14 16 22 19 | 16 15 19 18 | 12 16 25 19 | Tk 2m | | | | T GND | 14 16 23 19 | 16 16 20 18 | 13 17 26 19 | Max-ADI | 25 25 25 26 | 25 22 23 23 | 24 24 28 27 | Min Max | 12 25 | 15 21 | 10 26 | FROST | 3000 2900 3100 3100 | 3100 2900 2900 3000 | 3000 3200 3500 3500 | ------|------|------|------| W500 cm | 0.1 -0.8 6.7 1.8 | -0.2 -3.6 -0.3 0.0 | 3.7 -4.5 0.6 -3.7 | W700 -- | -0.4 0.4 2.6 -2.0 | 1.4 1.5 -1.1 -1.8 | -2.8 -0.8 0.6 0.6 | W850 s | 2.2 1.4 0.4 -0.1 | 2.3 0.4 -2.2 -2.3 | -3.1 0.0 1.4 0.8 | ------|------|------|------| H 700 | 3108 3106 3108 3107 | 3100 3082 3094 3106 | 3113 3113 3121 3107 | H 1000 | 154 150 141 127 | 134 144 150 155 | 166 155 127 118 | ------|------|------|------| RF 300 | 52 98 99 100 | 100 99 31 73 | 64 84 59 100 | RF 400 | 70 66 79 88 | 90 77 31 44 | 74 77 77 97 | RF 500 | 99 30 69 69 | 85 64 35 32 | 85 65 99 88 | RF 700 | 24 78 88 77 | 88 93 77 51 | 33 63 89 95 | RF 850 | 57 53 79 70 | 79 91 74 79 | 77 68 58 68 | RF 925 | 50 45 63 52 | 75 93 82 60 | 62 63 62 60 | ======

- 117 - Bibliography

Bibliography

Chapter II - Literature Review

- Anderson-Berry, L., Keenan, T., Bally, J., Pielke, R.Jr., Leigh, R., King, D., 2004: ‘The societal, social, and economic impacts of the Wolrd Weather Research Programme Sydney 2000 Forecast Demostration Project (WWRP S2000 FDP)’ – Weather and Forecasting, 19 , 168 – 178

- Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J., 1984: ‘Classification and Regression Trees’, Wadsworth, 358 pp.

- Crook, N.A., Sun, J., 2002: ‘Assimilation radar, surface, and profiler data for the Sydney 2000 Forecasting Demonstration Project’ – Journal of Atmospheric and Oceanic Technology, 19 , 888 – 898

- Dixon, M., Wiener, G., 1993: ‘TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting – A radar-based Methodology’ – Journal of Atmospheric and Oceanic Technology, 10 , 785 – 797

- Dreiseitl, E, 1975: ‘Gutachten zu den Olympischen Spielen in Innsbruck 1976’ - Expert opinion about the climatological conditions in Innsbruck, Austria for the International Olympic Committee, Institute of Meteorology and Geophysics, University of Innsbruck, Xerox copy, 16 pp.

- Hart, K.A., Steenburgh, W.J., Onton, D.J., 2005 – ‘Model Forecast Improvements with Decreased Horizontal Grid Spacing over Finescale Intermountain Orography during the 2002 Olympic Winter Games’ – Weather and Forecasting, 20 , 558 – 576

- Horel, J., Potter, T., Dunn, L., Steenburgh, W.J., Eubank, M., Splitt, M. and Onton, D.J., 2002: ‘Weather Support for the 2002 Winter Olympic and Paralympic Games’ – American Meteorological Society, 227 – 240

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- Houghton, D., 1992: ‘Wind Strategy’ – Fernhurst Books, 91 pp.

- Keenan, T.D., 1999: ‘Hydrometeor classification with a C-band polarimetric radar ’ – American Meteorological Society, 184 – 187

- Klein, W.H., Lewis B.M. and Enger, I., 1959: ‘Objective prediction of five-day mean temperatures during winter’ – J. Meteor., 16 , 672 – 682

- Kim, S.K., 1999: ‘Logistic regression for predicting sea breeze occurrence in Sydney Harbour’ – M.S. thesis, Department of Statistics, School of Mathematics, University of New South Wales, 91 pp.

- Lapczak, S et al, 1999: ‘The Canadian National Radar Project’ – American Meteorological Society, 327 – 330

- May, P. et al, 2004: ‘The Sydney 2000 Olympic Games Forecast Demonstration Project: Forecasting, Observing Network Infrastructure, and Data Processing Issues’ – Bulletin of the American Meteorological Society, 19 , 115 – 130

- Mueller, C. et al, 2003: ‘NCAR Auto-Nowcast System’ – Weather and Forecasting, 18 , 545 – 561

- Reiter, E.R., 1958: ‘Gutachten zu den Olympischen Spielen 1964 in Innsbruck’ - Expert opinion about the climatological conditions in Innsbruck, Austria for the International Olympic Committee, Institute of Meteorology and Geophysics, University of Innsbruck, Xerox copy, 16 pp.

- Spark, E. and Connor, G.J., 2003: ‘Wind Forecasting for the Sailing Events at the Sydney 2000 Olympic and Paralympic Games’ – Weather and Forecasting, 19 , 181 – 199

- Vergeiner, I., Dreiselt, E., Feichter, H., Pümpel, H., 1978: ‘Inversionslagen in Innsbruck’ , Wetter und Leben, Jahrgang 30, 69 – 86

- 119 - Bibliography

- Vivekanandan, J. et al, 1999: ‘Cloud microphysics retrieval using S-band dual- polarization radar measurements’ – Bulletin of the American Meteorological Society, 80 , 381 – 388

Internet – Sources: - Figure of Australian Climate: ‘http://www.worldbook.com/wb/Students?content_spotlight/climates/australian_ climate’

- Information about the 2002 Olympic Winter Games in Salt Lake City from ‘http://www.olympic.org/uk/games/’

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Chapter III - Forecast Verification

- Finley, J.P., 1884: ‘Tornado predictions’ – American Meteorological Journal, 1, 85 – 88

- ISAF, 1997: ‘International Sailing Federation (ISAF) race management manual’ - edition 4/97

- Jolliffe, I.T., Stephenson, D.B., 2003: ‘Forecast Verification’ – Wiley, 97 pp.

- Murphy, A.H., Epstein, E.S., 1989: ‘Skill scores and correlation coefficients in model verification’ – Monthly Weather Review, 117 , 572 – 581

- Schoenwiese, C.D., 2000: ‚Praktische Statistik fuer Meteorologen und Geowissenschaftler’ – Borntraeger, 298 pp.

- Spark, E. and Connor, G.J., 2003: ‘Wind Forecasting for the Sailing Events at the Sydney 2000 Olympic and Paralympic Games ’ – Weather and Forecasting, 19 , 181 – 199

- Wilks, D.S., 1995: ‘Statistical Methods in the Atmospheric Sciences’ – Academic Press, 627 pp.

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Chapter IV - World Sailing Games 2006

- Csekits, C., Haiden, T., Kann, A., Pistotnik, G., Stadlbacher, K., Wittmann, C., 2005: ‘Integrated nowcasting through comprehensive analysis (INCA)’ – ‘http://www.met.hu/pages/egows2006/EGOWS06_Csekits.ppt’

- Glahn, H.R., Lowry, D.A., 1972: ‘The Use of Model Output Statistics (MOS) in Objective Weather Forecasting’ , Journal of Applied Meteorology, 11, 1203- 1211 pp.

- Haiden, T., Kann, A., Stadlbacher, K., Pistotnik, G., Steinheimer, M., Wimmer, F., Wittmann, C., 2006: ’The analysis and nowcasting system INCA’ – ‘http://www.rclace.eu/File/Physics/2006/haiden_INCA_presentation_2006.pdf’

- Lotteraner, C, 2001: ‘Land- und Seewinde am Neusiedlersee’ – diploma thesis – Department of Meteorology and Geophysics - University Vienna, 99 pp.

- Jolliffe, I.T., Stephenson, D.B., 2003: ‘Forecast Verification’ – Wiley, 97 pp.

- ISAF World Sailing Games 2006, 2006: ‘Exposé WSG MetService’, handout in the run-up time of the event

- ISAF World Sailing Games 2006, 2006: ‘Looking Back on Austrian Adventure’ – ‘http://www.worldsailinggames2006.at’, visited in September 2006, no longer available

- Steinacker, R., 1991: ‘Eine Ostalpine Strömungslagenklassifikation’ – Institut für Meteorologie und Geophysik, Universität Wien, Austria

- Wilks, D.S., 1995: ‘Statistical Methods in the Atmospheric Sciences’ – Academic Press, 627 pp.

- Zwatz-Meise, V., 2006: ‘Wissenschaftliche Aktivitäten der ZAMG im Bereich der Synoptischen Meteorologie’ – personal communication

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Internet – Sources:

- Official data set for the accumulated rain average for all capitals and the city of Neusiedl from ‘http://www.zamg.ac.at/fix/klima/oe71-00/’

- Information about former ISAF World Sailing Games from ‘http://www.wikipedia.at’

- Information about the numerical global forecasting model of the ECMWF from ‘http://www.ecmwf.int’

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Acknowledgements

At this point, an exciting and marvelous period of my life ends with this thesis. Therefore, I have to thank those people, who made my studies possible and have encouraged me during the last years, when I was living and studying in Innsbruck. I am deeply indebted to my parents Brigitte and Alois Burgstaller , who did not only financially support me, but also approved all my interests and friends.

My special thanks go to:

- My supervisor ao. Univ. Prof. Dr. Georg Mayr for his professional guidance and mentorship at all time. He gave me the idea of associating my know-how with the meteorological demands of events

- Ao. Univ. Prof. Dr. Ekkehart Dreiseitl for his assistance and his incentive during the time, I was writing on this thesis and for his mentorship through my whole studies. By sharing his knowledge about meteorology, especially climatology, and secular affairs, he graded up this thesis and my studies in Innsbruck as well

- Dr. Herbert Pümpel, who told me many things I had to know about weather forecasting at events. Without his support, the weather service for the World University Winter Games 2005 would not have been as successful as it turned out to be. I am also obliged to his former colleagues Dr. Rudolf Kaltenböck and Dr. Markus Kerschbaum from Austrocontrol GmbH for their support in technical concerns

- Klaus Knüpfer, who supported me with his knowledge about MOS and the private calculation for the AWS Town Neusiedl. As the results of this thesis show, this tool makes weather forecasts often more precise than other prediction models do

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- Dr. Veronika Zwatz-Meise and Dr. Herbert Gmoser from the ZAMG for providing me any data for all my investigations for this thesis. Without these data, this master thesis could not be written

- The forecasting and technician team of the ZAMG for having a very marvelous and instructive time on the lake Neusiedl

- Erich Steiner , who spent me lot of his time with my special demands and wishes

- Ao. Univ. Prof. Dr. Günter Schauberger from the University of Veterinary Medicine Vienna and Ao. Univ. Prof. Dr. Erich Mursch-Radlgruber from the University of Natural Resources and Applied Life Science (Vienna) providing me the data from the additional weather stations, which were installed on the lake Neusiedl especially for the World Sailing Games

- Christa Eller , Mag a. Angelika Neuner and Maria Luise Zangerl for their administrative help and support all along my studies

- All my colleges and friends from the Institute of Meteorology and Geophysics Innsbruck. Especially, I want to mention Mag. Fritz Föst , Mag. Alexander Giordano , Mag a. Esther Grießer , Mag. Georg Haas , Philip Sacherer , Mag. Felix Schüller and Dr. Johannes Vergeiner for their technical assignments

- Mag. Thomas Bortenschlager , Roland Koch , Mag. Josef Lang , Mag. Alexander Niederl , Mag. Marc Olefs, Mag. Peter Rafelsberger and Mag a. Johanna Nemec for their friendship and the great time we had together in Innsbruck

- Robert Torlutter for the proof-reading of this master thesis

- Mag. Klaus Reingruber and Mag. Wolfgang Traunmüller, who gave me a very interesting and instructive job in their company and arranged me a convenient start in my professional life

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- My girl-friend Dr. Christina Schneider for her support and the marvelous time, we have together

Thank you!

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