Application of an object-based verification method to ensemble forecasts of 10‑m wind gusts during winter storms

The object-based method SAL (Structure, Amplitude and Location) was adapted for investigating the errors of forecasts of extreme 10‑m wind gusts associated with winter storms in Germany. It has been applied to a statistically downscaled version of the 51 member ECMWF (European Centre for Medium Range Weather Forecasts) operational ensemble forecast. The horizontal resolution of both downscaled data and of the German weather service's operational analysis data used for verification is 7 km. Forecast errors are subdivided in terms of storm intensity, location and extent. After identifying a set of storm events, objects of moderate and intense 10‑m wind gusts were identified with a local percentile-based threshold (90th percentile for moderate and 98th percentile for intense gust objects). Depending on the intensity of the storm, the gust objects differ in terms of size, shape and intensity. The characteristics of the ensemble forecasts of 10‑m wind gusts can basically be assessed in two different ways. Individual forecast members can be evaluated with respect to the location, intensity and extent of the gust field, and then address the ensemble characteristics by the score distributions. Alternatively, the gust fields' location, intensity and extent can be evaluated by directly using the ensemble mean forecast instead of the individual members. The results of the identified set of storms clearly indicate a high case-to-case variability in the predictability of 10‑m wind gusts objects, particularly when focusing on the structure of intense wind gust objects. It is found, that the gust fields' location and overall intensity can be better estimated from the ensemble mean forecast, compared to the individual forecast members. From a forecaster's perspective this means, that a storms' location and intensity can be well estimated by considering the ensemble mean wind forecasts. Considering the structure of the gust objects, results are different. While for longer lead times, there also seems to be a benefit from applying ensemble averaging, at short lead times the ensemble mean forecast performs equally or worse than most of the individual forecast members. The amplitude error is often the smallest component of the three error types. The findings are particularly relevant when deriving warning information, by giving guidance to forecasters when interpreting ensemble forecasts for severe storms

Characterisation and predictability of a strong and a weak forcing severe convective event – a multi-data approach

Two severe summer-time convective events in Germany are investigated which can be classified by the prevailing synoptic conditions into a strong and a weak forcing case. The strong forcing case exhibits a larger scale precipitation pattern caused by frontal ascent whereas scattered convection is dominating the convective activity in the weak forcing case. Other distinguished differences between the cases are faster movement of convective cells and larger regions with significant loss mainly due to severe gusts in the strong forcing case. A comprehensive set of various observations is used to characterise the two different events. The observations include measurements from a lightning detection network, precipitation radar, geostationary satellite and weather stations, as well as information from an automated cell detection algorithm based on radar reflectivity which is combined with severe weather reports, and damage data from insurances. Forecast performance at various time scales is analysed ranging from nowcasting and warning to short- range forecasting. Various methods and models are examined, including human warnings, observation-based nowcasting algorithms and high-resolution ensemble prediction systems. The analysis shows the advantages of a multi-sensor and multi-source approach in characterising convective events and their impacts. Using data from various sources allows to combine the different strengths of observational data sets, especially in terms of spatial coverage or data accuracy, e.g. damage data from insurances provide good spatial coverage with little meteorological information while measurements at weather stations provide accurate but pointwise observations. Furthermore, using data from multiple sources allow for a better understanding of the convective life cycle. Several parameters from different instruments are shown to have a predictive skill for convective development, these include satellite-based cloud-top cooling rates as measure for intensive convective growth, 3D-radar reflectivity, mesocyclone detection from doppler radar, overshooting top detection or lightning jumps to evaluate storm intensification and formation of severe weather. This synergetic approach can help to improve nowcasting algorihtms and thus the warning process. The predictability of the analysed severe convective events differs with different types of forcing which is reflected in both, convective-scale ensemble prediction system forecasts and human weather warnings. Human warnings show larger false alarm rates in the weak forcing case. Ensemble predictions are able to capture the characteristics of the convective precipitation. The forecast skill is connected strongly to the synoptic situation and the presence of large-scale forcing increases the forecast skill. This has to be considered for potential future warn-on-forecast strategies

Projections of global warming-induced impacts on winter storm losses in the German private household sector

We present projections of winter storm-induced insured losses in the German residential building sector for the 21st century. With this aim, two structurally most independent downscaling methods and one hybrid downscaling method are applied to a 3-member ensemble of ECHAM5/MPI-OM1 A1B scenario simulations. One method uses dynamical downscaling of intense winter storm events in the global model, and a transfer function to relate regional wind speeds to losses. The second method is based on a reshuffling of present day weather situations and sequences taking into account the change of their frequencies according to the linear temperature trends of the global runs. The third method uses statistical-dynamical downscaling, considering frequency changes of the occurrence of storm-prone weather patterns, and translation into loss by using empirical statistical distributions. The A1B scenario ensemble was downscaled by all three methods until 2070, and by the (statistical-) dynamical methods until 2100. Furthermore, all methods assume a constant statistical relationship between meteorology and insured losses and no developments other than climate change, such as in constructions or claims management. The study utilizes data provided by the German Insurance Association encompassing 24 years and with district-scale resolution. Compared to 1971–2000, the downscaling methods indicate an increase of 10-year return values (i.e. loss ratios per return period) of 6–35 % for 2011–2040, of 20–30 % for 2041–2070, and of 40–55 % for 2071–2100, respectively. Convolving various sources of uncertainty in one confidence statement (data-, loss model-, storm realization-, and Pareto fit-uncertainty), the return-level confidence interval for a return period of 15 years expands by more than a factor of two. Finally, we suggest how practitioners can deal with alternative scenarios or possible natural excursions of observed losses

Mechanismen, Auswirkungen und ihre Unsicherheiten

Abstract iii Zusammenfassung v 1 Introduction 1 1.1 Objectives of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Projected Changes in European Winter Storm Climate 9 2.1 Introduction and Current State of Research . . . . . . . . . . . . . . . . . 9 2.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.1 Reanalysis Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 Global Climate Model Output . . . . . . . . . . . . . . . . . . . . 11 2.2.3 Wind Field Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2.4 Storm Severity Index . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.2.5 Extreme Value Analysis . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Recent and Future Winter Storm Frequency . . . . . . . . . . . . . . . . . 20 2.3.1 Statistical Uncertainty and Natural Variability . . . . . . . . . . . 24 2.3.2 Scenario Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.3.3 Model Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 Intensities of Recent and Future Winter Storms . . . . . . . . . . . . . . . 30 2.4.1 Statistical Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.2 Scenario Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.4.3 Model Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.5 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3 Mechanisms Related to Changes in European Winter Storm Climate 45 3.1 Introduction and Current State of Research . . . . . . . . . . . . . . . . . 45 3.2 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.1 Reanalysis Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.2 Global Climate Model Data . . . . . . . . . . . . . . . . . . . . . . 47 3.2.3 Assessment of the North Atlantic Oscillation . . . . . . . . . . . . 48 3.2.4 Eady Growth Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.2.5 Hadley Cell Characteristics . . . . . . . . . . . . . . . . . . . . . . 50 3.3 Changes in the North Atlantic Oscillation (NAO) . . . . . . . . . . . . . . 50 3.3.1 The NAO in Historical and Recent Climate . . . . . . . . . . . . . 50 3.3.2 Future Changes in the NAO Strength . . . . . . . . . . . . . . . . 51 3.3.3 Changes in the NAO Shape . . . . . . . . . . . . . . . . . . . . . . 52 3.4 Changes in Baroclinicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.4.1 Zonal Mean Eady Growth Rate . . . . . . . . . . . . . . . . . . . . 54 3.4.2 Changes in North Atlantic Eady Growth Rate . . . . . . . . . . . 55 3.4.3 Relation to the NAO . . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.5 NAO Influences on European Winter Storms and their Impacts . . . . . . 57 3.5.1 Dependence of Winter Storm Frequency on the NAO . . . . . . . . 57 3.5.2 Dependence of Storm Damages on the NAO Phase . . . . . . . . . 59 3.6 Tropical Origins of Changes in the North Atlantic Oscillation . . . . . . . 62 3.6.1 Assessment of Hadley Circulation Changes . . . . . . . . . . . . . 62 3.6.2 The Influence of the Hadley Circulation on the NAO . . . . . . . . 64 3.6.3 A Rossby Wave Interpretation of the NAO . . . . . . . . . . . . . 68 3.7 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4 Estimation of Impacts for Future Winter Storms 77 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.2 Current State of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.3 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 4.3.1 Insurance Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 4.3.2 Reanalysis Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.3.3 Regional Climate Model Data . . . . . . . . . . . . . . . . . . . . . 83 4.3.4 Wind Field Tracking for High Resolution Model Output . . . . . . 84 4.4 Modeling of Storm Damages . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.4.1 Basic Loss Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.4.2 High-Resolution Refinement of the Storm Loss Model . . . . . . . 86 4.4.3 Optimization of Storm Damage Model . . . . . . . . . . . . . . . . 88 4.5 Uncertainties in Regional Loss Projections . . . . . . . . . . . . . . . . . . 91 4.5.1 Historical Losses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 4.5.2 Modeling Losses under Recent Climate Conditions . . . . . . . . . 94 4.5.3 Future Changes in Losses . . . . . . . . . . . . . . . . . . . . . . . 95 4.5.4 Uncertainties on Derived Changes . . . . . . . . . . . . . . . . . . 96 4.6 Assessment of Dynamical Downscaling Uncertainties . . . . . . . . . . . . 99 4.6.1 Ensemble Generation Technique . . . . . . . . . . . . . . . . . . . 100 4.6.2 Comparison of GCM and RCM . . . . . . . . . . . . . . . . . . . . 102 4.6.3 Deriving Uncertainties in Modeled Storm Impacts . . . . . . . . . 104 4.6.4 Implications for Climate Change Assessment . . . . . . . . . . . . 106 4.7 Estimates of Return Values for Loss Intensive Winter Storms . . . . . . . 106 4.7.1 Return Values of Historical Winter Storms . . . . . . . . . . . . . 106 4.7.2 Quantification of Uncertainties . . . . . . . . . . . . . . . . . . . . 110 4.7.3 Derived Climate Change Signal and its Uncertainties . . . . . . . . 117 4.8 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 5 Synthesis 125 Bibliography 131 A Supplementary Material 145 Acknowledgement 153The large scale fields of severe winds associated with deep extra tropical cyclones pose severe risks to society and economy by damaging both natural and man-made structures over vast areas. This work addresses anthropogenic changes in the frequency and intensity of European winter storm events, their potential impacts as well as the mechanisms related to such changes. On the basis of global climate projections it is found, that severe wind storms over the North Atlantic are generally decreasing in terms of their frequency, however on a band across the North Atlantic and parts of Europe increased frequency of severe storms is identified in connection with increases in their intensities. Changes are consistently identified amongst multiple model projections and for different scenarios on future greenhouse gas emission. The strength of identified changes is however found to depend on the scenario and particularly on the considered climate model. For central Europe, increases in frequency towards the end of the 21st century are identified under SRES-A1B conditions, ranging between -11% and +44% and an ensemble average of 21%. In terms of intensity, storms affecting central Europe occurring once a year are found to increase in strength by about +30%, with individual models projecting changes between -28% and up to +96%. Considerable robustness of results is found, with 7 out of 8 simulations projecting both increased frequency and intensity of winter storms affecting central Europe. With respect to underlying mechanisms for these changes, the relation to projected changes in the North Atlantic Oscillation (NAO), as well as changing baroclinic conditions of the atmosphere are investigated. It is found that the NAO undergoes fundamental changes with respect to both its phase as well as its shape. Consistent to diagnosed changes in storm frequency, the NAO is found shifting towards a more positive phase with its action centers shifting in north-eastward direction, which is related to more favorable growth conditions for cyclones over eastern parts of the North Atlantic and central Europe. The tropical influence on projected changes in the European storm climate are investigated by addressing the relation between the tropical Hadley circulation and the NAO, with a strong relation being identified between a projected northward expansion of the Hadley cell and changes in the NAO. Results from theoretical considerations, interpreting the NAO as a manifestation of a stationary Rossby wave induced by the overtopping zonal winds above the Rocky Mountains, are found to be well aligned with the projected eastward shift of the NAO action centers. Compared to changes in the European storm climate on large scales, the assessment of trends in storm related losses is associated with much larger uncertainty, which beside the large statistical uncertainty result from multiple uncertainty sources along the modeling chain. To quantify the uncertainty resulting from the dynamical downscaling of general circulation model (GCM) output, a methodology has been developed to generate high-resolution ensembles of potentially hazardous storms identified in GCM output. A large source of uncertainty is related to the modeling of local storm losses on the basis of near-surface wind gust estimates. Deriving storm-loss transfer functions on district level yields the advantage of including local differences in the vulnerability against severe winds, however uncertainties on determined vulnerability parameters are shown to be considerable. Grouping districts into larger regions is found to significantly reduce the involved uncertainty, correspondingly reducing the uncertainty inherent to future loss projections. Besides addressing single sources of uncertainty, a methodology has been developed to derive cumulative uncertainty ranges on estimates of future return levels and return periods within an extreme value analysis framework. Results indicate, that under SRES- A1B conditions, the accumulated German wide losses of a winter storm event occurring once in 5 years increases towards the end of the 21st century by about +30%, with an estimated uncertainty ranging between -5% and +87%. Correspondingly, the return period of a 5 year event is found to decrease to about 4.3 years with an uncertainty range between 3.7 to 5.2 years. Even larger increases in losses and decreases in return periods can be identified for events being even more infrequent, however associated with strongly increasing uncertainties on these estimates. Based on transient regional climate model (RCM) projections, German wide winter storm losses are found to increase by about +14% towards the end of the 21st century under SRES-A1B conditions, with individual RCM signals ranging between -14% and +39%, with 9 out of 12 models projecting increased losses. With respect to regional differences in such trends, north-western parts of Germany are found to be more affected with increases of up to 30% in ensemble average, while south- eastern parts feature only moderate increases in losses by about 5%.Die zerstörerische Wirkung extremer Winde im Zusammenhang mit intensiven extra-tropischen Zyklonen auf natürliche Ökosysteme und menschliche Strukturen stellt eine ernsthafte Bedrohung für Gesellschaft und Wirtschaft dar. Diese Arbeit untersucht anthropogene Änderungen in der Häufigkeit und Intensität Europäischer Winterstürme, potentielle Auswirkungen sowie Mechanismen welche im Zusammenhang zu diesen Änderungen stehen. Auf Basis globaler Klimaprojektionen findet sich eine generelle Abnahme in der Häufigkeit von Winterstürmen über dem Nordatlantik, wobei es auf einem Band über dem Nord Atlantik und Teilen Europas zu einer Zunahme potentiell schadenrelevanter Sturmsysteme kommt, verbunden mit einer Intensivierung dieser Systeme. Diese Änderungen können dabei in Projektionen mit unterschiedlichen Globalmodellen sowie für verschiedene Szenarien bzgl. zukünftiger Treibhausgasemissionen identifiziert werden, wobei die Stärke der diagnostizierten Änderungen vom untersuchten Szenario und vor allem vom untersuchten Modell abhängt. Für Zentraleuropa kann für das Ende des 21. Jahrhunderts unter SRES-A1B Bedingungen eine Änderung in der Häufigkeit von Sturmereignissen zwischen -11% und +44% festgestellt werden, wobei das Ensemble Mittel eine Zunahme um 21% zeigt. Darüber hinaus finden sich Änderungen in der Intensität einjährig wiederkehrender Winterstürme in Zentraleuropa, welche einer Zunahme um +30% entsprechen, wobei individuelle Modellläufe Änderungen zwischen -28% und +96% projizieren. Die Robustheit der Ergebnisse wird dadurch unterstrichen, dass 7 von 8 Simulationen eine Zunahme in sowohl der Häufigkeit als auch Intensität von Winterstürmen in Zentraleuropa projizieren. Im Hinblick auf zugrundeliegende Mechanismen wird in der Arbeit sowohl die Beziehung zu Änderungen in der Nord Atlantischen Oszillation (NAO), sowie die Änderung in den baroklinen Eigenschaften der Atmosphäre untersucht. Dabei können fundamentale Änderungen der NAO identifiziert werden, sowohl bezüglich ihrer Phase als auch ihrer Form. Konsistent zu den Änderungen in der Sturmhäufigkeit findet sich eine Verschiebung hin zu positiveren Phasen der NAO, wobei sich ihre Aktionszentren in nordöstliche Richtung verschieben, welches mit günstigeren Wachstumsbedingungen für Zyklonen über dem östlichen Nordatlantik und über Europa einhergeht. Ein tropischer Einfluss auf Änderungen im Europäischen Sturmklima wird untersucht, indem eine Beziehung zwischen Eigenschaften der Hadley Zirkulation und der NAO hergestellt wird. Dabei findet sich ein enger Zusammenhang zwischen einer nordwärts Ausdehnung der Hadley Zelle und den projizierten Änderungen der NAO. Theoretische Betrachtungen der NAO als Ausprägung einer stationären Rossby Welle, induziert durch die zonale Anströmung der Rocky Mountains können dabei in Einklang gebracht werden mit der diagnostizierten ostwärts Verschiebung der NAO Aktionszentren. Im Vergleich zu großskaligen Änderungen im Europäischen Sturmklima unterliegt die Abschätzung von Trends in sturminduzierten Schäden deutlich größeren Unsicherheiten, welche neben großen statistischen Unsicherheiten auf eine Reihe von Unsicherheitsquellen in der Modellkette zurückzuführen sind. Um die Unsicherheiten der dynamischen Regionalisierung globaler Klimamodelle (GCM) zu quantifizieren, wurde eine Methodik entwickelt um hochaufgelöste Ensemble Simulationen potentiell schadenrelevanter Sturmereignisse zu generieren. Eine große Unsicherheitsquelle stellt außerdem die Modellierung lokaler Sturmschäden auf Basis bodennaher Winden dar. Sturmschaden-Transferfunktionen können dabei auf Landkreisebene abgeleitet werden, mit dem Vorteil, dass lokale Vulnerabilitäten bzgl. extremer Winde abgebildet werden. Dabei entstehen jedoch große Unsicherheiten auf abgeleitete Modellparameter. Durch Zusammenfassen von Landkreisen zu größeren Regionen können diese signifikant reduziert werden, wodurch eine entsprechende Reduktion der Unsicherheit auf projizierte Schäden erreicht wird. Um die separat quantifizierten Unsicherheitsquellen zu Gesamtunsicherheiten zu integrieren, wurde ein Verfahren entwickelt um die kumulativen Unsicherheitsspannen auf abgeleitete Wiederkehrniveaus und Wiederkehrperioden im Rahmen der Extremwertstatistik zu berechnen. Dabei zeigen die Ergebnisse, dass unter SRES-A1B Bedingungen der Deutschlandweite Schaden von Winterstürmen mit einer Wiederkehrperiode von 5 Jahren um etwa +30% zunimmt, mit einer Unsicherheitsspanne zwischen -5% und +87%. Entsprechend reduzieren sich die Wiederkehrperioden von 5 Jahren auf etwa 4.3 Jahre, mit einer Unsicherheitsspanne zwischen 3.7 und 5.2 Jahren. Für seltenere Ereignisse finden sich sogar stärkere Zunahmen in Schäden und entsprechend Abnahmen in den Wiederkehrperioden, wobei diese Abschätzungen mit deutlich größeren Unsicherheiten behaftet sind. Auf Basis transienter regionaler Klimaprojektionen findet sich eine Zunahme Deutschlandweiter Schäden um +14% zum Ende des 21. Jahrhunderts unter SRES-A1B Bedingungen, wobei die Signale individueller Projektionen zwischen -14% und +39% liegen und 9 von 12 Modellsimulationen eine Zunahme projizieren. Im Hinblick auf regionale Unterschiede in diesem Trend, findet sich eine stärkere Betroffenheit der nordwestlichen Regionen Deutschlands mit einer Zunahme der Sturmschäden um etwa 30% im Ensemble Mittel. Für südwestliche Regionen hingegen finden sich nur moderate Änderungen der Schäden um etwa +5%

Estimating uncertainties from high resolution simulations of extreme wind storms and consequences for impacts

A simple method is presented designed to assess uncertainties from dynamical downscaling of regional high impact weather. The approach makes use of the fact that the choice of the simulation domain for the regional model is to a certain degree arbitrary. Thus, a small ensemble of equally valid simulations can be produced from the same driving model output by shifting the domain by a few of grid cells. Applying the approach to extra-tropical storm systems the regional simulations differ with respect to the exact location and severity of extreme wind speeds. Based on an integrated storm severity measure, the individual ensemble members are found to vary by more than 25 % from the ensemble mean in the majority of episodes considered. Estimates of insured losses based on individual regional simulations and integrated over Germany even differ by more than 50 % from the ensemble mean in most cases. Based on a set of intense storm episodes, a quantification of winter storm losses under recent and future climate is made. Using this domain shift ensemble approach, uncertainty ranges are derived representing the uncertainty inherent to the used downscaling method