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Fundamental research addressing the challenges of weather forecasts and climate monitoring is required in order to steadily improve the quality of meteorological and climatological services. However, the potential of transferring results from this research into operations increases significantly if carried out in collaboration with national meteorological and hydrometeorological services (NMHS). Out of this motivation and experience, the German “Hans-Ertel-Centre for Weather Research” (HErZ) has been established in 2011 as a virtual competence centre. HErZ develops and organizes a lasting national consortium along common objectives between the German Meteorological Service (DWD), German universities and scientific institutes to accelerate innovation in DWD’s weather, climate and environmental services. HErZ carries out excellent research, strengthens university education and promotes early career scientists, ensures personnel and strategical development, and facilitates the establishment and success of new cooperation, also outside of Germany. Moreover, HErZ grants access to DWD’s infrastructure, including (global) observation data and prediction models. With the strong support of the German Federal Ministry of Digital and Transport, HErZ was able to overcome the difficulties within initiating a cooperation between partners of different institutional communities and established an unprecedented sustainable and national transdisciplinary network. The current research and objectives of HErZ cover the whole value chain expected from this year’s EMS Annual Meeting’s additional focus on “Connecting Communities”: Bringing together fundamental and applied research, observations with prediction models, research institutions with NHMS, and developers with end-users. This intertwined and progressive structure in science and communication ultimately results in acquiring value for society as a whole. Establishing and managing HErZ and its scientific, strategic and structural goals is challenging. The complex tasks of achieving excellence in science and long-term success require a suitable balance between demands and needs of all institutions involved as well as an independent and strong governance by national and international high-level experts. In the future, HErZ must respond to new societal challenges in a targeted but flexible manner with new topics and competences, national and international partners, and a strengthened dialog with stakeholders and users. With this contribution, we would like to share with the audience the establishment, structure and peculiarities of HErZ, the lessons learnt from a transdisciplinary consortium connecting NHMS, universities and research institutes, and our visions towards future seamless science and services.

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.

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.

This paper describes an approach to derive probabilistic predictions of local winter storm damage occurrences from a global medium-range ensemble prediction system (EPS). Predictions of storm damage occurrences are subject to large uncertainty due to meteorological forecast uncertainty (typically addressed by means of ensemble predictions) and uncertainties in modelling weather impacts. The latter uncertainty arises from the fact that local vulnerabilities are not known in sufficient detail to allow for a deterministic assessment of damages. Thus to estimate the damage model uncertainty, a statistical model based on logistic regression analysis is employed, relating meteorological analyses to historical damage records. A quantification of the two individual contributions to the total forecast uncertainty is achieved by neglecting individual uncertainty sources and analysing resulting predictions. Results show an increase in forecast skill if both meteorological and damage occurrence uncertainties are taken into account. It is demonstrated that skilful predictions on district level are possible on lead times of several days. Skill is increased through the application of a proper ensemble calibration method, extending the range of lead times for which skilful damage predictions can be made.

A refined model for the calculation of storm losses is presented, making use of high-resolution insurance loss records for Germany and allowing loss estimates on a spatial level of administrative districts and for single storm events. Storm losses are calculated on the basis of wind speeds from both ERA-Interim and NCEP reanalyses. The loss model reproduces the spatial distribution of observed losses well by taking specific regional loss characteristics into account. This also permits high-accuracy estimates of total cumulated losses, though slightly underestimating the country-wide loss sums for storm "Kyrill", the most severe event in the insurance loss records from 1997 to 2007. A larger deviation, which is assigned to the relatively coarse resolution of the NCEP reanalysis, is only found for one specific rather small-scale event, not adequately captured by this dataset. The loss model is subsequently applied to the complete reanalysis period to extend the storm event catalogue to cover years when no systematic insurance records are available. This allows the consideration of loss-intensive storm events back to 1948, enlarging the event catalogue to cover the recent 60+ years, and to investigate the statistical characteristics of severe storm loss events in Germany based on a larger sample than provided by the insurance records only. Extreme value analysis is applied to the loss data to estimate the return periods of loss-intensive storms, yielding a return period for storm "Kyrill", for example, of approximately 15 to 21 years.

SummaryFlood generation is triggered by the interaction of the hydrological pre-conditions and the meteorological conditions at different space–time scales. This interaction results in floods of diverse characteristics, e.g. spatial flood extent and temporal flood progression. While previous studies have either linked flood occurrence to weather patterns neglecting the hydrological pre-conditions or categorised floods according to their generating mechanisms into flood types, this study combines both approaches. Exemplary for the Elbe River basin, the influence of pre-event soil moisture as an indicator of hydrological pre-conditions, on the link between weather patterns and flood occurrence is investigated. Flood favouring soil moisture and weather patterns as well as their combined influence on flood occurrence are examined. Flood types are identified and linked to soil moisture and weather patterns. The results show that the flood favouring hydro-meteorological patterns vary between seasons and can be linked to flood types. The highest flood potential for long-rain floods is associated with a weather pattern that is often identified in the presence of so called ‘Vb’ cyclones. Rain-on-snow and snowmelt floods are associated with westerly and north-westerly wind directions. In the analysis period, 18% of weather patterns only caused flooding in case of preceding soil saturation. The presented concept is part of a paradigm shift from pure flood frequency analysis to a frequency analysis that bases itself on process understanding by describing flood occurrence and characteristics in dependence of hydro-meteorological patterns.