While this sounds convincing and simple on paper, it involves various technical, methodological, and strategic requirements and transdisciplinary challenges. In particular, user engagement, co-design and stakeholder management are important prerequisites to develop successful operational products and services. This session therefore aims to assemble relevant actors and findings from all involved parties and disciplines at the interface of weather and climate risks and impact-based services. It seamlessly unites weather and climate scales and natural and social sciences to make the best use of risk and impact information for citizens and society. We therefore invite a broad international and interdisciplinary exchange on the following aspects:
- latest research and findings on risks and impacts of weather and climate extremes to societies, economies and environments, including terminology and concepts of risk,
- risk- and impact-based forecasts and warnings to enhance the value of weather and climate services in society, including probabilistic forecasts and uncertainty,
- demonstrators or operational services for weather and climate risk assessments,
- identification of gaps, needs and transdisciplinary challenges to co-design successful services and products,
- application of novel, ideally open data sources for exposure, vulnerability and socioeconomic impacts (losses and damages) for risk and impact assessments and their validation,
- methodologies, such as software and models, for the development and provision of risk and impact assessments
We reserve the option to convert talks into poster contributions to ensure a focused and impactful session.
The European Severe Storms Laboratory (ESSL) was founded in 2006 as a non-profit research organization building on a pre-existing informal network of European scientists. ESSL was created to foster pan-European collaboration in the study and forecasting of severe convective storms, tornadoes, and extreme weather events. Since then, it has grown into a leading centre for severe weather research, education, and operational support with various activities aimed at reducing the impact of extreme meteorological events on society.
ESSL’s core activities include the development and maintenance of the European Severe Weather Database (ESWD) together with its network of volunteers. The ESWD has grown to contain approximately 500,000 events and forms the basis for a wide range of research and development work as shown by its citations in over 400 peer-reviewed publications.
The Additive Regression Convective Hazard Models (AR-CHaMo), and the Weather Data Displayer and Radar Displayer, a sophisticated platform for visualizing and analysing meteorological data, are among the most important severe weather analysis tools that ESSL has developed. The AR-CHaMo models were originally developed for climate analysis and are an important tool for risk assessments by both public authorities and the reinsurance sector. They have also been adapted for use in the prediction of severe storm hazards, in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF). Since 2025, the Displayer and tailored AR-CHaMo-based products can be licensed by ESSL Services, a private sector spin-off of ESSL.
Over the years, ESSL has published a wide range of peer-reviewed publications on topics ranging from the relation between severe storms and climate change to analyses of severe storms, their impacts and their detection. Currently, ESSL is leading the initiative for a large-scale multi year field campaign named TIM for the collection of data on severe storms to improve the understanding of their evolution near mountain ranges.
ESSL's annual Testbeds and its training programs, including specialized courses for forecasters, often held at its Research and Training Centre in Wiener Neustadt, Austria. These initiatives are conducted in close cooperation with EUMETSAT, ECMWF and weather services, for example focusing on preparing meteorologists for new satellite capabilities, such as those offered by the Meteosat Third Generation. Joint workshops and expert-led sessions ensure that forecasters are equipped with the latest tools and knowledge to enhance severe weather prediction and response.
Through its partnerships with national or regional meteorological services and international organizations, many of whom are one of ESSL's 32 Full Institutional Members, the laboratory continues to advance the science of severe storms, and strengthen the resilience of European communities to high-impact weather events.
How to cite: Groenemeijer, P., Holzer, A. M., Renko, T., Antonescu, B., and Baart de la Faille, M.: The Twenty-Year Anniversary of the European Severe Storms Laboratory (ESSL), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-706, https://doi.org/10.5194/ems2026-706, 2026.
Climate change is increasingly affecting natural, societal and economic systems in Switzerland, requiring a better understanding of cross-sectoral risks and their interactions. This challenge is addressed by the programme “NCCS-Impacts” of the Swiss National Centre for Climate Services (NCCS), in which five interlinked projects cover (1) socioeconomic scenarios, (2) human and animal health, (3) ecosystem services, (4) supply chains, and (5) economic costs. The projects are closely connected and generate strong synergies. Results will be released progressively until the end of 2026.
In addition to generating new scientific insights, NCCS-Impacts places strong emphasis on developing actionable, user-oriented climate services. These are co-produced by researchers, practitioners, stakeholders and communication experts to maximise their usability and relevance for climate adaptation and mitigation. At the programme level, a key objective is to synthesise results across sectors and disciplines in a consistent and structured way making complex and heterogeneous findins more accessible, comparable and relevant for decision-making.
The presentation provides a synthesis of key results from the projects, including new socioeconomic pathways for Switzerland and associated greenhouse gas emissions, projections of heat-related mortality and vulnerability risks, climate risks for supply chains, and impacts on agricultural yields. It will also showcase selected web-based tools tailored to user needs, such as a hospital management tool for forecasting heat-related emergency visits, an interactive map for identifying supply chain risks, and a dashboard for exploring cross-sectoral impacts on ecosystem services. In addition, the presentation will introduce the overarching synthesis concept developed within NCCS-Impacts, illustrating how cross-sectoral findings can be integrated, structured and communicated to support informed decision-making.
How to cite: Fischer, A., Björnsen, A., Brown, S., Croci-Maspoli, M., Hama, A. M., Fussen, D., Füssler, J., Gubler, L., Huber, N., Oberpriller, Q., Rajczak, J., Röthlin, R., Roth, V., Szelecsenyi, A., Vicedo-Cabrera, A. M., and Zysset, A.: New insights on climate risks in Switzerland – findings from the cross-sectoral programme “NCCS-Impacts”, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-188, https://doi.org/10.5194/ems2026-188, 2026.
Germany’s new natural hazards portal for the general public, “Naturgefahrenportal” (NGP) was released in April 2025. The NGP aims to consolidate various sources of hazard-related data onto a single accessible platform, promoting public understanding and preparedness. To this end, the NGP website presents current warnings for Germany, location-based hazards and risks as well as concrete measures for preparedness and behavior in a case of emergency.
The NGP serves as a central hub of information for the general public, aggregating the most relevant information in a uniform way, while linking to regional or single-hazard portals for further interest. It does not provide push notifications but complements existing public alert systems with a situational overview for Germany and background information on natural hazards.
Since its release, the NGP has expanded its contents and uncovered areas for improvement of consistency between German institutions providing natural hazard-related data and information. The delicate balance between scientific correctness and communicating to an audience with limited prior knowledge or interest in natural hazards has been experienced, and User Experience (UX) has been identified as one major challenge for bringing natural hazard information to the general public.
The presentation gives a balanced overview of Germany's Natural Hazards Portal for Germany as well as the new features since its release, topics of consistency and User Experience.
The NGP represents a major step forward in the communication of natural hazards in Germany. It serves the mutual benefit of reaching the public and featuring existing services and platforms. The integration of all relevant information onto a single, unified platform offers a holistic view of natural hazards in Germany.
Acknowledgements
We would like to express our gratitude to various institutions in Germany for their support and their contributions to this DWD project, among them the German Federal Flood Forecasting Centers, the Bundesamt für Seeschifffahrt und Hydrographie (BSH), the Federal Office of Civil Protection and Disaster Assistance (BBK), the Bundesamt für Kartographie und Geodäsie (BKG), and the Bundesanstalt für Gewässerkunde (BfG), and our colleagues of various departments at DWD.
How to cite: Riede, H., Brendel, C., Erhardt, B., Hafer, M., Haller, M., Hüser, I., Koziar, C., Lengfeld, K., Rode, D. K., Rauthe-Schöch, A., Reetz, B., and Walawender, E.: Germany's national hazards portal NGP - Updates and developments, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-795, https://doi.org/10.5194/ems2026-795, 2026.
In recent years, we have unfortunately seen all too often how vulnerable our society is to weather and climate extremes (heatwaves, extreme precipitation events, etc.). In the context of climate change, it becomes even more important to better prepare society as a whole for such events. It turns out to be a difficult challenge to provide knowledge and data that truly resonate with actors in resilience. In Belgium, the Royal Meteorological Institute (RMI) and Climate Risk Assessment Center (CERAC) have joined forces to strengthen society’s resilience to future weather and climate extremes in a very concrete way through so-called Tales of Future Weather. The method starts by selecting a plausible future extreme event from climate projections. This case is then thoroughly framed within past climate and historical extremes, as well as within national climate projections. Next, the impact of this event is calculated as accurately as possible together with experts and various stakeholders from different sectors (health, economy, infrastructure, agriculture,...), and the results are brought together in a fictionalized narrative. Based on this narrative, sector-specific workshops are organized to reflect on vulnerabilities and preparedness for such an extreme event; are there certain cascading effects we do not yet know? Will our current crisis plans be able to cope with this situation? Based on these sector discussions, a parallel Tale will be developed again for the same extreme event, but this time set in a Belgium that is better prepared. In this way, a long-term vision (and a more hopeful message) can be developed. Finally, the entire cycle will conclude with communication to the broader public, in order to raise awareness about extreme events.
During the presentation, we would like to share how we developed a Tale of a Future Heatwave using this approach and what concrete impact it has had on various stakeholders.
How to cite: Caluwaerts, S., Beyens, T., Carlier, N., Delhaye, E., Gillerot, L., Vandelanotte, K., and Termonia, P.: Using tales of future weather to increase resilience for weather and climate extremes, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-725, https://doi.org/10.5194/ems2026-725, 2026.
Climate resilience is a defining challenge of the 21st century, yet public health authorities continue to face difficulties in operationalising state-of-the-art geospatial and environmental science. In Portugal, as in Europe more broadly, extreme temperatures have already increased in frequency and severity, contributing to measurable studies reporting impacts on excess mortality and morbidity. Of relevance, these impacts are often amplified by the simultaneous degradation of air quality. However, evidence on the compounding effect of temperatures and atmospheric composition has largely been event-specific, fragmented across case studies of individual heatwaves, cold waves, or air-quality exceedance episodes, limiting our ability to implement dedicated compound events early-warning systems. The ESA-funded AIR4health project, developed under the Early Digital Twin Components initiative, addresses these gaps by designing innovative algorithms and a user-driven climate service focused on predicting human mortality and morbidity excesses in Portugal, during compound extreme temperature and pollutant exceedance events. The project has developed two Machine Learning (ML)–based AIR4health Downscaling Algorithms and the corresponding Incidence Risk Ratio (IRR) Models depicting, in a 1 km x 1 km spatial resolution, and the daily mortality and hospital admissions excesses per municipality that are attributable to (i) Heat & Ozone (O3) and (ii) Cold & Nitrogen Dioxide (NO2) compound events, using a long (2000-2018) healthcare database for mainland Portugal. These indicators integrate EO data, in-situ air-quality records from the EEA, and CAMS/C3S model outputs to improve the spatial resolution. Results achieved included diverse ML architectures (random forest, XGboost, Neural Network, using linear regression as the non-ML benchmarking model) which were validated against observational records on withheld data, proving the ML models' superiority in spatially depicting O3 and NO2 exceedances, in space and time, proving the added-value of such architectures for hazard mapping, with implications for the health impact assessment, with mean absolute error metrics inferior to 10µg/m3 in almost all cases, including during extreme events. Furthermore, impact models showed the relevance of the combined effect, particularly concerning heat and O3 compound events during which the IRR may increase by 50%, compared to considering heat alone. In complement, users have been involved in defining the requirements for a graphical interface that resonates with the currently existing seasonal surveillance system, with the goal of delivering an interactive dashboard that automatically conveys risk indices into useful geospatial information for the public health sector. With these actions, AIR4health advances beyond current country-level systems by implementing fully spatiotemporal exposure–response modelling. Its dynamic and continuous framework will deliver a prototype DTC capable of providing fine-scale early warning for combined climate and air-quality extremes. By benchmarking results against European-level datasets, AIR4health will support scalable pathways towards relevant practices in planetary health and climate-preparedness, while contributing to the broader European Digital Twin ecosystem.
How to cite: Oliveira, A., Brito, A., Marques, B., Fonteles, C., Pereira, É., Silva, F., Girão, I., Figueiredo, L., Barros, L., Lima, M., Cunha, R., Alho, A., Oliveira, M., Nogueira, P., Castanheira, B., Trancoso, R., Teresa, V., Gasbarra, D., and Malina, E.: Climate and Environmental Digital Twins for Human Health: Leveraging Earth Observation for Compound Climate and Air Quality Extremes Early Warning, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-663, https://doi.org/10.5194/ems2026-663, 2026.
When hazardous weather conditions occur, it is essential that all societal actors are made aware in a timely and understandable manner. Much attention to development and implementation of actionable warning systems has therefore been given, not the least through the WMO EW4ALL initiative. For warning issuing authorities it is equally essential to gain in-depth understanding of how warning information is received, understood and acted upon. While this need is widely acknowledged, systematic post-event evaluation is still limited and often excludes collection of subjective data among affected populations.
Under the Norwegian Meteorological Institute’s project ‘Impact-based warnings for society’ (K2S), a multi-year series of post-event warning evaluation surveys has been implemented since 2025. In this presentation, we discuss the methodological procedure and provide key insights from surveys that have been conducted thus far. Four surveys were disseminated in 2025, respectively for 1. An orange level warning for wind gusts; 2. A yellow level warning for lightning; 3. An orange level warning for heavy rain, and; 4. A yellow level warning for a polar low. For each of the warned events, an invitation to participate in the online survey was sent to a selection of residents in municipalities covered by a given weather warning, in the days immediately after the event. Each of the surveys resulted in around 500 responses. Next to key demographics, the surveys included several validated constructs from the field of behavioural science. Measures included knowledge of, attitudes towards and trust in the warnings issued, as well as perceived relevance of warning information provided, behavioural response and experienced consequences.
Results show that most respondents have high trust in weather warnings, while trust levels are positively related to general interest in weather conditions and negatively with weather information literacy. While the majority understand the information provided in the warnings, knowledge of the content varies for different phenomena and between age groups. Most people who received the weather warnings took one or more measures, and relatively few experienced major negative consequences. Worry and fear was a relatively commonly mentioned consequence, emphasising that weather events not only have physical, but also emotional and mental impacts. Also, several reasons were mentioned for not acting upon a warning, including warning skepticism, high levels of perceived self-preparedness, and contextual irrelevance of possible impacts. We conclude with suggestions for how post-event surveys can feed into improvements of operational warning procedures, including a need to follow-up with public communication about evaluations after weather events to inform about warning accuracy and observed societal impacts, and provide topics for further in-depth research.
How to cite: Jeuring, J., Sivle, A., and Thorstensen Kindem, I. K.: Post-event surveys to assess public perceptions of and response to weather warnings in Norway, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-101, https://doi.org/10.5194/ems2026-101, 2026.
Resilience assessments are central to urban climate planning, yet when they fail to capture societal perceptions, uncertainties, and contested priorities, they risk producing strategies that lack legitimacy or misalign with public needs. These challenges are exacerbated by extreme heat, a deadly hazard that remains abstract to many residents and planners. This study develops a framework combining public surveys with two rounds of stakeholder surveys to link public concerns with institutional decision-making for heat resilience.
The framework comprises four phases: (i) scoping and stakeholder mapping; (ii) public surveys using hypothetical but plausible extreme heat scenarios to capture personal concern and sources of uncertainty; (iii) a first stakeholder survey to identify perceived readiness, common heat strategies and their evaluation criteria; and (iv) a second stakeholder survey that revisits these criteria in the context of salient scenarios identified from public responses. This framework would reveal where convergence and divergence emerge amongst public concerns, scientific thresholds, and institutional priorities.
Preliminary results from the ongoing public survey, deployed across cities in different Köppen-Geiger climates, reveal that scenario magnitude (e.g., maximum air temperature) drives concern more strongly than duration (e.g., how long the event lasts) or time or planning horizon (e.g., when the event happens). This finding suggests that public risk perception is shaped more by the intensity of the extreme than by its persistence or proximity, with implications for how heat risk is communicated and prioritised in resilience planning. The survey also captures sources of uncertainty, with early indications that unfamiliarity with heat impacts and insufficient information about protective actions are key drivers of perceived unpreparedness.
By explicitly capturing uncertainty as a resilience construct and embedding iterative stakeholder engagement, the framework translates perceived concerns into decision-relevant insights. It offers a practical, transferable template for participatory resilience assessment that aligns risk perceptions with planning priorities, ultimately supporting legitimate and actionable climate adaptation strategies for extreme heat.
How to cite: Heng, S. L., Balakrishnan, S., and Comes, T.: The Heat is On: A Scenario-based Resilience Framework to Capture Community and Planning Perceptions and Priorities for Urban Heat, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-534, https://doi.org/10.5194/ems2026-534, 2026.
Convective events, windstorms, and heavy precipitation frequently trigger fire brigade operations, including responses to fallen trees, flooded basements, and infrastructure damage. Such events often lead to sharp increases in emergency call volumes, potentially overwhelming control and dispatch centers. Anticipating operation volumes through impact forecasts could support operational decision-making, for example by enabling timely staffing adjustments to manage peak demand.
Within the WEXICOM project, we develop forecasts of weather-related fire brigade operations through a co-design process involving five German control centers. This process includes expert interviews, iterative model development, prototyping, and user testing. In an initial co-design cycle, we developed a simple Poisson regression model and implemented it in a real-time prototype application. The model combines convective nowcast data with building coverage as predictor variables to estimate operation volumes. User testing yielded positive feedback; however, model validation revealed that this simple approach tends to underestimate operation counts.
In a second co-design cycle, we investigate how additional meteorological and exposure data improve the predictive skill of statistical models for forecasting weather-related fire brigade operations at lead times of 1–6 hours. Meteorological inputs include convective nowcasts, ensemble forecasts of wind and precipitation, radar-based precipitation estimates, and station observations. Exposure variables comprise building coverage and counts, road length, land use, and topography. Results indicate that predictor importance varies with lead time: nowcasts dominate at short lead times (1 hour), whereas ensemble forecasts provide the greatest skill at longer lead times. Stepwise regression is applied to select relevant predictors from a large candidate set. The combined models reduce the logarithmic mean absolute error by more than 30%.
We further compare Poisson, negative binomial, and hurdle models to account for overdispersion and excess zeros. Overall, the hurdle negative binomial models provides the most appropriate representation of the data distribution.
To complete the co-design process, we will evaluate the impact of operation forecasts on the risk perception and decision-making of emergency managers in an experimental setting.
How to cite: Becker, N., Göber, M., and Rust, H. W.: Co-Designing Short-Term Forecasts of Weather-Related Fire Brigade Operations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-462, https://doi.org/10.5194/ems2026-462, 2026.
Extreme heat events can have severe consequences for public health and place considerable additional pressure on healthcare systems. In particular, the resulting increase in the number of emergency calls can push emergency medical services (EMS) to their operational limits. At present, control centre staff largely rely on their personal experience when analysing weather data and assessing the impact on the EMS workload. The ForMed project (Forecasting impacts of extreme weather conditions on emergency medical services), funded by the Deutscher Wetterdienst (DWD), addresses exactly this gap. Its goal is the development and evaluation of statistical models to forecast heat-related impacts on EMS demand. This contribution presents key findings from the data analysis phase and provides an overview of a prototype dashboard.
The analysis and modelling are based on emergency dispatch records from the Berlin Fire Brigade covering the period 2012–2025. These were linked with temporal predictors, including day of week, time of day, and public holidays, as well as an archive of COSMO-DE-EPS and ICON-D2-EPS forecasts. Generalized Additive Models (GAMs) were used to characterize the underlying dependencies. The central finding is an increase of 10–15% in EMS call volumes during strong heat events compared to climatological baseline conditions. This increase can lead to high demand on EMS, especially when accompanied by additional factors such as mass events or prolonged heat waves.
Building on these findings, a forecast dashboard has been developed that predicts EMS call volumes for the coming days based on current meteorological forecasts. The system is intended to enable operational planners to take anticipatory, quantitatively informed decisions. A prototype will be tested with the Berlin Fire Brigade's control centre in summer 2026 to evaluate its practical utility in an operational environment. Based on the outcome of this evaluation, it is planned to transfer the methodology to other EMS regions. Discussions with the fire department indicate that heat has played a minor role in planning up to now, but that, given the experience with climate change, there is now widespread interest in analyses of the impacts of heat.
How to cite: Kruppke-Hansen, D., Becker, N., and Rust, H.: Supporting Emergency Medical Services Through Weather-Based Forecasting of Demand, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-495, https://doi.org/10.5194/ems2026-495, 2026.
Climate change and the urban heat island (UHI) effect have exacerbated the health impacts of extreme heatwaves on aging populations. However, a significant gap exists between outdoor meteorological forecasts and the actual indoor heat stress experienced by vulnerable groups. Therefore, this study develops and validates a transdisciplinary, impact-based indoor thermal environment early warning model, providing a scientific basis for urban climate adaptation and public health responses.
The methodology of this study includes both the urban and indoor residential scales. First, at the urban scale, a spatial heat risk model is constructed using Geographic Information Systems (GIS). Data including the elderly ratio, low-income household density, medical accessibility, and impervious surface fraction are standardized using Z-scores. A comprehensive heat risk index is then calculated by Principal Component Analysis (PCA) to delineate urban heat hotspots urgently requiring social resource intervention. Second, to accurately assess the actual living environments of the elderly, the research shifts to the building scale, employing the EnergyPlus building energy model for dynamic indoor thermal simulations. The physical boundary conditions of the model are set using local meteorological data, while occupancy profiles and air-conditioning operational schedules are configured based on field surveys of 30 solitary elderly households in high-risk districts. This model accurately reflects the target population's high physiological vulnerability and low frequency of air conditioning use.
The results indicate that the cross-scale integration of spatial risk mapping and energy simulation for indoor temperatures achieves a more accurate assessment of heat hazards. Field surveys reveal that over 80% of the elderly suffer from temperature-sensitive chronic diseases. Under this extremely vulnerable scenario, the EnergyPlus model demonstrated the thermal delay effect caused by the heat storage of building envelopes. It also accurately predicts the time when the interior reaches critical heat stress, exhibiting high reliability in short-term temperature prediction.
By integrating urban-scale and building-scale analytical methods, this study establishes an accurate and operational climate assessment model. When the predicted indoor temperature exceeds 32°C, the system triggers an alert, providing social workers and government authorities with a 3-to-6 hour lead time for response. This model provides a data-driven strategy that helps to accurately deploy care visits and effectively mitigate the risk of mortality caused by high temperatures in an aging society.
How to cite: Chang, C., Lu, C.-L., and Lin, T.-P.: Heat Risk Assessment and Impact-Based Early Warning Modeling for Vulnerable Populations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-184, https://doi.org/10.5194/ems2026-184, 2026.
Under the influence of climate change, both the frequency and duration of extreme heat events have been increasing. Previous energy consumption studies have consistently identified a relationship between outdoor air temperature and residential energy use. In particular, under high-temperature conditions, increased demand for air conditioning often leads to a rise in household electricity consumption. Therefore, this study focuses on Taipei City and Tainan City, examining the impact of temperature on residential electricity consumption at the village scale, with the aim of capturing the characteristics of energy use variation under high-temperature conditions.
This study integrates the 2-km gridded historical climate reconstruction dataset (TReAD) provided by the TCCIP program of the National Science and Technology Center for Disaster Reduction, along with village-level monthly residential electricity consumption data published by Taiwan Power Company. A GIS-based area-weighting method was first applied to harmonize spatial scales. Considering the characteristics of residential electricity data in Taiwan, data calibration and grouped regression analyses were then conducted. A threshold of 23°C was adopted to represent the onset of cooling demand, allowing comparison between low- and high-temperature groups.
The results indicate that, due to Taiwan’s relatively mild winter climate and limited heating demand, electricity consumption is less sensitive to temperature variations in the low-temperature range. For example, in Tainan City, within the low-temperature group (<23°C), temperature explains only about 5.08% of the variance in residential electricity consumption (R² = 0.0508), with an estimated elasticity of approximately 0.8–1.2% increase in electricity use per 1°C rise. However, once temperatures exceed the cooling threshold, electricity consumption becomes highly responsive to temperature changes. In the high-temperature group (>23°C), a strong positive correlation is observed (R² = 0.932), indicating that temperature accounts for over 93% of the variation in electricity consumption.
Further estimation using elasticity analysis suggests that, within the high-temperature range, a 1°C increase in temperature leads to an approximate 7.57% rise in monthly electricity consumption per household. In practical terms, assuming an average monthly household electricity consumption of 400 kWh during summer, this corresponds to an additional 30–35 kWh per month for each 1°C increase.
These findings highlight that high-temperature environments not only elevate thermal discomfort for residents but also significantly increase energy demand. This, in turn, may impose additional financial burdens on households—particularly for vulnerable groups—thereby exacerbating issues of climate inequality.
How to cite: Lin, T.-P., Chang, C., Hsieh, C.-J., Lu, C.-L., and Tung, H. H.: The Impact of Temperature Variation on Urban Residential Electricity Consumption, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-366, https://doi.org/10.5194/ems2026-366, 2026.
The landscape of disaster risk management is undergoing a significant transformation, driven by two increasingly complementary approaches: Impact-Based Forecasting (IBF) and seasonal forecasts. While traditional forecasting focuses solely on weather predictions, IBF integrates hazard information—such as extreme temperature patterns—with socioeconomic exposure and vulnerability information, enabling quantitative risk assessments in areas as diverse as public health and infrastructure resilience. On the other side, seasonal forecasts spanning several months provide stakeholders with probabilistic predictions that enable forward-thinking adaptation planning well before conditions materialize.
A three-step conceptual framework illustrates the progression from prediction to actionable advice: 1) seasonal climate prediction, 2) impact modeling, and 3) actionable impact-based forecasting. While significant advances have been made in the first two steps separately, a critical gap remains in operational systems that systematically connect seasonal forecasts with impact models to enable the third step. To address this challenge, we utilize DWD’s operationally available seasonal heat predictions to develop heat-related impact-based forecasting prototypes. In particular, we showcase how the temperature-dependence of the demand for German short-work labor compensation (Kurzarbeitergeld) can be translated into regionally-resolved impact forecasts. We further discuss the forecast’s prediction skill as an important contribution for the relevance of such a service. In summary, this work helps bridge the strategic gap between traditional seasonal forecasts and impact-oriented services, contributing to the development of operational, reproducible workflows that connect probabilistic climate forecasts to warning systems and decision-making processes. This research received funding by the Federal Ministry of Research, Technology and Space (BMFTR) via the funding line “climate protection and finance”.
How to cite: Geiger, T., Castino, F., Dimke, P. P., Stomper, A., and Kreienkamp, F.: Bridging Seasonal Climate Predictions and Impact Models for Operational Risk Assessment in Labor Markets, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-804, https://doi.org/10.5194/ems2026-804, 2026.
From 10 to 15 April 2025, China experienced a rare persistent extreme wind-dust compound disaster that swept from north
to south. Based on in-situ observational data, historical disaster records, and situations of various exposed elements, this study
analyzed the formation mechanisms and evolution of this extreme weather event and conducted a rapid assessment of the associ-
ated loss and damage. The results indicate that the direct cause of this extreme wind-dust compound disaster was a strong
cold vortex system generated in Mongolia, which moved eastward and southward, combined with the amplification effects
of topography and urban structures, and the downward transmission of momentum from higher troposphere. The analysis
revealed that approximately 697.47 million people were exposed to strong winds, while about 1,374.54 million people were
exposed to high concentrations of PM10. The strong winds also caused varying degrees of damage to buildings, transporta-
tion networks, agricultural greenhouses, and forests. Based on vulnerability curves for wind-related loss and damage, it was
estimated that the number of victims affected by this extreme wind-dust compound disaster ranged from 0.209 to 1.044
million, with casualties between 5 and 13 individuals. The number of damaged buildings was estimated to be between 2115
and 4607, and the area of affected crops was between 229 and 783 km2. The direct economic losses could reach as high as
RMB 0.076–3.501 billion yuan. This study revealed the potential causes of this extreme wind-dust compound disaster and quantified
the disaster loss and impact, providing new insights for the prevention of strong wind and dust associated disasters.
How to cite: Zhang, G., Wang, Y., Lin, Z., Li, W., and Ma, H.: Process, Causes, and Loss Assessment of the Extreme Wind‑DustCompound Disaster in China in April 2025, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-599, https://doi.org/10.5194/ems2026-599, 2026.
Climate change presents increasing risks to agriculture, making it essential to support adaptation measures grounded in systematic climate risk assessment. This study presents the methodology used for a national climate risk assessment for Slovene agriculture, based on the IPCC AR5 framework and applied across nine major production sectors. The approach emphasises a transparent indicator structure, scenario-based assessment, and explicit integration of sensitivity and adaptive capacity indicators.
The methodology consists of four steps: (1) selection of climate hazards, including drought, heat, spring frost, extreme rainfall and erosion, floods, pests and diseases, and hail; (2) definition of exposure, sensitivity, and adaptive capacity indicators using national climate, soil, and agricultural datasets; (3) evaluation of hazard probability using historical data (1981–2010) and projections for RCP4.5 and RCP8.5 (2041–2070 and 2071–2100); and (4) integration of all components into risk matrices. Indicators include soil depth, texture, organic matter, water resource availability, crop distribution, protective systems such as irrigation, hail nets, and frost protection, and qualitative assessments for livestock and beekeeping. The selection of hazards, indicators, thresholds, and weighting factors was refined through two participatory workshops with experts from research institutions, ministries, advisory services, and sectoral stakeholders.
A key strength of this approach is the combination of spatially explicit biophysical metrics (12 × 12 km grid) with socio-technical adaptive capacity indicators that reflect sectors' preparedness. These include the very limited coverage of irrigation systems, the proportion of drought‑tolerant crops, the use of greenhouses and protective nets, and expert evaluations of livestock housing, ventilation, and water access. Although most hazards intensify under both RCP scenarios, risk levels differ markedly among sectors because adaptive capacity varies. Heat and pest/disease risks show the most pronounced increases due to rising numbers of warm and wet days. Drought and hail risks remain high where adaptive capacity is weak, whereas frost risk shows mixed signals depending on the indicator used. While results are presented at the national scale, the methodology is suitable for future regional refinement and for informing agricultural and climate policy planning.
Several limitations affect the assessment: precipitation projections carry considerable uncertainty; some indicators lack scientifically established thresholds; and adaptive capacity is difficult to quantify due to fragmented or incomplete datasets. These constraints underscore the need for improved data availability, refined thresholds and weighting schemes, and enhanced monitoring systems to strengthen future risk assessments.
This work provides a structured, transferable framework for national climate risk assessment in agriculture, linking scientific indicators to social preparedness and stakeholder knowledge. The study was funded by the Ministry of Agriculture, Forestry and Food of the Republic of Slovenia.
How to cite: Pogačar, T. and Glavan, M.: An Indicator‑Based Multi‑Hazard Methodology for National Agricultural Climate‑Risk Assessment of Slovenia, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-92, https://doi.org/10.5194/ems2026-92, 2026.
Local communities and cantonal authorities in Switzerland play a central role in responding to severe weather events, requiring timely, reliable, and actionable information to support operational decision-making. In practice, stakeholders rely on a range of information sources, from widely used third-party weather applications and the official MeteoSwiss platform to the expert-oriented Swiss national platform for natural hazards (GIN). This diversity reflects differing user needs, levels of expertise, and varying degrees of trust in information characteristics such as update frequency, spatial resolution, and visualization. As a result, stakeholders routinely combine and cross-reference sources to build situational awareness and increase decision confidence. Within the Horizon Europe project GOBEYOND, we investigate how different user groups interact with weather information, how it influences decision-making, and where critical gaps may hinder effective response. Our work focuses on two pilot regions, the Canton of Ticino and the Canton of Zurich, and involves close collaboration with first responders, civil protection authorities, cantonal offices, and natural hazard experts. To support this work, we developed a flexible local demonstrator platform that serves as both a prototyping and engagement tool, providing customizable layers of existing weather information alongside new products, including probabilistic forecast visualizations and impact-based approaches integrating stakeholder-relevant exposure data.
Stakeholder workshops and demonstration rounds using the prototype platform allowed us to assess how different types of information contribute to making data decision-ready. Information needs are highly context-dependent, varying between real-time situational management and anticipatory planning. Users balance independent data interpretation with reliance on expert guidance, highlighting the continued importance of mediated communication formats such as forecaster briefings tailored to operational needs. In addition, the usefulness of weather information depends strongly on its integration into existing workflows and decision processes. Our findings show that effective decision support is not only a matter of providing more or higher-quality data, but of ensuring its interpretation, contextualization, and alignment with established practices. Accordingly, we prioritize integrating new products into existing dissemination channels, including remote forecaster briefings and the Swiss national platform for natural hazards (GIN), to ensure usability, trust, and operational acceptance. We also explore opportunities to integrate weather information into situation management and situational awareness platforms used by stakeholders, further enhancing its operational relevance and accessibility. The poster presents key findings from stakeholder interactions, highlighting differences in user profiles and decision-making needs, as well as an overview of feature categories across weather and impact-oriented information, including feedback on visualizations, probabilistic products, and impact-based approaches.
How to cite: Kaderli, S., Mühlhofer, E., Moret, L., and Willemse, S.: Is Our Weather Information Decision-Ready? Insights from Exploratory Demonstrations with Authorities and First Responders in Switzerland, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-187, https://doi.org/10.5194/ems2026-187, 2026.
Pacific Island Countries (PICs) are exceptionally vulnerable to climate variability and extremes, which pose direct and increasingly severe threats to critical socioeconomic sectors. To address these urgent challenges, the Pacific Island Countries Advanced Seasonal Outlook (PICASO) system was developed. PICASO is an advanced hybrid seasonal prediction system that strategically integrates the strengths of both statistical and dynamical models to produce reliable, high-resolution climate information. Based on the APEC Climate Center Multi-Model Ensemble (APCC-MME), the platform provides user-friendly, high-quality climate predictions that are specifically tailored to individual observational stations within the region.
This study provides a comprehensive introduction to the PICASO system, detailing its sophisticated hybrid technical framework and foundational climate services. Furthermore, it introduces the recent development and integration of impact-based forecasting (IBF). This evolution represents a significant enhancement designed to bridge the critical gap between standard climate data and actionable, sector-specific guidance. We detail new content within PICASO that has been co-developed with national stakeholders to address specific local vulnerabilities.
Key implementation examples include:
- Niue: Wildfire risk assessments based on projected dryness and weather conditions.
- Marshall Islands and Tuvalu: Drought forecast.
- Palau: Forecast for dam water levels to support water resource management decisions.
- Cook Islands: Integrated monitoring and outlooks for sustainable water resource availability.
This transition from general climate outlooks to impact-based forecasting empowers Pacific stakeholders to shift from reactive responses to a proactive adaptation posture, substantially strengthening regional resilience against climate-related risks.
Key words: Seasonal climate forecasts, hybrid seasonal prediction system, PICASO, Pacific Island Countries, impact-based forecast
※ This work was supported GCF-UNEP CIS-PaC5 Project.
How to cite: Lee, H.-J., Yoo, J., Jeong, D., and Han, J.: Enhancing Climate Resilience in Pacific Island Countries: Integrating Impact-Based Forecasting into the PICASO, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-203, https://doi.org/10.5194/ems2026-203, 2026.
Flash floods and surface water flooding triggered by short-duration, high-intensity precipitation account for a substantial share of flood-related damage in Switzerland. Their rapid evolution in time and space makes them particularly difficult to represent in operational warning systems and poses important challenges for emergency management. In the context of the ongoing shift from hazard-based warnings toward more impact-based forecasting and warning approaches, this study explores how past events can support the development of warning products with higher operational relevance for stakeholders. Initial meetings with Swiss first responders helped to identify events ranging from purely surface-runoff-driven to more flash-flood-dominated events. These reference cases provide the basis for simulating such events using both observed and forecast precipitation data. Flood hazard is represented through two approaches: the physics-based hydrodynamic model LISFLOOD-FP and a data-driven approach that estimates precipitation return periods at the watercourse level and combines them with national and local flooding intensity maps to infer flood extent. Both are applied using the same event-specific precipitation forcing. The resulting hazard outputs are first assessed qualitatively in collaboration with Swiss first responders, including flood experts and representatives from local communities. In a subsequent step, impact estimates are derived for each approach by combining hazard outputs with building and road exposure data and a regional vulnerability curve for Switzerland. This allows us to assess differences between the hazard outputs, examine whether expressing results in terms of impact increases their usefulness for stakeholders, and evaluate the respective strengths and limitations of the different modelling approaches. Initial results indicate that model performance depends on the dominant event process, while the data-driven approach has limitations in capturing small events and small-scale effects. For one event in the Canton of Zurich, the resulting impact estimates are of the same order of magnitude as insurance records. These initial findings highlight both the potential of integrating hazard and impact information and the challenges that remain in developing more operationally relevant impact-based forecasting and warning approaches.
How to cite: Espejo, G. G., Kaderli, S., Mühlhofer, E., Martius, O., and Zischg, A. P.: Unraveling Opportunities and Challenges for Impact-Based Forecasting and Warning of Flash Floods and Surface Water Flooding, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-485, https://doi.org/10.5194/ems2026-485, 2026.
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