Verifying the quality of weather warnings is important for improving warnings, building trust, and enabling stakeholders to choose the right course of action. But what makes a warning valuable from a user’s point of view? 

The recipients of weather warnings, such as emergency response teams, commercial actors, or the general public, may each prioritize different aspects of weather warnings. For instance, some may value the correct intensity of an event over its correct timing, while others may prefer correct location over the correct duration. In addition, the trade-off between over- and under-forecasted weather events may vary between stakeholders as some might prioritize minimizing missed events while others prefer not having too many false alarms. To better capture these individual preferences, this work showcases the efforts made at the German Meteorological Service (Deutscher Wetterdienst, DWD) in the context of the renewal of its warning system (i.e., the RainBoW program: “Risk-based, Application-oriented and Individualizable Provision of Optimized Warning Information”) to understand how users perceive the quality of warnings.

To explore what properties of a warning are the most important to the general public - the largest group of recipients of weather warnings - we conducted two non-representative surveys during two ‘open house’ DWD events. Participants recorded how satisfied they were with forecasts if specific attributes such as intensity, timing, location, or persistence were incorrect and affected the outcome of their planned activities. 

In addition to this public survey, a workshop with stakeholders from disaster response teams provided further insights into their specific needs and interests towards forecast quality.

With this information from different stakeholders, we aim to develop verification methods that take these preferences into account when evaluating the accuracy of a warning. The development of new verification methods may be supported by user reports of the current weather collected through the official DWD weather app.

A key component of any user-oriented verification approach is an effective communication of the results. This requires translating complex statistical information into clear practical information that is accessible and meaningful for non-specialist audiences.

How to cite: Sauter, C., Wapler, K., Feige, K., Gehlen-Zeller, M., and Primo, C.: Towards User-Oriented Verification of Weather Warnings: Insights from Public and Stakeholder Preferences, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-348, https://doi.org/10.5194/ems2026-348, 2026.

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Weather warnings issued by national meteorological services are commonly derived through manual interpretation of numerical weather prediction output. While this approach allows for expert judgement, it limits update frequency and lead time and introduces variability between forecasters. Within the RainBoW program ("Risk-based, Application-oriented and INdividualizaBle Provision of Optimized Warning Information"), the German Meteorological Service (Deutscher Wetterdienst, DWD) is developing a prototype system that derives warning-relevant weather events from ensemble forecast data. This contribution presents the implementation and refinement of this prototype, with focus on frost and rain. It demonstrates how automated event detection can be used to supplement analysis that have traditionally been performed manually.

The prototype system integrates ensemble data of multiple configurations of the ICON numerical weather prediction model as input, to maximise the provided forecast lead time and accuracy. The different setups are ICON-D2 Rapid Update Cycle (RUC), ICON-D2, ICON-EU, and ICON with forecast lead times ranging from 14 hours up to seven days. These forecast datasets are supplemented by additional information, such as radar-based precipitation data. Event detection is performed per warning element, but similar across various input data sources. A rule-based approach is used to detect events in each ensemble member individually. Detected events are subsequently aggregated across ensemble members and model configurations in order to derive consistent event signals that are suitable for warning generation. This approach enables the combination of information from multiple models while also utilizing the ensemble character of the input data.

A key achievement of this work is the development of a processing chain that automatically generates updated event information whenever new model data become available. This enables a substantially higher update frequency than workflows based on human forecasters, while maintaining longer lead times. Currently, it operates as a research prototype and is not yet part of the operational warning workflow at DWD. Selected case studies demonstrate the detection of frost and rain events from ensemble forecasts. The results are compared with warning information derived from observational datasets to investigate the behaviour and interpretability of the prototype.

Future work will focus on comprehensive statistical verification, further refinement of the methodology, and the extension of the system to additional warning elements.

How to cite: Königer, L., Felsberg, A., Baumgartner, M., and Klink, M.: From Ensembles to Alerts: Deriving Event-Based Information from Forecasts, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-438, https://doi.org/10.5194/ems2026-438, 2026.

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How to cite: Ward, L., O'Loughlin, F., and Sweeney, C.: Evaluation of ECMWF subseasonal-to-seasonal forecast skill over Ireland for flood and drought events, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-397, https://doi.org/10.5194/ems2026-397, 2026.

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The Jakarta Metropolitan Area (JMA) of Indonesia officially becomes the most populated urban area with 40M inhabitants. Despite its limited diurnal temperature variability, JMA is currently confronted with an exacerbation of climate change and the urban heat island (UHI) that affects health of the urban communities. Nonetheless, the number of studies on the health implications of heat exposure within this tropical environment remain limited. Prior significant research has successfully incorporated both Indonesian National Health Insurance (BPJS) and meteorological observation datasets. However, these studies have exclusively relied upon observed daily mean air temperature as the thermal proxy. Consequently, the cumulative exposure arising from the diurnal variation associated with UHI effects is potentially underestimated.

This study aims to analyze and quantify the influence of heat-stress on CVD morbidity across the thirteen districts within JMA. Consequently, this study utilizes a five-year dataset (2020-2024) incorporating hourly meteorological parameters sourced from nine stations, thereby reflecting the diurnal variation of atmospheric dynamics in the analysis. Furthermore, rather than employing the 2-m air temperature, Physiological Equivalent Temperature (PET) is examined as offering a reliable indicator for human thermoregulation. The analysis concentrates on the correlation between heat stress and the morbidity of cardiovascular disease (CVD) within primary healthcare facilities.

Preliminary findings indicate that the UHI effect influences healthcare visits for CVD in urban areas. Cumulative exposure to extreme daytime heat stress environments, as quantified by PET values in urban areas, substantially increases the incidence of CVD admissions. Conversely, the daily maximum air temperature proves to be a more suitable indicator for rural areas. These results emphasize the necessity of integrating appropriate meteorological indicators to effectively address and mitigate heat-related health risks for urban populations. 

How to cite: Subasita, N., Septiono, W., Milošević, D., and Steeneveld, G.-J.: Extreme Heat Stress and Cardiovascular Diseases in the Tropical Megacity of Jakarta (Indonesia), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-726, https://doi.org/10.5194/ems2026-726, 2026.

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Against the background of climate warming and increasingly intensified extreme heat events, densely developed cities are facing ever-intensifying heat-risk challenges. In basin cities such as Taipei, restricted ventilation, complex built-environment conditions, and highly concentrated populations further exacerbate heat accumulation and retention. However, conventional meteorological station data are often insufficient to fully capture the heterogeneity of street-scale thermal environments, and they also make it difficult to identify high-risk areas where climatic hazards spatially overlap with socially vulnerable populations. Therefore, this study integrates multi-scale climate information and social vulnerability indicators to assess heat risk, with the aim of improving the local relevance of urban heat-risk identification and decision-making applications.

This study focuses on Taipei, the capital of Taiwan, to investigate the acute heat impacts faced by densely developed urban areas under a warming climate. Urban heat is not merely a meteorological outcome, but a complex microclimatic phenomenon shaped by the interaction of topographic conditions, built-environment geometry, atmospheric boundary-layer characteristics, and anthropogenic activities. To resolve the high degree of heterogeneity in urban microclimates, long-term observations with high spatial and temporal resolution are essential. This study integrates hourly air temperature data from the High-Density Street-Level Air Temperature Observation Network (HiSAN) in the Taipei metropolitan area for 2022, in situ observations from the Central Weather Administration (CWA), and the Taiwan ReAnalysis Downscaling dataset (TReAD) with a spatial resolution of 2 km. Through spatial interpolation in QGIS and ArcGIS, the study constructs comprehensive spatial models of urban heat distribution.

This study utilizes Daan Forest Park, a characteristic 'cool island' within the Taipei metropolitan area, was selected as the reference point. Monthly mean temperature differences between each observation site and the reference station were calculated to evaluate urban heat intensity, examine the relationship between built-environment characteristics and urban heat intensity, and provide a practical scientific basis for urban climate adaptation and hotspot identification. The analysis was further extended from heat-hazard assessment to risk assessment by overlaying urban heat hotspot distributions with socioeconomic vulnerability indicators, specifically targeting the distribution of older adults and low-income households, in order to identify heat-risk hotspots at different times and priority areas where high heat exposure coincides with high vulnerability. The results show that urban heat hotspots are mostly concentrated in densely built-up areas and exhibit a considerable degree of spatial overlap with areas where older adults and low-income populations are concentrated. This suggests that urban heat risk is determined not only by physical heat exposure but also closely linked to social vulnerability. Consequently,areas where high heat and high vulnerability overlap should be prioritized for subsequent urban climate adaptation and heat-risk management, and locally tailored adaptation and mitigation strategies should be proposed according to differences in built-environment characteristics and population structure across districts.

How to cite: Hsieh, C.-J., Lu, C.-L., Tung, H.-H., Chang, C., and Lin, T.-P.: Integrated Identification of Hazard Impact Areas from Urban Heat and Social Vulnerability: A Case Study of Elderly and Low-Income Populations in Taipei, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-186, https://doi.org/10.5194/ems2026-186, 2026.

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Regional heat extremes are intensifying under global warming, yet their evolution reflects the combined influence of anthropogenic forcing and internal climate variability. Here we investigate long-term changes in temperature extremes across South Korea using daily observations from 60 weather stations during 1974–2023. A non-stationary Generalized Extreme Value (GEV) framework incorporating global mean surface temperature (GMST) as a covariate reveals clear non-stationary behavior in both summer and winter extremes across event durations of 1–15 days. Winter cold extremes have weakened markedly, accompanied by a broadening of temperature distributions and a reduced frequency of severe cold events, indicating a systematic decline in cold-air outbreaks. In contrast, summer extremes exhibit greater societal relevance, with tropical night events showing the strongest sensitivity to global warming. These events have increased rapidly in both frequency and persistence, particularly along the west and south coasts, where enhanced moisture availability and elevated nighttime temperatures amplify heat stress. This intensification is likely linked to the strengthening of the North Pacific Subtropical High and warming of surrounding seas, which together enhance moisture transport and suppress nocturnal cooling over the Korean Peninsula.

Despite the persistent warming trend, heatwave activity exhibits pronounced decadal variability. A relative lull from the late 1990s to the early 2010s (period 1, P1) was followed by a rapid resurgence thereafter (period 2, P2). This shift is associated with changes in the North Atlantic Oscillation (NAO), whose planetary-scale teleconnections modulate atmospheric circulation over Northeast Asia. Positive NAO phases strengthen anticyclonic circulation over the Korean Peninsula, enhancing subsidence and surface warming. During P1, the NAO weakened with reduced interannual variability, likely linked to tropical Pacific forcing. In contrast, during P2, it strengthened, potentially driven by Atlantic forcing in conjunction with a phase shift in tropical Pacific variability. These results demonstrate that regional temperature extremes arise from the interplay between externally forced warming and internally generated climate variability, underscoring the importance of accounting for both processes in future climate risk assessment and adaptation.

How to cite: Ryu, J. H. and Kang, S. L.: Nonstationary Temperature Extremes in South Korea: Roles of Global Warming and Large-Scale Climate Variability, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-256, https://doi.org/10.5194/ems2026-256, 2026.

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Every year, the UK is impacted by major storms brought by the jet stream and associated Atlantic storm track. Exceptionally severe storms are well remembered by the public, including the ‘Burns’ Day Storm’ of January 1990, the ‘Boxing Day Storm’ of December 1998, and the ‘Great Storm’ of October 1987, all of which had impacts from extreme wind gusts. More recently, the UK experienced an exceptionally long period of stormy weather over the winter of 2013/2014 that has been described as the stormiest season in the UK since 1871.

Since 2015, impactful storms have been named in the UK by a storm naming group made up of the UK Met Office, the Dutch weather service KNMI, and the Irish weather service Met Éireann. The decision to name a storm is based on its forecast impact, not meteorological conditions, to allow for clear messaging around public warnings. This contributes to the complexity in analysing storms, as there are no definitive criteria for classifying a weather event as a storm. Additionally, trends in the frequency of storms in the UK can not be robustly examined because the record of named storms does not extend far enough back in time. 

Climatological analysis and comparison of storms is complex due to the number of variables involved: mean wind speed, maximum wind gust, wind direction, storm duration, spatial extent and storm track, all of which could have a bearing on how one storm could be judged to be “worse” than another.

This work aims to address that by examining the historical record with machine learning techniques. I explore the use of supervised classification models for identifying storms in the historical record pre-2015 based on station observation data and using the named storms as a guide. The model uses patterns in daily windspeed, rainfall, and MSLP across the UK to probabilistically classify each day in the test period as having a storm or not. This expands the catalogue of known storm dates in the UK, allowing further analysis of how storm frequency and severity have changed through time.

How to cite: Carlisle, E.: Identification of impactful storms in the UK using machine learning, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-213, https://doi.org/10.5194/ems2026-213, 2026.

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Anthropogenic activity has altered Earth’s climate at a rate unprecedented in millennia. Whether this rate itself has increased in recent decades remains uncertain, partly because natural variability obscures acceleration signals over short timescales. Here, we show that commonly used detection methods can yield different acceleration estimates, helping to explain divergent conclusions in the literature. We then examine whether acceleration emerges more clearly at regional than at global scales using observational, reanalysis, and simulation datasets. We find that large areas of the planet show significant acceleration of surface temperature. Densely populated regions, biodiversity hotspots, and Key Biodiversity Areas are particularly affected, with roughly half of the world’s population already living in areas undergoing accelerating warming. We show that this regional signal is correlated with the emergence of a global acceleration signal, indicating that a detectable global signal is expected around the present decade. We further demonstrate that regional aerosol forcing modulates the timing of acceleration emergence, delaying detection in heavily polluted regions while hastening it where aerosol emissions have declined. Finally, we assess when a deceleration signal is expected to emerge under different emissions pathways over the twenty-first century. Under a stringent mitigation pathway (SSP1-2.6), a deceleration signal in global warming is projected to emerge near mid-century, whereas under a moderate emissions pathway (SSP2-4.5) it is delayed by about three decades, emerging around 2080. These results indicate that the climate system is not only warming, but doing so at an increasing rate in many inhabited regions, underscoring the urgency of rapid mitigation and adaptation.

How to cite: Shmuel‬‏, ‪., Gottlieb, A. R., and Mankin, J. S.: Regional acceleration of climate change, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-543, https://doi.org/10.5194/ems2026-543, 2026.

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