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.
Extreme weather events pose significant challenges to communities worldwide, necessitating proactive measures for mitigation and adaptation. We will present a case study of the extreme weather event Hans, which struck Norway with remarkable intensity in August 2023 and led to large-scale flooding and landslides. Two characteristics set Hans apart from typical extreme rainfall events hitting Norway: 1) its unusual trajectory from the southeast, contrasting with the customary storms from the west, and 2) the large area of heavy rainfall, with return periods exceeding at least 100 years. The event caused heavy rainfall that lasted over several days, thus affecting a widespread area.
Through accurate forecasting, successful communication of risks, and well-organized collaboration between key actors, significant societal costs were saved and no lives were lost. We will present the perspectives of the forecasters in the onset of Hans, and on the preparedness efforts undertaken by municipalities in response to the forecasted threat. We will also discuss the value of climate adaptation to such events in an area where extreme precipitation is one of the main direct physical climate risks, including the importance of understanding Hans in a communication and outreach perspective.
The event itself is examined by its temporal evolution, spatial distribution, and associated meteorological phenomena. By analyzing observational data, we can interpret the key atmospheric dynamics driving the extreme weather event, thereby enhancing understanding of its underlying mechanisms. To assess the event’s rarity, we will also present the associated return periods for the observed precipitation amounts.
The presentation will highlight our experience from the event, emphasizing the role of communicating forecasts and the forecasters' close collaboration with preparedness and response services, while also highlighting the significance of climate adaptation efforts in regions vulnerable to extreme precipitation.
How to cite: Ødemark, K., Verpe Dyrrdal, A., and Mjelstad, H.: When “Hans” struck Norway: Forecasting, communication and municipal preparedness during an extreme weather event , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-847, https://doi.org/10.5194/ems2024-847, 2024.
Between 2000 and 2019, natural hazards and extreme weather events claimed approximately 1.23 million lives, affected over 4 billion people, and resulted in economic losses of around US$ 2.97 trillion worldwide. With climate change increasing the frequency and severity of severe weather events globally, it is critically important to develop effective strategies for disaster risk communication and reduction. One such strategy is the provision of early warning systems (EWS), shown to significantly reduce mortality and economic losses. To develop and improve EWS, it is critical to understand how (a) the public perceive and respond to risk information and (b) the factors that promote and inhibit risk preparedness.
Since 2011, in the UK, the Met Office have been responsible for issuing impact-based weather warnings for rain, thunderstorms, wind, snow, lightning, ice, fog, with extreme heat added in 2021. When amber or red warnings are issued, post event surveys are regularly used to capture public awareness, understanding, perceptions and behavioural responses to the warning. This talk presents the results of analysis of data collected from 32 surveys between 2014 and 2022 (n= 16,276). Here we examine whether perceptions and behavioural responses significantly differ according to the timing of the event, type of event the warning was issued for (e.g., snow, wind, heat etc), warning colour and whether storm naming was used. We also consider whether socio-demographic characteristics such as age, gender and location (urban/rural) predict perceptions and responses. Our results have implications for the communication of impact based weather warnings, and provide insights into how these can be best utilised to promote protective action and preparedness.
How to cite: Jenkins, S., Taylor, A., Summers, B., and Roberts, H.: Public Perceptions and Behaviour in Response to Weather Warnings: A UK Perspective, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-438, https://doi.org/10.5194/ems2024-438, 2024.
Severe weather poses significant threats to society, necessitating the development of effective forecasting and warning systems to mitigate their impacts. Integrating impact-based forecasts into warnings, with the premise of reducing people’s harm, currently consists in a paradigm change for many national weather services. Being at the intersection of probabilistic forecasts, uncertainty analysis and state-of-the art risk modelling, this new paradigm also entails significant challenges.
This contribution presents the ongoing efforts at MeteoSwiss to incorporate impacts into weather warnings. Through collaboration with stakeholders, including first responders and local authorities, MeteoSwiss is initiating a local pilot project focusing on heavy precipitation and deployment planning in the canton of Zurich. This impact case study, tailored to specific locations and use case, serves as a pilot to learn for further development and scaling up of impact-based warnings.
Furthermore, as a national-scale counterpart, we work on establishing a quasi-operational demonstrator for Switzerland, which integrates impact proxies (hazard-exposure) rather than full-fletched impact computations. We address the trade-offs inherent to developing impact-based warnings, including the need to balance data availability, specificity and utility across varying warning scales, and the need for seamless integration within an operational weather service.
This contribution discusses potentials and draw-backs of integrating many-dimensional impact considerations into weather warnings from the perspective of a national weather service, highlighting the importance of interdisciplinary collaboration and stakeholder co-development, and demonstrating the use of impact modelling tools in a (quasi-)operational context, with the ultimate aim to better cater to the diverse needs of society in the face of severe weather events.
How to cite: Mühlhofer, E. and Willemse, S.: From local impacts to country-wide warnings: progress and challenges of developing an operational impact-warning chain , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-652, https://doi.org/10.5194/ems2024-652, 2024.
The projected increase in heatwave intensity and frequency will have far-reaching consequences for the human and natural environment of Switzerland. Two particularly important consequences are heat-related excess mortality in the low-lying areas and heat-related acceleration of climate-change-induced alpine permafrost thawing in high-elevation areas. The latter will potentially have far-reaching impacts on alpine hazards, ecosystems, infrastructure, and tourism. In this interdisciplinary project, we assess the potential of using subseasonal heatwave predictions as a basis for early warning systems for the above-mentioned sectors in Switzerland. For the health sector, we show that the (observation-based) statistical relationship between temperature and mortality in combination with downscaled subseasonal temperature forecasts can be used to predict mortality attributable to heat. We demonstrate that for two densely populated areas of Switzerland (Cantons of Zurich and Geneva) and two past hot summers (2018 and 2022) this system is able to predict individual heat-related mortality peaks up to two weeks ahead and anticipate longer-lasting periods of heat-related excess mortality up to four weeks in advance. For the alpine sector, we show that individual summer heatwaves can play an important role in accelerating permafrost thawing, even though the process is driven by long-term climate change. We demonstrate this with idealized sensitivity experiments with the SNOWPACK model (a physical model that predicts the evolution of the snowpack and the ground temperature below). They indicate that both the duration of heatwaves as well as their timing within an individual summer are important for the intensity of the ground warming in permafrost regions. In summary, this project demonstrates a large potential for using subseasonal heatwave predictions for early warning systems for the health sector. For the alpine sector, it highlights the potential importance of individual heatwaves for permafrost thawing and raises the question if subseasonal heatwave predictions could support monitoring and early warning systems in high-elevation areas in some way.
How to cite: Büeler, D., Pyrina, M., Sharaborova, E., Sivaraj, S., Vicedo-Cabrera, A. M., Imamovic, A., Spirig, C., Lehning, M., and Domeisen, D. I. V.: Potential for subseasonal early warning systems for two heatwave-affected sectors of Switzerland: health and alpine permafrost, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-661, https://doi.org/10.5194/ems2024-661, 2024.
Hail is a severe meteorological hazard causing extensive damage to agriculture, buildings, and cars. Within the scClim hail research project, we prototype an application of the open-source risk assessment platform CLIMADA to quantify hail risk. This allows for the provision of impact-based forecasts in Switzerland based on operational radar data, crowdsourced hail reports, and ensemble weather forecasts with the HAILCAST diagnostic built into the operational COSMO model. Combining this with exposure and vulnerability information, we provide hail impact estimates for buildings and different crops in Switzerland. The platform is co-designed with relevant stakeholders from public and private institutions and is running in a pre-operational fashion during the 4-year research project. In addition to forecast products, the platform provides post-event assessments of hail impacts based on radar data and exposure information, which is being used by, e.g., building insurances to have an immediate first estimate for the extent of damages. However, radar-based damage estimates entail considerable uncertainties, which may be constrained locally by using crowdsourced hail reports.
Exchanges with stakeholders show that for bespoke preventive actions, such as sheltering cars and animals, or rolling up blinds to avoid hail damage, nowcasting information on expected hazard intensities is often sufficient. Nevertheless, for larger-scale actions such as a preterm crop harvest, preparation of temporary roof covers, or communication to the public, impact forecasts based on ensemble weather prediction may prove highly beneficial, depending on the forecast’s accuracy.
As a possible expansion, probabilistic hazard event sets were generated using kilometer-scale regional climate model simulations for Europe covering the present-day period from 2011 to 2021 and projecting a future climate using the pseudo global warming approach for a 3 K global warming level. The feasibility and utility of such an expansion are currently being evaluated, not least to assign return periods to predicted hail events.
How to cite: Schmid, T., Villiger, L., Raphael, P., and Bresch, D. N.: Opportunities and challenges of impact-based forecasts for hail damage in Switzerland, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-52, https://doi.org/10.5194/ems2024-52, 2024.
Physical activity is one of the most important factors for human health, but around 28 % of the world's population does not meet the World Health Organisation's recommendations. Physical activity can be increased in everyday life, for example through exercise or walking, with weather being a facilitator or a barrier. Due to climate change, extreme weather events are becoming more frequent, highlighting the relevance of further research into the associations between weather and physical activity. The future increase in heat waves may become a problem, particularly for vulnerable groups. Current research shows that higher air temperatures, more daylight hours, no precipitation and low wind speeds are associated with more physical activity. The present study aims to investigate these associations at the within-subject level (i.e., differences within individuals over time) by further exploring individuals’ fitness level as a moderating factor.
The Days in Motion study examined 676 participants (aged 18-80) from 338 heterosexual couples living in Berlin (Germany) at a baseline week and a 12-month follow-up week between 2013 and 2015. This secondary analysis used data from a sub-sample of 228 participants who did not receive an intervention, provided longitudinal data, and did not report to be ill, injured or on vacation during the assessment period. Minutes of moderate-to-vigorous physical activity were measured using accelerometers. Hourly physical activity data were linked to meteorological variables (air temperature, relative humidity, precipitation, wind speed, sunshine duration and cloud cover) from a central weather station in Berlin. Physical fitness level was assessed objectively by the 2-km indoor walking test. Mixed effects models were used for the analysis, with the following data structure: hours nested within days, weeks, and persons. Between-subject (i.e., differences between individuals) and within-subject associations can be analysed in mixed effects models.
Significant between-subject associations occurred only for wind speed, with higher mean wind speed reducing physical activity. Positive significant within-subject associations with physical activity were found for air temperature and sunshine duration, whereas a significant negative association was found for precipitation and no association for cloud cover. However, the effects were moderated by the fitness index. In the future more data is needed during heat waves to be able to adequately analyse when physical activity might start to decrease again, or when physical activity in heat might have a detrimental effect on health.
How to cite: Tunn, S., Keller, J., Becker, N., Knoll, N., and Rust, H.: Exploring within-subject associations with mixed effects models: How does weather affect physical activity?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-966, https://doi.org/10.5194/ems2024-966, 2024.
In the fields of research concerning heatstroke, air pollution, and disasters, it is necessary to distinguish between indoor and outdoor populations in order to evaluate the benefit of different measures indoors and outdoors. However, there are few studies such distinctions have been taken into account because it is difficult to obtain the dataset of the distribution of indoor and outdoor populations.
This study predicts the ratio of indoor populations on a 500 m grid basis in Tokyo by machine learning approach. In this prediction, time-series location data, building area, building attributes and temperature is used as explanatory variables and the ratio of indoor populations is set as objective variables. In particular, it focuses on improving the accuracy of predictions for times when the ratio of indoor to outdoor populations stabilizes, which is critical for assessing risks such as heatstroke.
The machine learning model, employing Random Forest, demonstrated high predictive accuracy with an average error of 4%. Additionally, the model's performance improved during morning commute hours, lunch breaks, and evening return times. Also, the effects of various factors such as whether it is a holiday, convenience of transportation, and commercial activities are investigated in terms of the respective increases and decreases in indoor and outdoor populations.
While our methods have some limitations such as accuracy of location data, and the scarcity of data points, these results are expected to have broad social applications in the future, including risk assessments for heatstroke, air pollution, and disasters.
Future work includes establishing more robust validation methods, using more accurate time-series location data, and adding explanatory variables that better represent the characteristics of each grid. This would lead to even more accurate predictions.
How to cite: Suzuki, T., Kimura, T., Ohashi, Y., Takane, Y., Yamaguchi, K., and Ihara, T.: Indoor and Outdoor Population Prediction Using Location-Based Services Based on Machine Learning Approach, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-221, https://doi.org/10.5194/ems2024-221, 2024.
Context. Extreme weather events are affecting society in unprecedented ways. Weather and climate services, including early warnings, play a fundamental role in preparing for and responding to these events, ultimately reducing loss and damage from the weather. In 2024, the EEA published the European Climate Risk Assessment, identifying different types of extreme weather as some of the most important climate change risks. To prepare for and respond to the rising weather and climate risks, the WMO has promoted the move towards impact-based forecasts (WMO, 2015, 2021). Knowing what to expect in terms of impacts is much more actionable for people and enables decision-makers to take targeted, cost-effective, and informed decisions (Harrowsmith et al., 2020). For some time, the Royal Netherlands Meteorological Institute (KNMI) and its Early Warning Centre (EWC) have been working towards impact-based warnings.
Approach. Within the context of early warning services, KNMI and its EWC are experimenting with quantitative impact forecasting: "not what the weather will be, but what the weather will do." Through collaboration with decision-makers and end-users, we apply data-driven methods to make quantitative forecasts of impact. We provide these quantitative forecasts as an ensemble, so that the stakeholders can base their decision on the expected impact and the uncertainty of the forecast. We apply a general machine learning framework to train a model on the observed weather and impact data for the Netherlands, the latter being provided by the stakeholders. Then, we use this model to derive quantitative two-week impact forecasts.
Results. We show results for three different fields: storm damage, human health, and wildfires. The storm damage results are aggregated to a 10 x 10 km spatial resolution and 24-hour time resolution, and we provide a two-week forecast of the expected number of daily storm damage emergency calls as received by the fire services at the combined emergency services dispatch centre. We are collaborating closely with the emergency service provider to communicate these quantitative impact forecasts in an efficient way. For human health (cardio-vascular and respiratory fatalities) as well as wildfires (nature fires and road-side fires), we are currently working on quantitative impact forecasts aggregated at the national and monthly levels. The spatial and temporal scale of forecasts will be further improved to the provincial and weekly levels. Lastly, we will highlight the current limitations and challenges of impact-based forecasts based on our practical experience, and further research needed in this area.
How to cite: de Baar, J. H. S., Pereira Marghidan, C., van der Schrier, G., and van den Besselaar, E. J. M.: Collaborative data-driven quantitative impact forecasting: applications in storm damage, human health and wildfires, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-671, https://doi.org/10.5194/ems2024-671, 2024.
Extreme weather events affect millions of lives each year. The impacts include displaced and injured population, casualties, as well as interruptions of access to public services and critical infrastructure. Humanitarian organizations support affected populations all over the globe and do so increasingly with anticipatory action to mitigate the impacts before they happen. Anticipatory actions may be contractually triggered by impact-based forecasts. One key challenge in implementing forecast-based action and financing is the timely availability and the contextual understanding of forecasts that provide the basis for anticipatory action.
We present an integrated framework for the creation and analysis of trigger mechanisms suitable for forecast-based financing and action. The core is an impact model using hazard, exposure and vulnerability to create globally consistent impact estimates. Using forecasts of extreme weather events as inputs, impact-based forecasts for past events are calculated using this impact model. The behavior of any contractual trigger can be modelled for the past events. Employing uncertainty and sensitivity analysis in a comprehensible way will help practitioners assess the behavior of such a trigger for their use case.
We exemplify the application of the framework globally. We estimate the skill metrics of the impact-based forecasts using globally available observations of past impacts, additionally we derive a simplified set of requirements for the trigger mechanism using current examples of forecast-based financing and action. These assumptions can be used to show if the forecast skill allows to take early action under such a simplified set of requirements for any location globally. In locations where the requirements for such a trigger mechanism are not yet met, we aim to provide a reasoning on the cause and possible suggestions for improving the skill.
We hope this framework is helping decision makers in the forecast-based financing and action space assess the expectations in trigger setups.
How to cite: Röösli, T., Probst, P., and Riedel, L.: A framework for triggering forecast-based action and financing using impact-based forecasts of extreme weather events, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1083, https://doi.org/10.5194/ems2024-1083, 2024.
Extreme weather conditions like wind gusts and heavy precipitation frequently lead to impacts like treefall or flooded roads. This in turn leads to a large number of emergency calls to fire brigades, which they have to deal with in addition to their routine operations. If suitable warning information about approaching hazardous weather is available in time, there is potential for preparatory actions like calling in additional staff or preparing equipment and vehicles. However, in order to properly prepare for an event, it is important to understand how a particular fire department is affected by different types of weather conditions.
Therefore, an event detection algorithm is applied to operation data provided by different fire brigades in Southern Germany. The algorithm is applied to time series of hourly numbers of weather-related emergency calls to detect time periods with high numbers of operations. Each of the identified time periods, which we refer to as events, is characterized by different parameters, like total number of operations, maximum hourly number of operations, duration and affected area. Furthermore, the meteorological conditions during the event are characterized using convective cell tracks, tracks of large-scale windstorms, radar-based precipitation amounts and wind gusts from reanalysis data. The 100 events with the highest number of operations are selected and their characteristics are compiled into an event catalog. To facilitate the analysis of the event catalog, an interactive dashboard is developed to visualize the mutual interactions between the different event characteristics.
It is shown that thunderstorms are responsible for most of the events, followed by windstorms and continuous rainfall. In general, events related to thunderstorms are short and intense and cover a smaller area, whereas events related to windstorms generally affect a larger area. Events related to continuous rainfall are shown to have the longest durations, leading to fire brigade operations for time periods of up to several days.
The event catalog is helpful to analyze past events, but can also be useful in the context of impact forecasting. In case of an approaching meteorological hazard, it allows to visualize the potential impacts on fire departments referring to previous events and their characteristics. Furthermore, the event catalog builds the foundation to the development models for the prediction of weather-related fire brigade operations in future work.
How to cite: Becker, N., Göber, M., Ulbrich, U., and Rust, H.: An event-based analysis of weather-related fire brigade operations, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-481, https://doi.org/10.5194/ems2024-481, 2024.
Evidence suggests that high temperatures may trigger preterm birth (PTB), which is associated with a higher risk of infant mortality and morbidity during childhood and adult life. However, there is limited evidence on the role of sociodemographic factors on the vulnerability of pregnant women. In a multi-location setting, we aimed to assess 1) the effect of heat on PTB of different gestational ages (extreme, very, late, standard preterm births) and at-term births and 2) how mother and child characteristics (sex, ethnicity, parity, age, education, marital status, socioeconomic class) influence the association between heat and PTB.
The analysis included all singleton births born in the warm season (5 warmer months) in 243 cities in 13 countries between 1979-2019. A two-stage design was applied with conditional quasi-Poisson regression with distributed lag nonlinear models to estimate the association between daily mean temperature and PTB or at-term birth (lags 0-4 days) in each location and for each category of characteristic of the mother and child. Then, a random-effects multilevel metaanalytical model was applied to report overall effects and by country level. Extreme heat effects were measured as the percentage change (ch%, 95%CI) in the outcome when the mean temperature increased from 1% to the 95% percentile.
Heat was positively associated with all endpoints, except for extreme or very PTB whose risks were very imprecise, with larger risks for late-PTB (5%, 0.8-9.3) and smaller in at term births (2%, 1-3.1). Younger, Caucasian mothers and those with low socioeconomic class seemed to be more vulnerable to heat. Female fetal gender was associated with higher risk than males.
This is the largest multi-location study assessing vulnerability patterns of heat-related PTB. It emphasises the need to integrate evidence from vulnerability assessments in designing public health interventions to face climate change effects.
How to cite: Salvador, C., Iñiguez, C., Kim, Y., Lavigne, E., Orru, H., Ragettli, M. S., Royé, D., de’Donato, F., Leon Guo, Y.-L., Chang, H., Astrom, C., Konishi, S., Tobías, A., Agay-Shay, K., Scovronick, N., Singh, T., Valdes Ortega, N., and Vicedo-Cabrera, A. M. and the Multi-Country Multi-City Collaborative Research Network: Exploring vulnerability patterns in heat-related preterm birth: a multi-country multi-city analysis, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-938, https://doi.org/10.5194/ems2024-938, 2024.
Current climate projections illustrate a drastic increase in the likelihood of heatwaves throughout the century. At the same time, population scenarios for many developed countries indicate a coeval rise in elderly population fraction, in particular, in rural areas. Research demonstrates that failing to consider projected changes in population and demographics leads to a significant underestimation of health burdens [1]. Thus, in addition to the fundamental increase in healthcare demand due to the increased baseline morbidity of older individuals, there are also additional infrastructural requirements to prepare for weather and climate change-related consequences. Incorporating population changes into climate projections is therefore crucial for a comprehensive assessment of heat-related health impacts.
Here, we pursue a risk analysis for Germany by mid-century by combining health-relevant heat indicators with demographic scenarios and data on the healthcare provision situation at the county level. Climate indicators are based on latest results from statistically-downscaled CMIP6 climate projections for Germany [2]. Demographic changes are inferred from socioeconomic model simulation conducted for the latest Climate Impact and Vulnerability Analysis by the German Federal Environment Agency, available at the county level until 2045 [3]. Data from the German Federal Statistical Office are used to depict the spatial healthcare provision in terms of the number of spatially-explicit emergency department beds. For the risk analysis selected literature-based heat indicators with demonstrated health impacts are evaluated and the potential consequences for the German hospital emergency care system are discussed. In particular, the unequal spatial distribution of risks for the elderly population as well as the “double exposure” dilemma for certain areas facing large demographic changes and intensified heatwaves are identified.
Interpreting the spatial-temporal pattern of German regional exposure of hospitals to future heatwaves and demographic changes will help health care practitioners and political decision makers to develop strategies to reduce heat-related health risks both in the short and long term.
References
[1] Vanos, J. K., Baldwin, J. W., Jay, O., & Ebi, K. L. (2020). Simplicity lacks robustness when projecting heat-health outcomes in a changing climate. Nature Communications, 11(1), 6079. https://doi.org/10.1038/s41467-020-19994-1
[2] Kreienkamp, F., Lorenz, P., & Geiger, T. (2020). Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany. Atmosphere, 11(11), Article 11. https://doi.org/10.3390/atmos11111245
[3] Buth, M., Kahlenborn, W., Greiving, S., Fleischhauer, M., Zebisch, M., Schneiderbauer, S., & Schause, I. (2017). Leitfaden für Klimawirkungs- und Vulnerabilitätsanalysen. Umweltbundesamt.
How to cite: Geiger, T., Potzauf, M., Detring, I., Castino, F., and Kreienkamp, F.: The Dilemma of Coeval Climate and Demographic Changes: Heat Risk Analysis for the German Hospital Care System, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1018, https://doi.org/10.5194/ems2024-1018, 2024.
Extreme weather and climate events have profound effects on societies, economies, and environments worldwide. As a result, meteorological and hydrological service providers and researchers are increasingly considering factors such as hazard, exposure, and vulnerability in order to gain a comprehensive understanding of the full impact of weather and to contribute to minimizing fatalities and losses, thus enhancing resilience. Implementing these goals involves various technical and methodological approaches and numerous challenges in developing effective operational products and services.
In the case of the Basque Country, as in other parts of the world, improving the resilience of our societies against weather impacts is a current priority and will become increasingly important in the future due to the potential local increase in severe weather events and exposure. As a first step in this complex process of characterizing weather impacts, accurate information from past events must be collected, prepared, and maintained. To this end, a severe weather catalogue has recently been implemented, focusing on local weather impacts, to record detailed information about severe or adverse weather events that affect our society. This catalogue serves as a repository of valuable information for characterizing and evaluating extreme weather events and its consequences.
In this contribution, we present an in-depth study of severe weather events that have occurred in our territory during this century, including an analysis of the associated environmental conditions and their impacts. The main objective of this work is to enhance our understanding of local weather impacts by examining synoptic and local hydro-ocean-meteorological conditions conveniently correlated with the degree of impact. We analyze different key aspects aggregated by season, event typology, hazard type, and other relevant factors. Throughout this work, we present and discuss statistical findings to provide perspective and characterize the main aspects of the common hazards and impacts experienced by our territory. Dealing with impact, various indicators are defined and categorized to characterize key aspects, including severe weather warning levels, economic damages, human fatalities, or disruptions to normal life.
How to cite: Gaztelumendi, S., Egaña, J., and Ruiz, M.: Understanding impact weather: insights from the Basque Country, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-457, https://doi.org/10.5194/ems2024-457, 2024.
Adaptation to climate change requires a timely implementation of effective adaptation actions. A major effort in this context is devoted to extreme weather and climate events that are a major driver of climate change impacts. Planning and designing effective and efficient adaptation actions ideally requires the assessment of risk posed by impactful events. Complex and conceptual risk models already cohabit in the scientific literature in the form of a disorganised ecosystem of non-homogeneous models. This study explores the concept and mathematical formalism of intermediate complexity (IC) models for assessing climate risk and their significance in supporting climate change adaptation efforts. Existing climate risk models are classified into categories of different complexity and intermediate-complexity climate risk models identified and conceptualised to fill gaps in the hierarchy. The formulation of an intermediate-complexity model for flood risk under future climate conditions is introduced, drawing inspiration from established large-scale methodologies and utilizing freely available data. The simulations combine data of hazard, exposure and vulnerability provided by the European Copernicus Services and local datasets to perform high resolution (order 100 m) risk assessments. We discuss the application of IC climate risk models as a conceptual laboratory to develop the concept of parametrizations to take into account the effect of small scale (order tents of meters) engineering actions in a risk model with a coarser resolution (typically hundreds meters or kilometers). In this sense, they will mimic traditional parametrizations used to model subgrid process for geophysical flows in numerical climate models. The use of IC models to rank the impact of extreme events simulated with climate models with the aim to replace hazard-based metrics with impact-based metrics for the definition of an extreme events is discussed. While acknowledging that these models may not encompass all factors influencing risk, the authors argue that they are a crucial component of model hierarchies, capable of promptly addressing critical knowledge gaps and providing valuable insights to decision-makers.
How to cite: Ruggieri, P. and Di Sabatino, S.: On the need and applications of intermediate-complexity climate risk models , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1068, https://doi.org/10.5194/ems2024-1068, 2024.
Climate change, primarily through extreme weather events, significantly impacts various societal sectors such as health, industry and agriculture, and it is expected that these impacts will further increase in the coming decades. Despite the abundance of knowledge and data on climate change, translating this information into actionable insights for stakeholders preparing for climate change proves challenging. Communication breakdowns often arise between scientists and stakeholders due to differing terminology, understanding of climate science and expectations.
In this presentation, we aim to showcase recent Belgian initiatives focused on effectively communicating climate risks to stakeholders. One notable initiative, triggered by the ongoing CORDEX.be II project to provide high-resolution climate information for Belgium, involves recreating historical heatwaves at future global warming levels based on long-term climatological datasets. Subsequent discussions with diverse sectors, including health authorities, event organizers, and the energy sector, were tailored to each sector's specific needs. For instance, we used the operational Belgian heat and health plan thresholds to visualize the occurrence of warning and alarm phases across various levels of global warming. In this way we effectively concretize the implications of climate change for health stakeholders in a comprehensible form. As a second case, we will present our ongoing collaboration with cities to monitor urban greening projects within their public spaces. Through this collaboration, we gather quantitative data on heat stress at the microscale, e.g. under a tree canopy or next to a water element. This approach allows cities and citizens to grasp the effectiveness of these measures, assisting in the formulation of future plans. The purpose of the presentation is to share our insights and inspire colleagues facing comparable challenges when engaging with stakeholders.
How to cite: Caluwaerts, S., Top, S., Hellebosch, I., Vergauwen, T., Vandelanotte, K., Van Schaeybroeck, B., and Termonia, P.: Preparing society for extreme weather: insights from Belgian initiatives to create actionable information for stakeholders, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-816, https://doi.org/10.5194/ems2024-816, 2024.
Hydrometeorological extremes cause widespread socio-economic disruption and damage worldwide. Assessing the probability and impact of such events is critical for improving risk preparedness and emergency management. Ensemble climate simulations allow to extend the sample size of plausible weather extremes and estimate the probability of disruptive events that may be unprecedented in the historical record. This approach, commonly known as UNSEEN (UNprecedented Simulated Extremes using ENsembles) has demonstrated a comparative advantage over traditional approaches that rely solely on observations for risk assessment. In this work, we apply the UNSEEN methodology to estimate the probability of river flooding in European medium-size catchments, using ensemble simulations of seasonal forecasts provided by the European Flood Awareness System (EFAS). Simulated values of river streamflow during rare events can be used as input for risk models to yield estimates of the socio-economic loss resulting from unprecedented floods. This framework promotes the co-design of flood risk storylines that can assist stakeholders and policy-planners in the development of mitigation and adaptation strategies to manage disasters.
How to cite: Bianco, E. and Ruggieri, P.: Assessing the risk of unprecedented flooding with event-based storylines , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-800, https://doi.org/10.5194/ems2024-800, 2024.
As part of the Climate Ready Barcelona project, a crucial aspect is the development of a Climate Vulnerability Index (CVI). This abstract highlights the creation of the climatological and meteorological foundation, at the microscale urban level, for the CVI models, which is a critical task within this project.
The CVI is visualized via an interactive interface, illustrating its geographical distribution in Barcelona under existing and potential climate change scenarios. The meteorological data sets used in this research offer a high-resolution grid, which facilitates a detailed examination of each building block. Furthermore, the system is capable of generating forecasts and alerts for imminent climate events, such as heatwaves or extreme nighttime temperatures.
The CVI and alerts are derived from an integration of diverse data types, including energy consumption, climate and weather data, socioeconomic factors, and building characteristics. The climate and meteorological base for the CVI models also involves the integration of diverse data types, which, in this case, only focuses on regional and European models. Both of these data are represented and analyzed using a knowledge graph, which encapsulates diverse urban environment concepts and aggregates data at various administrative levels.
A significant aspect of this work involves the construction of an ontology for structuring these highly heterogeneous datasets. This ontology forms the backbone of the knowledge graph, comprising a linked network of nodes encapsulating various concepts within the urban environment. These include data aggregated at different administrative levels and points of interest strategically distributed throughout the urban environment.
The connections established among the distinct node typologies facilitate the development of advanced geospatial data analytics modules. These modules enable the accurate estimation of a climate vulnerability index, providing valuable insights into urban heat risk dynamics and potential mitigation strategies.
Coupled with machine learning techniques, the knowledge graph predicts urban heat patterns, drawing insights from historical data and identifying underlying trends. This integration offers a platform for advanced analytical reasoning, simulation, and accurate forecasting, capturing the spatial and temporal components inherent in the data.
The resulting map highlights areas most susceptible to climate change impacts, providing invaluable information for policymakers and planners. This aids in the development of informed decisions to enhance the city's climate resilience and adaptation strategies. Furthermore, it provides Barcelona's municipal housing department with a detailed diagnosis of climate-vulnerable areas, crucial for energy renovation plans.
The Climate Ready Barcelona project has been funded by Local Governments for Sustainability (ICLEI) and the Google Foundation and demonstrates a commitment to proactive climate change readiness. The work presented here underscores the importance of creating a robust climatological and meteorological foundation to enhance the liability of urban resilience models at the building scale, a task that forms the core of this research.
How to cite: Comas Lázaro, A., Pariente, A., Mor, G., Broto, J. M., Sellart, M., and Cipriano, J.: Enhancing Climate Resilience Through Urban Microscale Weather Data Analysis, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-490, https://doi.org/10.5194/ems2024-490, 2024.
The proliferation of harmful algal blooms (HABs) is a significant environmental issue exacerbated by climate change. They have many detrimental impacts on public health, recreational activities, ecological equilibrium, animals, fisheries, microbiota, water quality, and the economy. HABs can be attributed to various factors, including water pollution resulting from agricultural activities, discharges from wastewater treatment plants, leakages from sewer systems, natural factors such as pH and light levels, and the impacts of climate change, such as increased water temperature, reduced water flows due to droughts, and water-related disasters like flooding and inundation. Although numerous causes of HABs are acknowledged, the mechanisms by which toxin-producing algae develop, as well as the crucial processes and components that contribute to their proliferation, remain unknown. This lack of understanding is primarily due to the unique dynamics of each lake's algal population and the nonlinear nature of the conditions that influence these dynamics. Modeling HABs in a changing climate is critical to meeting sustainable development targets for clean water and sanitation. Nevertheless, the absence of adequate and sufficient data on HABs poses a substantial obstacle for research endeavors. This study utilized the sparse identification nonlinear dynamics (SINDy) technique to model microcystin, which is a toxin produced by algae, using dissolved oxygen as a variable for water quality and evaporation as a meteorological parameter. SINDy is a state-of-the-art approach that integrates sparse regression and machine learning methodologies to reconstruct the analytical representation of a dynamic system. Furthermore, a web platform was developed that utilizes models and is accessible through the web. This tool aims to promote and enhance environmental education, increase the public's awareness of these events, and generate more efficient solutions to them by using what-if scenarios. This web platform allows tracking the status of HABs in lakes and observing the impact of specific parameters on harmful algae formation. On an interactive and user-friendly platform, users may effortlessly share photographs of HABs in lakes, enabling others to monitor the lakes' status.
How to cite: Baydaroğlu, Ö., Yeşilköy, S., Linderman, M., and Demir, I.: Unveiling Harmful Algal Bloom Dynamics with Sparse Identification of Nonlinear Dynamics and the Model-based Web Platform for Lakes, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-290, https://doi.org/10.5194/ems2024-290, 2024.
Urban heat island (UHI) effect poses a significant human-induced challenge to urban communities, jeopardizing their livelihoods. Amplified by the upward global temperature trends, as a consequence of climate change, the effects of UHI have increasingly become severe. This manifests in crucial social (e.g., increased heat-related illnesses and mortality rates, particularly affecting vulnerable populations), economic (e.g. difficulties of some groups to access to cooling infrastructure for mitigating heat stress) and environmental implications (e.g., higher heat stress on vegetation, increase risk of species loss, increase of ozone pollutant levels). The current study examined the daytime and nighttime UHI effect in the region of Paris from 1990 to 2022. The study presents a novel method for assessing UHI using readily available data from the NCEP-NCAR Reanalysis 1 dataset (at almost 1.8 km spatial resolution) and low computational resources, enhancing the feasibility of UHI monitoring and analysis. By integrating daily circulation weather type (WT) classification with UHI analysis, this study elucidates the influence of weather patterns on UHI events. Results showed that the most common WT were the anticyclonic (A) and the cyclonic (C) with almost 25% and 10% of occurrence respectively, combined with two additional directional types: the Southwestern (SW) and the Western (W) type which account for around 10% of the total cases for both WT. The anticyclonic type predominance is mainly due to the migration of the Azores anticyclone towards the area of study which is linked with higher urban heat island intensities either for maximum and minimum temperatures, especially during the autumn season. These anticyclonic situations were mainly associated with anomalous daytime and nighttime temperatures and accordingly a broader extension of Paris region affected by the UHI effects. It should be noted that a highest UHI effect was found during summer and especially noticeably for the nighttime. The findings of this study highlight the escalating severity of UHI impacts, exacerbated by global temperature trends due to climate change. Overall, this study highlights the urgent need for proactive measures to mitigate UHI effects in urban areas, encompassing policy interventions, infrastructure improvements, and community resilience strategies. By comprehensively understanding the dynamics of UHI and its interactions with broader climatic patterns, stakeholders can develop targeted interventions to safeguard urban communities against the escalating challenges posed by UHI in the context of climate change.
Keywords: Urban Heat Island assessment, climate risks, impacts, Lamb Weather Types, spatial analysis, Paris.
How to cite: Fernández-Duque, B., Pérez, I. A., García, M. Á., El Kenawy, A. M., and Vicente-Serrano, S. M.: Assessment of the Urban Heat Island Effect in Paris under different circulation weather types, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-724, https://doi.org/10.5194/ems2024-724, 2024.
It is important to determine the distribution of indoor and outdoor populations when assessing the risk of extreme summer temperatures in urban areas, the heat island effect, air pollution, and disasters. However, conventional indoor/outdoor determination using sensors and survey forms has problems in scale and immediacy, and it is difficult to determine the population distribution by time of day and by subregion. This study developed and verified a new estimation method using location-based service data with the aim of accurately estimating the distribution of indoor and outdoor population in urban areas and clarifying its temporal and spatial variation in some wards of Tokyo Metropolis, Japan.
The research methodology used point-type data from the global positioning system (GPS) communication, grid-type data acquired from mobile phone base stations, and building polygon data, and combined them to estimate the indoor and outdoor population by time and grid. Specifically, the grid-type demographics were used to recover confidential population data from point-type location service data, which were superimposed on building polygons and determined the indoor/outdoor population based on travel distances. The estimated indoor population was compared and validated with the person-trip survey (PT survey) data at the grid level.
As a result, validation with the PT survey in Nerima Ward confirmed that the estimated behavior of the indoor population, including confidential data restoration, was close to the actual behavior. The model is capable of capturing indoor and outdoor population movements by time of day and by grid, and its results are expected to have a wide range of social applications in the future, including risk assessment for heat stroke, air pollution, and disasters.
How to cite: Kimura, T., Suzuki, T., Ohashi, Y., Takane, Y., Yamaguchi, K., and Ihara, T.: Estimation of Distribution of Indoor and Outdoor Population Using Location-Based Serviced Data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-222, https://doi.org/10.5194/ems2024-222, 2024.
As global temperatures rise, there is a significant increase in the demand for cooling energy in buildings, leading to heightened attention on urban energy demands. The direct impact of cooling demand on urban energy burdens, coupled with a growing body of literature on energy, poverty, and other social factors, has underscored the importance of this issue. This study focused on exploring the relationship between Taiwan's regional climate conditions in high-temperature environments, social factors in geographical context, and energy burdens. The objective was to analyze the distribution of energy burdens affected by thermal shocks and social vulnerability, identifying vulnerable areas to facilitate climate change adaptation strategies.
Covering 20 counties and cities in Taiwan, this study employed administrative divisions as analytical units to discuss the distribution of energy burdens in Taiwan in 2018 and 2023. Regarding climatic factors, this study adopted the dynamical downscaling data produced by National Science and Technology Center for Disaster Reduction (NCDR). The data included the changes in energy demand under the current and IPCC-defined RCP8.5 warming scenarios. Additionally, the study examined the influence of social factors such as population structure, socioeconomics, and social vulnerability.
The results reveal variations in the distribution of energy burdens in 2023, ranging from a minimum of 0.2% to a maximum of 30%. Over half of the counties and cities have energy burdens concentrated between 5.4% and 7.9%. Cooling demand in Taiwan is highest in July, followed by August. Therefore, the high energy burden is reflected in the months of August and September. In the climate change scenario, the demand for cooling increases by up to 26.3% in August. The spatial distribution shows that energy burdens are higher in mountainous and suburban areas, despite lower cooling demand there. Conversely, high cooling demand is concentrated in major urban centers, where energy burdens are lower. Finally, the study integrates distribution maps of cooling demand and other social factors to explore the relationship between energy burdens and climate, population, and society.
Keywords: Energy Burden; Cooling Degree Hours; Spatial Analysis; Climate Change
How to cite: Chang, C. and Lin, T.-P.: The impacts of energy burden and social vulnerability in Taiwan under climate change and urban heat island, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-293, https://doi.org/10.5194/ems2024-293, 2024.
The enhancement in user communication is one of the key aspects in the RainBoW (Risk-based, Application-oriented and INdividualizaBle delivery of Optimized Weather warnings) programme, which encompasses the development of the new weather warning system for the German Meteorological Service (Deutscher Wetterdienst). Within RainBoW, a probabilistic weather warning system is developed that supplies both information to users with special requirements but also generates standardised warnings for the general public. It addresses a number of weather elements, such as wind, precipitation, and thunderstorm. Hence, a good understanding of the issued weather warnings by the recipients plays a crucial role in order to enable both professional forces but also individuals to take appropriate action in affected regions. In RainBoW, one of the means to achieve a better perception is to move impact information into the focus for future weather warnings provided by the German Meteorological Service.
In this contribution, we present a study on hazards of wind gusts, which is among the weather elements that show the largest impacts on infrastructure and society. In order to shed light on the severeness of impacting events, we discuss the generation of an impact proxy for Germany, which we model by evaluating historical wind gust data by means of extreme value statistics. Despite the available several years of detailed reanalysis data (e.g. from the COSMO REA6 dataset), one of the main challenges is the sparsely available extreme weather data. One reason for this lies in the very profound nature of extreme value statistics, but wind gusts are also not very well represented in the model data. This is further complicated by the rather short period of sufficiently detailed and consistent data, which is governed by few more or less local extreme weather situations that occured during roughly two decades. We discuss the potential of this approach and motivate its implementation into the future warning system (RainBoW) of the German Meteorological Service. Moreover, we complement this with user generated crowd sourcing data and discuss its suitability for an impact proxy of wind.
How to cite: Anger, F., Felsberg, A., Koser, D., Riedl, K., Lambert, A., Erhardt, B., Leschzyk, D.-K., Fundel, V., Winterrath, T., and Feige, K.: Modelling Wind Induced Impacts for Application in a New Weather Warning System, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-387, https://doi.org/10.5194/ems2024-387, 2024.
From the perspective of National Meteorological and Hydrological Services verification is needed to assess the quality of severe weather warnings, to determine their strengths and weaknesses and consequently to lead to improvements. Apart from the challenges that arise when analysing rare meteorological events, a set of statistical methods are suitable for comparing weather forecasts or warnings with observations to assess the (physical) accuracy of forecasts. For impact-based warnings, the complexity is increased since it is not enough to compare the forecasted meteorological values against observed values. The societal consequences of the hazard (e.g. comparison with damage compensation statistics) should also be taken into account, as well as the preventive measures taken by various actors - based on the information in the warning - to reduce risk and damages.
What determines how valuable a warning is from a user’s point of view? Do users and providers have the same understanding of the value of a warning, and the thresholds for issuing warnings? How can the value of a warning be best communicated to users to support their decisions to mitigate risk or prepare for it as best as possible? Some potential factors to consider in an evaluation could be how accessible the warning is, timeliness, how understandable the information about the weather, the consequences and the risk is, and how useful the information is in their decision-making processes. The latter might be affected by e.g. how trustworthy they consider the (provider of the) warning to be, how skillful or accurate the information is, or how relevant the information is to their specific situation or context. Among users, e.g. emergency services, authorities in civil protection, media and communication, and the general public, there is a wide variety in the way warnings are used and understood and in requirements concerning severe weather warnings.
A user-oriented verification and evaluation could help the NMHSs understand the capabilities and limits of warnings among the different user groups, and consequently improve their decision-making capabilities, such as safeguarding life and property.
Here, we will present results from a survey conducted at the second Weather and Society Conference (WMO) in February 2024, and at the seventh conference on weather warnings and communication (AMS) in June 2024. The respondents were experts from various fields, participating in the conference. They gave input on verification and evaluation activities in the field of weather warning and its communication to users, and opinions on how to best perform user-oriented evaluation of warnings.
Further, to increase the impact of the study, we would also like to invite all conference participants at the EMS annual meeting in Barcelona to respond to the short survey (ten minutes), by accessing this URL: https://forms.gle/eBe6S8qeweL1LukV8.
How to cite: Sivle, A. D. and Wapler, K.: Improved understanding of warning evaluation: will you contribute to the survey?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-404, https://doi.org/10.5194/ems2024-404, 2024.
In the last decades, climate-related extremes are becoming a recurrent feature in many hotspot regions, as southern Latin America, the Mediterranean, the sub-Saharan Africa, north-eastern China, and southern Australia. Depending on the type of the extreme, reported damages of single events – often on specific sectors – are becoming available with increasing detail, but this is limited to recent years and does not apply to all countries. Consequently, to estimate the socio-economic impacts of climate change, the usual approach is to regress climate variables versus indicators as the Gross Domestic Product (GDP), resulting in annual temperature being the major or the only driver for most sectors, with heterogeneous marginal influence of precipitation. In this presentation, we focus on the benefit of including climate extremes in such models, specifically – as single or compound events – heatwaves, cold waves, rainfall extremes, droughts, and snowfall. Using daily and high-resolution climate data and sub-national socioeconomic indicators over the past decades, we explore the role of the lagged effect between climate extremes and the economic response, represented by GDP, but also other indicators as the Human Development Index. To include the climate extremes in damage functions, a parameterization for such lagged effects is needed, but its explicit modelling is not free from uncertainties, which can be very large, to the point that they might suggest excluding some extremes over specific areas. We also present – in preliminary form – an SSP-based estimation of future impacts of climate extremes, using a set of different global damage functions, also tentatively including regional aggregation for specific climate extremes.
How to cite: Mastropietro, M., Chiani, L., Ghirri, J., Rodriguez-Pardo, C., Spinoni, J., and Tavoni, M.: Evaluating the role of time lag between climate extremes and socioeconomic impacts, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-557, https://doi.org/10.5194/ems2024-557, 2024.
Heat-related fatalities, predominantly from heatstroke, commonly occur indoors within residential settings. Consequently, accurate prediction of indoor temperatures is crucial for preventing such fatalities. However, few studies have measured the indoor thermal environment of general existing residential buildings rather than newly built residential buildings, and few studies that have predicted the indoor thermal environment based on the measurement.
This study measured the indoor thermal environment of 17 detached wooden homes in two towns in Ota Ward, Tokyo Metropolis, Japan from July 25 to September 20, 2018, spanning a total of 58 days. Indoor measurements included room temperature and relative humidity, globe temperature, atmospheric pressure, window open/closed status, and air conditioner operating status. Outdoor measurements included outdoor temperature and relative humidity. In addition, the wind speed, precipitation, and cloud cover observed by the Japan Meteorological Agency were obtained. Based on these data, we developed models to predict indoor temperatures and wet-bulb globe temperature (WBGT). The prediction models were constructed using linear regression and a three-layer neural network. The input variables include measured or observed values and characteristics of the residential buildings, such as the age of the buildings, the number of floors, the floor on which the room is located, and the number of occupants.
The model predicted room temperature and room WBGT with mean absolute errors (MAE) of 1.0°C and 0.8°C for non-air-conditioned rooms. The accuracy of room temperature and WBGT prediction improved by 0.1°C when the model was trained using data for the predicted room collected during the previous period. The linear regression model was as accurate as the neural network. The factors that are important for predicting the indoor thermal environment were identified using linear regression.
This research enables the development of government-led alert systems to mitigate heat-related risks in residential areas.
How to cite: Li, Y., Hashimoto, Y., Tsunematsu, N., and Ihara, T.: Modeling Indoor Thermal Environments in Detached Wooden Homes Using Linear Regression and Neural Networks, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-685, https://doi.org/10.5194/ems2024-685, 2024.
There has been a continuous year-on-year increase in mountaineering injuries and fatalities in Catalonia, partly related to adverse weather conditions such as thunderstorms, mist, strong winds, blizzard, cold temperatures, among others.
In this context, in November 2016, meteomuntanya.cat was launched, a specialized meteorological contents webpage resulting from the collaborative effort between the Meteorological Service of Catalonia (SMC) and the Catalan Mountain Clubs Federation (FEEC).
MeteoMuntanya provides operational, informative and awareness-raising information and products aimed at reducing accidentality.
Firstly, various tools are available for planning our route, climb or traverse in detail.
- “Mountain conditions” offers a written weather forecast focused on weekends and holidays. Special symbology is used to highlighting forecasted occurrences such as strong winds, reduced visibility due to mist, heavy or persistent rain, thunderstorms and lightning activity, snowfalls and blizzard, iced snow surface, and thermal comfort (cold/hot). Safety comments and recommendations may also be included.
- To complete the written forecast, the “peaks weather” provides daily semiautomated forecasts (3 km resolution WRF) for over 300 peaks over the Catalan massifs, with a 3-day window.
- Furthermore, users can also find real time and daily anomaly snow depth data, forecast focused in mountain routes conditions, and detailed conditions for some official competitions (as trail-running or mountain skiing).
To expand the “mountain culture” and reinforce awareness-raising, the “blog” section offers written content (2-5 minutes reading) on topics like climate change, Catalan massif climates, or mountain weather. Recent examples include “Forecasting mist”, “Winter anticyclone and snow” or “Climate change and mountains activities”, articles which are also permanently included in "Vèrtex," the FEEC review received by over 40,000 members (federates).
Finally, the section “mountain and safety” serve as a reference place for individuals, mountain clubs, and schools seeking education resources. Examples include YouTube videos on weather hazards during mountaineering featuring recognized mountaineers such Araceli Segarra, Ferran Latorre or Claudia Sabata. There is also free access to online seminars on topics like mountain blizzard or thunderstorms and lightning.
All MeteoMuntanya contents are consistently disseminated through the social networks of the Meteorological Service and the Mountain Federation, with more than 350.000 and 40.000 followers in X, respectively.
With this presentation, we aim to showcase our 8 years of experience and explore opportunities for coordinating efforts with other meteorological services to provide consistent mountain weather safety information and products.
How to cite: Esteban Vea, P., Serra Uró, A., and Pons Casadesús, Q.: MeteoMuntanya.cat: a project on weather, mountaineering, and safety , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-729, https://doi.org/10.5194/ems2024-729, 2024.
Understanding the compounding threats of floods and sea storms in the Mediterranean is crucial in the context of climate change. For this reason, a holistic analysis of their interaction and its implications for coastal resilience is needed. According to Zscheischler et al (2020), compound events are defined as the combination of multiple drivers and/or hazards that contribute to societal or environmental risk and that can be responsible for many of the most severe weather and climate related impacts. To manage these situations more effectively, it is essential to identify and prioritize elements that can be controlled, such as infrastructure planning and early warning systems, while also improving the resilience of those elements that cannot be controlled, such as natural ecosystems. Through the integration of scientific research, stakeholder collaboration, and management and policy interventions, coastal communities can enhance their capacity to mitigate risks and adapt to the impacts of compound hydrometeorological events.
In the framework of the C3Riskmed project, multidisciplinary research has been conducted to analyze compound events on the Spanish Peninsular Mediterranean coast. This communication introduces the compound events of floods, wind and sea storms recorded in the coastal municipalities covering a total 1,609 km of coastline between the Spanish-French border and Gibraltar. Their economic impact is analysed using Insurance Compensation Consortium (CCS) data and this is compared with the impact of non-compound events. A remarkability criterion, which integrates physical and socio-economic variables, and used to select the most important events, is introduced. Finally, a paradigmatic compound event such as the January 2020 storm "Gloria" is presented. Through detailed analysis of meteorological and impact data, key aspects of the dynamics and consequences of this compound event are shown. The lessons learned from Storm Gloria can be considered for the improvement of early warning systems, infrastructure planning, and community preparedness to mitigate the impacts of future extreme weather events.
This research has been done in the framework of the C3Riskmed project, Grant PID2020-113638RB-C22 funded by MCIN/AEI/10.13039/501100011033.
References:
Zscheischler, J. et al., 2020, A typology of compound weather and climate events. Nature Reviews.
How to cite: Llasat-Botija, M., Llasat, M. C., Marcos-Matamoros, R., Aguilera Vidal, M., Castán, S., and Jimenez, J. A.: Analysis of flood compound events in the Spanish Peninsular Mediterranean coast, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-790, https://doi.org/10.5194/ems2024-790, 2024.
Planning the scale of necessary road maintenance actions based on outputs from numerical weather prediction (NWP) models can be challenging. By employing machine learning, these actions can be estimated more objectively.
A machine learning model was developed to predict the hourly probability of salting and snow removal operations across different road maintenance categories. This model provides a three-day forecast of maintenance probabilities over a five-kilometer pixel grid covering mainland Finland.
The model was developed collaboratively by the Finnish Meteorological Institute and Destia, Finland's largest infrastructure service company. This development was part of the JääVe project, funded by the National Emergency Supply Agency. The model uses a gradient boosting approach and is trained on reports of snow removal and salting operations provided by Destia. Inputs to the model include MEPS NWP model outputs, cyclical temporal variables, and static landscape features.
The model has learned to account for varying regional and seasonal characteristics. For example, it predicts different probabilities for coastal versus inland areas and adjusts for variations between the sparse northern regions and the more populated southern settlements. The model's predictions are comprehensive, extending beyond the road station locations used for fitting to cover the whole Finland.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
How to cite: Kämäräinen, M., Haikonen, M., and Tuhkalainen, E.: Probabilistic forecasts of winter road maintenance operations based on machine learning, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-839, https://doi.org/10.5194/ems2024-839, 2024.
Urbanization intensifies the Urban Heat Island effect, exposing city dwellers to higher temperatures and increased heat stress risks, particularly during summer. This study proposes a comprehensive approach to assess heat-related risk in three European cities sampled from different European climate conditions: Turku (Finland), Jena (Germany), and Iași (Romania).
A mixed-methods approach combining geospatial analysis and statistics will be employed. Our aim is to create fine-scaled (at least 100 meters resolution) heat risk maps for each city and test whether rental data may be used as a proxy of the vulnerability variables in risk assessment. Rental data can be a valuable proxy for socioeconomic vulnerability in the context of heat stress. Higher rents often correspond to more modern buildings with better insulation and air conditioning, which can offer greater protection from extreme heat. Further we posit a correlation between rental prices and the vicinity of cooling structures such as green spaces and water bodies. Additionally, higher rents may be associated with better access to transport infrastructure, implying a higher connectivity to essential services like healthcare facilities or public buildings with air conditioning. This combined effect suggests that residents in higher-rent areas may have a greater ability to cope with extreme heat compared to those in lower-rent areas.
To ensure more relevance for current climate conditions, daily temperature data was acquired from meteorological agencies or research institutions for each city covering the period 2014-2023. We calculated the hazard layer, defined as the sum of temperature days exceeding 20°C for each location over the ten-year period. Population density data was obtained from Eurostat in a 1 km2 grid. To achieve a finer scale, this data was down-scaled using a building-volume-based method, providing a more accurate representation of population distribution within each city. Web scraping was used to collect rental data from online platforms, including various attributes. This data will serve as a proxy for socioeconomic vulnerability in the context of heat stress. We acquired data on established socioeconomic vulnerability indicators like income, education, and occupation for each city. Universal kriging is used to interpolate the hazard- and vulnerability layers to account for spatial autocorrelation and generate a continuous surface for each variable. This allows for a more nuanced analysis of the spatial relationships between these factors. The interpolated layers will be integrated into a spatial model to assess the risk of heat-related stress across each city, highlighting areas with high vulnerability. Statistical analysis is conducted to assess the correlation between rental data and established socioeconomic vulnerability indicators. This will validate the use of rental data as a vulnerability proxy and provide further insights into the sociodemographic makeup of vulnerable populations.
How to cite: Schlechtweg, P., Brenning, A., Käyhkö, J., and Sfica, L.: Assessing Excessive Heat-Related Risk in European Cities: A Multi-Criteria Approach for Medium-Sized Cities, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-864, https://doi.org/10.5194/ems2024-864, 2024.
The aviation sector is experiencing the impacts of climate change on both in-flight and ground operations. One meaningful example is that of heatwaves, as temperature extremes reduce aircraft lift and takeoff performance, requiring lighter loads—including fewer passengers and less cargo— and increasing noise pollution due to shallower climb angles. Heatwaves also deteriorate runway asphalt and compromise comfort inside airport terminals.
In this work, we analyze the occurrence of summer heatwaves at 30 European airports by comparing historical data from 1985-2014 with projections for 2035-2065, using data from 10 CMIP6 models across three Shared Socioeconomic Pathways (SSP126, SSP370, SSP585). We apply a dedicated bias correction and downscaling technique to model data to improve spatial resolution and address systematic distributional biases. Heatwaves are identified and measured using a Standardized Heatwave Indicator (SHI), enabling the assessment of any trends in the frequency, length, and intensity of this phenomenon at each airport.
Our findings reveal a significant increase in the frequency, duration, and intensity of summer heatwaves across all the airports considered for the period 2035-2065 compared to historical data. Heatwaves are anticipated to become an annual phenomenon, with Southern European airports expected to face the most severe conditions. These airports could in fact experience an average of up to four heatwaves each summer, lasting up to 14 days and reaching intensities exceeding the 99.97th percentile of historical events.
This work underscores the urgent need to identify and implement heatwave-aware adaptation measures and operational improvements in aviation, enhancing the climate resilience of this sector to ensure safety and efficiency in operations.
How to cite: Guerrini, F., Trentini, L., Venturini, M., Dal Gesso, S., Calmanti, S., and Petitta, M.: Climate Change and Aviation: The Increasing Impact of Heatwaves on European Airports, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-939, https://doi.org/10.5194/ems2024-939, 2024.
In recent years, interest in the study of natural disasters has grown considerably, linked both to the increase in the number of such events and to the worsening of their impact on the territory. The conservation strategies of cultural heritage (whether it is movable or immovable), in response to damage resulting from the natural phenomena of aging and decay but also from the occurrence of disasters, inevitably require a methodological approach aimed at planned conservation and preparedness for the risk event of the cultural site. In this sense, the Mediterranean basin which is a unique environment for the occurrence of atmospheric phenomena, results to be a relevant example, considering that Italy currently has the largest number of sites inscribed on the World Heritage List. We examined with this aim some case studies in Italy. We have employed different meteorological data (Satellite-based observations, ERA5 model, NWP models, GNSS, Lightning detection network, Radar) to characterize events related to some case studies of potential interest. The meteorological events investigated, which caused flooding and damage in the Italian study areas, showed how the sites responded in different ways to the stresses. The architectural peculiarities, the reciprocal relationship between buildings and environment, typical of spontaneous architecture in the Mediterranean basin, and the perception of heritage by inhabitants strongly condition protection policies. In this context, the properties of buildings need to respond to three fundamental aspects: climate, building materials and the morphology of the territory. These distinctive properties have therefore contributed to sites resilience, as explored in more detail in the different works performed (UNESCO archeological site of Villa Romana del Casale, in Piazza Armerina (Sicily), UNESCO site of “Sassi di Matera”, Alberobello UNESCO site, Como UNESCO Creative City and Trabocchi Coast (Abruzzo) UNESCO intangible cultural heritage candidates). Results have shown the great potential of the multi-sensors approach to describe in the most complete and detailed way the intense meteorological events and to show the agreement between the products involved in the studies in order to evaluate the joint use of different platforms. Future multi-instrumental applications and potential use of some products (e.g., satellite-based) for the analysis of events of interest in areas not covered by operative tools is an issue that emerged. In particular, in all presented case studies, we started from a long-term and large-scale analysis concerning the climatology of the area, to a short-term and more local scale where we described the most acute phases of the events. Different responses to stresses have to indicate the most proper approach to face up the risks and plan the actions to preserve the cultural heritage (e.g. risk management plan, mandatory for each UNESCO site since 2019).
How to cite: Mascitelli, A., Prestileo, F., Federico, S., Torcasio, R. C., Di Carlo, P., and Dietrich, S.: Multi-sensor approach for analysis of extreme weather events affecting areas of cultural interest, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-941, https://doi.org/10.5194/ems2024-941, 2024.
People's exposure to heat during the summer period can have serious repercussions on their health. This exposure is particularly problematic in cities, where it is exacerbated by the urban heat island (UHI), and can be associated with poor air pollution conditions. Today, preventing the risks associated with heat and air quality is a major public health issue in cities. A better understanding of urban-subscale variations in the exposure of inhabitants to heat and air pollutants in summer is necessary to adapt prevention measures and to improve the relevance of weather and air quality forecasts for the assessment of health impacts and for supporting decision-making.
The French research project Heat and Health in Cities (H2C) aims to improve the urban climate services to support heat risk prevention policies, with the Paris region (France) as a case study. In response, the three scientific objectives were identified: (1) Improve our understanding of the impacts of urban covers on UHI, local meteorology and air quality ; (2) Strengthen the synergy between numerical modeling and multi-source observations to assess the variations in population exposure to heat and air pollution, and the associated health risks ; (3) Develop and provide information that is useful for decision-making related to prevention.
Based on the cross-referencing of health, meteorological, socio-economic and urban data (at scale of the region municipalities and Paris' districts), the project has helped to clarify the environmental and social drivers of heat-related health impacts in the Paris region. In addition, an experimental campaign documented the multi-scale variability of meteorological conditions at the surface and in the atmospheric boundary layer. These observations are currently combined with weather and air quality numerical modelling, down to hectometric resolution, to gain a better understanding of the processes involved and the contrasts in exposure in the region and in Paris. The link with thermal comfort conditions in buildings is also explored on the basis of building simulations for a sample of Parisian housing stock. Finally, a major challenge is now the co-construction of territorialized indicators with the help of a group of stakeholders, for the definition of feasible, relevant and understandable urban climate services. For this, a multi-criteria analysis cartographic platform is being developed, fed by knowledge and data from research. The organization of workshops with stakeholders aims to collectively build and test different indicators thanks to a dynamic visual geographic and statistical restitution, and to guide the design of an operational tool.
How to cite: Lemonsu, A. and the Lemonsu A.1, Alessandrini J.-M.2, Capo J.1, Claeys M.1, Cordeau E.3, de Munck C.1, Dahech S.4, Dupont J.-C.5, Dugay F.6, Dupuis V.7, Forceville G.8, Garrigou S.3, Garrouste O.1, Goret M.1, Goria S.8 , Haeffelin M.9, Host S.3, Joly C.6, Keravec P.10, Kotth: The Heat and Health in Cities (H2C) project to support the prevention of extreme heat in cities, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-982, https://doi.org/10.5194/ems2024-982, 2024.
Citizens in every inhabited place on the planet are increasingly experiencing dramatic consequences of a changing climate. Within Europe the adaptation gap between the multi-hazard climate risk and the risk management capability is growing. The EU Horizon Europe project CLIMAAX (CLIMAte risk and vulnerability Assessment framework and toolboX) addresses this by providing financial, analytical and practical support to climate risk assessment community, allowing an improvement of regional Climate Risk Assessment (CRA) and management plans. In the context of the project, we have developed an inclusive and harmonised CRA framework and the toolbox for improved data access, manipulation, processing, modelling and visualisation.
The four main elements of the toolbox are:
1) A Handbook: the website to serve as a placeholder for the Framework and the tools, as well as user guide with full description of the tools involved;
2) A set of Jupyter notebook templates and examples of the workflows of case studies;
3) Access points to the data needed and tools for data manipulation and visualisation for the Climate Risk Assessment;
4) Access to computational and storage resources, using JupyterHub hosting all the repositories with the workflow examples
The heart of the CLIMAAX toolbox is Jupyter ecosystem with the JupyterBook used for the technology for Handbook, JupyterHub for the collaborative cloud environment and Jupyter notebooks for the executable examples. It will also have Jupyter lab enabled for users and Jupyter books for wiki, documentation and templates. With these resources at hand, both technical and non-technical persons have a tool that will provide a homogenous tool to perform local and regional climate risk assessments.
The project started in January 2023, and the operational version of the toolbox implemented during Spring 2024. This poster we will share the design and implementation of the CLIMAAX toolbox, as well as lessons learned and challenges from the process of building it.
How to cite: Vuckovic, M. and Wetterhall, F.: Bridging the gap between climate data and policy makers: The CLIMAAX project example, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1060, https://doi.org/10.5194/ems2024-1060, 2024.
