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.

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.

The standard strong-constraint four-dimensional variational data assimilation (SC-4DVar) approach was designed to correct random, zero-mean errors in both model forecasts and observations in the China Meteorological Administration Global Forecast System (CMA-GFS). However, global numerical weather prediction (NWP) models often exhibit significant systematic biases within the assimilation windows. To address this limitation, a weak-constraint 4DVar (WC-4DVar) approach, which explicitly accounts for model biases, has been proposed as a method for reducing the impact of these biases. In this study, the WC-4DVar approach is integrated into the CMA-GFS, and a one-month cycling assimilation and forecasting experiment is conducted to evaluate its effectiveness. The WC-4DVar approach incorporates a model bias term into the cost function, with the control variables and covariance matrix estimated from 240 samples collected over a one-year period from the CMA ensemble data assimilation (EDA) trial. The results show that WC-4DVar significantly reduces the model biases, particularly in regions with larger biases. Specifically, the analysis fields are improved, as evidenced by the negative root-mean-squared error reduction ratios obtained for the temperature and wind components. WC-4DVar exhibits an enhanced ability to forecast the geopotential height, temperature, and wind fields in the northern hemisphere within the first 192 hours. In the southern hemisphere, significant enhancements are observed in the upper atmosphere during the first 96 hours. Additionally, WC-4DVar improves its forecasting ability at the top of the model in tropical regions. These findings highlight the potential of WC-4DVar to serve as a valuable tool for improving operational NWP systems, particularly in regions where systematic biases are most pronounced. 

How to cite: Wang, L. and Liu, Y.: Development and impact of weak-constraint 4DVar with model bias consideration in the CMA-GFS , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-182, https://doi.org/10.5194/ems2026-182, 2026.

Accurate forecasting of extreme precipitation using mesoscale models remains a major challenge, particularly for warm-sector heavy rainfall events driven by complex cloud-microphysical processes that are poorly constrained by conventional observations. Satellite microwave radiances offer critical thermodynamic and hydrometeor information, yet their assimilation under cloudy and precipitating conditions requires careful treatment of radiative transfer errors. This study develops a physically constrained assimilation method for all-sky radiances from microwave temperature and humidity sounders (MWTS and MWHS) aboard the new-generation Fengyun-3 (FY-3) satellites, integrated into the China Meteorological Administration (CMA) MESO system. Within the CMA MESO 3DVAR system, the Advanced Radiative Transfer Modeling System (ARMS) is employed as the fast satellite observation operator, incorporating three key components: (1) a dynamically adaptive land emissivity parameterization, (2) a microphysics-consistent adaptive formulation for hydrometeor effective radius, and (3) a new delta-M multiple-scattering scheme. Three experiments were conducted for a record-breaking rainfall event over Hunan Province, China: a control run without all-sky radiance assimilation (CTRL), an all-sky configuration (EXPR1) utilizing the baseline ARMS, and an enhanced configuration (EXPR2) with an improved ARMS scattering module. Results show that EXPR2 reduces systematic biases in moisture-sensitive channels by approximately 7 K and decreases random errors by nearly 50% relative to CTRL, indicating substantially improved observation-minus-background statistics in cloudy scenes. This improvement further enables more realistic storm predictions, accurately reproducing the observed quasi-stationary convective core (> 45 dBZ) and the maximum rainfall exceeding 400 mm. Spatial verification using the Fractional Skill Score (FSS) demonstrates robust and statistically meaningful skill gains across multiple precipitation thresholds and spatial scales. At the extreme 100 mm threshold, EXPR2 is the only configuration that achieves useful skill at 50–100 km scales, whereas CTRL exhibits negligible skill. These findings underscore the value of physically constrained all-sky operator design for enhancing the prediction of high-impact precipitation events, with implications for operational convection-allowing data assimilation systems.

How to cite: chen, S., xie, Y., and weng, F.: Physically-Constrained Assimilation of All-Sky Radiances from FY-3 Microwave Sounders for Extreme Precipitation Forecast, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-227, https://doi.org/10.5194/ems2026-227, 2026.

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.

Standard verification approaches for AI weather prediction (AIWP) models often rely on scores derived from data-dense regions, while verification across the polar areas remains limited. Surface-level performance in data-sparse regions demands targeted evaluation using in-situ observations. We address this gap by evaluating GraphCast-predicted near-surface wind speeds against both ERA5 and in-situ observations at Antarctic stations. This study applies a three-way comparison between observations, reanalysis, and AIWP predictions to Antarctic stations, where such evaluation is currently absent; and extends the ERA5 vs in-situ observations (Obs) Antarctic wind evaluation by Rakoczy et al. [1].

We compare monthly mean 10 m wind speed from GraphCast T+24h forecasts, ERA5 reanalysis and SCAR Met READER station observations [2] at four Antarctic low-altitude stations for 2018–2020. Near-surface winds play a key role in Antarctic climate, influencing sea ice formation, precipitation, boundary layer stability and ice shelf dynamics [3]. Accurate representation in both reanalysis and forecast systems is therefore relevant to climate research as well as operational applications. Katabatic winds, channelled by ice sheet topography at scales below the model grid, may represent a particular challenge for AIWP and NWP models.

The three comparisons show a pattern. ERA5 vs Obs errors are substantial at all four stations, indicating that ERA5 shows substantial biases against observations. GraphCast vs Obs errors are systematically larger than ERA5 vs Obs at three of four stations, suggesting GraphCast may amplify ERA5's errors when evaluated against reality. GraphCast vs ERA5 errors are small at all Antarctic stations, indicating GraphCast closely replicates ERA5 patterns. The combination of these three comparisons is a noticeable preliminary finding: GraphCast faithfully reproduces ERA5 at Antarctic stations, but ERA5 deviates substantially from observations.

The station sample is too small to support strong conclusions, but these preliminary findings suggest that in-situ polar observations can expose verification blind spots not captured by reanalysis-based AIWP benchmarks, and that the fidelity of an AIWP model to ERA5 data is different from its ability to accurately predict in-situ observations. This study is ongoing; extensions to additional stations, longer records, and further AIWP models are planned prior to the conference.

—

[1] Rakoczy, B. C., Bromwich, D. H., & Wang, S. (2026). Evaluation of ERA5 Near-Surface Winds Over Antarctica: Spatial Variability, Biases, and Large-Scale Influences. Journal of Climate (published online ahead of print 2026), e250215, Article e250215. https://doi.org/10.1175/JCLI-D-25-0215.1

[2] https://legacy.bas.ac.uk/met/READER/data.html (accessed 25.03.2026) https://doi.org/10.5285/569d53fb-9b90-47a6-b3ca-26306e696706 

[3] Davrinche, C., Orsi, A., Amory, C., Kittel, C., and Agosta, C. (2025). Future changes in Antarctic near-surface winds: regional variability and key drivers under a high-emission scenario, The Cryosphere, 19, 6023–6042, https://doi.org/10.5194/tc-19-6023-2025 

How to cite: Guk, E.: GraphCast faithfully reproduces ERA5 near-surface wind speed at Antarctic stations - but ERA5 itself deviates substantially from in-situ observations: a preliminary three-way evaluation, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-434, https://doi.org/10.5194/ems2026-434, 2026.

For many applications and regions, radar coverage remains a challenge in weather prediction. Remote regions, oceanic areas, and mountain-shadowed terrain suffer from sparse or absent radar observations, and even where infrastructure exists, data accessibility and downtime create further gaps. Satellite imagery offers a promising opportunity to fill these spatial and temporal voids. Meteosat Third Generation (MTG) satellites provide unprecedented spatial resolution and temporal revisit rates across Europe and Africa, making them particularly well-suited for learning continuous radar-like reflectivity fields.

We present a UNet-based approach to synthesize 2D radar composites from MTG satellite channels and lightning data (LINET). Our architecture employs wavelet decomposition for multi-scale feature extraction, going beyond standard lowpass filtering to better capture the spatial structure of precipitation. To preserve the sharpness of convective features, we employ a loss function that explicitly emphasizes sharp edges in the target and penalizes the synthesized output accordingly. The bottleneck combines Swin-style local window attention with a global pooled attention branch, enabling the model to simultaneously capture fine-grained local structure and mesoscale spatial context. We also employ an efficient channel attention mechanism for very wide feature maps.

A central focus of this work is the geographic transferability of the trained model. Using radar observations from Europe, we systematically evaluate cross-regional generalization by training and validating on subsets of countries and testing on held-out regions. This design allows us to assess how well learned satellite-to-radar mappings transfer across different climatic regimes and precipitation characteristics, with implications for deploying such models in radar-sparse or radar-free regions globally.

How to cite: Hieronymus, M., Müller, R., and Blahak, U.: Cross-regional generalization of satellite-based radar synthesis with deep learning, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-570, https://doi.org/10.5194/ems2026-570, 2026.

Achieving Germany's national climate targets requires efforts to reduce emissions in all sectors. The German Ministry for Transport (BMV) established the ‘Network of Research’ (BMV-Forschungsnetzwerk), a network of German government agencies focused on the future-oriented transformation of transport in Germany. The focus of the main topic “Renewable energies” is the assessment of renewable energy potential along the transportation infrastructure. In this main topic, Germany’s national meteorological service DWD (Deutscher Wetterdienst), the Federal Highway and Transport Research Institute (BASt) and the German Centre for Rail Traffic Research at the Federal Railway Authority (DZSF/EBA) work closely together.

The transport infrastructure offers significant potential for renewable energy production. To optimize energy management, it is crucial to analyze the variability of renewable energy in Germany. In a previous study, weather situations which can possibly influence the energy production of wind, solar and water power plants were collected. Additionally, events with possible influence on the electricity transmission were taken into account. Afterwards, a simple assessment of the frequency of occurrence was carried out.

The current analysis focuses on solar energy production units since those are the most commonly used to generate renewable energy along transport infrastructure. The goal is to assess how frequent the occurrence of events like thunderstorms with hail, storms or dust effect the energy production.

Different data sets from DWD will be used to quantify the occurrence of those events for Germany overall as well as for exemplary locations with solar power plants near the transport infrastructure. Further, the occurrence of influential or destructive events will be quantified. The ratio between general occurrence and influential occurrence will then be used in a subsequent study considering climate change and the shifted occurrences of previously analyzed events.

How to cite: Dost, V., Drücke, J., and Deutschländer, T.: Assessing the impact of extraordinary weather events on solar energy production units in Germany, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-151, https://doi.org/10.5194/ems2026-151, 2026.

To meet the greenhouse gas targets of 2050, European countries plan a large expansion of offshore wind energy in the North Sea. This expansion is expected to cover 11% of the basin's surface area. The environmental impacts of this expansion are not fully understood yet. However, it is known that wind turbines alter the local microclimate by creating wakes, regions of decreased wind speed and enhanced turbulence. Mesoscale models are a promising tool to investigate the feedback effects of wind turbines on the environment. As such, recent studies show that offshore wind farms affect environmental factors such as sea surface temperature (SST). However, these studies are not conclusive and show both warming and cooling of SST induced by wind farms.

This study seeks to clarify the environmental feedback effects on SST of already installed wind farms using observations, as observational evidence of these effects remains limited. The study is focused on North Sea wind farms with over 100 turbines and examines both buoy measurements and satellite images. Time series of at least four years from before and after the construction of wind farms are investigated using conventional statistical models. Since buoy measurements are limited to a few offshore wind farms, satellite images provide additional spatial information and coverage at farms where buoy information is absent. In a second part of the study, the observational relationships between offshore wind farms and sea surface temperature derived from buoy and satellite data are used to train machine learning models. These models are then applied to projected offshore wind farm configurations to estimate potential future impacts on SST.

Preliminary results from observations show a consistent warming of SST in the wakes of large offshore wind farms, generally on the order of 0.2–0.5°C. This is in line with previous numerical modelling studies. The feedback effect of wind farms on SST has a strong seasonal component, being more pronounced in winter. During spring and summer months, SST cooling is also observed. Furthermore, a strong correlation with atmospheric stability is found, along with interdependent site variations. In future studies, these insights are expected to inform parameterizations in (coupled wave–ocean-)atmosphere mesoscale models.

How to cite: Barbieri Dumont, G., van de Wiel, B., Meyer, A., Lanzilao, L., and Porchetta, S.: Observational evidence of offshore wind farm impacts on sea surface temperature in the North Sea, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-496, https://doi.org/10.5194/ems2026-496, 2026.

Human health is directly influenced by environmental conditions associated with temperature, relative humidity, wind and incoming solar radiation. Among these, temperature and humidity exert the greatest influence on thermal comfort and human well-being. To assess thermal stress, various bioclimatic indices have been developed to simplify the complex interactions between atmospheric variables and human perception, enabling meaningful comparisons across different climatic regions. Thom’s Discomfort Index (TDI), introduced in 1957, is the most widely accepted and the first physiological index of its kind, primarily designed to describe discomfort at the population level, has been analyzed across the globe in areas with different climatology.

The city of Athens has undergone a continuous urbanization process that started in the 1950s, with the construction of new high-rise buildings, leading to the subsequent appearance of the urban heat island phenomenon, which contributed to Athens basin's local heating and modification of its climatic characteristics, especially during summers. Concurrently, Athens has experienced intense regional climate change in recent decades (since the mid-1980s).

The aim of the present study is to explore the temporal evolution of thermal sensation and discomfort at the historic center of Athens, incorporating air temperature (T) and relative humidity (RH) climate model projections, on 3-hour time step, for two future periods: the near (2031–2060) and the distant (2071–2100) one. Thermal discomfort is assessed using TDI index, which combines temperature and relative humidity into a single metric. TDI values were calculated from 3-hourly outputs of four EURO-CORDEX regional climate models under the RCP4.5 (intermediate) and RCP8.5 (high-emission) scenarios (IPCC AR5). Model data were bias-corrected against observations form the National Observatory of Athens (Thissio station), one of the longest and most homogeneous urban meteorological records in the Mediterranean region. The correction was performed over a reference period (1976-2005) and subsequently applied to future projections.

The results reveal a pronounced increase in the frequency and duration of high thermal discomfort conditions in the city center. Under the RCP4.5 scenario, the number of intense discomfort days is projected to increase by 21-39 days by mid-century and by approximately 1-2 months by the end of the century. Under the RCP8.5 scenario, the increase is substantially larger and becomes dramatic, with intense discomfort conditions potentially extending by up to 3 additional months annually. The contrast between the two scenarios highlights the critical role of emission mitigation in limiting future heat stress.

The study of TDI shows that climate change does not merely raise temperatures, but drastically increases perceived discomfort and heat related risk, transforming long parts of the year into thermally uncomfortable periods, highlighting the need for urgent adaptation measures in urban planning and public health to reduce vulnerability to extreme heat.

How to cite: Psiloglou, B., Gkinis, N., Machaira, P., and Giannakopoulos, C.: Temporal Discomfort Variation in the Eastern Mediterranean City of Athens, Greece. Part 2: Climate Model Predictions for Near and Distant Future., EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-365, https://doi.org/10.5194/ems2026-365, 2026.

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.

AgroClima and DataClima: a simple and interactive way to access climate data in Portugal. V. Pires (vanda.cabrinha@ipma.pt), C. Pereira (carlos.pereira@ipma.pt), R. Deus ricardo.deus@ipma.pt). Portuguese Sea and Atmosphere Institute, I.P.

ABSTRACT

Public and scientific interest in climate products has increased significantly in the last decade, driven not only by greater societal awareness of climate change but also by the growing frequency of extreme weather events. In this context, IPMA, as the national authority on climate matters, reinforces its role as a reference entity in the monitoring, analysis, and communication of climate variability and climate change. To this end, IPMA has developed two complementary platforms (DataClima and AgroClima) that integrate innovative climate modelling methodologies with in situ observational data. Together, these platforms constitute essential tools for civil society, providing reliable, high-quality, and easily accessible climate information. They support continuous climate monitoring and facilitate climate change adaptation, strengthening planning and decision-making processes across multiple socioeconomic sectors. The analysis of in situ observation data was carried out according to the standards of the World Meteorological Organization (WMO).

The information accessible through this two digital platform, which detailed information for the various regions of the country, aggregated by different Territorial Units, based on daily meteorological observation data from the IPMA network (in situ and remote) and numerical weather prediction (numerical model).

Dataclima is an interactive digital platform that provides access to modelled and observed climate indicators for the mainland and island regions of Portugal. Modelled climate indicators are available for the period from 1981 to the present day for different territorial units, with the aim of supporting various sectors of activity. The AgroClima platform facilitates digital access to climatological and agroclimate indicators, empower users in their interpretation, and provide up-to-date information and agroclimate warnings based on established thresholds.

How to cite: Pires, V., Pereira, C., and Deus, R.: AgroClima and DataClima: a simple and interactive way to access climate data in Portugal, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-377, https://doi.org/10.5194/ems2026-377, 2026.

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.

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.

Sea-ice cover exerts important controls on the Arctic climate and may form horizontally heterogeneous patterns, especially in the marginal ice zone (MIZ). Earth System Models (ESMs) represent the sea-ice heterogeneity within a grid cell as an ice fraction. The heterogeneous sea-ice cover, however, causes complex nonlinear surface-atmosphere interacting processes that cannot be quantified appropriately using solely the ice fraction. Among the nonlinear interacting processes are the secondary circulations in the atmospheric boundary layer (ABL) that are driven by the sea-ice and ocean water surfaces and their thermal contrast. An effective representation of the surface-atmosphere momentum, temperature and moisture exchanges for a grid cell of an ESM should accommodate for the occurrence of secondary circulations. This is of particular relevance when leads evolve in the sea ice. These elongated cracks in the sea-ice cover expose local regions of open ocean water with surface temperatures much higher than the surrounding sea ice. As a result, convective plumes develop above leads. Even if leads occupy a small areal fraction only, their impact on the regional temperature, atmospheric stability over sea ice, and surface-atmosphere fluxes in winter is disproportionally large.
To quantify and parameterise secondary circulations related to leads, we extend a thermal heterogeneity parameter [1], which defines the ratio between buoyancy effects of surface thermal contrasts to the inertia of the mean flow. This extension incorporates factors such as temperature difference between the sea-ice and water surfaces, the angle between geostrophic wind and lead orientation and typical length scales. Data are used from the Boundary layer and Aerosol and Cloud Study in the Arctic II (BACSAM II) flight campaign, where turbulence was measured at two different heights simultaneously: on an aircraft and 60 m below the aircraft using a passive trailing body called T-bird. The aircraft data are analysed with a wavelet transform, enabling a multiscale decomposition to extract a mesoscale contribution to the fluxes. Surface temperature characteristics are obtained from the Modis global Level-2 product (resolution: 1 km). A case study reveals a strong correlation between thermal heterogeneity parameters and mesoscale flux contributions for 20 km subintervals with 1 km rolling steps along the flight legs. The correlation is enhanced for leads oriented normal to wind, and when fetch dependent downstream effects are included.

[1] Margairaz, Fabien & Pardyjak, Eric & Calaf, Marc. (2020). Surface Thermal Heterogeneities and the Atmospheric Boundary Layer: The Thermal Heterogeneity Parameter. Boundary-Layer Meteorology. 177. 1-20. 10.1007/s10546-020-00544-7.

How to cite: Staudinger, I. and Vercauteren, N.: Representing sea-ice heterogeneities and the Arctic boundary-layer using a thermal heterogeneity parameter, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-639, https://doi.org/10.5194/ems2026-639, 2026.

On August 14, 2018, Typhoon Yagi (2018) moved northward and impacted Shandong Province of China, resulting in widespread rainstorm with a round-shaped heavy rain distribution. Specifically, an outer spiral rainband appeared on the typhoon periphery in southeastern Shandong, bringing short-term heavy rainfall and local heavy rainstorms. To study the mechanisms of the outer spiral rainbands, the characteristics and causes of the spiral rainbands are investigated in this study by using radar data and the observations from ground-based stations, radiosonde stations and aircraft. Numerical experiments are also conducted based on the Advanced Research WRF (Weather Research and Forecasting) model and its Hybrid-3DVAR (three-dimensional variational) data assimilation system. The model adopts 12 km and 4 km one-way nested grids, with 44 vertical layers. The initial ensemble perturbation fields are generated by using a stochastic perturbation method, and the Ensemble Transform Kalman Filter (ETKF) method is used for the bias correction of ensemble forecast, providing flow dependent background errors for the Hybrid-3DVAR assimilation module.

The results indicate that the outer spiral rainbands are formed by the merging and development of several linear mesoscale convective systems (MCSs). The outer spiral rainbands exhibit distinct characteristics of the linear MCSs with leading stratiform precipitation. There are several stronger linear MCSs merging laterally into other linear MCSs. Broad stratiform echoes appear in the front (eastern part) of the linear MCS in its maturity stage, and the convection develops up to 10 km or more. Short-term heavy rainfall occurs along the linear MCS at the maturity stage. The water vapor of heavy rainfall mainly comes from the near-surface layer (below 850 hPa) around the typhoon, and the water vapor flux convergence is mainly concentrated near the wind field convergence line. Before convection initiation, the middle and lower levels over Shandong are thermally unstable with high temperature and high humidity, and the wind rotates clockwise with height, which favor the development of convective systems. As the typhoon slowly moves northward, downward intrusion of cold air appears at 500 hPa. Below 900 hPa, on the southeast of the typhoon over central Shandong there are local convergence between southwesterly wind and southerly wind, and between southerly wind and southeasterly wind. The convergence-induced dynamic uplift triggers the release of unstable energy, stimulating several local linear MCSs. The MCSs develop northward along the steering flow. The linear MCSs merge and strengthen for several times, and finally the elongated spiral rainbands occur. At the mature stage of the convective systems, dry and cold downdrafts appear in the lower levels in the front of the MCS. Convective systems at the heights above 600 hPa move rapidly eastward with the upper-air steering flow, leading to the gradual weakening and dissipation of the linear MCS.

How to cite: Sheng, C., Fan, S., Qu, Q., Liu, S., and Zhu, W.: Causes of the Outer Spiral Rainbands Induced by Typhoon Yagi (2018) in Shandong Province of China, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-75, https://doi.org/10.5194/ems2026-75, 2026.

Supercells represent the most intense and vigorous type of convective storms, often associated with severe weather phenomena such as large hail, damaging winds, and tornadoes. This study investigates a supercell embedded within a quasi‑linear convective system (QLCS) that occurred over Beijing on 12 June 2022. This storm produced large hail with diameters of 3–5 cm in Miyun and Shunyi Districts, resulting in significant economic losses. By assimilating high spatiotemporal‑resolution observations from a C‑band phased array radar (PAR) into a four‑dimensional variational data assimilation system, we examine the dynamical processes responsible for the supercell’s development and its associated mesocyclone.   

Our findings reveal that prior to convective initiation, a pronounced convergence zone formed west of the terrain, generating several meso‑γ‑scale vortices near the surface. During the vertical merger of the convective cell with the QLCS, a strong QLCS-driven downdraft enhanced low‑level horizontal convergence, stretching the embedded vortices and markedly increasing vertical vorticity. In the mature stage, a mesocyclone developed with its rotational center reaching 4.5 km altitude and a maximum rotational velocity of 20 m s⁻¹.

The analysis demonstrates that the surface convergence lines and the meso‑γ‑vortices along them strengthen low‑level convergence and generate strong updrafts, triggering the initial storm. These intense updrafts effectively convert horizontal vorticity into vertical vorticity and transport it upward. Furthermore, the process of convective merging reinforces low‑level horizontal convergence, which forcibly stretches the mesovortex, enhancing vertical vorticity. These mechanisms enable the convective storm to develop in a strong, organized manner, ultimately leading to the formation of the supercell storm.

 Overall, this study highlights the critical role of terrain‑induced convergence, meso‑γ‑scale vortices, and convective merging in the initiation and intensification of supercell storms within QLCS environments. The high‑resolution PAR observations and assimilation techniques employed here provide valuable insights into the multiscale interactions that govern severe convective weather, with important implications for operational forecasting and early warning systems.

How to cite: Liu, R., Chen, M., and Wu, J.: Revealing Key Dynamical Mechanisms of a Severe Supercell within a QLCS Using Rapid Update 4DVar Assimilation of C‐band Phased Array Weather Radar Data, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-106, https://doi.org/10.5194/ems2026-106, 2026.

Sub-daily intensity duration frequency (IDF) curves underpin flood risk management and infrastructure design, yet they remain unavailable or highly uncertain in many Mediterranean and semi-arid regions due to sparse high-resolution gauge networks. Meanwhile, satellite precipitation products provide spatially continuous coverage but can exhibit systematic biases in magnitude, frequency, and extremes, particularly at short durations. This study examines whether machine-learning-adjusted, high-resolution satellite precipitation can support reliable sub-daily IDF estimation across an entire region, using Historical Palestine (Israel and the West Bank) as a climatically heterogeneous Mediterranean case study.

We propose a regional extreme-value framework that links bias-corrected satellite precipitation to sub-daily Intensity Duration Frequency (IDF) curves through a Peak-Over-Threshold approach with an Extended Generalized Pareto Distribution (POT-EGPD) parameterized using L-moments. Regionalization is performed using Gaussian Mixture Models (GMM), trained on calibration gauges only, with predictors combining extreme-shape information (L-moment ratios) and physiographic metadata (elevation, latitude, longitude, and climatic class). To evaluate generalizability, we implement station-based cross-validation, ensuring regional representativeness during splitting while preventing leakage.

We compare three methodological variants designed to isolate the effects of frequency and magnitude biases in satellite extremes: (i) a baseline regional POT model (U1) using calibration-gauge thresholds, tail parameters, and exceedance rates; (ii) a frequency-adjusted variant (U2) that corrects satellite exceedance rates using calibration-derived regional scaling; and (iii) an annual-maxima benchmark using regional GEV modelling (AM-GEV) with satellite index scaling. Satellite inputs include raw and machine-learning-adjusted products (e.g., IMERG raw versus IMERG adjusted via LightGBM).

Preliminary results indicate that machine-learning adjustment improves the consistency of satellite-based extreme DDF behaviour relative to gauges, and that explicitly correcting exceedance frequency further stabilizes tail behaviour across regions. The framework is intended for scalable regional IDF production in environments where fine-resolution gauges are scarce, supporting design-rainfall estimation and climate-resilient planning.

How to cite: Jayousi, F. and O'Loughlin, F.: A Cross-Validated Regional POT-EGPD Framework for Sub-Daily IDF Curves from Adjusted Satellite Precipitation in Mediterranean Climates, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-32, https://doi.org/10.5194/ems2026-32, 2026.

Reliable precipitation monitoring is crucial for hydrological and water resource management, particularly in semi-arid regions like Morocco where ground-based observation networks remain sparse. In this context, the present study evaluates and validates three distinct precipitation products, specifically the soil moisture-derived SM2RAIN-ASCAT, the reanalysis-based ERA5, and the satellite-based CHIRPS, against observed data from 36 synoptic stations distributed across Morocco's diverse climatic zones over the period 2007–2022. Different results for the different timescales have emerged, with performance differing markedly by both source and temporal scale. Despite strong detection capabilities, ERA5 showed the strongest overall performance, achieving the highest correlation and probability of detection (POD) throughout the study domain. In contrast, SM2RAIN-ASCAT exhibited a systematic overestimation, while CHIRPS showed a widespread tendency toward underestimation. Based on daily assessments, all products showed poor accuracy and elevated false alarm ratios, in particular during the dry summer months (JJA), where convective and sparse rainfall makes precise satellite retrieval especially challenging. A Quantile Mapping (QM) bias correction methodology was applied to address these disparities and improve the quantitative reliability of each dataset. The correction revealed that, particularly at the monthly and seasonal scales, the explained variance (R²) for ERA5 and CHIRPS increased significantly (R² > 0.6), indicating a tighter alignment with ground observations. While SM2RAIN-ASCAT showed improved consistency following correction, it remained less reliable than ERA5 and CHIRPS in representing temporal dynamics across the study area. Finally, these results highlight that bias-corrected ERA5 and CHIRPS are the most dependable sources for hydrological applications in Morocco, while bias-corrected SM2RAIN-ASCAT stands as a particularly valuable alternative for monthly assessments in data-scarce environments where soil moisture-based retrieval offers a distinct and independent estimation pathway.

How to cite: El Goumi, S., Koohi, S., Bouras, E. H., EL Alaouy, N., Guendour, R., Nait-Taleb, O., Essbiti, M. C., Chouidda, H., Krimissa, S., Elaloui, A., and Namous, M.: Toward Reliable Precipitation Estimation in Data-Scarce Semi-Arid Regions: A Multi-Source Evaluation and Bias Correction Study over Morocco, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-37, https://doi.org/10.5194/ems2026-37, 2026.

The frequency and severity of heatwaves are on the rise globally, with several European cities in particular experiencing the most extreme phenomena in recent history. Over the past few years people have become increasingly aware regarding the implications of thermal stress on dwellings and living conditions. However, the links between outdoor weather drivers and indoor stressors are still under investigation. This study aims to shed light on those links, using the "living lab" of the Green Village at TU Delft as a reference site.

The MSc thesis-based research will incorporate field measurements and modelling output from both building physics and atmospheric science. Outdoor meteorological parameters such as air temperature, wind speed, humidity and solar fluxes will be used for the derivation of biometeorological indices from the Rayman modelling platform. The indices of interest are the Physiological Equivalent Temperature (PET) and the Universal Thermal Climate Index (UTCI), which will be used as boundary conditions for the study of the indoor environment. Indoor monitoring will include data for operative temperature, relative humidity and light irradiation. Furthermore, CO2 concentrations will be used as proxies for the study of occupant-driven ventilation. EnergyPlus simulations will quantify heat transfer, thermal storage and radiative exchanges in the building envelope. Results from the monitoring and the modelling campaigns will be complemented with the addition of human-centred research in the form of questionnaires and/or semi-structured interviews, in order to address thermal exposure from a biometeorological perspective.

All in all, this multi-layered research will attempt to quantify the impact of outdoor heat on indoor spaces while taking human perception into account. Enhanced understanding of these dynamics will facilitate future designs for houses in the face of climate change and extreme urban heat.

Keywords: Human biometeorology, Heat stress indices (PET, UTCI), Indoor thermal comfort, Urban climate adaptation, Indoor–outdoor thermal coupling

How to cite: Papanikolaou, N. and Droste, A.: Coupling Outdoor Heat Stress and Indoor Thermal Exposure in Dwellings: A Human Biometeorology Approach, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-219, https://doi.org/10.5194/ems2026-219, 2026.

Extreme rainfall and urban heat events are intensifying across Europe under climate change, with compound occurrences where heatwaves are followed by convective precipitation posing particular challenges for urban flood risk and heat stress management. While the urban heat island effect and its influence on local atmospheric dynamics are well documented, the net impact of urban land cover on compound hydrometeorological extremes remains poorly understood, particularly at the convection-permitting scales needed to resolve city-scale processes.
This study aims to quantify the net effect of urban land cover on a compound hydrometeorological extreme that struck the Netherlands in July 2025: a sustained heatwave followed by a cold front-driven convective rainfall episode over the Amsterdam metropolitan region. Using the Weather Research and Forecasting (WRF) model, this is achieved by comparing simulations against a no-urban control case in which urban land cover is removed from the domain. To ensure this comparison is robust and not contingent on a single model configuration, the analysis is conducted across two dimensions of uncertainty. First, three urban landscape configurations of increasing heterogeneity are tested: the standard MODIS land use classification, a Local Climate Zone (LCZ) dataset from World Urban Database and Access Portal Tools (WUDAPT), and a high-resolution data derived from realistic 3D urban morphology, allowing the urban signal to be assessed independently of how the city is represented. Second, a physical scheme ensemble combining three turbulence treatments (YSU and MYJ planetary boundary layer schemes, and a Large Eddy Simulation approach without PBL parameterization) and three microphysics schemes (WSM6, Thompson, and Morrison double-moment) is used to constrain the sensitivity of the results to physical parameterization choices.
Model performance is evaluated against hourly observations from the KNMI station at Schiphol, covering 2 m air temperature, 10 m wind speed, specific and relative humidity, and incoming solar radiation, with precipitation evaluated against radar observations. Preliminary results from the baseline configuration show strong agreement with observed diurnal temperature cycles (R² = 0.90, RMSE = 1.85°C ), while systematic biases emerge in wind speed and near-surface humidity, and simulated convective precipitation is delayed and underestimated relative to observations.
These initial findings establish a credible baseline from which the full ensemble analysis will provide a rigorous and representation-independent estimate of how urban land cover modulates heatwave intensity, convective triggering, and precipitation distribution during compound hydrometeorological extremes.

How to cite: Smook, C., Droste, A., Schleiss, M., and Chen, X.: Urban Land Cover Effects on a Compound Heatwave and Convective Rainfall Event: A WRF Ensemble Study over Amsterdam, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-473, https://doi.org/10.5194/ems2026-473, 2026.

Due to the increase in extreme weather events, climate patterns are emerging that differ from those of the past, and there is a rising need for effective disaster prevention measures to proactively respond to such hazardous weather conditions. In particular, while some areas are currently managed as a single weather alert zone including coastal and insular regions, the need for further subdivision has arisen because meteorological characteristics (such as precipitation and snowfall) vary even within a single weather alert zone. Accordingly, this study conducted a research project on the subdivision of weather alert zones, focusing on six places in the Western Coastal region where the need for subdivision has been raised, while taking into account the meteorological characteristics of each location. This study utilized observation data from the Korea Meteorological Administration’s AWS and ASOS systems from 2018 to 2025 to quantify the occurrence patterns of weather alerts at each station. Based on this, the study investigated methods to classify regions based on similar weather characteristics from those with dissimilar characteristics. To this end, binary matrix, Jaccard-based clustering, hierarchical cluster analysis, and Dynamic Time Warping (DTW) analysis were employed. We evaluated the simultaneity (correlation) among observation stations within the same special weather alert area to verify spatial heterogeneity, and then estimated the optimal number of clusters. In addition, we verified whether the observed clustering differences were attributable to time-lag effect and confirmed whether the regions exhibiting distinct clustering characteristics were indeed validly separated. As a result, an analysis of six weather advisory zones and 30 observation points along the western coastal regions revealed that the characteristics of hazardous weather occurrence differed among some points, suggesting the potential for spatial subdivision within weather alert zones.

How to cite: Shin, M. and Kim, J.: A Study on the Method of Subdivision Weather Alert Areas including Coastal and Insular regions, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-33, https://doi.org/10.5194/ems2026-33, 2026.

Coastal regions of the Mediterranean basin are increasingly experiencing the impacts of climate change. Among these, sea-level rise (SLR), combined with land subsidence and storm surges, represents a major hazard for coastal environments, affecting urban areas, transport systems, ports, and other critical infrastructures. Low-lying coastal zones are particularly vulnerable to accelerating SLR, which enhances the exposure of coastal communities to flooding, shoreline retreat, and erosion. Rising sea level can also amplify the impacts of extreme events such as storm surges and tsunami. In this context, the Northern Adriatic Sea represents one of the most vulnerable sectors of the Mediterranean due to the combined effects of global SLR and significant vertical land motion (VLM), with prevailing subsidence. This study investigates future relative sea-level rise (RSLR) scenarios along the Emilia-Romagna coast (Italy) up to 2150, with particular attention to the role of differential subsidence and its implications for coastal infrastructures. The analysis integrates multiple datasets: (i) InSAR data from the Copernicus European Ground Motion Service and GNSS geodetic data from local networks to quantify the subsidence rates in the investigated area; (ii) Airborne LiDAR data provided by the Italian Ministry of the Environment to generate high-resolution digital elevation models (DEMs) for projecting potential flooding extents; and (iii) SLR projections from the IPCC AR6 under different SSP climate scenarios for the Mediterranean basin, revised for current rates of VLM. Finally, we provide a classification of the different areas according to their exposure and risks to the combined effects of VLM, SLR and storm surges, producing heterogeneous patterns of coastal vulnerability and identifying critical coastal infrastructures at risk of inundation. Our findings provide new insights into the combined effects of climate-driven SLR and land subsidence in the Northern Adriatic, support the development of adaptation strategies and coastal risk mitigation measures also for other vulnerable Mediterranean coastal systems.

How to cite: Iacono, F., Alberti, T., Anzidei, M., Bisson, M., Trippanera, D., Bosman, A., Serpelloni, E., Tolomei, C., and Mastronuzzi, G.: Relative sea-level rise in the Northern Adriatic (Italy): future scenarios for the Emilia-Romagna coast up to 2150, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-78, https://doi.org/10.5194/ems2026-78, 2026.

Quantifying the exchange of mass, momentum and energy between the earth’s surface and the atmosphere is essential for understanding - and therefore modeling - weather and climate processes. Compared to flat terrain, exchange processes in complex terrain are especially efficient due to valley and slope wind circulations. The international TEAMx observational campaign (TOC) in summer 2025 focused on the observation of exchange processes on different scales in the mountain boundary layer. 

The LIVAVERT(EX)² project - linking valley flow and vertical exchange in complex terrain - is embedded in TEAMx and focuses on observing valley winds in combination with exchange processes in the Sarntal Alps region, a local hotspot of convective initiation within the Alps. As part of the project, a novel airborne Doppler lidar (ADL) was used for its first extended measurement campaign in complex terrain. The new ADL system, called AIRflows, measures profiles of 3D wind at 100 m along-track and vertical resolution, and thereby provides spatially resolved insight into valley wind systems and vertical exchange.

During TEAMx, AIRflows was used on 33 research flights onboard the TU Braunschweig Cessna F406 research aircraft. Additionally, an extensive ground-based Doppler lidar (GDL) network was established as part of a KITcube deployment in the Sarntal Alps region. The obtained GDL observations, as well as synchronized airborne in-situ measurements by a second aircraft (DLR Cessna), allow for the first real-world validation of high-resolution ADL observations in complex terrain. This contribution provides estimates of the ADL and GDL wind profiling accuracy and their spatial representativeness. In a second step, the spatially resolved ADL wind profiles are used to validate existing GDL-based volume flux estimation methods. The goal of the validation is to obtain accurate volume flux budgets in the valleys surrounding the Sarntal Alps. Combining volume flux budget and direct vertical exchange observations then allows for a more quantitative insight into valley flow and its relation to convective initiation over the surrounding mountains than ever before.

Key words: TEAMx, KITcube, airborne research, Doppler lidar, validation, valley flow, volume flux budget, complex terrain, convective initiation, boundary layer meteorology

How to cite: Schaeffler, L., Gasch, P., Gohm, A., and Stiperski, I.: Novel airborne and ground-based Doppler lidar observations of mountain wind systems: Validation of volume flux estimates during TEAMx, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-59, https://doi.org/10.5194/ems2026-59, 2026.

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.

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.

Understanding the influence of climate change on environmental extremes is essential to inform adaptation and mitigation policies. Here, we employ an event-focused storyline methodology to evaluate changes in total precipitation linked to Storm Boris, which struck Central Europe in September 2024. Our study addresses both historical (attribution) and future (projection) changes, and builds on our previous work by exploring how results vary with the stringency of applied dynamical constraints. Simulations are conducted with the global CMIP6 coupled climate model AWI-CM1, in which the winds observed before and during Storm Boris—including the jet stream evolution—are imposed (nudged). Such simulations are performed with those same winds under a range of climate states: preindustrial, present-day, and possible future states with 2, 3, and 4 °C of global warming relative to preindustrial levels. We test two nudging regimes: (1) a "weak constraint," nudging only synoptic- and planetary-scale winds from ERA5 in the free troposphere to allow partial dynamical adaptation, and (2) a "strong constraint," imposing winds across all vertical levels and scales to fully inhibit dynamical changes.

Both approaches successfully represent the event, with the strongly constrained setup yielding higher present-day precipitation totals, yielding a present-day rainfall closest to observations. The intensification of accumulated rainfall from preindustrial to present-day is robust, showing increases of 7% (weak constraint) and 4% (strong constraint). For up to +3ºC of global warming, both methods display roughly linear increases in total rainfall. However, at +4ºC, the results diverge: under weak constraints, precipitation changes are minimal or slightly negative relative to present-day, whereas under strong constraints, they continue to increase linearly. These differences are due to thermally-induced dynamical adaptations allowed under the weak constraint. It remains unclear whether these responses represent actual physical responses or are influenced by methodological limitations, and whether similar divergence would be observed in other extreme events. Then, these discrepancies reinforce the need to study a broader set of events and to adopt multi-method approaches to project extreme precipitation changes.

How to cite: Sánchez Benítez, A., Athanase, M., and Goessling, H. F.: Storm Boris' rainfall: Robust increases at moderate warming levels, large uncertainty at higher warming, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-21, https://doi.org/10.5194/ems2026-21, 2026.

Wildfires are dynamic components of the Earth system, responding to both natural climate variability, ecological dynamics and human activities. While global burnt area (BA) has declined in recent decades, regional fire severity has increased, impacting ecosystems and infrastructure. Observational data, including satellite-derived products, enable monitoring of wildfire activity and the quantification of BA. However, they cannot fully separate the effects of anthropogenic climate forcing from internal climate variability. Earth system models offer a way to systematically investigate these drivers, providing critical insights into the causes of changing fire patterns. 

Using a storyline approach, we separate the thermodynamic and dynamic components of climate change, largely driven by human-induced forcing, and assess their impacts on BA. This allows us to attribute wildfire responses to external forcing versus internal variability. We analyze nudged circulation simulations from the Community Earth System Model Version 2 (CESM2; Danabasoglu et al., 2020) under different anthropogenic forcing scenarios. The pre-industrial simulation is based on a CO₂ concentration of 282 ppm, whereas the historical simulation uses time-varying historical CO₂ concentrations. Both simulations are nudged to horizontal winds from the ERA5 reanalysis ensuring the representation of large-scale circulation patterns. 

We present a first evaluation of wildfire characteristics in the nudged CESM2 simulations by comparing simulated output with observational data. Specifically, we compare the simulations with observational data from GFED5 (Chen et al., 2023), to investigate mean values and trends in BA, fire season length and fire weather indices for the period 2001–2020. Further, we present a first assessment of historical BA trends, highlighting how circulation-driven variability and thermodynamic changes can be separated across regions.

This evaluation provides a foundation for future studies using nudged CESM2 simulations to represent key wildfire characteristics and enables attribution studies that disentangle the relative roles of changes in climate and land use between pre-industrial and historical periods.

How to cite: Eifler, L., Dunkl, I., Sippel, S., and Bastos, A.: From Observation to Attribution: nudged atmospheric circulation simulations to attribute burned area trends, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-158, https://doi.org/10.5194/ems2026-158, 2026.

In 2025, Türkiye experienced a series of compound climate extremes that led to significant cross-sectoral impacts. The year was recorded as the fifth-warmest in the last 55 years, with a mean temperature of 15.1°C, 1.2°C above the 1991-2020 normal. A new national absolute maximum temperature record was set in Silopi, located in the Southeastern Anatolia region, at 50.5°C. In parallel with these high temperatures, the country experienced its most severe meteorological drought since 1964, with annual precipitation 27.6% below the long-term average. This study uses a storyline approach to analyze how these atmospheric conditions created cascading risks for the agricultural sector. Meteorological data from 220 stations showed that 268 days of the year had positive temperature anomalies. Heatwave analysis revealed a marked increase in both frequency and duration compared to the 1991-2020 base period. While the historical average for heatwave duration is generally between 5 and 10 days, this period reached 34 days in Şırnak, situated near the southeastern border. Additionally, Tokat, located in the inner Black Sea transition zone, experienced the highest frequency with 5 separate heatwave events. These thermal extremes occurred alongside widespread water stress, as confirmed by Standardized Precipitation Index (SPI) and Percent of Normal Index (PNI) assessments, which showed varying levels of drought across nearly all regions. The combined effects of these record heatwaves and rainfall deficits led to production declines across primary crop categories. According to 2025 agricultural statistics, wheat and barley production decreased by 13.7% and 25.9%, respectively. Significant losses were also noted in industrial crops and resilient perennials such as olives, 34.7%, reflecting the severity of the moisture deficit. These findings show how localized temperature and precipitation extremes can cascade into national food security risks, highlighting the need for integrated, cross-sectoral adaptation strategies in the Eastern Mediterranean basin.

Keywords: Compound Extremes, Heatwave Frequency, Drought Monitoring, Crop Yield Loss, Eastern Mediterranean.

How to cite: Gurkan, H.: Compound Climate Extremes in Türkiye: A Storyline of Record-Breaking 2025 Heatwaves and Cascading Agricultural Impacts, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-450, https://doi.org/10.5194/ems2026-450, 2026.