This session presents and explores the increasingly sophisticated systems developed to aid, and often automate, the forecasting and warning process, encompassing also downstream links to users that form part of the "warning value chain". The rapid proliferation of data available, including probabilistic and rapidly-updating NWP as well as a plethora of observations, combined with a growing appreciation of user needs and the importance of timely and relevant forecasts, has brought the development of these systems to the fore.
As a legacy of WMO's HIWeather programme, we also invite discussion of the interdisciplinary challenges, gaps, and opportunities in evaluating the warning value chain from observing, nowcasting and forecasting to warning and response. Understanding the true added value that each contribution brings to decision-making and community outcomes is critical.
Meanwhile, ongoing rapid developments in machine learning bring both opportunities and challenges for the warning process, and with the conference theme in mind contributions at this intersection point are also particularly welcome this year.

Topics may include:
• Nowcasting systems
• Links to severe weather and severe weather impacts
• Automated first guess warning systems
• Post-processing techniques
• Seamless deterministic and probabilistic forecast prediction
• Integrating systems and information within a forecast and warning value chain
• Use of machine learning and other advanced analytic techniques
• Can output of data-driven (AI) models contribute to warning systems?

This session will focus on recent advances in data assimilation and ensemble forecasting across a wide range of temporal scales, from short-range forecast to subseasonal and seasonal prediction. Emphasis will be placed on the links between data assimilation strategies and the ability of ensemble prediction systems to produce skillful, reliable, and actionable forecasts, particularly for high-impact and extreme weather events.
We welcome contributions addressing both traditional and machine learning–based approaches to data assimilation, ensemble generation, and ensemble utilization. Of particular interest are studies that explore how these techniques evolve with forecast lead time, including the transition from short-range to medium-range, extended-range, and seasonal forecasting systems. Contributions highlighting the perspective of operational forecasters and the practical use of ensembles in forecasting and decision-making, especially for extreme events, are strongly encouraged.
The conveners invite papers on a broad range of topics related to Data Assimilation and Ensemble Forecasting for weather and climate prediction, including (but not limited to):
- intercomparison and assessment of the complementarity between different data assimilation techniques, such as Kalman filtering, variational methods, hybrid approaches, and nudging techniques for frequent or rapid update analysis cycles;
- variational data assimilation with extended assimilation windows, including weak-constraint formulations that allow for the explicit representation of model error;
- ensemble data assimilation systems and flow-dependent estimation of background, observation, and model error statistics;
- representation of uncertainties in initial conditions, model physics, boundary conditions, and coupling strategies in global and limited-area ensemble prediction systems, across time scales from short-range to seasonal;
- strategies for bridging weather and climate prediction, including ensemble methods tailored for subseasonal to seasonal forecasting;
- verification, calibration, and post-processing methods for ensemble prediction systems, with attention to scale-dependent performance;
- use of multi-model and ensemble databases;
- applications of ensemble forecasts across different sectors, including energy, health, transport, agriculture, insurance, finance, and climate-sensitive decision support.

This session invites presentations on various aspects of scientific and operational collaboration related to weather and climate modelling, including atmosphere, land and ocean components. The session will be split into two sub-sessions which will focus on the following topics:

- Challenges in developing high-resolution mesoscale models with a focus on end-users and the EUMETNET forecasting programme. This involves cooperative operational systems as well as developments of different parts of the operational modeling chain, from nowcasting to forecasting, deterministic and probabilistic, and post-processing. Observation impact studies to assess the importance of different parts of the observing system for global and limited area NWP models are also welcome.

- Numerics and physics-dynamics coupling in weather and climate models. This involves the development, testing and application of novel numerical discretization techniques and sub-grid models, variable-resolution modelling, as well as performance aspects on current and emerging computing architectures.

Additionally, we welcome contributions to hybrid approaches that extend physical models with data-driven machine learning (ML) techniques. Specifically (but not exclusively), we are interested in how ML techniques can be used to identify and address deficiencies in physical models.

This session addresses recent advances and emerging challenges in the verification of numerical weather prediction (NWP), artificial intelligence–based weather prediction (AIWP), and climate modelling systems across a broad range of spatial and temporal scales. Contributions are welcome across the full verification spectrum, from methodological research to operational practice and user-oriented applications.

The scope encompasses established verification approaches for physical NWP models, as well as novel methodologies required for AIWP and hybrid systems, including their extension to subseasonal-to-seasonal and climate applications
• Use and interpretation of new and emerging observational datasets for verification, including non-traditional observations, impact-based data, and applications related to high-impact and user-oriented services such as warnings for hazardous weather.
• Advances in verification approaches tailored to different modelling systems (physical, artificial intelligence-based and hybrid models, and climate models), including suitable metrics, techniques, and effective communication of forecast skill and uncertainty.
• Methodological innovations such as spatial, temporal, and object-based verification, extremes verification, process-based evaluation, and probabilistic methods.
• Verification strategies adapted to high-resolution, convection permitting, ensemble, and variable-resolution modelling systems.
• Verification approaches aimed at supporting decision-making and end-user needs, including sector-specific verification, evaluation of risk-relevant events, and applications bridging weather and climate services.

This session focuses on machine learning (ML) methodology applied in the meteorological context. The topics include model architectures, training strategies, uncertainty quantification, evaluation and validation schemes underpinning reliable ML for the Earth system. We aim to bring together contributors from meteorology, climate science, computer science, and applied mathematics who are advancing the theoretical and methodological foundations of ML for weather and climate. We also welcome approaches applied to weather extremes across time scales, with a strong emphasis on uncertainty quantification and probabilistic prediction in operational settings. In particular, we encourage studies that bridge AI-based forecasts with impact-based forecasting, risk assessment, and decision support, including applications to floods, droughts, heatwaves, storms, atmospheric rivers, and compound or cascading hazards.

We invite contributions on topics including, but not limited to:

* Novel model architectures with potential to be applied in meteorology/climatology.
* Novel applications of ML architectures for geophysical data.
* Training strategies and objectives
- including e.g. loss functions, self-supervision, pre-training and fine-tuning, transfer learning, and data augmentation, ...
* Integration of physical knowledge
- physics-informed and hybrid models, constraints and regularisation, stability and robustness, ...
* Uncertainty quantification and reliability
- probabilistic ML, ensembles, Bayesian approaches, decision-relevant evaluation, ...
* Evaluation strategies and evaluation studies
- Intercomparison of different architectures, comparison with physical methods, benchmark strategies.
* Interpretability, explainability and fairness
- methods to understand, diagnose and stress-test ML models...
* Human aspect -- how AI changes our work, organisations, and culture?
* ML and hybrid approaches for extreme event prediction
* Evaluation of AI forecasts for rare and high-impact events
* Integration of AI methods into operational workflows: Case studies demonstrating operational feasibility and societal benefits
* Translation of probabilistic AI forecasts into impact-based warnings and user-oriented products

Renewable energy sources are currently investigated worldwide and technologies undergo rapid developments. However, further basic and applied studies in meteorological processes and tools are needed to understand these technologies and better integrate them with local, national and international power systems. This applies especially to wind and solar energy resources as they are strongly affected by weather and climate and highly variable in space and time. Contributions from all energy meteorology fields are invited with a focus on the following topics:

• Wind and turbulence profiles with respect to wind energy applications (measurements and theory) including wakes within a wind farm;
• Clouds and aerosol properties with respect to solar energy applications (measurements and theory);
• Marine renewable energy (wind, wave, tidal, marine current, osmotic, thermal);
• Meteorology and biomass for energy;
• Impact of wind and solar energy farms and biomass crops on local, regional and global meteorology;
• The use of numerical models and remote sensing (ground based and from satellites) for renewable energy assessment studies;
• Research on nowcasting, short term forecasts (minutes to day) and ensemble forecasts and its application in the energy sector;
• Quantification of the variability of renewable resources in space and time and its integration into power systems;
• Impacts of long term climate change and variability on power systems (e.g., changes in renewable resources or demand characteristics);
• Practical experience using meteorological information in energy related applications.

Weather conditions directly influence forests and agriculture. Hail, diseases, drought and extreme events in general can have devastating effects on crops, natural vegetation and forests’ health. However, meteorology-related risks can be reduced through better timing of agricultural management, harvests, improving climate-smart forest management, application of pesticides or through use of irrigation systems. A clear picture of current and future weather conditions, especially along with better understanding of extreme weather events and fine-scale (microclimatic) variations, is relevant, for example, to ensure resilient forestry, food security and biodiversity.

Microclimatic conditions contrast strongly with the macroclimatic conditions measured by standard weather stations and commonly represented by gridded climate data. This is evident for instance in forests, where variations in structures (e.g. canopy openness) form temperature and humidity regimes that are significantly buffered from the conditions outside forests. Although there is ample evidence that microclimates drive many ecosystem functions and ecological processes, fine-scale variation in climate is still rarely considered in environmental research, management and applications. Thus, a better understanding of the current and future microclimates can support the provision of ecosystem services and enhance the efficacy and benefits of nature conservation.  

This session aims to advance our understanding of interactions between weather and climate variability and change across scales and forest, agricultural, and natural vegetation systems. We invite presentations related but not limited to: 

- Micrometeorology and microclimate, measuring (e.g. ground-based, remote-sensing, citizen science, Big Data etc.) and modeling (both statistical and mechanistic) at scales operating below the conventional climate grids, from meters to hundreds of meters
- Impact of weather and climate extremes on agriculture and forests
- Biometeorology and bioclimatology, agrometeorological modeling 
- Climate-smart management in mitigating the impacts of weather and climate induced disturbances (e.g. droughts, fires, pests, diseases) 
- Development of approaches to produce future climate projections operating at fine-spatial scales 
- Decision support systems & the representation of uncertainty and added values of increased resolution for end-users
- Interactions/feedback of forestry and agriculture end users

This session "Human biometeorology" deals with the interactions between atmospheric conditions and humans beings in an interdisciplinary manner. The core question is how atmospheric conditions impact the well-being and health of humans, and how to transfer such knowledge in a widely understandable way in order to ensure the appropriate use of such kind of information. Atmospheric conditions include transient ones driven by weather patterns and long-term climatology but as well how potential climate change trends may affect these interactions.

In this context, the session will address issues concerning health, warning systems and measures in place to mitigate adverse impacts, and the models used to evaluate the heat load and cold stress on organisms. This will include the thermal component from the environment, weather sensitivity, actinic and chemical components of stress factors. Modelling studies and experimental studies on how environmental management, urban planning and design or traffic regulation can improve living conditions and decrease emissions are particularly welcome.

In addition, the session will consider the impacts of weather processes on human well-being and health. Since several methods are in use to compile bio-weather forecasts, we are looking forward to discussing such approaches and the way to convey such information to the public, but also to special target groups. Another aim is to describe ways, how climate data and information should be transferred and addressed for issues on tourism, recreation and other economic sectors.

The session will also address efforts to combine different environmental impacts on humans into one single index, as it is well known that humans react to the whole mix of atmospheric stimuli. Our aim is to improve the requested information and to look for more efficient ways of conveying the message on a regular basis in order to enable citizens to make the best use of such information in their everyday activities.

Wildfires pose a growing challenge for weather and climate science, with major impacts on ecosystems, air quality, infrastructure, and human safety. Climate change is increasing wildfire risk through rising temperatures, more frequent droughts, shifting precipitation patterns, and changing wind and humidity conditions. As a result, extreme wildfires are becoming more widespread and frequent, shifting from occasional hazards to a persistent feature of the Earth system.

Advances in field observations, remote sensing, reanalysis products, and high-resolution modelling now provide unprecedented access to meteorological and environmental data. These datasets create new opportunities to study wildfires. A key challenge is combining these diverse data sources into modelling frameworks that support reliable wildfire prediction. Turning data into skilful, actionable forecasts requires ongoing innovation in numerical modelling, statistical methods, and machine learning.

Extreme wildfire events deserve special focus. In these cases, large fire plumes generate their own weather and alter the atmospheric boundary layer. The formation of pyrocumulus and pyrocumulonimbus clouds can dramatically accelerate fire spread. Such fires exhibit new behaviour that we are only beginning to understand and that is not yet captured well in fire spread models.

This session brings together researchers and fire analysts working at the intersection of weather, climate, and wildfire science. We encourage contributions from communities in high-resolution modelling, numerical weather prediction, climate modelling, Earth observation, and climate services, with a focus on understanding fundamental physics, as well as improving how fire-relevant processes and uncertainties are represented in forecasts and projections.

Topics include, but are not limited to:
• Observational and modelling approaches to extreme wildfire dynamics and
• pyroconvective fire spread
• Fire-weather and fire-climate relationships, including extremes and compound events
• Statistical, dynamical, and machine-learning methods for predicting wildfire occurrence and spread
• Integration of satellite data, reanalysis, and in situ observations into forecasting
• frameworks
• Uncertainty assessment of wildfire-relevant variables in weather and climate models
• Transdisciplinary research involving collaboration with operational firefighters
• Applications in climate services, early-warning systems, and risk-based decision support

This session invites researchers, developers, and operational teams to showcase modern concepts for processing meteorological and climatological data. Emphasis will be placed on workflows leveraging Python-based tools, cloud computing, and GIS technologies to efficiently handle large-scale datasets and deliver actionable insights.

Contributions can include innovative data pipelines, integration of diverse datasets, visualization strategies, reproducible analysis workflows, and operational applications. The session aims to foster knowledge exchange on practical implementations, highlight cutting-edge approaches, and demonstrate how modern computational techniques can enhance the usability and impact of weather and climate data.

Robust and reliable climatic studies, particularly those assessments dealing with climate variability and change, greatly depend on availability and accessibility to high-quality/high-resolution and long-term instrumental climate data. At present, a restricted availability and accessibility to long-term and high-quality climate records and datasets is still limiting our ability to better understand, detect, predict and respond to climate variability and change at lower spatial scales than global. In addition, the need for providing reliable, opportune and timely climate services deeply relies on the availability and accessibility to high-quality and high-resolution climate data, which also requires further research and innovative applications in the areas of data rescue techniques and procedures, data management systems, climate monitoring, climate time-series quality control and homogenisation.
In this session, we welcome contributions (oral and poster) in the following major topics:
• Climate monitoring , including early warning systems and improvements in the quality of the observational meteorological networks
• More efficient transfer of the data rescued into the digital format by means of improving the current state-of-the-art on image enhancement, image segmentation and post-correction techniques, innovating on adaptive Optical Character Recognition and Speech Recognition technologies and their application to transfer data, defining best practices about the operational context for digitisation, improving techniques for inventorying, organising, identifying and validating the data rescued, exploring crowd-sourcing approaches or engaging citizen scientist volunteers, conserving, imaging, inventorying and archiving historical documents containing weather records
• Climate data and metadata processing, including climate data flow management systems, from improved database models to better data extraction, development of relational metadata databases and data exchange platforms and networks interoperability
• Innovative, improved and extended climate data quality controls (QC), including both near real-time and time-series QCs: from gross-errors and tolerance checks to temporal and spatial coherence tests, statistical derivation and machine learning of QC rules, and extending tailored QC application to monthly, daily and sub-daily data and to all essential climate variables
• Improvements to the current state-of-the-art of climate data homogeneity and homogenisation methods, including methods intercomparison and evaluation, along with other topics such as climate time-series inhomogeneities detection and correction techniques/algorithms, using parallel measurements to study inhomogeneities and extending approaches to detect/adjust monthly and, especially, daily and sub-daily time-series and to homogenise all essential climate variables
• Fostering evaluation of the uncertainty budget in reconstructed time-series, including the influence of the various data processes steps, and analytical work and numerical estimates using realistic benchmarking datasets

Spatially comprehensive representations of past weather and climate are an important basis for analyzing climate variations and for modelling weather-related impacts on the environment and natural resources. Such gridded datasets are also indispensable for validation and downscaling of climate models. Increasing demands for, and widespread application of grid data, call for efficient methods of analyses to integrate the observational data, and a profound knowledge of the potential and limitations of the datasets in applications.

Modern spatial climatology seeks to improve the accuracy, coverage and utility of grid datasets. Prominent directions of the actual development in the field are the following:

• Establish datasets for new regions and extend coverage to larger, multi-national and continental domains, building on data collection and harmonization efforts.
• Develop datasets for more climate variables and improve the representation of cross-variable relationships.
• Integrate data from multiple observation sources (stations, radar, satellite, citizen data, model-based reanalyses) with statistical merging, machine learning and model post-processing.
• Extend datasets back in time, tackling the challenges of long-term consistency and variations in observational density.
• Improve the representation of extremes, urban climates, and small-scale processes in complex topography.
• Quantify uncertainties and develop ensembles that allow users to trace uncertainty through applications.
• Advance the time resolution of datasets to the sub-daily scale (resolve the diurnal cycle), building on methods of spatio-temporal data analysis.

This session addresses topics related to the development, production, and application of gridded climate data, with an emphasis on statistical analysis and interpolation, inference from remote sensing, or post-processing of re-analyses. Particularly encouraged are contributions dealing with new datasets, modern challenges and developments (see above), as well as examples of applications that give insights on the potential and limitation of grid datasets. We also invite contributions related to the operational production at climate service centers, such as overviews on data suites, the technical implementation, interfaces and visualisation (GIS), dissemination, and user information.

The session intends to bring together experts in spatial data analysis, researchers on regional climatology, and dataset users in related environmental sciences, to promote a continued knowledge exchange and to fertilise the advancement and application of spatial climate datasets.

The prediction of changes in the climate mean state, variability and extremes remains a key challenge on decadal to centennial timescales. Recent advances in climate modelling, downscaling, artificial intelligence (AI) and post-processing techniques and ensemble techniques provide the basis for generating climate information on local to regional and global scales. To make such information actionable for users, relevant information needs to be derived and provided in a way that can support decision-making processes. This requires a close dialogue between the producers and wide-ranging users of such a climate service.

National climate change assessments and scenarios have become an essential requirement for decision-making at international, national and sub-national levels. Over recent years, many European countries have set up quasi-operational climate services informing on the current and future state of the climate in the respective country on a regular basis (e.g. KNMI'23 in the Netherlands, UKCP in the UK, CH2018 and CH2025 in Switzerland, ÖKS15 and ÖKS26 in Austria, National and federal states Climate Reports in Germany). However, the underpinning science to generate actionable climate information in a user-tailored approach differs from country to country. This session aims at an international exchange on these challenges focusing on:

- Practical challenges and best practices in developing national, regional and global climate projections and predictions to support adaptation action and impact assessments.

- Developments in dynamical and statistical downscaling techniques, process-based model evaluations, AI techniques and quality assessments.

- Methods to quantify uncertainties from climate model ensembles, combination of climate predictions and projections to provide seamless user information.

- Examples of tailoring information for climate impacts and risk assessments to support decision-making and demonstration on evaluation steps taken to monitor the uptake of climate information.

Even though a wealth of climate datasets has become available over the past decades, challenges remain in the assessment local and regional climate risks.

Many countries in the EU develop ‘National Climate Scenarios’ and related products that can be used for impact and risk assessments and the exploration of adaptation measures. At the same time, within the EU and its associated countries, many regions still rely on globally available climate projections to make a first estimate of regional risk.

Advances are being made in downscaling climate projections, implementation of hazard specific climate impact modelling chains, multi-risk climate assessment and equally relevant the communication of the risk assessment outcomes to sectoral end-users. Yet, common challenges still encountered by the providers and users of scenarios are the communication of uncertainties, requests for very high spatial and temporal resolutions, advice on the use of climate scenarios, interactions with users, integration of climate scenarios with impact information, the value and quality of data for multi-hazard and -risk assessment and how to deal with new scientific insights.

The Horizon Europe project CLIMAAX aimed to address some of the challenges associated with implementing regional climate risk assessments by developing a climate risk assessment framework and toolbox to assess climate risks for a variety of hazards, relying on climate projections from European and global repositories.

For this session we encourage submissions on constructing, delivering, using national climate scenarios and implementing regional climate risk assessments, including:

• Challenges in the provision of National climate information – including information gaps and the challenges in communication and providing information in ways that is relevant and accessible;
• Understanding user needs and the way users use climate scenarios, and the role of co-development of climate information and services;
• Best practices to bridge the gap between large scale climate projections and local relevance for multi-risk assessment (particularly examples that have used the CLIMAAX-developed Handbook and Framework)
• Comparisons of approaches in different countries, and examples of cross-border assessments;
• Future outlook and new opportunities in the science of scenario products drawing from novel types of information or techniques, e.g. Extreme event Attribution, decadal predictions, high resolution models, AI/ML, and new insights in the climate system and/or policy developments