The keynote on Data-driven weather models: A new era in meteorology will be given by Mariana Clare and the ECMWF Colleagues.

Mariana Clare is a researcher at the European Centre for Medium Range Weather Forecasts (ECMWF), where she helps develop AIFS, ECMWF’s data-driven weather forecasting model. She is particularly interested in how to capture the model uncertainty in these data-driven approaches and in evaluating their physical realism. She recently received a PhD from Imperial College London, focussing on developing advanced numerical and statistical techniques to quantify uncertainty in coastal ocean models. By training she is a mathematician, having done her undergraduate degree in Mathematics at the University of Oxford.

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?

The session will focus on the latest developments in data assimilation and ensemble forecasting techniques, ranging from links with nowcasting to the ability to produce and deliver skillful and reliable forecasts of high-impact extreme events to the medium and extended range.
We welcome any methods and ideas, both traditional and machine learning-based, on how to assimilate data, but also on approaches to create and use an ensemble forecast, and how these techniques can vary with the forecast lead-time. Of particular interest will be the perspective of forecasters and the use of ensembles in forecasting extreme weather events.
The conveners invite papers on various issues associated with Data Assimilation and Ensemble Forecasting for weather prediction, such as:
- intercomparison and study of the complementarity between different assimilation techniques: Kalman filtering, variational assimilation, nudging techniques for frequent analysis cycles, etc;
- variational techniques with longer assimilation windows and weak constraint methods to allow for the inclusion of model error estimates;
- ensemble data assimilation systems and flow dependent estimation of background and on-the-fly error statistics;
- representation of uncertainties in initial conditions, model and boundary coupling in Global and Limited-Area Ensemble Prediction Systems;
- verification and calibration methods of Ensemble Prediction Systems;
- use of TIGGE database;
- application of ensemble forecast in different sectors, including energy, health, transport, agriculture, insurance, finance, etc.

This session will handle various aspects of scientific and operational collaboration related to weather and climate modelling. The session will be split into three 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. Observation impact studies to assess the importance of different parts of the observing system for global and limited area NWP models.

- Numerics and physics-dynamics coupling in weather and climate models: This encompasses the development, testing and application of novel numerical techniques, the coupling between the dynamical core and physical parameterizations, variable-resolution modelling, as well as performance aspects on current and future supercomputer architectures.

- Model verification: Developments and new approaches in the use of observations and verification techniques. It covers all verification aspects from research to applications to general verification practice and across all time and space scales. Highly welcome verification subjects including high-impact, user oriented applications, warnings against adverse weather events or events with high risk or user relevance.

Artificial Intelligence (AI) is revolutionizing the weather-prediction value chain and is becoming a key technology for all climate-related sciences. This session focuses on machine learning techniques and aims at bringing together research with weather and climate-related background with relevant contributions from computer sciences using these techniques.

Contributions from all kinds of machine learning studies in weather and climate are encouraged, including but not limited to:

* Global, regional and local weather prediction, including both NWP emulators and training the model directly from observations, data driven models
* ECMWF Anemoi Framework contributions, relevant Destination Earth contributions
* Postprocessing of Numerical Weather Prediction (NWP) output
* Nowcasting studies, studies using satellite data, radar data, and observational weather data
* Seasonal forecasts
* Climate-related studies, including dimensionality reduction of weather and climate data, extraction of relevant features
* Operational frameworks (MLOps), cloud ecosystems, and data flows related to AI
* AI/ML projects in the European Weather Cloud (EWC)
* Benchmark datasets and validation of the model outputs
* Quantifying the impacts of weather and climate, connecting meteorological data with non-meteorological datasets
* Human aspect -- how AI changes our work, organisations, and culture?

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 and drought can have devastating effects on forests’ health and crops. However, meteorology-related risks can be reduced through better timing of harvests, improved, 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 in non-urban environments is relevant, for example, to ensure resilient forestry, food production 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 forest 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 the role of weather and climate variability and change across spatial scales on forests and agriculture. 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 

- Wildfires and forest fires

- Decision support systems & the representation of uncertainty and added values of increased resolution for end-users

- Interactions/feedback of forestry and agriculture end users

Our European transport infrastructure is vulnerable to disruption by the weather and from other natural hazards. For example, we know that fog, snow, thunderstorms and volcanic ash all have potential to severely disrupt aviation. On land, rail and road networks may be greatly affected by factors such as snow, ice, flooding and strong winds. At sea, wind, fog, ice but also wind-driven sea motions such as waves, currents and sea ice can strongly affect traffic. Such disruptions can have significant consequences at both national and international level, and can be one of the most costly effects of bad weather.
Increasingly as transport networks expand, with climate change and as our dependence on technology increases, we see that there is a need to mitigate against the disruption of land, sea and air transport.
This session invites contributions from those involved in developing weather-based solutions for reducing risk to air, sea and/or land transport. In particular, participants are encouraged to discuss strategic risk reduction in transport at organizational or national level, perhaps achieved through engagement with the aviation or marine community, stakeholders and users in road and rail networks.
In addition, the session welcomes presentations on other aspects of transport meteorology, including impact studies and verification of forecasts, meteorological services in the cockpit, and environmental impacts of aviation and other forms of transport.

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.

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.

Climate predictions on timescales of several weeks to months to years are becoming increasingly important for society, particularly in the context of adaptation to climate change. Advancing the quality of these forecasts requires further research on the physical processes acting on these different timescales and on how well prediction models capture these processes, as well as on methods extracting the most skilful information from these model forecasts. While contributions to both topics are welcome, the session will particularly focus on the latter aspect. More specifically, we invite contributions on:
i. advancing the climate forecasts with new initialization and ensemble strategies as well as improved model physics of the earth climate system,
ii. post-processing raw model output (e.g., bias correction, (re)calibration, or downscaling with classic or machine-learning-based statistical methods),
iii. translating physical knowledge on local and remote physical drivers of predictability into tools to detect and indicate “windows of forecast opportunity” (e.g., subsampling or weighting of ensemble members or models),
iv. coupling raw model forecasts to impact models to support early warning systems and adaptation strategies (related to extreme events and hazards in the atmosphere, biosphere, and lithosphere, to health, or to energy).