This session presents and explores the increasingly sophisticated systems developed to aid, and often automate, the forecasting and warning process. 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. The opportunities afforded by the WMO's HiWeather programme will also be discussed in this session.

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
• Use of machine learning and other advanced analytic techniques
• Can output of data-driven (AI) models contribute to warning systems?

Successful warnings require information and expertise to be integrated across a multitude of domains including environmental observation, weather/climate and hazard modelling, impact prediction, warning communication and decision making. This comes with many challenges including building effective partnerships between the different players involved in the warning process who may have different expectations about the spatio-temporal detail of the warning, different needs for uncertainty information, different abilities to handle missing information, and so on.

The value chain (or the value cycle or network) provides a useful framework for describing and understanding the many different groups, skills, tools, relationships, and data/information flows that combine to produce and deliver warnings. It can characterise who does what and how groups interact and exchange data and information to provide critical services during a warning situation (information flow mainly "down the chain"). It can also support the co-design, co-creation and co-provision of services during the service development phase (user needs propagated "up the chain"). The effectiveness of the value chain may be measured using different, yet complementary, methods and metrics that emphasise different characteristics of the value chain such as accuracy, timeliness, relevance, and socioeconomic outcomes.

Case studies of existing warning chains and high impact events can apply value chain approaches to characterise and measure the effectiveness of the tools, processes, partnerships, and infrastructure. This provides the evidence to identify shortfalls and propose investments in new capability and partnerships.

This session welcomes contributions on:
• Assessments of high-impact case study events using value chain approaches
• Challenges, gaps and opportunities arising from using value chains
• Value chain approaches, metrics and measures
• Warning system approaches covering a range of time-scales

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 skilful 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.

The Weather Research and Forecasting model (WRF) is a widely used high-resolution meteorological model for operational weather forecasting, fundamental and applied research in meteorology, air quality, wind energy engineering, and consultancy studies. Its user’s community consists of universities, weather forecasters, and consultancy agencies world-wide. The goal of this session is to create a European forum to discuss research results concerning all aspects of the WRF and MPAS modelling frameworks.
Papers are invited on:
• Initialization, and meteorological and land surface boundary conditions.
• Numerical and grid spacing aspects
• Studies concerning data assimilation.
• Development of physical parameterization schemes.
• Model evaluation and validation against a broad range of available observations.
• Future WRF development.
• Tailored WRF versions, e.g. polar WRF, WRF-LES, WRF-Chem, H-WRF, the WRF single-column model
• WRF applications in weather forecasting, air quality studies, wind energy engineering.
• Regional climate studies
• Mesoscale meteorological phenomena studied with WRF.
• Analogous studies using Model for Prediction Across Scales (MPAS)

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 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 and local weather prediction, including both NWP emulators and training the model directly from observations
* Postprocessing of Numerical Weather Prediction (NWP) data
* 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 the AI
* 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 agricultural yields. Hail, disease and drought can have devastating effects on crops. However meteorological-related risks can be reduced through better timing of harvests, application of pesticides or through use of irrigation systems. A clear picture of current and future weather conditions, along with appropriate farm actions, can increase the likelihood of improved yields.

Climate change also influences crop suitability in certain regions where livestock can be negatively affected by migrating diseases and available food. To complicate matters the agricultural sector is also trying to become more sustainable and environmentally friendly in an attempt to meet greenhouse gas emission targets.

This session intends to examine our increasing knowledge of agricultural meteorology, while also attempting to identify opportunities in our changing environment.

We invite presentations related but not limited to:
• Agrometeorological modelling (e.g. modelling agrometeorological related diseases, frost protection warning methods, drought indices etc.)
• Impact of weather and climate extremes on agriculture
• Methods of measurements and observations (e.g. ground based equipment, remote sensing products, citizen science, Big Data etc.)
• Decision support systems & the representation of uncertainty
• Interactions/feedback of farmers and other end users
• Use of future climate projections on agrometeorological models

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.

Global climate change has resulted in various transformations, including changes in temperature, precipitation, land productivity, water resources, natural hazards, and atmospheric chemistry. These changes have significant impacts on ecosystem functions and services, particularly in critical regions (e.g. mountains, deserts, coasts, and Arctic areas). These environments have experienced more intense warming and higher frequencies of extreme events, such as heat waves, droughts, heavy rainfall, and wind storms, compared to the global average. In this regard, the comparison between paleo and recent data inside the same environments can provide better reconstructions of past variations and more reliable comparisons with current scenarios, opening the way to the use of previously overlooked archives.
This session will focus on the investigation of new indicators to characterize and understand meteorological, hydrological, and bio-geochemical processes in these critically endangered areas. Specific topics of interest include:
● Changes in mean climate, but also variability, frequency, and intensity of extreme events for the main climatological variables (e.g. precipitation, temperature, snow depth) to identify impacts on the ecological and hydrological systems.
● The effects of climate change on surface gas fluxes and in particular on carbon cycling.
● Feedback mechanisms between the atmosphere, the biosphere, and the soil, including the effects of changes in land use and land cover.
● Adaptation strategies and management practices that can help mitigate the impacts of climate change in extreme environments.
● Comparisons between proxy data from natural archives before and during the last 150 years and the introduction of instrumental measurements.
This session aims also at identifying the key research gaps and provide recommendations for future research to better understand and manage the effects of climate change in critical environments.

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, statistical downscaling and post-processing techniques such as bias correction 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'14 and KNMI'23 in the Netherlands, UKCP18 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 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).