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

Successful hazardous weather warnings require information and expertise to be integrated across a multitude of domains including environmental observation, weather 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/cycles 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 weather case study events using value chain/cycle approaches
• Challenges, gaps and opportunities arising from using value chains/cycles
• Value chain/cycle approaches, metrics and measures

Intense floods in small/meso-scale catchments and intense precipitation over cities from severe local storms may pose a serious threath to society. For the timely prediction of such events, the value of high-resolution and high-quality quantitative precipitation estimation and corresponding forecasts cannot be overrated. Harmonization of different forecast methods (results and uncertainty from nowcasting and NWP) across a forecast time range from minutes to days is a big challenge but would greatly help to improve warnings. The session invites contributions along the entire process chain from quantitative precipitation estimation (QPE), observation-based nowcasting (QPN), combination of nowcasting with NWP and flash-flood prediction (FFP). Area-covering and high-resolution polarimetric weather radar observations provide core information for QPE/QPN giving precipitation intensity, hydrometeor types, and wind, accompagnied by satellite and lightning observations. With the perspective to enable/improve seamless predictions of surface precipitation from the time of observation to days ahead, topics of this session are advances in polarimetric precipitation retrievals, synergy with other sensors, advances in nowcasting (e.g., life cycle effects), advances in NWP data assimilation (e.g., radar, satellite, lightning information) and concepts and new strategies to merge observation-based QPN with NWP.

The session will focus on the most recent developments in the field of ensemble techniques, ranging from its close connections with data-assimilation and nowcasting at short and medium ranges to their capacity to produce and deliver skillful and reliable forecasts of high-impact extreme events at sub-seasonal to seasonal (S2S) timescales.
As such it may provide a platform for exchanging ideas on how to create and use an ensemble system, techniques varying according to the forecast lead time. In particular, the forecaster perspective and the use of ensembles in predicting hazardous weather will be of interest.

The conveners invite papers on various issues associated with Ensemble Forecasting for weather prediction, such as:
• representation of initial uncertainties in Global and Limited-Area Ensemble Prediction Systems, including interlinks between data-assimilation and probabilistic forecasting;
• representation of model or boundary uncertainties in Global and Limited-Area Ensemble Prediction Systems;
• results from experiments including THORPEX Regional Campaigns, HyMeX, FROST-2014, etc.;
• results from recent studies using TIGGE and TIGGE-LAM databases;
• use, verification and calibration methods of Ensemble Prediction Systems;
• applications of probabilistic forecasts in the sectors of energy, health, transport, agriculture, insurance and finance;
• challenges tackled by the S2S WWRP/THORPEX-WCRP joint project (http://s2sprediction.net), including discussion on S2S sources of predictability, forecasts and socioeconomic applications of high-impact climate services.

Participants are especially encouraged to present contributions and discuss strategies to bridge gaps between stakeholders and actionable S2S tailored products.

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.

Numerical modeling has become a cornerstone of atmospheric science. Applications cover all relevant scales, from microphysical processes (e.g., radiation, chemistry, cloud physics) to planetary-scale dynamics (weather and climate prediction). However, models are usually developed to represent a specific process on a specific scale, and processes on larger or smaller scales that may affect the problem of interest (e.g., due to scale interactions or scaling cascades) are simplified or neglected. Thus, a hierarchy of models and methods is often used to consider the effects of unresolved processes in a computationally efficient yet realistic way. Parameterization schemes often consider the mean effects of smaller, unresolved physical processes through effective descriptions and empirical correlations but with low fidelity and limited predictability. Larger scales are considered by boundary conditions that drive the development on the scale of interest. While increasing computational resources enable high-fidelity models to directly simulate complex physical processes on multiple scales, this is only possible for idealized configurations and a small fraction of the relevant parameter space. Thus, new and advanced methods are required to address the challenge of regime-overarching modeling and to bridge the gap between scales and processes. In this context, autonomous stochastic and machine-learning tools are increasingly explored, combining computational efficiency with validity across multiple scales and parameter regimes. Ranging from traditional statistical emulation to deep learning, data-driven approaches are expected to allow for qualitatively new atmospheric modeling and building digital twins of the atmosphere.

To provide a platform for the interdisciplinary exchange on accurate and economical representations of multi-scale physical processes in operational and research applications, the focus of this session is on novel modeling approaches in fluid dynamics, radiative transfer, cloud physics, atmospheric chemistry, and related disciplines, as well as their coupling in more complex frameworks, joining the UP and OSA sections of the meeting. We invite contributions on meteorological applications of fluctuation modeling, stochastic dynamics, data-driven and machine learning approaches, model coupling, nesting, grid adaptation, and regime-independent modeling, while welcoming promising approaches that have not yet been used in atmospheric science.

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)

Artificial Intelligence (AI) is nowadays a central key for many modern applications and research areas, e.g. autonomous driving, image / face recognition as well as system simulation and optimization. Consequently, AI gains more and more importance also in weather and climate related sciences. This session focuses on machine learning and computer vision 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 and computer vision studies in weather and climate on a wide range of time-scales are encouraged, including
• All kinds of postprocessing studies of Numerical Weather Prediction (NWP) forecasts (including projects such as DeepRain, etc.)
• Nowcasting studies, studies using satellite data, radar data, and observational weather data
• Seasonal forecast studies
• Climate related studies

These studies may e.g. deal with one or more of the fields
• Pre-processing of weather and climate data for machine learning purposes (e.g. forecasts from Numerical Weather Prediction (NWP) models, observational / satellite / radar data, etc.)
• Dimensionality reduction of weather and climate data, extraction of relevant features
• All kinds of supervised and unsupervised learning techniques
• Application of computer vision algorithms
• Regression and classification tasks
• Artificial Neural Networks, Deep / Convolutional / Recurrent Neural Networks, LSTMs, Decision Trees, Support Vector Machines, Ensemble Learning and Random Forests, etc.
• Using cloud infrastructure to train and run AI-applications

Cloud computing has emerged as the dominant paradigm, supporting practically all industrial applications and a significant number of academic and research projects. Since its introduction in early 2010s and its widespread adoption thereafter, migration to the cloud computing has been a considerable task for many organizations and companies. Meteorology is no exception, there is a great diversity in the adoption of cloud computing and related technologies like fog/edge computing, microservices etc. The processing of big meteorological data close to their physical location is a perfect use case for cloud technologies and cloud storage infrastructures which offer all the necessary infrastructure and tools, especially if cloud infrastructures are offered together with HPC resources. European Weather cloud, coordinated by ECMWF and EUMETSAT, is one of the notable cloud computing infrastructures following this paradigm and is provided to the meteorological services of these Organization’s Member and Co-operating States. Moreover, in DestinE, the new EC coordinated initiative implemented by ESA, ECMWF and EUMETSAT is envisaged that most of the processing workloads will be across different cloud/HPC facilities and distributed cloud-based storage infrastructures will be hosting the produced and stored data.
This session focuses on Cloud/Fog/Edge computing use cases in meteorology (in combination with HPC) and aims to identify the current status and the steps ahead towards a wider cloud computing adoption in Meteorology.
We encourage contributions describing all kinds of Cloud/Fog/Edge computing efforts in the Meteorological domain, such as (but not limited to):
• Cloud Applications, Infrastructure and Platforms (IaaS, PaaS SaaS and XaaS).
• Cloud federations and cross domain integrations.
• Service-Oriented Architecture in Cloud Computing
• Cloud Storage, File Systems, Big Data storage and Management.
• Networks within Cloud systems, the Storage Area, and to the outside
• Virtualization in the Context of Cloud Computing Platforms
• Fog and Edge Computing
• Operational systems on the cloud.
• Big Data processing use cases, techniques, models, and algorithms on the cloud.
• Big Data Infrastructures and platforms
• Data lakes and warehouses on the cloud.
• Machine Learning Techniques for Edge, Fog, and Cloud.
• Cloud computing and HPC convergence in Meteorology
• Edge/Fog/Cloud computing and HPC workload unification.

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

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 impacts the built environment in various ways. Optimally, when designing and operating an infrastructure of any kind, the local climatic conditions at present and in the future are considered to ensure the long lifetime and well-functionality of the structure. Besides, buildings are responsible for a significant proportion of energy-related greenhouse gas emissions, 36% at EU level. In many European countries, the national building code specifies the general conditions concerning the building, technical requirements, energy efficiency etc. However, the type of weather data to support the optimal design of different infrastructure depends on the infrastructure in question. For example, a nuclear power plant should operate in any kind of weather conditions, even in the case of a record storm, floods, icing, heat, etc, which means the design should consider weather events that are not present in the observational datasets.

This session focuses on the exploitation of weather and climate data in multi-disciplinary research to support the optimal planning and operation of built environment. We welcome contributions from both data providers (e.g., national hydrometeorological institutes), but also from the data users (engineers, city planners, authorities, etc). We welcome contributions from the following topics:

- weather and climate data to support design of built environment;
- weather extremes and critical infrastructure;
- energy-efficiency, energy demand and climate resilience of buildings;
- nature-based solutions to support climate adaptation;
- meteorological reference years for the current and future climate;
- planning, construction and maintenance of built environment with respect to changing weather conditions and mitigation of climate change.

This session “Atmospheric effects on humans” 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, for example in the form of gridded datasets, are an important basis for analyzing climate variations and for modelling weather-related impacts on the environment and natural resources. They are also indispensable for validation and downscaling of climate models. Increasing demands for, and widespread application of grid data, call for efficient methods of spatial analysis from observations, and profound knowledge of the potential and limitations of these datasets in applications. At the same time, the growing pool of observational data (radar data, satellite based data…) offers the opportunity to improve the accuracy and reduce uncertainty of gridded climate data. Modern spatial climatology therefore deals with a wide range of space and time scales. As a result, actual developments in the field are concerned with a range of challenging issues. These include for example the spatial characteristics and representation of extremes, the representation of small-scale processes (auxiliary variables), the integration of several observational data sources (e.g. station, radar, satellite, re-analysis data), the quantification of uncertainties, the analysis at sub-daily time scales, and the long-term consistency as well as cross-variable consistency in grid datasets.

This session addresses topics related to the development, production, quality assessment and application of gridded climate data with an emphasis on statistical methods for spatial analysis and interpolation applied on observational data. Contributions dealing with modern methodological challenges and applications giving pertinent insights are particularly encouraged. Spatial analysis by applying e.g. GIS is a very strong tool for visualizing and disseminating climate information. Examples showing developments, application and dissemination of products from such analyses for climate services are also very welcome.

The session intends to bring together experts, scientists and other interested people analyzing spatio-temporal characteristics of climatological elements, including spatial interpolation and GIS modeling within meteorology, climatology and other related environmental sciences.

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. KNMI14 and KNMI21 in the Netherlands, UKCP18 in the UK, CH2018 in Switzerland, ÖKS15 in Austria, National Climate Report 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.

- Developments in dynamical and statistical downscaling techniques, process-based model evaluations and quality assessment of the resulting simulations.

- 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