Understanding and predicting the climate system, especially high-impact events such as extremes and tipping points, is an urgent task due to the ongoing climate crisis. This session highlights contributions at the interface of Earth sciences, mathematics, and physics that bring new perspectives and methods to environmental and geoscientific challenges. We are particularly interested in advances that improve the theoretical understanding of complex climate dynamics, enhance numerical modelling with both theory-informed and data-driven approaches, develop innovative data analysis techniques, and quantify the impacts and uncertainties associated with global warming.
Specific topics include: extreme events, tipping points, dynamical systems , statistical mechanics, model reduction techniques, model uncertainty and ensemble design, PDEs, stochastic processes, numerical methods, parametrisations, data assimilation, and machine learning. We invite contributions both related to specific applications as well as more speculative and theoretical investigations. We particularly encourage early career researchers to present their interdisciplinary work in this session.

Climate modeling is pushing the frontier towards increasingly complex, high-resolution earth system models (ESMs). At the same time, nonlinearities and emergent phenomena in the climate system are often studied by means of conceptual models, which offer qualitative understanding and permit theoretical approaches. Recent advancements in statistical and physical emulators, from reduced-complexity models to machine learning techniques, are enabling rapid and computationally efficient assessments of climate trajectories, impacts and risks.

Between these approaches, a persistent “gap between simulation and understanding” (Held 2005, see also Balaji et al. 2022) challenges our ability to transfer insight from conceptual models to reality, and distill the physical mechanisms underlying the behavior of state-of-the-art ESMs. This calls for a concerted effort to learn from the entire model hierarchy — or rather, model spectrum — to understand the differences and similarities across its various levels of complexity for increased confidence in climate predictions.

A diverse and well-integrated model ecosystem is also an indispensable prerequisite for the timely assessment of climate risks and effective decision-making. This places renewed emphasis on the concept of fit-for-purpose modeling, which is intrinsically linked to the climate model spectrum through the need to understand what levels of complexity are required and sufficient for a given scientific question or application. The climate community is increasingly interested in making models useful also beyond the academic domain (Mansfield et al., 2023).

In this session, we bring together contributions from all subfields of climate science that showcase how different modeling approaches advance our understanding of the Earth system, highlight inconsistencies in the model spectrum, and/or enable applications in climate impact projections. The key goal is to foster exchange between researchers working on different rungs of the model complexity ladder, focusing on process understanding. Contributions may employ dynamical systems models, physics-based low-order models, explainable machine learning, fast climate models and Earth System Models of Intermediate Complexity (EMICs), simplified or idealized setups of ESMs, CMIP models, and km-scale models. Processes and phenomena of interest include the Earth system response to transient forcing, tipping behavior, climate variability and extremes, and predictability.

This session invites contributions focused on the understanding, modeling, and prediction of extreme events in weather, climate, and broader geophysical systems, from both theoretical and applied perspectives. We aim to bring together researchers from the traditional geophysical sciences with those working in mathematical, statistical, and dynamical systems approaches, fostering an interdisciplinary dialogue and discussions.
By highlighting the complementary nature of physical intuition and mathematical formalism, this session seeks to advance our understanding of the processes that give rise to extremes, improve predictive capabilities, and assess the extremes' societal and environmental impacts.
Topics of interest include, but are not limited to:
- Variability and projected changes in extremes under climate change
- Representation and performance of climate models in simulating extreme events
- Attribution of extreme events
- Emergent constraints on extreme behavior
- Predictability of extremes across meteorological to climate timescales
- Connections between extremes in dynamical systems and observed geophysical extremes
- Theoretical and applied studies of extremes in nonlinear and chaotic systems
- Downscaling techniques for extreme events
- Linking the physical dynamics of extreme events to their impacts on society and ecosystems.
We particularly encourage submissions that bridge disciplines, propose novel methodologies, or offer new insights into the mechanisms and consequences of extreme geophysical phenomena. We encourage submissions from the "Transdiscipinary Newtork to bridge Climate Science and Impacts on Society" (FutureMED) and the "Seasonal-to-decadal climate predictability in the Mediterranean: process understanding and services" (MEDUSSE) COST action communities.

Extreme weather and climate conditions, such as recent events unprecedented in the observational record, have extensive impact globally. Some of these events would have been nearly impossible without human-made climate change, and broke records by large margins. Furthermore, compounding hazards and cascading risks resulting from these high-impact extremes are becoming evident. Continued warming does not only increase the frequency and intensity of such extremes, it also potentially increases the risk of crossing tipping points and triggering abrupt unprecedented impacts. To increase preparedness for high-impact climate events, developing novel methods, models and process-understanding that capture these hazards and their associated impacts is paramount.

This session aims to bring together the latest research quantifying and understanding high-impact climate events in past, present and future climates. We welcome studies across all spatial and temporal scales, and covering compound, cascading, and connected extremes as well as worst-case scenarios, with the ultimate goal to provide actionable climate information to increase societal preparedness to such extreme high-impact events.

We invite work addressing high-impact extreme events via, but not limited to, model experiments and intercomparisons, diverse storyline approaches such as event-based or dynamical storylines, climate projections including large ensembles and unseen events, insights from paleo archives, and attribution studies. We also especially welcome contributions focusing on physical understanding of high-impact events, on their ecological and socioeconomic impacts, as well as on approaches to potentially limit societal impacts.

The session is sponsored and closely linked to the World Climate Research Programme lighthouse activitIES on 'Understanding High-Risk Events' and 'Explaining and Predicting Earth System Change'.

The Earth system is a complex, multiphysics system with nonlinear interactions on multiple spatial and temporal scales. Understanding constituent processes (linear, nonlinear, stochastic, etc.) on the one hand, and the complexity of individual subsystems or the full integrated system on the other, is key to being able to better model the Earth System in a predictive fashion. The renaissance of machine and deep-learning in the past decade has led to rapid progress in the development of advanced approaches in, e.g., nonlinear time series analysis, dynamical and stochastic systems theory, critical slowing down theory, complex systems theory, and these approaches, in turn show promise in facilitating further advances in modeling the Earth system.



In this context, this session seeks contributions on all aspects of complexity, nonlinearity, tipping points and stochastic dynamics of the Earth system, including the atmosphere, the hydrosphere, the cryosphere, the solid earth, etc. Communications on theoretical, experimental and modeling studies are all welcome, where the latter modeling studies can span the range of model hierarchy from idealized models to complex Earth System Models (ESM). Studies based on emerging approaches such as data driven models, Artificial Intelligence approaches, complex network methods, critical slowing down analysis, dynamical and stochastic systems theory, etc., are particularly encouraged.

Rossby wave dynamics stands at the intersection of several open research questions, ranging from our basic understanding of mid-latitude variability, to the short- and medium-range predictability of high-impact weather events, and to the circulation changes expected from anthropogenic global warming. Rossby waves exist and propagate along vorticity gradients such as the one related to the tropopause-level jet stream, whose complex meandering often "breaks" creating nonlinear circulation features, such as atmospheric blocking.

Recent extreme weather and climate episodes, like heavy rainfall events leading to flash floods, recurrent and concurrent summer heatwaves or unforeseen winter cold spells, highlight the need to improve our understanding of jet streams and of the associated linear and non-linear, planetary and synoptic-scale Rossby wave dynamics in the atmosphere to better constrain the impacts of Rossby waves and of atmospheric blocking on extreme weather and climate events.

Abstracts are invited on a wide range of topics, with a focus on, but not limited to, the following areas:

(1) Theoretical developments in the dry and moist dynamics of Rossby waves, wave breaking, atmospheric blocking, and of jet streams acting as atmospheric Rossby waveguides. This includes the role of local and remote drivers (e.g., the tropics, Arctic, or stratosphere) in affecting Rossby wave evolution.
(2) Linkages between extreme weather/climate events and the jet stream, as well as the associated linear and non-linear Rossby wave evolution during such events, including wave breaking, cut-off formation and re-absorption, and atmospheric blocking.
(3) Application of cutting-edge methods to study the multi-scale interaction of Rossby waves from the convective scale to the large-scale dynamics, and its representation in existing weather and climate models (e.g. hierarchical and/or high-resolution modelling, machine learning/AI-based approaches).
(4) Exploring the effect of Rossby wave packets on predictability at lead times from medium range (~2 weeks) to seasonal time-scales. This includes the potential role of blocking and of teleconnections involving Rossby wave propagation.
(5) Projected future changes in planetary or synoptic-scale Rossby waves, or in their future connection to weather and climate events.

In recent years, the field of geostatistics has seen significant advancements. These methods are fundamental in understanding spatially and temporally variable hydrological and environmental processes, which are vital for risk assessment, input for other models, and management of extreme events like floods and droughts.
This session aims to provide a comprehensive platform for researchers to present and discuss innovative applications and methodologies of geostatistics and spatio-temporal analysis in hydrology and related fields. The focus will be on traditional approaches and the assessment of uncertainties, whereas Machine Learning approaches have their specific and other dedicated sessions.

We invite contributions that address the following topics (but not limited to):

1. Spatio-temporal Analysis of Hydrological and Environmental Anomalies:
- Methods for detecting and analyzing large-scale anomalies in hydrological and environmental data.
- Techniques to manage and predict extreme events based on spatio-temporal patterns.

2. Innovative Geostatistical Applications:
- Advances in spatial and spatio-temporal modeling.
- Applications in spatial reasoning and data mining.
- Reduced computational complexity methods suitable for large-scale problems.

3. Geostatistical Methods for Hydrological Extremes:
- Techniques for analyzing the dynamics of natural events, such as floods, droughts, and morphological changes.
- Utilization of copulas and other statistical tools to identify spatio-temporal relationships.

4. Optimization and Generalization of Spatial Models:
- Approaches to optimize monitoring networks and spatial models.
- Techniques for predicting regions with limited or unobserved data e.g., using physical-based model simulations or using secondary variables.

5. Uncertainty Assessment in Geostatistics:
- Methods for characterizing and managing uncertainties in spatial data.
- Applications of Bayesian Geostatistical Analysis and Generalized Extreme Value Distributions.

6. Spatial and Spatio-temporal Covariance Analysis:
- Exploring links between hydrological variables and extremes through covariance analysis.
- Applications of Gaussian and non-Gaussian models in spatial analysis and prediction.

This session addresses the interdisciplinary and challenging issue of extreme variability across scales, from theory to applications. Because this variability is ubiquitous this session focuses on edge-cutting research in various geophysical domains.

Reliability in water research depends on two key aspects: the availability of robust observational data and the rigorous selection and validation of model frameworks. This session highlights the importance of data acquisition, quality control, and curation in supporting reliable methodologies across hydraulic and hydrologic engineering.
In hydraulics, flume experiments provide controlled, high-quality datasets but are resource-intensive and limited in scalability. Numerical modeling offers greater flexibility to simulate diverse flow conditions, yet its accuracy is highly sensitive to parameterization, boundary conditions, and discretization schemes. In hydrology, sparse and uncertain field data further complicate model calibration and validation.
Recent advances in artificial intelligence (AI) and machine learning (ML) allow researchers to analyze large and heterogeneous datasets. However, risks arise when dataset adequacy, representativeness, or validation are overlooked, leading to ambiguous outcomes. These issues intensify when experimental, numerical, and AI-driven approaches are not cross-validated or integrated, weakening robustness and transferability.
This session aims to strengthen understanding of data curation and model selection as critical, though often overlooked, components in solving water resource challenges. Topics of interest include:
1. Strategies for data acquisition, handling, and curation across laboratory, field, numerical, and AI/ML approaches.
2. Best practices in optimization, calibration, and hyper-parameterization to improve model performance.
3. Frameworks for integrating laboratory, field, and computational datasets for consistency and cross-validation.
4. Data curation methods that enhance efficiency, reproducibility, and reliability in modeling.
Through interdisciplinary dialogue, the session seeks to generate methodological insights and practical guidelines that enhance accuracy in data handling and model selection. The overarching goal is to advance high-quality, validated, and context-relevant outcomes that strengthen resilience and reliability in water research.

Rainfall is a “collective” phenomenon emerging from numerous drops. It reaches the ground surface with varying intensity, drop size and velocity distribution. Understanding the relation between the physics of individual drops and that of a population of drops remains an open challenge, both scientifically and for practical implications. This remains true also for solid precipitation. Hence, it is much needed to better understand small scale space-time precipitation variability, which is a key driving force of the hydrological response, especially in highly heterogeneous areas (mountains, cities). This hydrological response at the catchment scale is the result of the interplay between the space-time variability of precipitation, the catchment geomorphological / pedological / ecological characteristics and antecedent hydrological conditions. Similarly to the small scales, accurate measurement and prediction of the spate-time distribution of precipitation at hydrologically relevant scales still remains an open challenge.

This session brings together scientists and practitioners who aim to measure and understand precipitation variability from drop scale to catchment scale as well as its hydrological consequences. Contributions addressing one or several of the following topics are encouraged:
- Novel techniques for measuring liquid and solid precipitation variability at hydrologically relevant space and time scales (from drop to catchment scale), from in-situ measurements to remote sensing techniques, and from ground-based devices to spaceborne platforms. Innovative comparison metrics are welcomed;
- Drop (or particle) size distributions, small scale variability of precipitation, and their consequences for precipitation rate retrieval algorithms for radars, commercial microwave links and other remote sensors;
- Novel modelling or characterization tools of precipitation variability from drop scale to catchment scale from various approaches (e.g. scaling, (multi-)fractal, statistic, deterministic, numerical modelling);
- Novel approaches to better identify, understand and simulate the dominant microphysical processes at work in liquid and solid precipitation.
- Applications of measured and/or modelled precipitation fields in catchment hydrological models for the purpose of process understanding or predicting hydrological response.
- Rainfall simulators developed to investigate the accuracy of disdrometer measurements in assessing drop size and fall velocity.

The statistical characterization and modelling of precipitation are crucial in a variety of applications, such as flood forecasting, water resource assessments, evaluation of climate change impacts, infrastructure design, and hydrological modelling. This session aims to gather contributions on research, advanced applications, and future needs in the understanding and modelling of precipitation, including its variability at different scales and its sources of uncertainty.

Contributions focusing on one or more of the following issues are particularly welcome:
- Process conceptualization and approaches to modelling precipitation at different spatial and temporal scales, including model parameter identification, calibration and regionalisation, and sensitivity analyses to parameterization and scales of process representation.
- Novel studies aimed at the assessment and representation of different sources of uncertainty of precipitation, including natural climate variability and changes caused by global warming.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source ground-based, remotely sensed, and model-derived precipitation products.
- Estimation of precipitation variability and uncertainty at ungauged sites.
- Modelling, forecasting and nowcasting approaches based on ensemble simulations for synthetic representation of precipitation variability and uncertainty.
- Machine-learning approaches for precipitation modelling, forecasting, and downscaling: Machine-learning and hybrid (physics-informed) methods for precipitation simulation, uncertainty quantification, bias correction, and spatio-temporal downscaling, including baseline comparisons, cross-climate transfer tests, and evaluations of explainability and robustness.
- Scaling and scale invariance properties of precipitation fields in space and/or in time.
- Dynamical and statistical downscaling approaches to generate precipitation at fine spatial and temporal scales from coarse-scale information from meteorological and climate models.

Understanding plasma energisation and energy transport is one of the major challenges in the field of space plasma physics. Key regions where fundamental processes such as plasma heating, shock formation and re-formation, magnetic reconnection, turbulence, wave-particle interactions, plasma jets braking, and combinations of these initiate and govern particle energisation and energy transport include the solar atmosphere, the solar wind and the Earth's foreshock, bow shock, magnetosheath, magnetopause, magnetotail current sheet, and transition region.

Due to their proximity, these regions provide excellent laboratories in which to study such processes. Near Earth, the ESA/Cluster and NASA/MMS four-point constellations, as well as the large-scale multipoint NASA/THEMIS mission, have greatly improved our understanding of these plasma processes compared to earlier single-point measurements. However, these missions have also revealed that these processes operate across multiple scales, ranging from large fluid scales to smaller kinetic scales. This implies that multi-scale in situ observations are critical. To resolve scale coupling and ultimately fully understand plasma energisation and energy transport processes, simultaneous measurements at both fluid and kinetic scales are required. Building on previous single-scale missions, the Plasma Observatory (PO) mission represents the next generation of space plasma physics investigations. PO is a seven-spacecraft, multi-scale mission concept designed to study plasma energisation and energy transport in the Earth's magnetosphere simultaneously at fluid and ion scales. These are the scales at which the largest amount of electromagnetic energy is converted into energised particles and energy is transported. Any modelling approach, from global to kinetic, can be applied here.

We particularly welcome studies integrating numerical modeling, theoretical investigations and in-situ measurements/remote observations from past, current and future space missions such as Cluster, MMS, PO, Parker Solar Probe, Solar Orbiter, Bepi Colombo, SMILE, HelioSwarm, SPO...

Machine learning is reshaping the modelling of many physical processes in Earth system models, offering new routes for parameterisation, emulation, and hybrid modelling. This session focuses on the use of machine learning to emulate computationally expensive and unresolved processes, accelerate physical models, simulate across weather and climate, and improve representation across domains such as convection, turbulence, radiation, hydrology, sea ice, and other components of the Earth system.

Topics include (but are not limited to):

- Subgrid-scale parameterization via machine learning (for example those related to air-sea & land-atmosphere interactions)
- Emulators of physical processes, model components, or whole weather and climate models (including end-to-end learning)
- Hybrid ML-physics modelling frameworks
- Foundation Models
- Reinforcement learning (such as for ensuring physical consistency, stability, optimising model behaviour and improving time-series modelling)
- Physics-informed neural networks, neural operators, and differentiable programming
- Verification of data-driven models (including AI forecasting)
- Physical behaviour, encoding and analysis of AI parametrisations, emulators and whole models (such as through feature-based evaluation/conditional vs unconditional evaluation)
- Calibration and parameter optimization using ML
- Coupling of ML models with physical models
- Cross-domain applications (atmosphere, ocean, cryosphere, land).

This session provides a critical overview of current progress and emerging directions in the application of ML across parametrisations, emulation and hybrid modelling.

Time series are a very common type of data sets generated by observational and modeling efforts across all fields of Earth, environmental and space sciences. The characteristics of such dynamical data may however vastly differ from one another between data of different origins – short vs. long, linear vs. nonlinear, univariate vs. multivariate, single- vs. multi-scale, etc., equally calling for both specifically tailored methodologies as well as more generalist approaches. Similarly, also the specific tasks of time series analysis may span a vast body of problems, including (among others)

- characterization of nonlinear variability patterns in time and/or frequency domain,
- quantification of various aspects of time series and big data complexity and predictability,
- identification and quantification of different flavors of statistical interdependency within and between time series,
- discrimination between mere co-variability and true causality among two or more time series,
- dimensionality/complexity reduction and identification of statistically and/or dynamically meaningful modes of (co-)variability, and
- statistical and/or dynamical modeling of time series using stochastic or deterministic approaches.

According to this broad range of potential analysis goals, there exists a continuously expanding plethora of time series and big data analysis methods. This session focusses on geoscience problems from different fields covered by the EGU community that exhibit considerable degrees of dynamical and/or structural complexity, and applications of methods that specifically address this complexity. We anticipate that the presentation of novel methodological developments and/or successful showcases of applications of complexity science concepts (like statistical complexity measures, entropies, multi-scale and cross-scale analysis, predictability quantifiers, information transfer, causal discovery or complex networks, to mention only a few examples) across various disciplines will stimulate inter-disciplinary exchange and cross-fertilization among the EGU community.

The complex interactions and interdependencies of hydrological and land surface processes within the Earth system pose major challenges for prediction and understanding. Machine learning has become a powerful tool for prediction across these domains, but leveraging its scientific potential goes beyond applying existing algorithms and data. It requires detailed understanding and problem-specific integration of domain knowledge with data-driven techniques to make models more interpretable and enable new process understanding. This session explores how machine learning techniques are currently used to integrate, explain, and complement physical knowledge in hydrology and land surface modeling, including studies of surface and subsurface water dynamics, soil-vegetation interactions, land-atmosphere exchanges, and eco-hydrological processes. Submissions are welcome on topics including, but not limited to:

- Explainability and transparency in data-driven hydrological and land surface modeling;
- Integration of process knowledge and machine learning;
- Domain-specific model development;
- Data assimilation and hybrid modeling approaches;
- Causal learning and inference in machine learning models;
- Data-driven equation discovery;
- Challenges and solutions for hybrid models and explainable AI.

Submissions that present methodological innovation, critically assess limitations, or demonstrate contributions to process understanding across scales are especially encouraged.

frequent and impactful weather-related disasters. Conversely, declines in water availability make monitoring surface water dynamics, including seasonal water body variations, wetland extent, and river morphology changes crucial for environmental management, climate change assessment, and sustainable development. Remote sensing is a critical tool for data collection and observation, especially in regions where field surveys and gauging stations are limited, such as remote or conflict ridden areas and data-poor developing nations. The integration of remotely-sensed variables—like digital elevation models, river width, water extent, water level, flow velocities, and land cover—into hydraulic models offers the potential to significantly enhance our understanding of hydrological processes and improve predictive capabilities.
Research has so far focused on optimising the use of satellite observations, supported by both government and commercial initiatives, and numerous datasets from airborne sensors, including aircraft and drones. Recent advancements in Earth observation (EO) and machine learning have further enhanced the monitoring of floods and inland water dynamics, utilising multi-sensor EO data to detect surface water, even in densely vegetated regions. However, despite these advancements, the update frequency and timeliness of most remote sensing data products are still limited for capturing dynamic hydrological processes, which hinders their use in forecasting and data assimilation. This session invites cutting-edge presentations on advancing surface water and flood monitoring and mapping through remotely-sensed data, focusing on:
- Remote sensing for surface water and flood dynamics, flood hazard and risk mapping including commercial satellite missions and airborne sensors
- The use of remotely-sensed data for calibrating or validating hydrological or hydraulic models
- Data assimilation of remotely-sensed data into hydrological and hydraulic models
- Enhancements in river discretization and monitoring through Earth observations
- Surface water and river flow estimation using remote sensing
- Machine learning and deep learning-based water body mapping and flood predictions
- Ideas for developing multi-satellite data products and services to improve the monitoring of surface water dynamics including floods
Early career and underrepresented scientists are particularly encouraged to participate.

This session explores forecasting in geosciences using statistical methods. Ranging from linear regression to the most advanced machine learning (ML) or artificial intelligence (AI) algorithms, the session welcomes all contributions developing and/or using these tools for various applications such as AI-based numerical weather prediction and nowcasting, time series forecasting in geosciences, forecast blending, statistical post-processing, and downscaling.
This session also welcomes contributions advancing the assessment of AI-based forecasts. Aiming at a proper and in-depth assessment of the strengths and weaknesses of AI-based models, the session will report on benchmarking activities, new verification methodologies, diagnostics of forecast realism, and progress in the interpretability of AI-based models.

This session is designed to foster interdisciplinary discussions among geoscientists from meteorology, climate, hydrology, and other related communities, promoting the use of statistical methods in forecasting, verification, and beyond.

The recent revolution of data-driven forecasting systems based on artificial intelligence (AI) has opened new research possibilities in weather forecasting, climate science, and various other areas. At the same time, many open questions remain–such as how to properly evaluate the model outputs in terms of generalizability under climate change, whether models extrapolate to unseen extremes, and to what extent they are consistent with physical principles. This session focuses on new scientific approaches emerging from this AI revolution, limitations of current models, and strategies to overcome them. We encourage submissions that explore a wide range of topics, including evaluations of outputs and comparisons to numerical models, technical advancements in initial condition optimization or model fine-tuning, novel techniques from explainable AI, and other relevant studies. Bringing together experts from AI, climate sciences, statistics, and applied math will foster interdisciplinary collaborations and guide scientific progress in this quickly evolving field of research.

This session covers climate predictions from seasonal to multi-decadal timescales and their applications. Continuing to improve such predictions is of major importance to society. The session embraces advances in our understanding of the origins of seasonal to decadal predictability and of the limitations of such predictions. This includes advances in improving forecast skill and reliability and making the most of this information by developing and evaluating new applications and climate services.
The session welcomes contributions from dynamical models, machine-learning or other statistical methods and hybrid approaches. It will investigate predictions of various climate phenomena, including extremes, from global to regional scales, and from seasonal to multi-decadal timescales (including seamless predictions). Physical processes and sources relevant to long-term predictability (e.g. ocean, cryosphere, or land) as well as predicting large-scale atmospheric circulation anomalies associated with teleconnections will be discussed. Analysis of predictions in a multi-model framework, and ensemble forecast initialization and generation will be another focus of the session. We are also interested in approaches addressing initialization shocks and drifts. The session welcomes work on innovative methods of quality assessment and verification of climate predictions. We also invite contributions on the use of seasonal-to-decadal predictions for risk assessment, adaptation and further applications.

Space and astrophysical plasmas are typically in a turbulent state, exhibiting strong fluctuations of various quantities over a broad range of scales. These fluctuations are non-linearly coupled, and this coupling leads to the transfer of energy (and other quantities, such as cross helicity and magnetic helicity) from large to small scales and to dissipation. Turbulent processes are relevant for the heating of the solar wind and the corona, and the acceleration of energetic particles. In these environments, many aspects of turbulence are not well understood, in particular, the injection and onset of the cascade, the cascade itself, the dissipation mechanisms, as well as the role of coherent structures and waves. Specific phenomena such as magnetic reconnection, shock waves, solar wind expansion, plasma instabilities, wave activity and their relationship with the turbulent cascade and dissipation are under debate. The session will explore these open questions through observational, theoretical, numerical, and laboratory studies, aiming to advance our understanding of these processes. For observational studies, we welcome contributions utilizing data from a wide range of relevant spacecraft missions, including WIND, CLUSTER, MMS, STEREO, THEMIS, Van Allen Probes, and DSCOVR, with particular emphasis on recent findings from Solar Orbiter and Parker Solar Probe.

This session focuses on the non-linear processes taking place in space, laboratory, and astrophysical plasma. In many cases, these processes are not separated but appear interlinked. For instance, magnetic reconnection is an established ingredient of the turbulence cascade, and it is also responsible for the production of turbulence in reconnection outflows; shocks can be accountable for turbulence formation, for example, in the turbulent magnetosheath, or can be efficient particle accelerators through their interaction with the ambient turbulence.

The study of these processes has seen significant progress in recent years thanks to a synergistic approach based on simulations and observations. On the one hand, simulations can deliver output in a temporal and spatial range of scales, going from fluid to electron kinetic. That is partially also due to the advent of GPU facilities that contribute to increasing computational algorithms' power in plasma physics. On the observational side, high cadence measurements of particles and fields and high-resolution 3D measurements of particle distribution functions are currently provided by the missions MMS, Parker Solar Probe, and Solar Orbiter, opening new research scenarios in heliophysics and providing a consistent amount of new data to be analysed. Furthermore, other present and future missions that will give unique plasma measurements around solar system compact objects, such as Bepi Colombo, Juice, Comet Interceptor, and HelioSwarm are demanding the development of new numerical tools for a successful interpretation of the observations.

This session welcomes simulation, observational, and theoretical works relevant to studying the abovementioned processes. We also encourage papers proposing new methods in simulation techniques and data analysis, for example, those rooted in Artificial Intelligence or those based on multi-point satellite observations.

Geophysical and planetary flows in stratified media exhibit stratified turbulence that give rise to a variety of flow phenomena spanning a range of spatial scales from the Kolmogorov to planetary scales. Stratified turbulence significantly influences the flow dynamics on various temporal scales via complex nonlinear interactions, which continue to be challenging to understand, diagnose, and quantify from both theory and numerics. This understanding is fundamental to advance our knowledge of turbulent flow dynamics, and a prerequisite for improved turbulent closures and parameterizations for robust predictions of weather and climate.

Gravity-driven flows, driven by density differences, due to temperature (e.g. katabatic winds) and/or salinity (e.g. density currents) differences, and/or the presence of particles (e.g. snow avalanches, debris-flows turbidites, pyroclastic flows) are an ubiquitous geophysical phenomenon involving stratified turbulent effects. These effects are coupled with entrainment, particle–fluid interactions, non-Newtonian rheologies, and interactions with ambient stratified environments. Although occurring in various planetary environments and similar physical processes governing their dynamics, a universal description of gravity-driven flows remains elusive, as the feedback on the aforementioned flow processes is particularly difficult to predict.

This session aims to bring together the recent advancements in stratified turbulence and gravity-driven flows in geophysical, planetary, and astrophysical flows. We welcome fundamental and applied contributions from theoretical, numerical, and modelling perspectives, as well as complementary approaches based on field observations, laboratory and analogue experiments, and numerical simulations (including data-driven and IA-based methods). Topics include, but are not limited to:
— turbulent fluxes and transports; turbulent decay, mixing, and dissipation
— stable atmospheric boundary layer flows and intermittent turbulence
— wave turbulence and wave-vortex dynamics in various turbulent regimes
— turbulence in weakly and strongly stratified flows and stratified shear flows
— snow avalanches, dust storms, landslides, turbidity currents
— river, volcanic and oceanic plumes, mud, debris and pyroclastic flows
— katabatic winds, oceanic density currents

We particularly encourage participation from early career researchers.

Weather forecasting and its application is one of the most important subject in meteorology. This session will focus on R&D on weather forecasting techniques and applications, in particular those AI based techniques and application. Contributions related to nowcasting, meso-scale and convection permitting modelling, ensemble prediction techniques, and statistical post-processing are very welcome.

Topics may include:

- AI based Nowcasting methods and systems, use of observations and weather analysis
- Physics and AI driven Mesoscale and convection permitting modelling
- Development on AI for Ensemble prediction techniques and products
- AI for weather forecasting application
- AI for Seamless prediction and application
- Statistical and AI NWP Post-processing
- Use of machine learning, data mining and other advanced analytical techniques
- Presentation of results from relevant international research projects of EU, WMO, and EUMETNET etc.

Connect with colleagues across disciplines at the 4th Lagrangian session!

This session provides an open venue for scientists to share the latest advances in Lagrangian techniques, explore diverse applications, and build new connections.

We invite presentations on topics including, but not limited to:
- Planetary circulations and variability (fundamental processes shaping jets, gyres, waveguides, overturning circulations, transport barriers across atmosphere and ocean)
- Mesoscale eddies and coherent structures (eddy transport, wave-mean flow interactions, blocking)
- Turbulence and mixing (turbulent and convective entrainment, breaking internal waves, boundary layers)
- Numerical and computational advances (incl. data-driven techniques, GPU acceleration, graph-theoretical formulations, adaptive methods, data assimilation)
- Inverse modeling techniques (long-range transport of volcanic plumes, wildfire smoke, hazardous material, aerosols, plastics, micro-organisms, and their impacts on global composition, health, and climate)
- Field campaigns (drifters, floats, superpressure balloons, etc)

Waves in the Earth’s crust are often triggered by fractures in the process of sliding and/or propagation. Conversely, the waves can trigger fracture sliding and propagation. Analysis of wave propagation and their interaction with pre-existing or emerging fractures is central to geophysics. Recently new observations and theoretical concepts were introduced pointing out to the limitations of the traditional concepts. These are:
• Multiscale nature of wave fields and fractures in geomaterials
• Rotational mechanisms of wave and fracture propagation
• Strong rock and rock mass non-linearity (such as bilinear stress-strain curve with high modulus in compression and low in tension) and its effect on wave propagation
• Triggering effects and instability in geomaterials
• Active nature of geomaterials (e.g., seismic emission induced by stress and pressure wave propagation)
• Synchronization in fracture processes including earthquakes and volcanic activity

It is anticipated that studying these and related phenomena can lead to breakthroughs in understanding of the stress transfer and multiscale failure processes in the Earth's crust, ocean and atmosphere and facilitate developing better prediction and monitoring methods.

The session is designed as a forum for discussing these and similar topics.

Internal gravity waves (IGWs) still pose major questions in the study of both atmospheric and ocean sciences, and stellar physics. Important issues include IGW radiation from their various relevant sources, IGW reflection at boundaries, their propagation through and interaction
with a larger-scale flow, wave-induced mean flow, wave-wave interactions in general, wave breaking and its implications for mixing, and the parameterization of these processes in models not explicitly resolving IGWs. The observational record, both on a global scale and with respect to local small-scale processes, is not yet sufficiently able to yield appropriate constraints. The session is intended to bring together experts from all fields of geophysical and astrophysical fluid dynamics working on related problems. Presentations on theoretical, modelling, experimental, and observational work with regard to all aspects of IGWs are most welcome, including those on major collaborative projects, which seek to accurately parameterize the role of IGWs in numerical models.

We invite presentations on ocean surface waves, and wind-generated waves in particular, their dynamics, modelling and applications. This is a large topic of the physical oceanography in its own right, but it is also becoming clear that many large-scale geophysical processes are essentially coupled with the surface waves, and those include climate, weather, tropical cyclones, Marginal Ice Zone and other phenomena in the atmosphere and many issues of the upper-ocean mixing below the interface. This is a rapidly developing area of research and geophysical applications, and contributions on wave-coupled effects in the lower atmosphere and upper ocean are strongly encouraged

Europe is warming faster than any other continent, with climate-related hazards such as heatwaves, droughts, floods, and wildfires becoming more frequent and intense. These events not only pose direct threats to human systems but also trigger cascading effects across ecosystems, biodiversity, and biogeochemical cycles. This panel discussion explores the complex interplay between climate change and compounding natural hazards—such as wildfires, landslides, and extreme weather—and their cascading impacts on ecological systems, biogeochemical processes, and carbon dynamics. It will examine how these interactions affect ecosystem services, resilience and adaptation, drawing on insights from ecological modelling, Earth observation, and multi-risk analysis.

To effectively address these complex and cascading risks, the session also draws on expertise in governance and science-policy communication, recognising that scientific insights must be translated into actionable strategies, informed decision-making, and inclusive policies that enhance societal and ecological resilience.

This session brings together experts in ecological modelling, Earth Observation, multi-risk assessment, governance, and science-policy communication, including members of the EGU Climate Hazards Task Force. Panellists will respond to questions from the chairs and the audience, addressing how scientific research can better inform policy, what tools are needed to anticipate complex hazard-ecosystem interactions, and how to foster resilience in the face of uncertainty. The session aims to bridge disciplinary boundaries and spark dialogue between scientists, policymakers, and civil society, encouraging a shift from reactive to proactive risk and ecosystem management.

Land surface processes play a crucial role in shaping the Earth's climate and in modulating hydrometeorological variability as well as the occurrence of compound extreme events. As a core component of state-of-the-art Earth System Models (ESMs), their representation critically influences and enables climate feedbacks essential for predictions and climate-change projections. However, land hydrology and its interactions with other components of the Earth system (e.g. biosphere, biogeochemical cycles, anthropogenic disturbances/practices) remain poorly represented in most ESMs, potentially inducing erroneous responses to anthropogenic climate forcings at global to local scales and leading to misrepresentations of the occurrence, intensity and sequencing of droughts, floods and their compound manifestations. For instance, ESMs do not represent the observed decline of groundwater levels in water-limited regions, threatening the subsistence of groundwater-dependent ecosystems and exacerbating persistence and impact of droughts, thereby increasing the risk of ecosystem shifts and to progressive desertification.
This session is therefore open to observational and modeling contributions advancing the understanding and representation of hydrological, biophysical and biogeochemical processes and couplings in land surface models, including the simulation and predictability of compound extreme events across time scales. Particular attention will be dedicated to the representation of the interaction between hydrological processes and the biosphere (including the human component) to properly characterize the carbon-water nexus, human-water feedbacks, and the effects of land-based mitigation/adaptation options to climate change.
The session also welcomes contributions on high-resolution ESMs, advanced observation systems, and emerging data-driven and Artificial Intelligence approaches that enhance early warning capabilities and support resilience to compound hydrometeorological hazards.
The overarching aim of this session is to provide an open and collaborative space to bridge disciplinary gaps within and across communities involved in land surface modeling, and to strengthen links between land surface process representation and downstream applications in climate prediction and climate-change studies, with particular relevance for compound extreme events and transitions, while highlighting priorities and emerging opportunities for the development of next-generation ESMs.

Sitting under a tree, you feel the spark of an idea, and suddenly everything falls into place. The following days and tests confirm: you have made a magnificent discovery — so the classical story of scientific genius goes…

But science as a human activity is error-prone, and might be more adequately described as "trial and error". Handling mistakes and setbacks is therefore a key skill of scientists. Yet, we publish only those parts of our research that did work. That is also because a study may have better chances to be accepted for scientific publication if it confirms an accepted theory or reaches a positive result (publication bias). Conversely, the cases that fail in their test of a new method or idea often end up in a drawer (which is why publication bias is also sometimes called the "file drawer effect"). This is potentially a waste of time and resources within our community, as other scientists may set about testing the same idea or model setup without being aware of previous failed attempts.

Thus, we want to turn the story around, and ask you to share 1) those ideas that seemed magnificent but turned out not to be, and 2) the errors, bugs, and mistakes in your work that made the scientific road bumpy. In the spirit of open science and in an interdisciplinary setting, we want to bring the BUGS out of the drawers and into the spotlight. What ideas were torn down or did not work, and what concepts survived in the ashes or were robust despite errors?

We explicitly solicit Blunders, Unexpected Glitches, and Surprises (BUGS) from modeling and field or lab experiments and from all disciplines of the Geosciences.

In a friendly atmosphere, we will learn from each other’s mistakes, understand the impact of errors and abandoned paths on our work, give each other ideas for shared problems, and generate new insights for our science or scientific practice.

Here are some ideas for contributions that we would love to see:
- Ideas that sounded good at first, but turned out to not work.
- Results that presented themselves as great in the first place but turned out to be caused by a bug or measurement error.
- Errors and slip-ups that resulted in insights.
- Failed experiments and negative results.
- Obstacles and dead ends you found and would like to warn others about.

For inspiration, see last year's collection of BUGS - ranging from clay bricks to atmospheric temperature extremes - at https://meetingorganizer.copernicus.org/EGU25/session/52496.

The subsurface is increasingly recognised as a finite, shared and strategic space in which multiple, often competing uses must coexist over long time horizons. Energy storage and production, carbon dioxide removal, groundwater supply, underground infrastructure, and the storage or disposal of waste all rely on carefully a managed subsurface resource. How these uses interact has become a critical question for sustainable development.

This challenge is particularly acute in urban regions, where pressures from climate change, growing energy demand, environmental pollution, and social transformation converge. Densely populated cities are hotspots of resource consumption and vulnerability, but also key sites of innovation where resilient, low-carbon and just futures can be actively co-designed. At the same time, urban growth increasingly depends on subsurface resources located beyond city boundaries that support cities through the provision of energy and water, as well as through the removal of waste.

Understanding urban resilience and transformation therefore requires an integrated view of surface and subsurface systems across urban and extra-urban spaces, and across spatial and temporal scales. Yet, the role of surface and subsurface geo-processes within broader socio-technical and socio-ecological dynamics remains underexplored. Addressing this gap is essential for ensuring that subsurface resources are used in ways that are environmentally sustainable, socially just and mindful of future needs.

Real-time monitoring and modelling are essential for supporting decision making in water and environmental management. By combining observational data with numerical models, one can forecast how hydrological and environmental systems respond to different management actions or environmental changes. Numerical models can thus provide reliable guidance for decision-making.
Real-time modelling frameworks face several challenges, including the integration of diverse data streams and practical constraints on modelling, as well as the need to deliver timely and reliable outputs. Addressing these challenges is key to improve how water and environmental systems are managed under growing pressures from climate change and human activities. This session invites contributions on innovative approaches, tools, and case studies that demonstrate how observations and models can be combined to support operational and strategic decisions.
Topics of interest include, but are not limited to:
1) Integration of modelling with ground-based and satellite observations
2) Real-time data assimilation and forecasting for decision support
3) Conceptual, numerical, and data-driven approaches for decision-relevant modelling
4) Optimised monitoring, site characterisation, and data processing
5) Real-world case studies showing how modelling informs operational water and environmental management

Cities are intricate multi-scale systems, composed of diverse sub-components such as population, energy, transport, and climate. These components interact on various time scales, from hourly to seasonal to annual and beyond. Effective urban models and digital twins, crucial for urban planning and policy-making, must account for these complex interactions as they govern the growth and functioning of cities, often giving rise to emergent large-scale phenomena. However, our ability to quantitatively describe city behaviour remains limited due to the myriad of processes, scales, and feedbacks involved.
This session invites studies focused on modelling and monitoring the dynamics of multiple sectors and city-biosphere interactions. Topics of interest include, but are not limited to:
• Demography
• Urban transport networks
• Energy consumption
• Anthropogenic emissions and Pollution
• Urban climate
• Urban hydrology
• Urban ecology

Our aim is to elucidate the complex dynamics within urban environments and explore how urban form and function can be optimised to enhance the liveability and well-being of their citizens.

Several subsystems of the Earth have been suggested to possibly react abruptly at critical levels of anthropogenic forcing. Examples of such potential Tipping Elements include the Atlantic Meridional Overturning Circulation, the polar ice sheets, tropical and boreal forests, as well as the tropical monsoon systems. Interactions between the different Tipping Elements may either have stabilizing or destabilizing effects on the other subsystems, potentially leading to cascades of abrupt transitions. The critical forcing levels at which abrupt transitions occur have recently been associated with Tipping Points.

It is paramount to determine the critical forcing levels (and the associated uncertainties) beyond which the systems in question will abruptly change their state, with potentially devastating climatic, ecological, and societal impacts. For this purpose, we need to substantially enhance our understanding of the dynamics of the Tipping Elements and their interactions, on the basis of paleoclimatic evidence, present-day observations, and models spanning the entire hierarchy of complexity. Moreover, to be able to mitigate - or prepare for - potential future transitions, early warning signals have to be identified and monitored in both observations and models.

This multidisciplinary session invites contributions that address Tipping Points in the Earth system from the different perspectives of all relevant disciplines, including

- the mathematical theory of tipping points
- methods to anticipate critical transitions from data
- tipping points in climate models across the hierarchy, including comprehensive Earth system models
- climatic, ecological and socioeconomic impacts of tipping events
- decision theory in the presence of uncertain tipping point estimates and uncertain impacts

Are you unsure about how to bring order in the extensive program of the General Assembly? Are you wondering how to tackle this week of science? Are you curious about what EGU and the General Assembly have to offer? Then this is the short course for you!

During this course, we will provide you with tips and tricks on how to handle this large conference and how to make the most out of your week at this year's General Assembly. We'll explain the EGU structure, the difference between EGU and the General Assembly, we will dive into the program groups and we will introduce some key persons that help the Union function.

This is a useful short course for first-time attendees, those who have previously only joined us online, and those who haven’t been to Vienna for a while!

The course seeks to introduce attendants to the use of the SCENFIRE R package (https://github.com/rmmarcos/SCENFIRE_package). SCENFIRE is a post-processing algorithm that allows the user to combine wildfire simulated perimeters into burn probability based estimates of exposure to wildfires.

The EU-funded project AquaINFRA (https://aquainfra.eu/) aims to help marine and freshwater researchers restore healthy oceans, seas, coastal and inland waters. To achieve this goal, a large part of the work is dedicated to designing and implementing a research data infrastructure composed of the AquaINFRA Interaction Platform (AIP) and the Virtual Research Environment (VRE). This effort is part of the ongoing development of the European Open Science Cloud (EOSC) as an overarching research infrastructure, the EU flagship initiative to enable Open Science practices in Europe.

The AIP is the central gateway for scientific communities to find, access, and reuse aquatic digital resources such as FAIR multi-disciplinary data and analysis workflows. The basis for this is the Data Discovery and Access service which performs a live query to a number of data providers from the aquatic realm, for instance, Copernicus Marine and HELCOM. The data found can be used in the VRE, which is composed of a web API service hosting a number of OGC API Processes, a virtual lab based on the tool MyBinder, and the Galaxy platform as a workflow management system.

In this short course, we will start with providing an overview of the research data infrastructure. Then, we will show how the AIP and VRE can help to find data and use it in the Galaxy platform to create reproducible and readily-shareable analysis workflows. We will use a hydrological demonstrator in the form of a Data-to-Knowledge Package (D2K-Package) for this purpose [1]. The D2K-Package is a collection of links to digital research assets, including data, containerized code enriched by the computational environment, virtual labs, OGC API Processes, and computational workflows.

Although we will use a hydrological demonstrator, the course is not limited to hydrologists but open to everyone interested in making computational research more reusable. To follow this course, the attendees will need to register on Galaxy (https://usegalaxy.eu/login/start). We kindly ask the attendees to do so in advance to avoid delay. No prior knowledge in Hydrology or Galaxy is required to follow this course. Some understanding of scripting languages (e.g., R) can be helpful but the basic concepts do not depend on a particular technology.

Konkol, M. et al. (2025). Encouraging reusability of computational research through Data-to-Knowledge Packages - A hydrological use case https://doi.org/10.12688/openreseurope.20221.2.

The study and attribution of extreme weather and climate events has increasingly moved toward process-based approaches that explicitly account for the atmospheric dynamics leading to an event. Indices issued from dynamical systems theory enable one such approach. These indices rely on identifying past atmospheric situations with similar dynamics -- so called analogues. They quantify the similarity of large-scale circulation patterns between extreme events, informing on the extremes' predictability and on how the occurrence and characteristics of the events change in time.
This short course will introduce the theory and calculation of analogue-based dynamical systems indices, and show how they can be applied to study the predictability of extreme events, and attribute their occurrence to climate change (as implemented in the ClimatMeter platform). We will further explore the potential for attribution of climate impacts impacts. The course will combine a methodological overview with real-world applications.

Earth System Sciences (ESS) datasets, particularly those generated by high-resolution numerical models, are continuing to increase in terms of resolution and size. These datasets are essential for advancing ESS, supporting critical activities such as climate change policymaking, weather forecasting in the face of increasingly frequent natural disasters, and modern applications like machine learning.

The storage, usability, transfer and shareability of such datasets have become a pressing concern within the scientific community. State-of-the-art applications now produce outputs so large that even the most advanced data centres and infrastructures struggle not only to store them but also to ensure their usability and processability, including by downstream machine learning. Ongoing and upcoming community initiatives, such as digital twins and the 7th Phase of the Coupled Model Intercomparison Project (CMIP7), are already pushing infrastructures to their limits. With future investment in hardware likely to remain constrained, a critical and viable way forward is to explore (lossy) data compression & reduction that balance efficiency with the needs of diverse stakeholders. Therefore, the interest in compression has grown as a means to 1) make the data volumes more manageable, 2) reduce transfer times and computational costs, while 3) preserving the quality required for downstream scientific analyses.

Nevertheless, many ESS researchers remain cautious about lossy compression, concerned that critical information or features may be lost for specific downstream applications. Identifying these use-case-specific requirements and ensuring they are preserved during compression are essential steps toward building trust so that compression can become widely adopted across the community.

This short course is designed as a practical introduction to compressing ESS datasets using various compression frameworks and to share tips on preserving important data properties throughout the compression process. After completing the hands-on exercises, using either your own or provided data, time will be set aside for debate and discussion to address questions about lossy compression and to exchange wishes and concerns regarding this family of methods. A short document summarising the discussion will be produced and made freely available afterwards.

To learn more about recent advances in data compression, please also join the ESSI2.2 oral and poster sessions.

Join our tutorial on discovering, sharing, and learning with Earth System Sciences data: 1) How to find high-quality datasets for your data-driven projects, including scientific and governmental sources, 2) Tips for selecting the right (disciplinary) repository for sharing your data - according to your needs and particularly addressing the FAIRness and Openness principles, and 3) How to find and use open online courses and educational materials (OER) to leverage discovered data.
We will demonstrate tips, tricks, and how-tos using the NFDI4Earth services OneStop4All (https://onestop4all.nfdi4earth.de/) and the Knowledge Hub (https://knowledgehub.nfdi4earth.de/).
You are invited to share your experiences, best practices, or favorite repositories with the community, and take away practical skills and knowledge to enhance your research.

AI is a gamechanger in the quest for better understanding Earth data. ML allows training of models for virtually any purpose, and many of them are published on open platforms like HuggingFace and Kaggle. However, in practice it is not easy particularly for non-experts to use such models, due to several blockers: Models typically need highly specific data preprocessing requiring skillful python coding. Model metadata are sparse and not standardized. In particular, they are not machine-readable so human intervention is required. A model's comfort zone is not always delineated clearly, and outside of it model accuracy and reliability can drop drastically, such as below 20%.

Recent work in research and standardization is aiming at overcoming these obstacles in the quest for easy-to-use, zero-coding, reliable ML use on spatio-temporal Earth Data. Based on ongoing research in the EU-funded FAIRgeo project we discuss AI-Cubes as a novel paradigm which embeds ML inference seamlessly into the geo datacube query standard, WCPS. Further, the concept of Model Fencing aims at deriving hints about a model's comfort zone so that the server can automatically decide about model applicability on the region selected and warn the user.

Live demos, several of which can be recapitulated by the audience, serve to illustrate the challenges and solution approaches. Ample time will be reserved for active discussion with the audience.

Data assimilation (DA) is widely used in the study of the atmosphere, the ocean, the land surface, hydrological processes, etc. The powerful technique combines prior information from numerical model simulations with observations to provide a better estimate of the state of the system than either the data or the model alone. This short course will introduce participants to the basics of data assimilation, including the theory and its applications to various disciplines of geoscience. An interactive hands-on example of building a data assimilation system based on a simple numerical model will be given. This will prepare participants to build a data assimilation system for their own numerical models at a later stage after the course.
In summary, the short course introduces the following topics:

(1) DA theory, including basic concepts and selected methodologies.
(2) Examples of DA applications in various geoscience fields.
(3) Hands-on exercise in applying data assimilation to an example numerical model using open-source software.

This short course is aimed at people who are interested in data assimilation but do not necessarily have experience in data assimilation, in particular early career scientists (BSc, MSc, PhD students and postdocs) and people who are new to data assimilation.