Modern challenges of climate change, disaster management, public health and safety, resources management, and logistics can only be addressed through big data analytics. A variety of modern technologies are generating massive volumes of conventional and non-conventional geospatial data at local and global scales. Most of this data includes geospatial data components and are analysed using spatial algorithms. Ignoring the geospatial component of big data can lead to an inappropriate interpretation of extracted information. This gap has been recognised and led to the development of new spatiotemporally aware strategies and methods.
This session discusses advances in spatiotemporal machine learning methods and the softwares and infrastructures to support them.

Machine learning (ML) is currently transforming data analysis and modelling of the Earth system. While statistical and data-driven models have been used for a long time, recent advances in machine learning now allow for encoding non-linear, spatio-temporal relationships robustly without sacrificing interpretability. This has the potential to accelerate climate science, by providing new physics-based modelling approaches; improving our understanding of the underlying processes; reducing and better quantifying climate signals, variability, and uncertainty; and even making predictions directly from observations across different spatio-temporal scales. The limitations of machine learning methods need to also be considered, such as requiring, in general, rather large training datasets, data leakage, and/or poor generalisation abilities, so that methods are applied where they are fit for purpose and add value.

This session aims to provide a venue to present the latest progress in the use of ML applied to all aspects of climate science and we welcome abstracts focussed on, but not limited to:
- Causal discovery and inference: causal impact assessment, interventions, counterfactual analysis
- Learning (causal) process and feature representations in observations or across models and observations
- Hybrid models (physically informed ML, emulation, data-model integration)
- Novel detection and attribution approaches
- Probabilistic modelling and uncertainty quantification
- Explainable AI applications to climate data science and climate modelling
- Distributional robustness, transfer learning and/or out-of-distribution generalisation tasks in climate science

Please note that a companion session “ML for Earth System modelling” focuses specifically on ML for model improvement, particularly for near-term time-scales (including seasonal and decadal) forecasting, and related abstracts should be submitted there.

The increasing amount of data from an increasing number of spacecraft in our solar system shouts out for new data analysis strategies. There is a need for frameworks that can rapidly and intelligently extract information from these data sets in a manner useful for scientific analysis. The community is starting to respond to this need. Machine learning, with all of its different facets, provides a viable playground for tackling a wide range of research questions in planetary and heliospheric physics.

We encourage submissions dealing with machine learning approaches of all levels in planetary sciences and heliophysics. The aim of this session is to provide an overview of the current efforts to integrate machine learning technologies into data driven space research, to highlight state-of-the art developments and to generate a wider discussion on further possible applications of machine learning.

A Digital Twin of the Earth (DTE) is an interactive, dynamic digital replica of our planet that combines observations with simulations from physical models and advanced AI-based analysis. It aims to replicate the Earth's complex ecosystem, allowing us to estimate our planet’s response to changes under both the current climate state and future climate projections. A DTE is an emerging concept that is capable of simulating what-if scenarios before they occur, which is crucial for natural hazard mitigation and adaptation plans (e.g., floods, heatwaves, wildfires, droughts, etc.). A DTE calls for an advanced, federated, multi-computing architecture that works across organizations and agency boundaries. For a DTE to be successful, it needs to be open source and developed for and by the community. It needs to be an extendable framework that encourages continuous contributions, integration, and development of advanced AI solutions in order to have an accurate digital representation of the physical environment. This session welcomes presentations on current open-source frameworks and enabling technologies for DTE including, but not restricted to:
• Open-source DTE framework
• Computer infrastructure to move virtual data from DTE repositories to service platforms
• Surrogate models for missing observations and unresolved physical processes
• Hybrid AI / physics-based modelling
• Extreme value predictions
• Uncertainty quantification and representation
• Post-processing (event detection and downscaling/super-resolution)

Proper characterization of uncertainty remains a major research and operational challenge in Environmental Sciences, and is inherent to many aspects of modelling impacting model structure development; parameter estimation; an adequate representation of the data (inputs data and data used to evaluate the models); initial and boundary conditions; and hypothesis testing. To address this challenge, methods for a) uncertainty analysis (UA) that seek to identify, quantify and reduce the different sources of uncertainty, as well as propagating them through a system/model, and b) the closely-related methods for sensitivity analysis (SA) that evaluate the role and significance of uncertain factors (in the functioning of systems/models), have proved to be very helpful.

This session invites contributions that discuss advances, both in theory and/or application, in methods for SA/UA applicable to all Earth and Environmental Systems Models (EESMs), which embraces all areas of hydrology, such as classical hydrology, subsurface hydrology and soil science.

Topics of interest include (but are not limited to):
1) Novel methods for effective characterization of sensitivity and uncertainty
2) Analyses of over-parameterised models enabled by AI/ML techniques
3) Single- versus multi-criteria SA/UA
4) Novel approaches for parameter estimation, data inversion and data assimilation
5) Novel methods for spatial and temporal evaluation/analysis of models
6) The role of information and error on SA/UA (e.g., input/output data error, model structure error, parametric error, regionalization error in environments with no data etc.)
7) The role of SA in evaluating model consistency and reliability
8) Novel approaches and benchmarking efforts for parameter estimation
9) Improving the computational efficiency of SA/UA (efficient sampling, surrogate modelling, parallel computing, model pre-emption, model ensembles, etc.)

Understanding Earth’s system’s natural processes, especially in the context of global climate change, has been recognized globally as a very urgent and central research direction which needs further exploration. With the launch of new satellite platforms with a high revisit time, combined with the increasing capability for collecting repetitive ultra-high aerial images through unmade aerial vehicles, the scientific community have new opportunities for developing and applying new image processing algorithms to solve old and new environmental issues.

The purpose of the proposed session is to gather scientific researchers related to this topic aiming to highlight ongoing research and new applications in the field of satellite and aerial time-series imagery. The session focus is on presenting studies aimed at the development or exploitation of novel satellite times series processing algorithms and applications to different types of remote sensing data for investigating longtime processes in all branches of Earth (sea, ice, land, atmosphere).

The conveners encourage both applied and theoretical research contributions focusing on novel methods and applications of satellite and aerial time-series imagery in all geosciences disciplines, including both aerial and satellite platforms (optical and SAR) and data acquired in all regions of the electromagnetic spectrum.

Among various fields of exploration in artificial intelligence (AI), the availability of high-quality training datasets is an exciting area of research that holds great potential to make accurate predictions or perform a desired task. Training data is the initial dataset used to train machine learning algorithms and models. Training data is also known as training dataset (TDS), learning set, and training set. The goal of this session is:
1) to discuss the cutting-edge topics of machine learning training data for the geospatial community;
2) to describe the spatial, temporal and thematic representativeness of TDS and their uncertainties;
3) to focus on sharing and reusability of TDS to increase the adaptation of TDS for geospatial analysis.

This session will focus on the following topics around training datasets:
-How to describe a training dataset to enable efficient re-use in ML/AI applications?
-What are the main characteristics of the training dataset, and what additional information needs to be provided to sufficiently understand the privacy, nature and usability of the dataset?
-Exploring the effect of training data accuracy level, uncertainty of the measurement, labelling procedure used to generate the training data, original data used to create labels, external classification schemes for label semantics, e.g. ontologies or vocabularies;
-What metadata is required, recommended, or optionally provided?
-How to express the quality of a TDS? Is it possible to auto-generate quality indicators?
-Evaluating the effect of training data size, spatial resolution and structure, temporal resolution and currency, the spectral resolution of imagery used for annotation, and annotating accuracy.
-Methods for documenting, storing, evaluating, publishing, and sharing the training datasets;
-Transfer learning and impact of combining various training datasets;
-Open standards and open source training datasets;
-How to enable FAIR (findable, accessible, interoperable and reusable) data principles to be at the heart of future TDS standardization.

Machine learning (ML) applied to earth observation (EO) data provides an ample source to distill insights about our planet and societal activities. Typically, such investigations run as scientific research projects or as industrial proof-of-concept studies with significant manual interaction. In practice, corresponding solutions operate on a local or regional scale considering individual events or limited time periods. Advancing platform technologies and adherence to Open Science principles to enable scalable and reproducible workflows of high complexity are key to drive innovation in EO science and applications.

In our session presenters discuss the design of platforms and methods to scale-up and develop end- to-end repeatable, reusable and/or reproducible ML-model workflows based on multi-modal EO data to global and real-time services. These methods support the organization of input data, the efficient model training, continuous evaluation & testing, and deployment for federated operations on hybrid compute systems.

In particular, the following five topics will be addressed:
1. big geospatial data hubs for efficient preparation of analysis-ready data and features, 2. large-scale ML training on high-performance computing and cloud infrastructure,
3. frameworks for ML-operations at global scale considering complex workflows and hybrid systems,
4. reusability and reproducibility of complex EO-based workflows across platforms, as well as
5. big geospatial data and GeoML-model federation to reach maximal scale by efficient data sharing and model training & inference across institutions around the globe.

We target to not exclusively provide insights into frameworks and methods, but will also discuss the challenges faced en route from research experiments to a successfully integrated, real-time and global service.

Deep Learning has seen accelerated adoption across Hydrology and the broader Earth Sciences. This session highlights the continued integration of deep learning and its many variants into traditional and emerging hydrology-related workflows. Abstracts are solicited related to novel theory development, new methodologies, or practical applications of deep learning in hydrological modeling and process understanding. This might include, but is not limited to, the following:

(1) Development of novel deep learning models or modeling workflows.
(2) Integrating deep learning with process-based models and/or physical understanding.
(3) Improving understanding of the (internal) states/representations of deep learning models.
(4) Understanding the reliability of deep learning, e.g., under non-stationarity.
(5) Deriving scaling relationships or process-related insights with deep learning.
(6) Modeling human behavior and impacts on the hydrological cycle.
(7) Extreme event analysis, detection, and mitigation.
(8) Natural Language Processing in support of models and/or modeling workflows.

Unsupervised, supervised, semi-supervised as well as reinforcement learning are now increasingly used to address Earth system-related challenges for the atmosphere, the ocean, the land surface, or the sea ice.
Machine learning could help extract information from numerous Earth System data, such as in-situ and satellite observations, as well as improve model prediction through novel parameterizations or speed-ups. This session invites submissions spanning modeling and observational approaches towards providing an overview of state-of-the-art applications of these novel methods for predicting and monitoring the Earth System from short to decadal time scales. This includes (but is not restricted to):
- The use of machine learning to reduce or estimate model uncertainty
- Generate significant speedups
- Design new parameterization schemes
- Emulate numerical models
- Fundamental process understanding

Please consider submitting abstracts focused on ML applied to observations and modeling of the climate and its constituent processes to the companion "ML for Climate Science" session.

Artificial intelligence (in particular, machine learning) can be used to predict and respond to natural disasters. The ITU/WMO/UNEP Focus Group AI for Natural Disaster Management (FG-AI4NDM) is building a community of experts and stakeholders to identify best practices in the use of AI for data processing, improved modeling across spatiotemporal scales, and providing effective communication. This multidisciplinary FG-AI4NDM-session invites contributions addressing challenges and opportunities related to the use of AI for the detection, forecasting, and communication of natural hazards and disasters. In particular, it welcomes presentations highlighting innovative approaches to data collection (e.g., via sensor networks), data handling (e.g., via automating annotation), data storage and transmission (e.g., via edge- and cloud computing), novel modeling or explainability methods (e.g., integrating quantum computing methods), and outcomes of operational implementation.

Landslides, debris flows and avalanches are common types of unsteady bulk mass movements. Globally, the risk from these mass movements is expected to increase, due to changes in precipitation patterns, rising average temperatures and continued urbanisation of mountainous regions. Climate change also reduces the power of site-specific empirically-based predictions, requiring updated approaches for effective and robust management of the associated risk.

Given sustained improvements in computational power, the techniques involving artificial intelligence and explicit hydromechanical modelling are becoming more and more widespread. Both techniques have the advantages of reducing our dependence on empirical approaches. This session thus covers two main domains:

1) New approaches and state-of-the-art artificial intelligence techniques on remote sensing data for creating and updating landslide inventories.
2) Advances in hydromechanical numerical models and digital tools for geophysical mass flows.

The ultimate goal of both is integration into the wider context of hazard and/or risk assessment and mitigation.

Contributions to this session may involve:
(a) Regional scale analysis for landslide detection and applications for establishing multi-temporal inventories.
(b) Data processing, fusion, and data manipulation, as well as novel AI model tuning practices.
(c) Evaluating the quality of landslide detection through AI techniques.
(d) Comparing the performance of different AI segmentation models.
(e) Novel constitutive and hydromechanical modelling of flows, both at the field- and laboratory-scales.
(f) Hydromechanical modelling of the interaction of mass movements with structural countermeasures.
(g) Advances in risk analysis through the integration of digital technologies and multidisciplinary viewpoints (potentially including combining AI and hydromechanical modelling techniques).

Landslide susceptibility, the spatial likelihood of occurrence of landslides, is the subject of countless scientific publications. They use heterogeneous data, and apply many different methods, mostly falling under the definition of statistical and/or machine learning with the common feature of considering many input variables and a single target output, denoting landslide presence. It is a classification problem: given N input variables assuming different values, each combination associated with a 0/1 possible outcome, a model should be trained on some dataset, tested, and eventually it applied to unseen data.
Relevant input data (“predictors”, “factors”, “independent variables”) is usually a mixed set of topographic, morphometric, environmental, climatic, and a landslide inventory. Choice of a specific method depends on software availability, personal background, and existence of relevant literature in the area of interest. New methods are proposed regularly and very often is difficult to judge their relative performance based with respect to existing methods.
A meaningful comparison of many different methods would require a common dataset – a benchmark - to train and test each of them in a systematic way. This is a standard procedure in machine learning science and practice, for virtually all the fields: benchmark datasets exist for medical sciences, image recognition, linguistics, and in general any classification algorithm. The “Iris dataset” is a famous example of a benchmark in classification of numerical data into three different variants of the flower Iris. This session aims at establishing one or more benchmark datasets that could be helpful in landslide susceptibility research, to compare the plethora of existing methods and new methods to come.
We propose an interactive session: the organizers will single out benchmark datasets, share them with participants at due time, prior to the conference venue. We expect abstract proposals to describe the method(s) they intend to apply, the type of data it requires, and an independent case study for which the method proved successful. Participants should be ready to disclose minimal computer code (in any major programming language) to run their method, to apply the code to the benchmark dataset prior to the conference, and present their results. We aim at collecting all of the results in a journal publication, including datasets, benchmark and computer codes in collaboration with the participants.
Download dataset at: http://dx.doi.org/10.31223/X52S9C

Geophysical methods have great potential for the characterization of subsurface properties and processes to inform geological, reservoir, hydrological, and biogeochemical studies. In these contexts, the classically used geophysical tools only provide indirect information about the characteristics and heterogeneities of subsurface rocks and their associated processes (e.g., flow, transport, biogeochemical reactions). Petrophysical relationships hence have to be developed to provide links between physical properties (e.g. electrical conductivity, seismic velocity, or attenuation) and the intrinsic parameters of interest (e.g. fluid content, hydraulic properties, pressure conditions). In addition, geophysical methods are increasingly deployed in increasingly complex environments for time-lapse, continuous, and distributed monitoring. Here again, there is a great need for accurate and efficient physical relationships such that geophysical data can be correctly interpreted (e.g., included in fully coupled inversions). Establishing such petrophysical models requires multidisciplinary approaches and diverse theoretical frameworks. While each physical property has its own intrinsic dependence on pore-scale interfacial, geometrical, and biogeochemical properties or on external conditions such as pressure or temperature, each associated geophysical method also has its own specific investigation depth and spatial resolution. Such complexity poses great challenges in combining theoretical developments with laboratory validations and scaling laboratory observations to field practices. This session consequently invites contributions from various communities to share their models, their experiments, or their field tests and data in order to discuss multidisciplinary ways to advance petrophysical relationship development and to improve our knowledge of complex processes in the subsurface.

Non-destructive testing (NDT) methods are employed in a variety of engineering and geosciences applications and their stand-alone use has been greatly investigated to date. New theoretical developments, technological advances and the progress achieved in surveying, data processing and interpretation have in fact led to a tremendous growth of the equipment reliability, allowing outstanding data quality and accuracy.

Nevertheless, the requirements of comprehensive site and material investigations may be complex and time-consuming, involving multiple expertise and equipment. The challenge is to step forward and provide an effective integration between data outputs with different physical quantities, scale domains and resolutions. In this regard, enormous development opportunities relating to data fusion, integration and correlation between different NDT methods and theories are to be further investigated.

This Session primarily aims at disseminating contributions from state-of-the-art NDT methods and new numerical developments, promoting the integration of existing equipment and the development of new algorithms, surveying techniques, methods and prototypes for effective monitoring and diagnostics. NDT techniques of interest are related–but not limited to–the application of acoustic emission (AE) testing, electromagnetic testing (ET), ground penetrating radar (GPR), geoelectric methods (GM), laser testing methods (LM), magnetic flux leakage (MFL), microwave testing, magnetic particle testing (MT), neutron radiographic testing (NR), radiographic testing (RT), thermal/infrared testing (IRT), ultrasonic testing (UT), seismic methods (SM), vibration analysis (VA), visual and optical testing (VT/OT).

The Session will focus on the application of different NDT methods and theories and will be related –but not limited to– the following investigation areas:
- advanced data fusion;
- advanced interpretation methods;
- design and development of new surveying equipment and prototypes;
- real-time & remote assessment and monitoring methods for material and site inspection (real-life and virtual reality);
- comprehensive and inclusive information data systems for the investigation of survey sites and materials;
- numerical simulation and modelling of data outputs with different physical quantities, scale domains and resolutions;
- advances in NDT methods, numerical developments and applications (stand-alone use of existing and state-of-the-art NDTs).

The session gathers multi-disciplinary geoscientific aspects such as dynamics, reactions, and environmental/health consequences of radioactive materials that are massively released accidentally (e.g., Chernobyl and Fukushima nuclear power plant accidents, wide fires, etc.), future potential risk of leakage (e.g., Zaporizhzhia nuclear power plant) and by other human activities (e.g., nuclear tests).

The radioactive materials are known as polluting materials that are hazardous for human society, but are also ideal markers in understanding dynamics and physical/chemical/biological reactions chains in the environment. Therefore, man-made radioactive contamination involves regional and global transport and local reactions of radioactive materials through atmosphere, soil and water system, ocean, and organic and ecosystem, and its relations with human and non-human biota. The topic also involves hazard prediction, risk assessment, nowcast, and countermeasures, , which is now urgent important for the nuclear power plants in Ukraine.

By combining long monitoring data (> halftime of Cesium 137 after the Chernobyl Accident in 1986, 12 years after the Fukushima Accident in 2011, and other events), we can improve our knowledgebase on the environmental behavior of radioactive materials and its environmental/biological impact. This should lead to improved monitoring systems in the future including emergency response systems, acute sampling/measurement methodology, and remediation schemes for any future nuclear accidents.

The following specific topics have traditionally been discussed:
(a) Atmospheric Science (emissions, transport, deposition, pollution);
(b) Hydrology (transport in surface and ground water system, soil-water interactions);
(c) Oceanology (transport, bio-system interaction);
(d) Soil System (transport, chemical interaction, transfer to organic system);
(e) Forestry;
(f) Natural Hazards (warning systems, health risk assessments, geophysical variability);
(g) Measurement Techniques (instrumentation, multipoint data measurements);
(h) Ecosystems (migration/decay of radionuclides).

The session consists of updated observations, new theoretical developments including simulations, and improved methods or tools which could improve observation and prediction capabilities during eventual future nuclear emergencies. New evaluations of existing tools, past nuclear contamination events and other data sets also welcome.

Europe’s green transition and response to environmental challenges will depend on a parallel digital transition, supporting decision-makers and actors with fit-for-purpose digital technologies and assets. The EU’s “European strategy for data” and Data Governance Act identify data spaces as the instruments to achieve a single market for data, global competitiveness and data sovereignty through “a purpose- or a sector-specific or cross-sectoral interoperable framework of common standards and practises to share or jointly process data”. Shortly, European data spaces will ensure that more data becomes available for use in research, economy and society while keeping data rights-holders in control.

Several projects and initiatives are building thematic data spaces, allowing researchers, industries, and governments to access high-quality, interoperable data and related services from multiple providers and giving data holders and providers tools to manage, control and provide access to their data. The benefits of data spaces for a FAIR data ecosystem and potential users are clear, but a deeper understanding of the design, set up and evolution of data spaces is needed.

This session seeks contributions from any group, project or initiative that has established or is establishing a data space in the context of environmental and Earth sciences. Talks in this session should provide a general overview of the designing, building, running, and governing data spaces and share best practices in this respect. Practical use cases on different levels (regional, national, European or global) demonstrating the value of data spaces for access, combining data from various sources and flexible environmental/Earth system data processing are also welcome including initiatives bridging into data spaces such as Destination Earth Data Lake. Finally, we welcome presentations from projects and initiatives working to consolidate the complex landscape of different data ecosystems, both within and beyond environmental and Earth sciences.

Several projects and initiatives are building thematic data spaces, allowing researchers, industries, and governments to access high-quality, interoperable data and related services from multiple providers and giving data holders and providers tools to manage, control and provide access to their data. The benefits of data spaces for a FAIR data ecosystem and potential users are clear, but a deeper understanding of the design, set up and evolution of data spaces is needed.

This session seeks contributions from any group, project or initiative that has established or is establishing a data space in the context of environmental and Earth sciences. Talks in this session should provide a general overview of the designing, building, running, and governing data spaces and share best practices in this respect. Practical use cases on different levels (regional, national, European or global) demonstrating the value of data spaces for access, combining data from various sources and flexible environmental/Earth system data processing are also welcome including initiatives bridging into data spaces such as Destination Earth Data Lake. Finally, we welcome presentations from projects and initiatives working to consolidate the complex landscape of different data ecosystems, both within and beyond environmental and Earth sciences.

Research data infrastructures (RDIs) aim to manage and share research products and metadata systematically to enable research across all scales and disciplinary boundaries. Their services support researchers throughout the entire research lifecycle, especially during data management and collaborative analysis, and they foster FAIRness and openness, e.g., by applying established standards for metadata, data, and/or scientific workflows. Through their offerings and services, RDIs can shape research practices and are strongly connected with the communities of users that identify and associate themselves with them.

Naturally, the potential of RDIs faces many challenges. Even though it is clear that RDIs are indispensable for solving big societal problems, their wide adoption requires a cultural change within research communities. At the same time RDIs themselves must be developed further to serve user needs. And, also at the same time, the sustainability of RDIs must be improved, international cooperation increased, and duplication of development efforts must be avoided. To be able to provide a community of diverse career stages and backgrounds with a convincing infrastructure that is established beyond national and institutional boundaries, new collaboration patterns and funding approaches must be tested so that RDIs foster cultural change in academia and be a reliable foundation for FAIR and open research. This needs to happen while academia struggles with improving researcher evaluation, with a continuing digital disruption, with enhancing scholarly communication, and with diversity, equity, and inclusion.

In the Earth System Sciences (ESS), several research data infrastructures and components are currently developed on different regional and disciplinary scales, all of which face these challenges at some level. This session provides a forum to exchange methods, stories, and ideas to enable cultural change and international collaboration in scientific communities, to bridge the gap between user needs, and to build sustainable software solutions.

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 the early 2010s and its widespread adoption thereafter, migration to cloud computing has been a considerable task for many organisations and companies. Processing of big data close to their physical location is a perfect use case for cloud technologies and cloud storage infrastructure which offer all the necessary infrastructure and tools, especially if cloud infrastructure is offered together with HPC resources.
Pangeo (pangeo.io) is a global community of researchers and developers that tackle big geoscience data challenges in a collaborative manner using HPC and Cloud infrastructure.
This session's aim is threefold:
(1) Focuses on Cloud/Fog/Edge computing use cases and aims to identify the status and the steps towards a wider cloud computing adoption in Earth Observation and Earth Modeling.
(2) to motivate researchers that are using or developing in the Pangeo ecosystem to share their endeavors with a broader community that can benefit from these new tools.
(3) to contribute to the Pangeo community in terms of potential new applications for the Pangeo ecosystem, containing the following core packages: Xarray, Iris, Dask, Jupyter, Zarr, Kerchunk and Intake.
We encourage contributions describing all kinds of Cloud/Fog/Edge computing efforts in Earth Observation and Earth Modeling domains, such as:
- Cloud Applications, Infrastructure and Platforms (IaaS, PaaS SaaS and XaaS).
- Cloud federations and cross domain integration
- 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
- Fog and Edge Computing
- Operational systems on the cloud.
- Data lakes and warehouses on the cloud.
- Cloud computing and HPC convergence in EO data processing.
Also presentations using at least one of Pangeo’s core packages in any of the following domains:
- Atmosphere, Ocean and Land Models
- Satellite Observations
- Machine Learning
- And other related applications
We welcome any contributions in the above themes presented as science-based in other EGU sessions, but more focused on research, data management, software and/or infrastructure aspects. For instance, you can showcase your implementation through live executable notebooks.

In the big-data era, science attempts to address global societal problems (such as climate change, pandemics, environmentally sustainable exploitation of our resources) often with computationally expensive advanced methodologies that require machine-to-machine (m2m) interaction and interoperable data and services. At the same time, data science and technologies are rapidly evolving and offer new tools to address scientific demands, revealing yet more technological challenges as the complexity increases at all stages of the data and service development lifecycle.

To serve the complex, heterogeneous and diverse demands of their end-users, data providers try to work on federated solutions using FAIR enabling resources. Scientists and developers investigate the requirements for integrating systems that operate not necessarily using the same technologies but rather adopting technical solutions that facilitate the interoperability among them. Starting at the very low levels of data (e.g. near real-time, minimally processed, etc) and reaching higher-level derivative data collections, products and services, many issues are still open: metadata models, authentication and authorisation systems, ontologies, machine actionable licenses and PIDs that support the findability, accessibility and sustainable future of data (enabling proper citation and attribution to both creators and funders, usage tracking, etc.).

Groups facilitating global data sharing, networks and services are e.g. Federation of Digital Seismograph Networks (FDSN), OneGeology, OneGeochemistry, WorldFAIR, Earth Systems Grid Federation (ESFG), OGC, W3C, GEO, CODATA/DDI Cross-Domain Data Initiative), the European Open Science Cloud (EOSC) and the paneuropean cluster of environmental research infrastructures (ENVRI).

This session brings together diverse disciplines and a variety of experts (data centre architects, data stewards, developers, ontologists, scientists). We seek contributions that demonstrate scientific use cases (in the field of earth/environmental sciences), discuss ICT and data challenges and recommend best practices based on experience from interoperability frameworks. We welcome abstracts from small scale scientific methodologies developed by (multi)disciplinary groups aiming at multi/inter-disciplinary science to larger scale integrated platforms that offer interoperational data and services by multiple discipline-focused providers and/or cross-domain communities of providers

Destination Earth (DestinE) is an ambitious initiative of the European Union aiming to develop – on a global scale - a highly accurate digital model of the Earth that will help understand and simulate the evolution in the state of our planet, better predict impact on human system processes, ecosystem processes and their interaction.

DestinE will exploit state-of-the-art technologies, including high-performance computing, high-resolution Earth system models and novel approaches in analytics, including artificial intelligence, and offer unprecedented interactivity with the system for users.

A number of tangible outcomes are expected from these developments: Earth system simulations will become more skillful, the intensity and magnitude of natural disasters will be predicted more reliably, decision makers will have tools to tackle more efficiently the effect of climate change and much more.

Work is currently ongoing by the three implementing agencies (ESA, EUMETSAT, ECMWF) to develop the three components of the DestinE system: the Core Service Platform, the Data Lake and the Digital Twin Engine. This session aims at presenting progress towards the implementation of the DestinE system. It will also highlight opportunities to contribute to this challenging and ambitious endeavor and co-evolve the system together.

Need for Smart Solutions in earth, environmental and planetary sciences: Tackling data challenges and incorporating applied earth and planetary sciences into artificial intelligence (AI) models opened a new avenue for creating comprehensive methodologies and strategies to answer a wide variety of theoretical and practical questions from detecting, modelling, interpreting and predicting changes in the earth and environment’s ecosystems in response to climate change to understanding interactions among the ocean, atmosphere, and land in the climate system. Therefore, AI and Data Science (DS) in earth, environmental and planetary sciences are one of the fastest growing areas. The performance of the AI/DS models improves as it gains experience over time. Various mathematical and statistical models need to be investigated to determine the performance of AI models. Once the learning process is completed, then the model can then be used to make an assumption, classify and test data. This is achieved after gaining experience in the training process. This session aims to make available to the world community of earth, environment and planetary sciences-related professionals a collection of scientific papers on the current state of the art and recent developments of AI and DS applications in the field. This session will shed light on many recent research activities on applying AI/DS techniques into a single comprehensive document to address engineering, social, political, economic, safety, health, and technological issues of earth, environment and planetary sciences challenges and opportunities. The purpose of this session is to improve and facilitate the application of intelligent systems for the earth, environmental and planetary sciences to highlight new insight for creating comprehensive methodologies for analyzing/processing/predicting/management strategies in the fields of fundamental and applied sciences problems through the decision-making abilities of artificial intelligence and machine learning techniques.

Visualisation of scientific data is an integral part of scientific understanding and communication. Scientists have to make decisions about the most effective way to communicate their results everyday. How do we best visualise the data to understand it ourselves? How do we best visualise our results to communicate with others? Common pitfalls can be overcrowding, overcomplicated plot types or inaccessible color schemes. Scientists may also get overwhelmed by the graphics requirements of different publishers, for presentations, posters etc. This short course is designed to help scientists improve their data visualization skills in a way that the research outputs would be more accessible within their own scientific community and reach a wider audience.
Topics discussed include:
- Choosing a plot type – keeping it simple
- Color schemes – which ones to use or not to use
- Creativity vs simplicity – finding the right balance
- Producing your figures and maps – software and tools
- Figure files – publication ready resolutions
This course is co-organized by the Young Hydrologic Society (YHS), enabling networking and skill enhancement of early career researchers worldwide. Our goal is to help you make your figures more accessible by a wider audience, informative and beautiful. If you feel your graphs could be improved, we welcome you to join this short course.

Science impacts human society in many ways but of
particular importance is the application of scientific
results to the design of forecasting systems.
Forecasting systems are indispensable for making
informed decisions under risk. Informative and reliable
weather forecasts for instance help to better prepare
for or to reduce the exposure to adverse weather.
Therefore, there is a need for an objective and well
understood framework for ``forecast verification'',
i.e. qualitative and quantitative assessment of
forecast performance.

Statistical methods compare historical forecasts with
corresponding verifications, indicating whether the
forecasting system behaved significantly different (in
a statistical sense) from what was expected.

This short course will introduce the participants to
the fundamentals of statistical forecast verification.
Some necessary statistical theory will be discussed as well, and some hands-on numerical experiments will take place using freely available code. More specifically, the course will cover the following topics (more or less in that order)

* Forecast types and scoring rules
* Tests and p-values
* How to cope with dependent data
* How to cope with forecasts of spatial fields
* Code, literature, and further resources

Target audience are researchers (both from academic institutions and operational centres) who are either new to forecast verification or who have practical experience but want to know more about the theory. The course is NOT restricted to atmospheric forecasts, nor exclusively to the assessment of operational forecasting systems. The discussed methods are applicable in many other fields such as parameter estimation, data assimilation, model evaluation, and machine learning.

Humans have been successfully mapping the remotest and most inhospitable places on Earth, and the surfaces and interiors of other planets and their moons at highest resolution. The remaining blank spots are located in areas that are hardly accessible either through field surveys, geophysical methods or remote sensing due to technical and/or financial challenges. Some of these places are key areas that would help to reveal geologic history, or provide access to future exploration endeavors.

Such extreme and remote locations are commonly associated with the ocean floor, or planetary surfaces, but these extreme worlds might also be found in hot deserts, under the ice, in high-mountain ranges, in volcanic edifices, hidden underneath dense canopy cover, or located within the near-surface crust. All such locations are prime targets for remote sensing mapping in a wider sense. The methodological and technical repertoire to investigate extreme and remote locations is thus highly specialized and despite different contexts there are commonalities not only with respect to technical mapping approaches, but also in the way how knowledge is gathered and assessed, interpreted and vizualized regarding its scientific but also its economic value.

This session invites contributions to this field of geologic mapping and cartography of extreme (natural) environments with a focus on the scientific synthesis and extraction of information and knowledge.

A candidate contribution might cover, but is not limited to, topics such as:

- ocean mapping using manned and unmanned vehicles and devices,
- offshore exploration using remote sensing techniques,
- crustal investigation through drilling and sampling,
- subsurface investigation using radar techniques,
- planetary geologic and geophysical mapping,
- geologic investigation of desert environments,
- subglacial geologic mapping...

The aim of this session is to bring together researchers mapping geologically and geophysically inaccessible environments, thus relying on geophysical and remote sensing techniques as single source for collecting data and information. We would like to keep the focus on geologic and geophysical mapping of spots for which we have no or only very limited knowledge due to the harsh environmental conditions, and we would thus exclude areas that are inaccessible for political reasons.

The session aims to focus on the implementation of GEO data sharing and management and FAIR principles to In Situ Data for environmental and climate purposes. This effort also benefits to the implementation of the UN Sustainable Development Goals (SDG) and other global challenges.
In the context of Earth Observation, common understanding exists regarding the application of FAIR principles and GEO Data Sharing and Management principles for remote sensing satellite data, while in-situ data still faces issues related to its fragmentation, legal, organizational, technical aspects including a lack of standardization, harmonization, and integration to better solve the global challenges the planet and our society. It is then a major bottleneck to upscale from local to global. Global initiatives such as GEO, its work on Essential Variables and the European component EuroGEO; the Copernicus programme through the activities of the In-Situ Component; the activities conducive to the Common European Green Deal data space; and the work around the UN SDGs are providing important approaches to this aim.
In this context, topics as:
• Promoting free, full, open and timely access to in situ data; implementation of the GEO Data Sharing and Management principles;
• Methodological approaches to collect and manage in situ data requirements, including the application of the Essential Variables concept as a potential common framework;
• Pushing in situ data and its fit-for-purpose as a driver to define data collections: temporal and spatial resolution, thematic accuracy, model harmonisation and interoperability, formats, spatial and temporal coverage;
• Challenges in in situ data accessibility and re-usability (including both technical and licensing aspects);
• In-situ data gap analysis and best practices and recommendations

will be appreciated.

Research data repositories can store a variety of research outputs, ranging from raw observational data derived from monitoring infrastructures including satellites, drones, aircraft, remote sensors, in situ analytical laboratories, etc. to downstream derived products from scientific projects and the full suite of small and highly-variable data from the long-tail communities as well as sample descriptions. Repositories play an important part in the support of open data and open science, whilst their curation of datasets, together with operation and continuous development of access services, are key to making their data assets FAIR. Many data and sample repositories and their data provider communities are continually working on improving dataset identification and attribution, versioning control, as well as provenance recording and tracking, and improving and enriching relevant metadata records.

Traditionally, data centers monitored their usage by logging access to data sets (number, volume, success/fail rate) and used connection IP addresses for rough geographic differentiation. That usage data, together with user feedback received through other channels, supported data center governance and was sufficient for reporting to funders and other stakeholders. However, in recent times many repositories are being asked by their sponsors and funding agencies to provide information on what data and services are used by whom and for what purpose in greater detail than customary in the past: these more detailed requests can be in conflict with privacy legislation. More sophisticated systems now have to be implemented to enhance usage statistics, and the costs often compete with activities such as improving customer service and user experience.

This session will showcase a range of best practices in research data repositories that are working on making data and metadata Open, FAIR and accessible to both humans and machines. Contributions are welcome on unique identification of datasets and physical samples in a repository, attribution to ensure both funders and creators are credited (particularly of the raw observational datasets), provenance tracking and on making QA/QC assessments of the published datasets publicly available and FAIR. Methodologies and systems for implementing user identification and usage tracking are also invited, including protecting privacy and balancing scarce resources vs the need to continuously improve the user experience.

Awareness of the importance of the reproducibility of research results has increased considerably in recent years. Knowledge must be robust and reliable in order to serve as a foundation to build further progress on it. Reproducibility is a complex topic that spans technology, research communities, and research culture. In the narrow sense, reproducibility refers to the possibility of another researcher independently achieving the same result with the identical data and calculation methods. Put simply, one could say that research is either reproducible or not, but more practically there is a continuum of reproducibility where some factors weigh more heavily on influencing results. Replicability or replication, on the other hand, is a broader term and refers to one’s ability to replicate their own research. One problem, however, is that a large percentage of existing studies cannot be successfully reproduced or replicated. This endangers trust in science.

However, with the increasing complexity, volume and variety of Earth System Science (ESS) data - where data can be of multiple types like source code, entire workflows, observational or model output data - and the continuing push towards compliance with the FAIR data principles, achieving reproducibility is challenging. Dedicated solutions do exist only for a subset of implementation factors, but are mostly focused on single institutions or infrastructure providers. Current developments to establish the FDOs (FAIR Digital Objects) and corresponding frameworks go one step further to eventually enable a global interoperable data space to achieve scientific reproducibility. The adoption of Artificial Intelligence (AI), especially machine learning (ML), and other computational-intensive processes complicate this even further.

This session will explore current practices, methods and tools geared towards enabling reproducible results and workflows in ESS. We promote contributions from the areas of infrastructures, infrastructure requirements, workflow frameworks, software/tools, description of practices or other aspects (e.g. provenance tracking, quality information management, FDOs, AI/ML) that must be considered in order to achieve and enable reproducibility in Earth system sciences. These can be contributions that are generally valid and/or transferable or focus on certain areas of application. Finally, best practice examples (or as a counter-example bad practice) are also invited.

In recent decades, the use of geoinformation technology has become increasingly important in understanding the Earth's environment. This session focuses on modern open-source software tools, including those built on top of commercial GIS solutions, developed to facilitate the analysis of mainly geospatial data in various branches of geosciences. Earth science research has become more collaborative with shared code and platforms, and this work is supported by Free and Open Source Software (FOSS) and shared virtual research infrastructures utilising cloud and high-performance computing. Contributions will showcase practical solutions and applications based on FOSS, cloud-based architecture, and high-performance computing to support information sharing, scientific collaboration, and large-scale data analytics. Additionally, the session will address the challenges of comprehensive evaluations of Earth Systems Science Prediction (ESSP) systems, such as numerical weather prediction, hydrologic prediction, and climate prediction and projection, which require large storage volumes and meaningful integration of observational data. Innovative methods in open frameworks and platforms will be discussed to enable meaningful and informative model evaluations and comparisons for many large Earth science applications from weather to climate to geo in the scope of Open Science 2.0.

Research in Earth and environmental sciences benefits from interdisciplinary approaches (e.g. to understand and model multi-scale processes). The study of complex environmental processes may involve diverse collections of samples and associated field or laboratory measurements, sensors, remote sensing data, across international dimensions. Research benefits from practices that use easily-portable and reproducible tools and techniques. Best practices of sharing our data and software are now well-established and the earth science community needs to move forward with generally accepted methodologies of software and data distribution that can expand easily to include complex system and multi-domain challenges.

This session seeks innovative presentations for interdisciplinary research and applications, including but not limited to, on Earth Science data and service activities. Presentations addressing the specific societal needs, best practices, learned lessons and new challenges in data provenance, information access, visualization, and analysis, are highly encouraged, as well as presentation on the ways to adopt FAIR data principles towards sustainable solutions in Earth Science and the path to open science are . Discussion of challenges for future data services or European infrastructure are also welcome.

Remote sensing products have a high potential to contribute to monitoring and modelling of water resources. Nevertheless, their use by water managers is still limited due to lack of quality, resolution, trust, accessibility, or experience.
In this session, we look for new developments that support the use of remote sensing data for water management applications from local to global scales. We are looking for research to increase the quality of remote sensing products, such as higher resolution mapping of land use and/or agricultural practices or improved assessments of river discharge, lake and reservoir volumes, groundwater resources and drought monitoring/modelling. We are interested in quality assessment of remote sensing products through uncertainty analysis or evaluations using alternative sources of data. We also welcome contributions using a combination of different techniques (physically based models or artificial intelligence techniques) or a combination of different sources of data (remote sensing and in situ). Finally, we wish to attract presentations on developments of user-friendly platforms providing smooth access to remote sensing data for water applications.
We are particularly interested in applications of remote sensing to estimate the human water interactions such as dam operations and/or irrigations.

Policies and decisions are often based on data products, such as dynamic maps and time series. The underlying data is ideally of high quality, but generating complete and accurate data is often a costly endeavour. Integrating sparse accurate sensors and low-cost instruments is a way to overcome this issue but it results in challenges related to interoperability. Moreover, the quality of combined data and how the resulting data product (e.g., a map showing an interpolation) is generated needs to be communicated transparently to users. An aggravating factor is that quality is not an absolute indicator but might depend on the use case and other factors (e.g, accuracy/precision of the sensors, deployment, data management). A computational notebook (e.g., R Markdown) can help to communicate how the quality of a dataset and the data product are calculated. For example, the notebook can show which observations are included/excluded in a map showing an interpolation.
In this short course, we will show how reproducible computational notebooks can help to communicate information on data quality effectively and transparently allowing users to understand, verify, and build on top of shareable workflows. To achieve that, we will demonstrate a use case from the EU-funded project MINKE on how the cooperation between the metrology and the oceanographic community can lead to an improved data reliability and use to address wicked problems related to “Life below water” (SDG 14). MINKE focuses on data quality and interoperability and aims to improve the use of existing research infrastructures and stimulate collaborations across research fields and citizen science.
In this hands-on course, we will apply tools to publish reproducible research, including R, R Markdown, Binder, and git. Furthermore, we will touch upon issues related to the computational environment and data management, thus covering Open Science principles (e.g., open code and data). This course is open to everyone interested in reproducibility of R-based workflows. We invite participants to follow the use case on their laptops and experiment with the computational workflow. Basic knowledge in R is needed, whereas knowledge in the other technologies is recommended but optional. The workflows will be reproducible in the browser. While the use case is from MINKE, the reproducibility concepts are applicable to other scenarios based on computational workflows.
Please register: https://forms.gle/34uD45xH3UKY6tiHA

Almost all scientific studies rely to some extent on correct statistical analyses. While statistical software packages for scientists offer great opportunities and provide many powerful tools (e.g., in data mining and exploratory statistics), there are many pitfalls, which may result in wrong or nonreproducible manuscripts. This problem has been known for a long time and has been addressed explicitly in some research fields other than the geosciences. This short course aims to address potential problems in geoscientific studies and to reduce the number of non-reproducible studies.

A. Fundamental issues in design of experiments and statistical analyses
The following fundamental issues will be addressed:
• Time spent for experimental designs. Advantages and disadvantages of selected experimental designs. Missing randomization. Observational study vs. controlled experiments
• Pseudo-replication vs. true replications and how to deal with it. Wrong model formulations
• “Obsession” with p values: Statistical significance and geoscientific relevance
• Statistical tests: conditions for the application of modelling and hypothesis testing
• Dealing with suspected outliers
• Logistic vs. linear regression
• Number of experimental treatments vs. power of tests. Number of replicates required for predictive modelling
• Use and misuse of correlation analyses
• Investigating and dealing with interactions between factors or predictors

B. Selected advanced issues in geoscientific studies
The following topics will be addressed:
• Validation or cross-validation instead of a sole focus on calibration.
• Model types
• Use of contrasts instead of multiple mean testing
• Different experimental designs – completely randomized (CRD), randomized complete block (RCBD), Latin square (LSD), balanced incomplete bock (BIBD), and split plot design
• RCBD with one treatment factor: analysis of variance and mixed effects model
• Blocked observational study with one predictor: multiple linear regression and mixed effects model
• CRD, RCBD, LSD, split plot design and BIBD: advantages, disadvantages, equations and modelling
• Analysing nested (multi-stratum) designs

Examples will be shown using the programming languages R and SAS

Due to the continuous increase in geographical data set sizes and the number of computations that have to be performed in numerical modelling or data analyses, there is often a need to improve the performance and scalability of the software used. Developing such software can be challenging.

In this short course we will introduce the asynchronous many-tasks (AMT) approach, which can be used to develop software that performs and scales well over cores in a single computer as well as over nodes in a computer cluster. We will explain the general principles behind AMT, and show how the HPX C++ software library [1] can be used to develop an example algorithm, calculating hill shading from a digital elevation model, in parallel.

One advantage of using the HPX library is that it provides a single high-level API for performing parallel computations on both shared and distributed memory systems. This contrasts with a popular approach of using multiple APIs - and their associated programming models - for these, like OpenMP and MPI.

The HPX library is successfully being used in various HPC applications, one of which is the LUE numerical modelling framework [2, 3, 4]. With LUE model developers can implement their models using Python and execute them unchanged on their laptop or on a computer cluster.

The goal of this short course is to introduce the attendants to the principles behind AMT and the HPX library, and allow them to be able to decide whether the approach is applicable in their own use-cases. The short course is especially relevant for research software engineers, but we welcome everybody interested in the topic.

- [1] HPX website, https://hpx.stellar-group.org
- [2] LUE website, https://lue.computationalgeography.org
- [3] De Jong, K., Panja, D., Van Kreveld, M., Karssenberg, D. (2021), An environmental modelling framework based on asynchronous many-tasks: scalability and usability, Environmental Modelling & Software, doi: 10.1016/j.envsoft.2021.104998
- [4] De Jong, K., Panja, D., Karssenberg, D., Van Kreveld, M. (2022), Scalability and composability of flow accumulation algorithms based on asynchronous many-tasks, Computers & Geosciences, doi: 10.1016/j.cageo.2022.105083

Historical terrestrial oblique images are a unique and invaluable resource for quantifying early changes of the alpine environment after the Little Ice Age. Becoming available in the second half of the 19th century, these images are the only visual sources documenting our environment in its nearly unaltered state. Hence, historical terrestrial images pose an incredible potential for many research areas including botany, hydrology, glaciology and geomorphology. Despite their unprecedented potential, historical terrestrial images are seldom used. The processing is time consuming, requires basic knowledge in photogrammetry and available tools are often difficult to use. Hence, researchers often fear investing time considering the uncertain outcome. In this short course, participants will learn the basics of photogrammetry necessary to understand the underlying concepts. This will enable them to assess the potential and limitations of historical terrestrial images for their respective research prior to the processing. Together with the participants we will evaluate and explore freely available tools discussing their pros and cons, focusing on the processing of selected historical images. After the short course, participants will be able to decide on their own if historical terrestrial images can be a valuable asset for their research, knowing their potential and limitations. Further, they will be able to use the available tools to incorporate historical terrestrial images into their respective research.

Python is one of the most popular programming languages for data science and analytics, with a large and steadily growing community in the field of Earth and Space Sciences. In this short introductory course, we will help participants with a working knowledge of Python to familiarize themselves with the world of geospatial raster and vector data. We will introduce a set of tools from the Python ecosystem and show how these can be used to carry out practical geospatial data analysis tasks. In particular, we will consider satellite images and public geo-datasets and demonstrate how these can be opened, explored, manipulated, combined, and visualized using Python. The tutorial will be based on the lesson “Introduction to Geospatial Raster and Vector data with Python” [1], which is part of the Incubator program [2] of The Carpentries [3].

[1] https://carpentries-incubator.github.io/geospatial-python
[2] https://carpentries-incubator.org/
[3] https://carpentries.org

Python is an open-source language at the very forefront of climate science. To understand past, present and future climate, climatologists analyze and interpret large amounts of historical data obtained from multiple sources such as weather stations, radar, satellites or computer models, to name but a few. Therefore, Earth scientists spend a great deal of time processing multidimensional climate data in order to better understand and explain climate systems.

This short-course covers basic tools to get started with Python in climate science. For example, this short course will briefly touch upon subjects, such as (i) packages mosted used by climate scientists, (ii) Python for beginners, and (iii) data extraction, basic analysis, and visualization. Specifically, participants will become familiar with datasets and learn how to manipulate geospatial and multidimensional data from commonly used reanalysis climate datasets. Additionally, we will also cover how to take advantage of the powerful, versatile and widely used package Xarray (https://xarray.dev/) to apply simple operations over multidimensional data in just a few lines of code! By the end of the course, participants will be able to compute and visualize anomalies and climatologies.

This short-course promotes open-source and collaborative environments for climate scientists. To accomplish this goal, this course will be conducted using Jupyter notebooks in Google Colab. Participants are recommended to open a google account prior to the course. We expect all participants to have some basic programming experience (including basic knowledge of coding concepts such as loops, conditional statements, functions and data types, among others), but no previous exposure with Python language is necessary. Attendees will be provided with an installation guide, as well as with complementary examples (i.e., notebooks) to illustrate how useful these tools can be for a climate scientist.

We highly encourage early career researchers and programming enthusiasts in climate and wider environmental sciences to attend this course.

GOLDENEYE project (https://www.goldeneye-project.eu/) offers a multi-source Earth Observation Data (EOD) platform to improve mine safety, environmental footprint and overall profitability. The project as the main objective of developing the GOLDENAI platform for mine site monitoring.

The GOLDENAI platform considers the ingestion of different data sources into a data cube representation for a given area of interest (AOI). The platform consists of two main components: the back-end (mentioned as ‘OCLI’ hereafter) and the front-end (mentioned as ‘GOLDENAI GUI’ hereafter).

OCLI provides a pipeline that processes AI knowledge packs (AIKPs) and handles the data mining process. It involves automated modules for data acquisition, data preprocessing, image processing (e.g., denoising, edge detection, etc.), and AI processing. In particular, for a given area of interest (AOI), it first collects the related satellite data products from the DIAS services, Euro Data Cube (EDC), EOS or Supplier’s API. Then, it prepares analysis ready data (ARD) by performing data cleaning, transformation and filtering.

The GOLDENAI GUI acts as a general repository of data in Cloud Optimized GeoTIFF (COG) format (processed from OCLI) which is straightforward to explore and exploit by the platform users. The GUI is an architectural approach for implementing a modern 2D and 3D visualization platform. It’s powered by OGC web services and deliver EO, Drone (UAV) Sensing and Proximal Sensing data. This data could be interactively explored by the public and mine site owners, with different authentication and authorization access levels, in an easy, quick and efficient way as assisted by a conversational AI agent.

In this short course, we will briefly demonstrate how OCLI works, the main concepts and the structure and components of project tasks integrating AIKPs dedicated to different use cases in the mining sector. We will also explore the functionalities of the GOLDENAI GUI. All participants can register in the GUI and then try to apply it to a use case they are interested in. This course is both for the novices as well as for data-analysis experts.

The catalogue of marine data and services available from EUMETSAT continues to grow. Between mandatory missions and those operated under the Copernicus programme, and their respective downstream services, the opportunities for users to access data relevant for marine applications have never been greater. However, with increasing volume and diversity of data comes challenges. This short course will provide an overview of the suite of services and training resources available from EUMETSAT to support users to work with data relevant to the marine community. There will be a strong focus on practical aspects of accessing and working with data, with a particular focus on open source tools. The course will support participants in the use of suite of Python based Jupyter notebooks, and API clients, in both local and cloud computing environments. Trainers will be available to support participants in designing their own workflows for using satellite data in their own marine applications.

Participants will learn:
- What data is available from EUMETSAT via its mandatory and Copernicus missions, satellite applications facilities, and through contributions to the Copernicus services. There will be a strong focus on the Sentinel-3 and 6 missions.
- How to access data using EUMETSATs data access services, including harmonised data access through the Copernicus WEkEO service.
- How to work with data using open source tools, based around repositories of Python based code and Jupyter Notebooks.
- About the options presented to work with Copernicus data by cloud computing in WEkEO.

Open Science is a redefinition of scientific collaboration and output around principles and values of transparency, rigor, inclusivity, and trust. It is a culture designed to promote science and its social impact. It reflects how science has evolved into 21st Century, including the huge growth in data, instrumentation, computational power and resources, and complexity as well as its importance for addressing large societal challenges. Open science creates new opportunities for all stakeholders including researchers, funders, institutions, decision makers, and public participants, and communities.
In this short course, we will introduce participants to Open Science, the ecosystem that supports Open Science, and the values, practices and tools that enable that ecosystem. Participants will have the opportunity to explore the practical impact of Open Science, the tools that advance research and collaboration. This course is designed for researchers new to open science, open science practices and tools that enable and support open science.
Participants in this course will be able to define open science, discuss the benefits and challenges of open science, and identify the practices that enable open science. Participants will be able to identify tools and resources that can be used to practice open science in their own research. Participants will be able to develop a plan to implement open science practices in their own contexts.

If taken with Practicing Open Science: Data, Software, and other Results, participants will gain a broad overview of open science and how to practice it with immediately applicable actions.

Access to open data, open software and open results is important for transparency and supports reproducibility of research findings. It is critical to supporting disaster emergency responses all over the world, to advancing the response to the global pandemic, to advancing science in response to big and small questions, and making science more inclusive, impactful, and focused on the public good.

This course is designed to introduce researchers to the practices, characteristics, and benefits of open data, open software, and open results via the researcher workflow and research life-cycle. This course is an opportunity to review key practices that support preservation, sharing, using, and attribution of open data, software, and other results to advance science.

Participants will be able to articulate the definitions and characteristics of open data as well as the concepts of metadata, primary, and secondary data. They will be able to identify open software practices and resources for sharing, use, maintaining, and evolving open software while using open software to streamline workflow. Participants will be able to explain how, when, and where to make research outputs open and accessible while discussing the challenges and benefits of open results practices. Finally, participants will be able to create a plan to implement open research in their contexts.

If taken with Practicing Open Science: The principles, ecosystem, and tools, participants will gain a broad overview of open science and how to practice it with immediately applicable actions.

Why do so many early career scientists find it such a challenging task to create a well-written and eye-catching illustrated research paper? Why are articles mostly lacking coherence among story and visual aids?

Well, writing research articles is different from just reporting your field or lab work or pulling up some graphs out of a hat. In brief, one must put together a concise presentation on paper that has to invite the reader, be engaging to read, and be graphical attractive to the audience. To draw readers to your articles, one must pair the seven graphic design principles with the five key characteristics of scientific writing.

If you feel overwhelmed with scientific writing, need more structure, or just want to improve your publishing skills, this course is for you. If you are looking for a few hacks that could improve your graphic and writing skills, we have you covered.

You will discover how to break down the article creation into clearly defined tasks. You will be shown how writing and graphic design can evolve together into a harmonizing piece of literature that your target audience will enjoy while saving you time in the progress. Scroll up and click the “star” to add this course to your personal program.

The visualization and user-friendly exploration of information from scientific data is one of the main tasks of good scientific practice. But steady increases in temporal and spatial resolutions of modeling and remote sensing approaches lead to ever-increasing data complexity and volumes. On the other hand, earth system science data are getting increasingly important as decision support for stakeholders and other end users far beyond the scientific domains.

This poses major challenges for the entire process chain, from data storage to web-based visualization. For example, (1) the data has to be enriched with metadata and made available via appropriate and efficient services; (2) visualization and exploration tools must then access the often decentralized tools via interfaces that are as standardized as possible; (3) the presentation of the essential information must be coordinated in co-design with the potential end users. This challenge is reflected by the active development of tools, interfaces and libraries for modern earth system science data visualization and exploration.

In this session, we hence aim to establish a transdisciplinary community of scientists, software-developers and other experts in the field of data visualization in order to give a state-of-the-art overview of tools, interfaces and best-practices. In particular, we look for contributions in the following fields:

- Developments of open source visualization and exploration techniques for earth system science data
- Co-designed visualization solutions enabling transdisciplinary research and decision support for non-scientific stakeholders and end-users
- Tools and best-practices for visualizing complex, high-dimensional and high frequency data
- Services and interfaces for the distribution and presentation of metadata enriched earth system science data
- Data visualization and exploration solutions for decentralized research data infrastructures

All contributions should emphasize the usage of community-driven interfaces and open source solutions and finally contribute to the FAIRification of products from earth system sciences.

Earth observation (EO) technologies are valuable tools for providing the evidence necessary for decision making through the systematic monitoring, prediction and assessment of natural resources in a wide range of spatial and temporal scales, covering a range of multidisciplinary scientific communities and related applications.
Novel integrated systems can emerge by combining EO technologies with other sources of data and modeling tools that improve the availability, access and use of EO for a sustainable planet. With the access to EO data archives, past dynamics and trends can be identified and enable the training of dynamic models that can detect and predict various incidents. Both, monitoring and mapping are essential components of designing appropriate policies to prevent, for example, desertification and accelerate soil and water quality restoration.
The objective of this session is to explore the main challenges and the future directions of EO-driven approaches in two main pillars: environment and resilient society. A non-complete list of possible applications includes:
- develop decision making tools for improving agribusiness productivity, optimization of land and water management, explore the spatio-temporal dynamics of ecohydrological processes
- provide predictions of precipitation and monitor the meteorological drought using radar remote sensing
- investigate the interactions between atmospheric mechanisms and solar-related applications in a wide range of scales
- estimate the variations of sea surface levels with the use of satellite altimetry and tide gauge measurements
- monitor and model the evolution of aerosol and clouds in their natural environment using atmospheric remote sensing multi-platforms
- assess the risks to cultural heritage sites and critical infrastructure (CH/CI) due to natural hazards (i.e., fires, floods, earthquakes, etc.), propose preventive measures, integrate different sources of tools and data (e.g. EO imagery, machine learning, and geo-information data) for CH/CI and archaeolandscapes.
- Detect forest phenological changes and forest disturbances via various EO data (radar, multispectral, hyperspectral), identify possible abrupt changes in the forest phenology trend
- elaborate large amount of data using modelling tools for predicting and monitoring land, water and climate changes, and infer human origins and archaeological networks through the vast amount of EO data with the use of pattern recognition techniques.

Downscaling aims to process and refine global climate model output to provide information at spatial and temporal scales suitable for impact studies. In response to the current challenges posed by climate change and variability, downscaling techniques continue to play an important role in the development of user-driven climate information and new climate services and products. In fact, the "user's dilemma" is no longer that there is a lack of downscaled data, but rather how to select amongst the available datasets and to assess their credibility. In this context, model evaluation and verification is growing in relevance and advances in the field will likely require close collaboration between various disciplines.

Furthermore, epistemologists have started to revisit current practices of climate model validation. This new thread of discussion encourages to clarify the issue of added value of downscaling, i.e. the value gained through adding another level of complexity to the uncertainty cascade. For example, the ‘adequacy-for-purpose view’ may offer a more holistic approach to the evaluation of downscaling models (and atmospheric models, in general) as it considers, for example, user perspectives next to a model’s representational accuracy.

In our session, we aim to bring together scientists from the various geoscientific disciplines interrelated through downscaling: atmospheric modeling, climate change impact modeling, machine learning and verification research. We also invite philosophers of climate science to enrich our discussion about novel challenges faced by the evaluation of increasingly complex simulation models.

Contributions to this session may address, but are not limited to:

- newly available downscaling products,
- applications relying on downscaled data,
- downscaling method development, including the potential for machine learning,
- bias correction and statistical postprocessing,
- challenges in the data management of kilometer-scale simulations,
- verification, uncertainty quantification and the added value of downscaling,
- downscaling approaches in light of computational epistemology.

Environmental systems often span spatial and temporal scales covering different orders of magnitude. The session is oriented toward collecting studies relevant to understand multiscale aspects of these systems and in proposing adequate multi-platform and inter-disciplinary surveillance networks monitoring tools systems. It is especially aimed to emphasize the interaction between environmental processes occurring at different scales. In particular, special attention is devoted to the studies focused on the development of new techniques and integrated instrumentation for multiscale monitoring of high natural risk areas, such as volcanic, seismic, energy exploitation, slope instability, floods, coastal instability, climate changes, and another environmental context.
We expect contributions derived from several disciplines, such as applied geophysics, geology, seismology, geodesy, geochemistry, remote and proximal sensing, volcanology, geotechnical, soil science, marine geology, oceanography, climatology, and meteorology. In this context, the contributions in analytical and numerical modeling of geological and environmental processes are also expected.
Finally, we stress that the inter-disciplinary studies that highlight the multiscale properties of natural processes analyzed and monitored by using several methodologies are welcome.

Observations from aircraft, remotely piloted aircraft systems (RPAS/UAV/UAS) and balloons are an important means to obtain a broad view of processes within the Earth environment during measurement campaigns. The range of available instruments enables a broad and flexible range of applications. It includes sensors for meteorological parameters, trace gases and cloud/aerosol particles and more complex systems like high spectral resolution lidar, hyperspectral imaging at wavelengths from the visible to thermal infra-red, solar-induced fluorescence and synthetic aperture radar. The use of small state-of-the-art instruments, the combination of more and more complex sets of instruments with improved accuracy and data acquisition speed enables more complex campaign strategies even on small aircraft, balloons or RPAS.
Applications include atmospheric parameters, structural and functional properties of vegetation, glaciological processes, sea ice and iceberg studies, soil and minerals and dissolved or suspended matter in inland water and the ocean. Ground based systems and satellites are key information sources to complement airborne datasets and a comprehensive view of the observed system is often obtained by combining all three. Aircraft and balloon operations depend on weather conditions either to obtain the atmospheric phenomenon of interest or the required surface-viewing conditions and so require detailed planning. They provide large horizontal and vertical coverage with adaptable temporal sampling. Future satellite instruments can be tested using airborne platforms during their development. The validation of operational satellite systems and applications using airborne measurements has come increasingly into focus with the European Copernicus program in recent years.
This session will bring together aircraft, balloon and RPAS operators and researchers to present:
• an overview of the current status of environmental research focusing on the use of airborne platforms
• recent observation campaigns and their outcomes
• multi-aircraft/balloon/RPAS and multi-RI campaigns
• using airborne and ground-based RI to complement satellite data, including cal/val campaigns
• identifying and closing capability gaps
• contributions of airborne measurements to modelling activities
• airborne platforms to reduce the environmental footprint of alternative observation strategies
• airborne instruments, developments and observations
• future plans involving airborne research

Remote sensing (RS) plays a fundamental role in the impact analysis and mitigation of the impacts of climate change and human activities, supporting the achievement of Sustainable Developments Goals of United Nations (e.g. SDG2, SDG11, SDG13, SDG15). For three decades, RS from satellite has been established as a powerful monitoring tool able to cover extended areas at low cost and with regular revisit capability. However, to face the current and expected future increase in the frequency of natural hazards, new technologies have been developed with the aims to improve the flexibility in data collections and resolution. A branch of these new developed technologies are the uncrewed aerial systems (UASs) equipped with different sensors (optical, microwave, near-infrared, thermal infrared sensors). They allow bridging the gap among spaceborne and ground-based RS data providing ultra-high resolution spatial data, with a significant advantage on the flexibility of flight scheduling and the environmental data collection. These multi-source UAS-sensing data drive new developments in the field of RS applications: the mapping of the modification induced by climate change, as by the erosion and landslides, by tectonic, volcanic or human processes, as well as the improvement of crop monitoring to support a sustainable precision agriculture. On those bases a number of synergy was observed between the “sensing technologies” in the Geosciences community. The session will really focus on the several aspects of this cooperation in terms of technology and team-work and how they answer to the needing of the SDG of United Nations. In this context, we encourage who is involved concurrence development and applications to show their most recent findings focused for example on: (i) reviewing the trends of satellite RS in terrain surveys before and after the geological phenomena in integration with UAS measurement systems; (ii) UAS configuration and specifications for precision agriculture, vegetation management; (iii) the changes with the use of UAS, for those doing remote measurement with the ability to control every segment of the RS chain.

There are multiple environmental pathways that impact human, animal, and plant health. Increasing climatic variability, including extreme weather events, coupled with human-environmental interactions leads to increased risks of disease outbreaks including vector- (e.g. Zika, Dengue, Chikungunya, Malaria, Rift Valley Fever), water- (e.g. Cholera, Dysentery, Typhoid) and air-borne (e.g. Coronavirus, Influenza) diseases. These phenomena have a spatiotemporal distribution driven by the interactions of climate and environmental variables (e.g. precipitation, specific humidity, runoff, vegetation indices) with that of the vectors and hosts of each individual disease. This session is seeking research that advances the state-of-the-art in disease early warning. This can range from developing the system for which these disease models can reside to advancing the science behind individual routes of transmission using climatic, weather, and remote sensing data products.

Ground-based networks for monitoring of atmospheric chemical composition and meteorology improve our understanding of local, regional, and continental scale atmospheric events and long-term trends, and inform decisions critical to air quality, climate change, weather forecasting, and human health. Monitoring networks serve an important role within the research community, providing a backbone of data to support modeling, satellite data product validation, and short-term measurement campaigns. Ongoing collaboration, communication, and promotion of monitoring network developments and data products is necessary in order to fully leverage the bene t from such networks. This session explores how ground-based atmospheric monitoring networks can be utilized to:
- promote cross-network and -discipline engagement
- develop and test new technologies and sensors
- expand quality assurance methods and techniques
- support modelling and satellite data products

Remote sensing measurements, acquired using different platforms - ground, UAV, aircraft and satellite - have increasingly become rapidly developing technologies to study and monitor Earth surface, to perform comprehensive analysis and modeling, with the final goal of supporting decision systems for ecosystem management. The spectral, spatial and temporal resolutions of remote sensors have been continuously improving, making environmental remote sensing more accurate and comprehensive than ever before. Such progress enables understanding of multiscale aspects of high-risk natural phenomena and development of multi-platform and inter-disciplinary surveillance monitoring tools. The session welcomes contributions focusing on present and future perspectives in environmental remote sensing, from multispectral/hyperspectral optical and thermal sensors. Applications are encouraged to cover, but not limited to, the monitoring and characterization of environmental changes and natural hazards from volcanic and seismic processes, landslides, and soil science. Specifically, we are looking for novel solutions and approaches including the topics as follows: (i) state-of-the-art techniques focusing on novel quantitative methods; (ii) new applications for state-of-the-art sensors, including UAVs and other close-range systems; (iii) techniques for multiplatform data fusion.

Instrumentation and measurement technologies are currently playing a key role in the monitoring, assessment and protection of water resources.
This session focuses on measurement techniques, sensing methods and data science implications for the observation of water systems, emphasizing the strong link between measurement aspects and computational aspects characterising the water sector.
This session aims at providing an updated framework of the observational techniques, data processing approaches and sensing technologies for water management and protection, giving attention to today’s data science aspects, e.g. data analytics, big data, cloud computing and Artificial Intelligence.
Building a community around instrumentation & measurements for water systems is one of the aims of the session. In particular, participants to the EGU2020 edition of this session contributed to this book: A. Di Mauro, A. Scozzari & F. Soldovieri (eds.), Instrumentation and Measurement Technologies for Water Cycle Management, Springer Water, ISBN: 978-3-031-08261-0, 2022.
We welcome contributions about field measurement approaches, development of new sensing techniques, low cost sensor systems and measurement methods enabling crowdsourced data collection also through social sensing. Therefore, water quantity and quality measurements as well as water characterization techniques are within the scope of this session.
Remote sensing techniques for the monitoring of water resources and/or the related infrastructures are also welcome.
Contributions dealing with the integration of data from multiple sources are solicited, as well as the design of ICT architectures (including IoT concepts) and of computing systems for the user-friendly monitoring of the water resource and the related networks.
Studies about signal and data processing techniques (including AI approaches) and the integration between sensor networks and large data systems are also very encouraged.