The goal of the eWaterCycle project is to facilitate hydrological modelling being done Findable, Accessible, Interoperable & Reproducible (FAIR).

High (hyper) resolution and / or large sample hydrological modelling, including those driven by Destination Earth (DestinE) Digital Twin (DT) inputs, often require HPC infrastructure for model runs. Designing such studies, however, benefit from users working on interactive Cloud Infrastructures. Migrating workflows from Cloud Infrastructure to HPC infrastructure requires deep knowledge of the systems in place, which typical (hydrological expert) users don’t have. A core design philosophy of the eWaterCycle platform is that domain (hydrology) users should not need to become computer science experts to carry out their hydrological research.

To address this, we have developed a workflow that seamlessly upscales any hydrological workflow designed on Cloud Infrastructure to a SLURM high performance compute cluster, with small changes compared to working from the cloud environment: setting up paths (e.g. scratch folders) and supplying key argument parameters (e.g. the specified region). Users are not required to have any prior knowledge of HPC systems beforehand.

This 'seamless' workflow can be run from any jupyterhub environment. We are using and are in development of integrating with the services that are part of DestinE.

As a ‘large sample hydrology’-example: we run a climate change impact on flood frequency analysis on each of the 6830 catchments in the entire . We facilitate using ERA5, ERA-Interim and CMIP6 data as well as the data provided by the Digital Twin (DT) as input to these model runs. In this presentation we will be sharing our results obtained using eWaterCycle with the DT data. This workflow will serve as an example of our seamless scaling between Cloud Infrastructure and HPC systems and provide lessons learned for others setting up similar services.

How to cite: Melotto, M., Hut, R., and Vitolo, C.: Hydrological simulations with seamless scaling between Could and High Performance Computing environments on DestinE from the comfort of your own browser using eWaterCycle., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3863, https://doi.org/10.5194/egusphere-egu26-3863, 2026.

Data accessibility is crucial in modern research across the Natural and Engineering Sciences (NES), including Geosciences, and is central to the push toward Open Science. Yet, accessing and efficiently processing rapidly growing datasets, such as Earth-related spatiotemporal data, remains challenging as sources diversify and collection frequency increases. Most of these datasets are hosted on the Cloud, and cloud-native data access and processing are ramping up as modern digital competences. Cloud-based processing is especially beneficial because bringing computation close to the data boosts efficiency and reduces analysis time. Despite this, many researchers still rely on the inefficient approach of downloading data for local analysis. Sometimes this is unavoidable because the data is not provided in cloud-friendly formats, but often it also reflects a lack of skills for cloud-based access and processing. A similar problem occurs in data publishing, where research datasets are frequently shared in formats that impede efficient cloud access and interoperability, even though cloud-optimized formats could be used at no additional cost.

The CLOUD-NES project, funded by the Dutch Research Council (NWO) via the Thematic Digital Competence Centre NES (TDCC-NES), aims to advance cloud-native tools and workflows for publishing, accessing, and processing research data in the Netherlands. The project demonstrates the benefits of cloud-native approaches through reproducible performance benchmarks and equipes researchers with practical training to strengthen digital competencies. A public cloud-native data repository with co-located analysis capabilities is being developed, featuring object-based scalable storage and STAC-compliant data catalog, and hosting selected datasets from large-scale geospatial data providers in the Netherlands such as PDOK and KNMI, transformed into cloud-optimized formats. Through iterative benchmarking, we are assessing the performance of cloud-native storage formats, access patterns, and analysis workflows, generating reproducible evidence of efficiency gains to support community adoption. All infrastructure, ingestion pipelines, and benchmarking code will be open-source, accompanied by detailed guidelines and documentation. To further accelerate adoption, domain-specific open training materials will be developed and hands-on workshops for researchers and data providers will be organized. Training covers cloud-native data access, workflow design, dataset publishing, and infrastructure deployment, using common domain-specific workflows as case studies. Community events and mini symposia will foster community building and knowledge exchange, while lessons learned and best practices will be disseminated nationally and internationally.

By combining demonstrable benchmarks, practical training, and clear guidance for data providers, CLOUD-NES aims to accelerate the adoption of cloud-native research practices across the Dutch research community and beyond, improving efficiency, reproducibility, and accessibility of large, complex datasets. This presentation provides an overview of the CLOUD-NES project, covering the design and operation of its reproducible cloud-native benchmarking framework and the structure of its open training materials. Planned project activities, including community-building events and mini-symposia on effective cloud-native practices, will also be highlighted.

How to cite: Girgin, S., Nattino, F., Brandt, M., and Plieger, M.: Advancing cloud-native data access and processing for Natural and Engineering Sciences: CLOUD-NES, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21049, https://doi.org/10.5194/egusphere-egu26-21049, 2026.

How to cite: Fornari, F., Antonacci, M., Pisa, C., Baousis, V., Kaprol, T., and Albughdadi, M.: A Cloud-Based Infrastructure for EO-Driven Climate Resilience Services , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7440, https://doi.org/10.5194/egusphere-egu26-7440, 2026.

Modern climate archives are increasingly distributed across heterogeneous storage systems, while analysis workflows are becoming more interactive, distributed, and cloud-native. Moreover, many high-performance computing centres (HPC) host large climate datasets on traditional file-based storage infrastructures, whereas computational resources are often located at different sites. This separation between data location and compute resources creates significant barriers to efficient, interactive, and scalable data access.

This situation calls for climate data access services that are scalable, flexible, and independent of specific client-side environments, while supporting common climate data formats such as NetCDF, GeoTIFF, Zarr, HDF5, and GRIB. Nevertheless, efficient remote access to large and heterogeneous climate archives remains a major bottleneck for modern scientific workflows.

We present aservice, the Freva Data Loader, which implements the logic required to open datasets from diverse storage backends and expose them as Zarr chunks  through a lightweight, web-friendly REST interface with modern authentication mechanisms.

The Freva Data Loader is implemented as a stateless, worker service exposing a REST interface for dataset access and Zarr endpoint generation. Upon receiving an authenticated request, the service resolves dataset metadata, opens the underlying data from the appropriate storage backend (e.g. POSIX file systems or object storage), and exposes the data as a Zarr-compatible, chunked stream. Authentication and authorisation are handled centrally using OAuth2, ensuring secure and controlled access across institutional boundaries. Requests are coordinated by a Loader component and distributed to worker instances via a message broker (Redis), enabling asynchronous execution and horizontal scalability.

The service decouples data access from client-side tooling and enables users and applications to access data stored on traditional posix HPC file  systems, tape archives, as well as cloud-based object storage through a unified Zarr interface. Instead of transferring complete files between data centres or downloading them in full, clients retrieve only the required data chunks on demand. Users and client applications can request chunked array access over the network and process data incrementally, supporting interactive exploration and scalable downstream computation using cloud-native, chunked storage semantics, while remaining compatible with existing analysis stacks based on Zarr, xarray, and Dask.

How to cite: Hadizadeh, M., Bergemann, M., Lucio Eceiza, E., Fast, A., and Kadow, C.: A Service-Oriented Distributed Zarr Solution for Climate Data Access Across Heterogeneous HPC and Storage Infrastructures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9640, https://doi.org/10.5194/egusphere-egu26-9640, 2026.

Programmatic access to remote high-volume multi-dimensional geophysical data was nearly impossible before the advent of high-speed networks and public cloud storage. Even then, data often had to be made "analysis-ready" before such access was possible. However, once analysis ready data is available, remote access becomes possible, with only the bytes needed by the client transferred across a network to the local client. In many cases such access will be faster and more energy efficient than downloading the entire dataset that contains the relevant variables (or parts of variables). Additionally, even when it is not more efficient than downloading data on a case-by-case basis, it may not be possible to locally cache the data, and remote access may be the only possibility. Hence, the notion of analysis ready data has become very popular, and this has often been understood to mean "made available on an object store in Zarr format". However, the key aspects of analysis ready data can be delivered via other interfaces and formats, provided the right software stack is available.  Here we present such a stack in the context of how we expect to enable remote access to NetCDF4 data from the upcoming CMIP7 Assessment Fast Track (and other data to be held in the newly upgraded Earth System Grid Federation, ESGF). The new ESGF will expose data via http servers which will support remote range-get to portions of files, which essentially provides the same remote access capabilities as an object store. The requirements for using such a stack, for the new ESGF, and for object stores  are (1) the data to be accessed must be appropriately chunked (partitioned into suitably dimensioned hyperslabs), (2) the chunk indices must be efficiently stored, and (3) the reading software using tools such Dask must be fully parallelisable. If either of the first two criteria are not met, data access can be impossibly slow even for relatively small problems, and if the third is not met, large problems cannot be efficiently addressed.  To address the first two issues, we present:  `cmip7-repack` a tool to ensure that key aspects of the  CMIP7 data are chunked appropriatel
y; `pyfive`,  a pure-Python thread-safe library for reading HDF data performantly in both serial and parallel applications; and a `pyfive`-enabled version of the `h5netcdf` library for facilitating remote and/or parallel data access using the NetCDF4 API.  With these tools we are able to show that reformatting data from the NetCDF4 data preferred by modellers into additional formats such as Zarr, and/or maintaining duplicate copies of chunk indices made by tools such as kerchunk, will no longer be necessary for most workloads.

How to cite: Hassell, D., Predoi, V., Lawrence, B., Cimadevilla, E., and Mühlbauer, K.: Supporting remote access to HDF5 datasets, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21900, https://doi.org/10.5194/egusphere-egu26-21900, 2026.

Geoscience research faces enormous data growth, larger, more versatile datasets from satellites, IoT devices, and measurement instruments. To make full use of these data opportunities and the demand for integrated analysis, there is a need for new IT-solutions. SURF, the Dutch national digital infrastructure provider for research and education, is investigating a data lakehouse architecture in the context of an innovation project and the project of the SAGE European Green Deal Data Space (https://www.greendealdata.eu/). In SAGE we collaborate with geoscientists from the Department of Geography at Utrecht University in processing heterogeneous environmental monitoring datasets into data products for further research.  

The data lakehouse architecture combines the flexibility of a datalake for handling heterogeneous data and ML workflows with the properties of a database (ACID transactions) and the governance of data warehouses. We explore this architecture using SURF services, like the object store, and open-source software from existing geoscience ecosystems like Pangeo and Earthmover. The exact properties of the data lakehouse depend on the software packages used. We present the lakehouse solution for UU use-case of serving and publishing exposome data products. Currently, data-processing of the data products is handled by a batch service. We will discuss how the lakehouse architecture could be extended to both serve the resulting data products and cover the processing stage and subsequent analysis-workflows. 

How to cite: Griffioen, R., Loffredo, L., Bood, R.-J., Oonk, R., Kuipers, E., Schmitz, O., and Karssenberg, D.: A data lakehouse solution for geoscience workflows, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12863, https://doi.org/10.5194/egusphere-egu26-12863, 2026.

How to cite: Bhawiyuga, A., Girgin, S., de By, R. A., and Zurita-Milla, R.: Energy Efficiency in Cloud-Based Earth Observation Data Processing: Gap Analysis and Research Directions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18770, https://doi.org/10.5194/egusphere-egu26-18770, 2026.

Earth System Models (ESMs) produce output on a wide range of structured and unstructured grids. However, exploring these heterogeneous datasets remains challenging, often requiring specialized software, access to high-performance computing resources, or time-consuming regridding to regular latitude-longitude grids that can introduce interpolation artifacts.

We present GridLook, an open-source, browser-based WebGL visualization tool that enables interactive exploration of cloud-hosted Zarr datasets directly on their native grids without any software installation. GridLook leverages the Pangeo ecosystem by consuming Zarr stores from any CORS-enabled cloud storage (including S3, Swift, and Google Cloud), making it immediately compatible with FAIR data principles and cloud-native workflows.

Key features include: (1) client-side GPU rendering; (2) automatic grid type detection from CF-compliant metadata, supporting a wide variety of grids; and (3) shareable URLs that encode the complete visualization state including dataset location, variable selection, and view parameters.

The architecture follows a serverless design in which all rendering occurs in the user’s browser, removing the need for backend infrastructure and enabling real-time interaction with large datasets through Zarr's chunked access patterns. By combining cloud-native data formats (Zarr), standardized metadata conventions (CF), and modern web technologies (WebGL), GridLook reduces time-to-plot and supports lightweight, shareable visualization workflows. This facilitates rapid visual inspection of model output by both data users and model developers, enabling quicker communication of spatial features and identification of potential bugs during model development.

The tool is freely available at https://gridlook.pages.dev with source code on GitHub, and we invite community contributions for additional grid types and features.

How to cite: Fast, A., Kölling, T., and Wachsmann, F.: GridLook: A browser-based ESM data-viewer, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9862, https://doi.org/10.5194/egusphere-egu26-9862, 2026.

Discrete Global Grid Systems (DGGS) and in particular HEALPix have become increasingly popular in the Earth sciences over the past few years, mainly due to its equal-area nature and readily available python libraries. However, this adoption and the ever increasing amounts of data come with its own set of challenges. In particular, the existing python libraries are written for use in astronomy and thus only work on a sphere, resulting in small but often non-negligible variations in the cell areas when applied to the surface of the earth.

Additionally, a large spatial coverage at high resolutions require a large amount of memory just to represent the spatial information in memory (e.g. roughly 100GB for full Earth coverage at 100 m resolution).

Finally, the storage format of HEALPix was not standardized until very recently with the release of the CF conventions version 1.13 and the upcoming zarr DGGS convention.

We present healpix-geo, a HEALPix implementation library for python built on top of the cdshealpix, moc, and geodesy rust crates with minimal python dependencies (numpy and optionally shapely). It supports the most common HEALPix indexing schemes (nested, ring, zuniq), allows the conversion of cell indices to and from ellipsoidal coordinates, and contains a range-based data structure suitable to index a large amount of cells with a small memory footprint.

We further show how healpix-geo integrates with xdggs, an xarray extension that enables high-level interaction with DGGS datasets, including efficient subsetting, analysis-ready representations, and visualization within Pangeo workflows. 

 xdggs also provides an extensible mechanism to easily import/export DGGS data from/to a variety of models or conventions, with built-in support of the CF HEALPix conventions and the zarr DGGS conventions. Together, healpix-geo and xdggs provide an end-to-end, standards-aligned pathway for scalable HEALPix-based geospatial analysis on the ellipsoid.

How to cite: Magin, J., Bovy, B., Richard, P., Delouis, J.-M., and Odaka, T.: Ellipsoidal HEALPix for the Earth sciences: healpix-geo and xdggs integration into the Pangeo ecosystem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18208, https://doi.org/10.5194/egusphere-egu26-18208, 2026.

The quality of analysis-ready Earth Observation (EO) products strongly depends on the ability of the processing chain to accurately model the mapping from the Earth’s surface to the detector geometry. This mapping involves a convolution with the instrumental Point Spread Function (PSF) and an orthorectification step that corrects for terrain-induced geometric distortions using a Digital Elevation Model (DEM).

These distortions are challenging, as they can lead to spatially varying PSF deformations. Even worse, some areas with strong topographic gradients lead to an effective PSF with multiple modes and may cause the failure of standard operational orthorectification algorithms.

To anticipate these failures, we introduce critical incidence maps. For a given point on a DEM, such a map provides the maximum sensor incidence angle at which this point remains visible, i.e. not occluded by surrounding terrain in any azimuthal direction. We show that, for moderate incidence angles (typically below ~20°), rugged areas responsible for orthorectification failures cover only a very small fraction of Earth’s surface, leaving therefore ample room for more robust algorithms to tackle these thorny points.

For smooth regions, we introduce and compare various semi-analytical orthorectification schemes that achieve appealing trade-offs between computational cost and geometric precision. We then combine two distinct orthorectification strategies, tailored respectively to smooth and rugged terrain, and express the overall ground-to-detector mapping as a sparse linear operator.

This formulation yields an efficient forward model that accurately captures terrain-induced PSF distortions, including multimodality. Finally, we apply this model to invert the data projection, from the detector to the Earth’s surface, in the context of a HEALPix discrete grid.

How to cite: Richard, P., Delouis, J.-M., Magin, J., and Odaka, T.: Modeling orthorectification and PSF distortions on a HEALPix grid, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17943, https://doi.org/10.5194/egusphere-egu26-17943, 2026.

JupyterGIS is an open-source web-GIS (Geographic Information System) designed to bring the iterative and interactive workflows of Jupyter to geospatial data analysis. By leveraging the Jupyter ecosystem, it seamlessly interleaves code and visualization, providing access to the vast range of existing geospatial libraries and interfaces.

The architecture of JupyterGIS is based on a single, serializable JSON project document that encapsulates all project information. This document is
implemented as a collaborative Conflict-free Replicated Data Type (CRDT), a "ydoc," ensuring real-time synchronization when edited by multiple instances or components simultaneously. This design enables teams to collaboratively work on geospatial data in real time, a feature particularly valuable for organizations. Additionally, it opens possibilities for coediting with LLM-based AI agents, greatly expanding the potential for automation and advanced analysis.

JupyterGIS offers very flexible deployment options. It can run on high-performance backend servers, including scalable Kubernetes clusters, to handle large-scale datasets and computationally intensive tasks, such as those commonly encountered in Earth Observation applications. Or, it can be deployed as a static website via WebAssembly and JupyterLite, executing computations directly in the user's browser. The latter eliminates the need for any backend infrastructure, making JupyterGIS suitable for creating embeddable, highly scalable, and accessible applications, such as lightweight embedded maps.

Young, initiated in 2024, JupyterGIS is a rapidly growing project that has garnered significant attention, community contributions, and organizational support, bundled in the Pangeo and GeoJupyter initiatives. As a fully open-source and sovereign solution, it provides a self-hostable alternative to proprietary platforms. This is particularly advantageous for handling sensitive data, as all components are auditable and under the user's control. Its modular and extensible architecture also ensures easy integration into existing systems and adaptability to new use cases.

JupyterGIS thus serves multiple roles for working with geospatial data: as a local or remote Integrated Development Environment (IDE), as an interface integrated into large-scale organizational portals, and as an embedded solution for small maps and web applications.

Our overview of JupyterGIS will include its underlying architecture, showcase the UI and features with examples, comparing its strengths and weaknesses to other platforms. The goal is to provide a comprehensive understanding of this novel tool, enabling listeners to assess its applicability to their use cases and to guide them on how to get started.

How to cite: Corlay, S., Meschede, M., Renou, M., Mooney, G., and Verma, A.: JupyterGIS: A Flexible, Open-Source Platform for Geospatial Analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18919, https://doi.org/10.5194/egusphere-egu26-18919, 2026.