Solar phenomena such as flares, coronal mass ejections (CMEs), and solar energetic particles (SEPs) are actively monitored and assessed for space weather hazards. In recent years, machine learning has demonstrated considerable success in solar flare forecasting. Accurate SEP forecasting remains challenging in space weather monitoring due to the complexity of SEP event origins and propagation. We introduce SEPNET, an innovative multi-task neural network that integrates forecasting of solar flares and CME summary statistics into the SEP prediction model, leveraging their shared dependence on space-weather HMI active region patches (SHARP) magnetic field parameters. SEPNET incorporates long short-term memory and transformer architectures to capture contextual dependencies. The performance of SEPNET is evaluated on the state-of-the-art SEPVAL SEP dataset and compared with classical machine learning methods and current state-of-the-art pre-eruptive SEP prediction models. The results show that SEPNET achieves higher detection rates and skill scores while being suitable for real-time space weather alert operations.

How to cite: Chen, Y., Yu, Y., Zhao, L., Whitman, K., Manchester, W., and Gombosi, T.: SEPNET: a multi-task deep learning framework for SEP forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11945, https://doi.org/10.5194/egusphere-egu26-11945, 2026.

Many geoscientific datasets, such as those produced by climate and weather models, are stored in the NetCDF file format.  These datasets are typically very large and often strain institutional data storage resources. While lossy compression methods for scientific data have become more studied and adopted in recent years, most advanced lossy approaches do not work easily and/or transparently with NetCDF files. For example, they may require a file format conversion or they may not work correctly with “missing values” or “fill values” that are often present in model outputs.  While lossy quantization approaches such at BitRound and Granular BitRound have built-in support by NetCDF and are quite easy to use, such approaches are generally not able to reduce the data size as much as more advanced compressors (for a fixed error metric), like SPERR, ZFP, or SZ3.

We are particularly interested in reducing the data size of the CONUS404 dataset.  CONUS404 is a publicly available unique high-resolution hydro-climate dataset produced by Weather Research and Forecasting (WRF) Model simulations that cover the CONtiguous United States (CONUS) for 40 years at 4-km resolution (a collaboration between NSF National Center for Atmospheric Research the U.S. Geological Survey Water Mission Area). 

Here, we investigate one advanced lossy compressor, SPERR [1], together with its plugin for NetCDF files, H5Z-SPERR [2], in a Python-based workflow to compress and analyze CONUS404 data.  SPERR is attractive due to its support for quality control in terms of both maximum point-wise error (PWE) and peak signal-to-noise ratio (PSNR), enabling easy experimenting of storage-quality tradeoffs. Further, given a target quality metric, previous work has shown that SPERR likely produces the smallest compressed file size compared to other advanced compressors. It leverages the HDF5 dynamic plugin mechanism to enable users to stay in the NetCDF ecosystem with minimal to no change to existing analysis workflows, whenever a typical NetCDF file is able to be read. And, importantly for our work, the SPERR plugin supports efficient masking of “missing values,” which are common to climate and weather model output.  The support for missing values enables compression on many variables which are not naturally handled by other advanced compressors that rely on HDF5 plugins. Further, because H5Z-SPERR directly handles missing values, they can be stored in a much more compact format (and are restored during decompression), further improving compression efficiency. (Note that built-in NetCDF quantization approaches can work with missing values.) 

Our experimentation demonstrates the benefit of enabling advanced lossy (de)compression in the NetCDF ecosystem: adoption friction is kept at the minimum with little change to workflows, while storage requirements are greatly reduced.

[1] https://github.com/NCAR/SPERR

[2] https://github.com/NCAR/H5Z-SPERR

How to cite: Li, S., Baker, A., and Xue, L.: Application of advanced lossy compression in the NetCDF ecosystem for CONUS404 data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6232, https://doi.org/10.5194/egusphere-egu26-6232, 2026.

Numerical modeling is a fundamental tool for understanding physically driven processes in geosciences. In multiparametric settings, the Finite Element Method is widely used because it can accommodate irregular geometries and complex boundary conditions. However, this advantage critically depends on the quality of the computational mesh, which must faithfully represent geological features such as faults, stratigraphic interfaces, and wells. In practice, mesh generation remains a major bottleneck, requiring specialized expertise and significant manual effort. We present Geo2Gmsh, an automated, lightweight workflow built on Gmsh (Geuzaine & Remacle, 2009), that generates geological meshes directly from simple text‐based descriptions of topological elements, including surfaces, lines, and points. These elements correspond to geologically meaningful features, allowing users to define faults, horizons, wells, and domain boundaries in a transparent, reproducible, and solver‐independent way. The workflow is demonstrated using two contrasting case studies: (1) Ringvent, an active sill‐driven hydrothermal system in the Guaymas Basin, and (2) the Eastern Llanos Basin, a foreland basin in eastern Colombia. To evaluate solver compatibility, we solved the heat equation in SfePy (https://sfepy.org/doc-devel/index.html) using the Eastern Llanos Basin model as the computational domain. Although the simulation is illustrative and not calibrated to observations, it confirms that meshes produced by Geo2Gmsh can be readily incorporated into numerical solvers. By explicitly embedding wells, faults, and geological interfaces in the mesh, Geo2Gmsh enables boundary conditions to be applied directly to physically meaningful features and allows model outputs to be extracted along them, simplifying both model setup and post‐processing. Meshes can be exported in standard formats (e.g., VTK, MSH, and Exodus via meshio), ensuring broad interoperability. Overall, Geo2Gmsh provides a lightweight, scalable, and reproducible workflow that dramatically lowers the technical barrier to geological mesh generation. This contribution establishes a practical foundation for reproducible, open-source numerical modeling in geosciences, facilitating the integration of geological knowledge into high-fidelity computational simulations.

How to cite: Buitrago, H., Contreras, J., and Neumann, F.: Geo2Gmsh: A Scalable Workflow for Automated Mesh Generation of Geological Models Using Gmsh, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6022, https://doi.org/10.5194/egusphere-egu26-6022, 2026.

Accurate high resolution wind field prediction is essential for wind resource as-
sessment, renewable energy planning, and regional weather analysis. Although
Numerical Weather Prediction (NWP) models such as the Weather Research
and Forecasting (WRF) model provide physically consistent wind forecasts, their
outputs often suffer from systematic biases arising from uncertainties in surface
characteristics, simplified physical parameterizations, and resolution limitations.
Furthermore, increasing model resolution to the kilometer scale significantly
raises computational cost. To address these challenges, this study presents a
machine learning–based framework for bias correction of WRF-simulated wind
fields over the Southern Tamil Nadu region, with particular focus on the Mup-
pandal wind farm area.
An extensive validation of WRF configurations was first performed using mul-
tiple physics scheme combinations and domain setups, evaluated against ERA5
reanalysis data. The optimal configuration was identified and used to gener-
ate three years (2023–2025) of wind simulations at 3 km × 3 km resolution.
Significant biases were observed in the raw WRF outputs, motivating the appli-
cation of an Artificial Neural Network (ANN) based bias correction approach.
A Random Forest algorithm was employed for feature selection, followed by
Principal Component Analysis (PCA) to reduce dimensionality while retaining
95% of the variance. A feedforward neural network with multiple hidden layers
was trained to correct the U10 and V10 wind components, with the hyperbolic
tangent activation function yielding the best performance. The bias-corrected
wind fields exhibited substantial improvement in mean and extremes, achieving low error metrics and
strong correlation with ERA5 data.
The results demonstrate that combining physically based NWP simulations with
machine learning driven bias correction provides an accurate and computation-
ally efficient approach for generating high-resolution wind fields. This hybrid
framework offers significant potential for wind energy assessment and localized
meteorological applications in data-sparse regions.

How to cite: Pm, V. and Chakravarthy, B.: Bias Correction of Numerical Weather PredictionWind Fields in Southern Tamil Nadu RegionUsing Machine Learning Techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16232, https://doi.org/10.5194/egusphere-egu26-16232, 2026.

Characterizing the thermal state of urban surfaces is fundamental for mitigating the impacts of the Surface Urban Heat Island (SUHI) effect. This study presents an intensive in-situ thermal infrared monitoring campaign in the high-density urban core of Athens, Greece. Utilizing a calibrated handheld TIR sensor (7.5–14 μm), surface temperatures were recorded across strategic locations in the center of Athens during hot weather conditions. The methodology emphasizes the critical role of material-specific parameterization, where thermographic data were post-processed to account for emissivity (ε) variations and surface temperature, ensuring high-fidelity measurements.

Experimental results reveal extreme thermal stress, with maximum surface temperatures reaching 56.0°C on conventional paving materials, while the mean ambient air temperature was close to 35.0°C during peak solar hours (13:00–18:00LT). Spatial analysis and visualization of the results were performed using QGIS, correlating thermal signatures with urban geometry, shading conditions, and vegetation density. The aim of this study was to highlight the significant cooling potential of specific urban materials and nature-based solutions.

How to cite: Gkountaras, O., Georgakis, C., Velissaridis, T., and Assimakopoulos, M. N.: In-situ Thermal Infrared Monitoring in an Urban Area: A Case Study of Micro-scale Thermal Transitions during Hot Weather Conditions in Athens, Greece., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3363, https://doi.org/10.5194/egusphere-egu26-3363, 2026.

Quantifying soil properties such as Soil Organic Carbon (SOC), texture, and Calcium Carbonate (CaCO₃) is essential for assessing soil health and ensuring food security. While Visible, Near Infrared, and Short Wave Infrared (VSWIR) remote sensing is a standard operational tool, the Longwave Infrared (LWIR, 8-14 μm) offer complementary information on mineralogy and moisture that are still not yet fully explored for this specific application. This study investigates the synergy between VSWIR and LWIR data that will be available with future hyperspectral satellite missions. Among them, the European Space Agency's Copernicus Expansion missions that will add to the EO capacity the Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM) mission. Alongside are the NASA's Surface Biology and Geology (SBG and SBG-TIR) missions.

The research focuses on Jolanda di Savoia (Italy), an agricultural landscape resulting from land reclamation projects in the late 19th century. Ground truth data were collected during a field campaign on June 22, 2023, providing 59 topsoil samples further analysed for SOC, texture, and CaCO₃. Field campaign was coincident with an airborne survey carried out with the LWIR Hyperspectral Thermal Emission Spectrometer (HyTES) sensor. HyTES captured data across 256 spectral bands from 7.5 to 11.5 μm, providing a pixel size of approximately 2.3 meters.

To evaluate the multi-frequency potential, we developed a workflow combining a soil composite from PRISMA (VSWIR) satellite time-series with simulated SBG-TIR (LWIR) data. The SBG-TIR simulation chain included as input a surface emissivity map derived from the airborne HyTES survey. To cover the LWIR wide spectral range (up to 12 µm), the emissivity spectrum was extended using an autoencoder neural network procedure trained on the ECOSTRESS Soil Spectral Library. Top-Of-Atmosphere (TOA) radiance was then simulated using the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-14) model, incorporating the optical depth and cloud/aerosol optical properties coefficients specific to SBG-TIR. Furthermore, these simulated data were atmospherically corrected to produce the target satellite emissivity products according to the TES algorithm.

Soil properties prediction models were developed using supervised machine learning algorithms. We benchmarked two scenarios: 1) the proposed combined approach using PRISMA and the simulated SBG-TIR L2 emissivity product; and 2) a VSWIR-only approach using PRISMA. A quantitative assessment by 10-fold cross-validation using common literature metrics (R², RMSE, RPD) highlighted the benefits of the multi-sensor approach. For SOC retrieval, the standalone VSWIR (PRISMA) model yielded an R² of 0.55 (RPD = 1.5), while the synergistic integration of PRISMA with simulated SBG-TIR data improved the retrieval accuracy, reaching an R² of 0.65 and increasing the RPD to 1.69. This work indicates that, on the agricultural test site of Jolanda di Savoia, the combined use of SVWIR and LWIR spectral range slightly improves the SOC retrieval. Further validation across diverse agricultural scenarios will be essential to test the real advantage of combining next-generation imaging spectroscopy missions.

How to cite: Rossi, F., Casa, R., Marrone, L., Mirzaei, S., Pascucci, S., and Pignatti, S.: Evaluating the combined potential of VSWIR and Thermal Infrared data for soil characterisation., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13611, https://doi.org/10.5194/egusphere-egu26-13611, 2026.

As part of the French Ocean data cluster ODATIS (from the Data Terra Research Infrastructure), we will showcase the Virtual Research Enviroment (VRE) tools and services offered by CNES and Ifremer. In particular, we will present the CNES JupyterHub platform for hosting projects (high computing power with CPU and GPU capacities, very fast and optimized remote access to data products, etc.) together with altimetry specific Pangeo-based libraries, powerful tools, dedicated tutorials to illustrate simple use cases (intercomparison with different satellite data, cyclone monitoring, coastal water quality applications, etc.) and a technical support (helpdesk) for smooth sailing on the platform. In addition, the synergy between satellite and in-situ data will be also illustrated for several applications, such as surface currents, and comparisons between (BGC-)Argo profiling float observations and satellite matchups.

How to cite: Germineaud, C., Caer, G., and Piollé, J.-F.: Virtual Research Environment initiatives as part of ODATIS, the French Ocean data cluster, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22048, https://doi.org/10.5194/egusphere-egu26-22048, 2026.

The sustainable management of water resources in anthropogenic contexts requires a holistic transition from mere monitoring to comprehensive assessments of water habitats. Urban reservoirs are particularly vulnerable ecosystems, where pressures such as agricultural runoff and untreated sewage discharge drive eutrophication, compromising water quality. Traditional monitoring methods often lack the spatiotemporal resolution required to capture the complex dynamics of these environments. To address this gap, this study evaluates the efficacy of close-range remote sensing combined with Machine Learning (ML) and Explainable Artificial Intelligence (XAI) for estimating optically active water quality parameters (turbidity, chlorophyll-a, and phycocyanin). The research was conducted at the Ibirité Reservoir (Minas Gerais, Brazil), a highly eutrophic urban system serving as a petrochemical industry water supply. Ten monthly field campaigns (August 2024 to May 2025) were conducted, covering both dry and wet seasons, to capture seasonal variability. Data acquisition employed Unmanned Aerial Vehicles (UAVs) equipped with two distinct multispectral sensors: the DJI Phantom 4 Multispectral (P4M – 5 bands) and the MicaSense RedEdge Dual-P (MSR – 10 bands). This setup allowed for a comparative analysis of spectral resolution impacts on model performance. The methodology tested three ML algorithms: Random Forest, CatBoost, and XGBoost. To ensure physical consistency, SHAP (SHapley Additive exPlanations) values were used to interpret the models. This ML-XAI approach assessed: (1) the comparative performance of each sensor and algorithm; and (2) the robustness of the models by identifying the most influential spectral bands for each parameter. Results indicate that ensemble learning algorithms, specifically Random Forest and CatBoost, consistently outperformed others across datasets. The MSR sensor achieved the highest overall accuracy, particularly for Phycocyanin estimation using Random Forest (R² = 0.93), compared to the P4M's best result for the same parameter (R² = 0.90). Explainable AI analysis revealed the physical drivers behind this performance: for Phycocyanin, the MicaSense models relied heavily on the specific 717 nm and 705 nm (RedEdge) bands. This explains the superior performance, as these narrow bands better resolve the specific spectral features of cyanobacteria compared to the single RedEdge band available on the DJI sensor. Conversely, for Chlorophyll-a, the NIR (842 nm) and Red (650 nm) bands were the dominant predictors. Since both sensors possess these bands, the performance gap was narrower (R² = 0.79 for MSR vs. 0.77 for P4M), validating the cost-effectiveness of the 5-band sensor for general pigment monitoring. However, for Turbidity, the additional spectral resolution of the MSR (specifically the 717 nm band) proved decisive, raising accuracy to R² = 0.84 compared to 0.78 for the P4M. Findings demonstrate that integrating high-resolution multispectral sensing with interpretable ensemble learning offers a scalable and physically consistent tool for monitoring water habitat health, supporting data-driven decision-making in complex urban environments.

How to cite: Mello, C., Salim, D., Souza, B., Pereira, G., and Amorim, C.: Benchmarking UAV multispectral sensors and machine learning for water quality estimation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14855, https://doi.org/10.5194/egusphere-egu26-14855, 2026.

Large-scale and long-term satellite observations are essential for environmental monitoring and for detecting gradual ecosystem responses to climate variability and land-use change.

This study presents a remote sensing–based framework to characterize vegetation phenology and its stability across Europe over four decades (1982–2022), using the temporally consistent and cross-sensor-calibrated PKU GIMMS NDVI dataset. The framework integrates NDVI time-series analysis with a newly developed Phenology Variability Index (PVI), designed to assess phenological stability at climatic scales and to complement established methods. Monthly NDVI time series are analyzed using non-parametric statistical tests and long-term mean seasonal profiles to delineate phenologically coherent regions through spatial clustering. Land Surface Phenology (LSP) metrics and the Phenology Variability Index are subsequently derived to characterize seasonal timing, trends, and phenological stability within and across regions. In this way, we integrate spatially explicit, pixel-level NDVI statistics and PVI-based evaluations with analyses of phenologically homogeneous clusters, providing a comprehensive understanding of vegetation dynamics across ecosystems.

Five spatially coherent clusters were identified, each characterized by distinct seasonal signatures linked to major European eco-climatic zones. Results reveal pronounced spatial and temporal heterogeneity, with consistent greening trends in temperate, montane, and Mediterranean regions, weaker and seasonally constrained greening in semi-arid areas, and largely stable winter NDVI conditions in mountainous forests and continental regions. LSP metrics indicate shifts in the timing and duration of the growing season, reflecting combined effects of climate variability and land-use change. The PVI further highlights higher phenological stability in Mediterranean and semi-arid landscapes, contrasted with greater variability in temperate and montane ecosystems.

Overall, this study demonstrates how long-term, high-temporal-resolution satellite data can support ecosystem assessment and environmental monitoring across continental scales. The proposed framework provides a transferable and robust methodological basis for analyzing vegetation dynamics, contributing to remote sensing–driven environmental monitoring and climate change research.

Keywords:
Remote sensing; Environmental monitoring; Vegetation dynamics; NDVI time series; Europe; Phenology Variability Index (PVI); Monthly trend analysis; Land Surface Phenology.

How to cite: Samela, C., Imbrenda, V., Coluzzi, R., and Lanfredi, M.: Monitoring Long-term Vegetation Phenology across Europe Using Satellite NDVI Time Series (PKU GIMMS), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12633, https://doi.org/10.5194/egusphere-egu26-12633, 2026.