National Observatory of Athens

Abstract Medicanes are cyclones with tropical characteristics in the Mediterranean basin. They typically show an eye with neighboring spiraling bands, but the surrounding region of maximum winds is not well documented, especially near the sea surface, due to limited available observational data. Estimating such characteristics accurately is essential for a better understanding of the specific fine‐scale wind structures and their relationship with the development mechanisms of medicanes. Here we first demonstrate the capacity of synthetic aperture radar (SAR) spaceborne instruments to diagnose important medicane structural characteristics. Building on an unprecedented collection of SAR high‐resolution observations and subsequent sea‐surface wind‐speed estimates, the eye patterns, heavy rainfall, convective cells, and boundary‐layer roll vortices are evidenced, with attributes comparable with those of tropical cyclones of similar intensities. Motivated by this resemblance, we estimate parameters characterizing the cyclone surface wind structure (the intensity, inner and outer size, and Rossby number) from high‐resolution observations of medicanes. We show that their inner size is of magnitude comparable with that of tropical cyclones of similar intensities. Conversely, typical outer sizes and Rossby numbers are found to be smaller. The misrepresentation of these characteristics in medium‐resolution global reanalyses brings into question their suitability and the application of diagnostic tools to these data to monitor the life cycles of medicanes. This invites us to develop dedicated observation‐based monitoring frameworks. Medium‐resolution scatterometers provide more reliable structural estimates despite the small medicane size. Along with SAR surface wind‐speed estimates, and complemented by upper‐level measurements, they may support future reanalysis efforts. Further investigation is needed to understand how the high‐resolution features reported in our work connect with kilometer‐ and subkilometer‐scale physical processes.

The Quick Look Content (QLC) suite provides automated forecast verification and analysis capabilities optimized for the Copernicus Atmosphere Monitoring Service (CAMS) and emerging AI-integrated forecasting systems. QLC addresses the growing need for systematic, reproducible evaluation of atmospheric composition forecasts through an end-to-end workflow from data retrieval to publication-quality visualizations.The system integrates direct access to ECMWF's MARS archive with currently 16 observation networks including EBAS, AirNow, and GHOST-harmonized datasets, covering 7,855 atmospheric variables. Native GRIB support preserves forecast step information critical for analyzing temporal forecast evolution. QLC handles the complete verification workflow: automated MARS data retrieval, model-observation collocation with configurable spatial and temporal matching, statistical analysis including bias, RMSE, and correlation metrics, and generation of comprehensive visualizations (e.g., maps, time series, scatter plots, Taylor diagrams).Recent development of an AIFS-specific workflow enables systematic comparison of AI-integrated forecasts against traditional IFS-COMPO runs and observational data. QLC supports multiple evaluation modes: single experiment validation, multi-experiment intercomparison, and observation-only analysis. Processing scales from single-station quick looks to continental-scale multi-variable assessments. Integration with the evaltools package (CNRM/Météo-France) provides advanced statistical diagnostics including diurnal cycles, station score maps, and exceedance analysis.Here we introduce QLC and demonstrate its capabilities through verification examples comparing IFS-COMPO and AIFS-COMPO forecasts for ozone, nitrogen oxides, and particulate matter against multi-network observations. Results highlight the tool's utility for operational forecast monitoring, model development support, and scientific analysis. The open-source package (PyPI: rc-qlc) is designed for use on both HPC systems and workstations, with one-command installation and comprehensive documentation at docs.researchconcepts.io/qlc.This work is supported by CAMS2_35_bis_KNMI: "Developments on reactive gases and aerosol in the global system" (https://atmosphere.copernicus.eu/).

Landslides in the Western Corinth Rift reflect a mix of long-term “set-up” conditions—such as terrain, rock type, and fault-related structure—and short-term triggers linked to transient deformation and changing rainfall patterns. To represent these interacting processes in a clear and interpretable way, we propose a two-phase, multi-scale landslide susceptibility workflow based on explainable XGBoost. At Phase 1 (watershed scale) we develop a baseline susceptibility model using a standardized set of conditioning factors. These include (i) terrain and geomorphometric variables (elevation, slope, aspect, profile curvature, plan curvature and topographic wetness index (TWI) and (ii) lithological and structural controls (lithology and hydrolithology classes, distance from river network and fault-influence proxies such as distance to faults). The model is trained using historical landslide inventories, whereas interpretability was built in through explainable AI tools, such as SHAP, allowing us to quantify both global and site-specific contributions of conditioning factors, including key interactions. The result is a set of susceptibility maps paired with readable diagnostics that explain why certain areas are critical. At Phase 2 (local refinement and activity confirmation) focuses on the Krini–Gkrekas–Pititsa sector, where observations are denser and more reliable. Here, we evaluate whether susceptibility hotspots from Phase 1 align with evidence of ongoing or emerging instability. We add dynamic indicators and independent validation using: European Ground Motion Service InSAR ground motion, SBAS historical InSAR data; GNSS trend metrics and antecedent precipitation indices from station data. The goal is not just to refine local interpretation, but to test whether predicted patterns make physical sense, by checking consistency between (a) areas predisposed by lithology and structure and (b) present-day deformation signals and rainfall forcing. The workflow aims to produce decision-ready, interpretable outputs at two complementary scales: (1) watershed-scale susceptibility that highlights where failures are more likely based on relatively stable controls, and (2) a localized assessment that strengthens confidence where susceptibility coincides with measured deformation and hydrometeorological conditions. This improves trust and usability of AI-assisted landslide hazard assessment in tectonically active landscapes.KeywordsLandslide susceptibility; XGBoost; explainable AI; SHAP; multi-scale modeling; watershed analysis; lithology; active faults; EGMS; InSAR ground motion; GNSS; antecedent precipitation index; Western Corinth Rift.

Environmental observatories provide powerful real-world contexts for advancing climate change education and fostering engagement with Earth system science. The Navarino Environmental Observatory (NEO), located in southwestern Greece, integrates long-term environmental monitoring with interdisciplinary research, generating high-resolution datasets on atmospheric conditions, ecosystem dynamics, soil and hydrological processes, and biodiversity change in a Mediterranean climate hotspot. By linking empirical observations to education and outreach activities, NEO supports learning experiences that connect scientific evidence to place-based climate impacts and societal challenges.This contribution presents how NEO observational data are embedded in participatory education initiatives to enhance climate literacy, critical thinking, and data competencies across diverse learner groups. Drawing on examples from international field courses, summer schools, living lab activities, and community workshops, we show how students and stakeholders engage directly with real environmental datasets to interpret trends, explore uncertainty, and understand feedbacks between climate, ecosystems, and land management. Particular attention is given to how data-driven learning influences climate perceptions, supports interdisciplinary understanding, and encourages informed dialogue between scientists and society.Our experience demonstrates that combining long-term environmental observations with experiential and participatory educational approaches strengthens climate change education, promotes trust in scientific evidence, and supports the development of actionable knowledge for climate adaptation and sustainability.

For many critical energy-related applications, such as reliable PV power production, accurate Global Horizontal Irradiance (GHI) short-term forecasts are crucial. Forecasts of GHI, as analyzed in this study, are produced by the state-of-the-art, high-resolution forecasts developed within the Destination Earth initiative, the ECMWF Digital Twin Engine using the ExtremeDT (Weather-Induced Extremes Digital Twin) dataset, that produces daily global simulations at resolutions of 4.4 km kilometres up to four days ahead. However the GHI ExtremesDT forecasts do not take into account the aerosol effects, which may introduce systematic biases, especially during periods of high aerosol load events. We investigated the impact of aerosols on DT GHI forecasts and the associated PV power predictions within the context of the DestinE Destination Renewable Energy (DRE) use case.We analyze almost one year of data comprising two-day-ahead DT GHI forecasts and the corresponding aerosol optical depth (AOD) forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The DT GHI forecasts are corrected for aerosol effects using fast radiative transfer model techniques utilising the lidRadtran [1, 2] package.Within the DRE project, and based on co-design activities with the project's end user, site-specific PV power production forecasts tailored to the user’s needs and infrastructure were developed, using DT GHI forecasts and a machine learning (ML) model that was trained on the user’s historical data. The impact of aerosol correction was also evaluated by comparing PV power production forecasts derived from GHI forecasts with and without aerosol correction against the actual PV power production of the plant.A machine learning (ML) model, trained on historical, site-specific production data from the QUEST PV park, was created within the DRE project to convert predicted solar irradiance into PV park power output in order to evaluate the impact on power production. The ML model propagates the original and aerosol-corrected GHI forecasts, which are then compared to actual production.The results show how CAMS-based aerosol correction of GHI forecasts can reduce bias and consistently improve PV power prediction. Results show the value of incorporating atmospheric composition data with ML-based power conversion models for operational energy applications, as well as the significance of aerosol representation in solar forecasting.AcknowledgmentsDRE project has received funding from the European Space Agency under the DESTINATION EARTH USE CASES – DESP USE CASES - ROUND 1. The duration of the project is 12 months (November 2023 - November 2024).We would like also to acknowledge the COST Action HARMONIA (International network for harmonization of atmospheric aerosol retrievals from ground based photometers), CA21119.Bibliography(1) Mayer, B.; Kylling, A. Atmospheric Chemistry and Physics 2005, 5, 1855–1877.(2) Emde, C.; Buras-Schnell, R.; Kylling, A.; Mayer, B.; Gasteiger, J.; Hamann, U.; Kylling, J.; Richter, B.; Pause, C.; Dowling, T.; Bugliaro, L. Geoscientific Model Development 2016, 9, 1647–1672.

Thermal imaging of the Milos volcano (Cyclades, Greece) is used to monitor its active hydrothermal system, specifically focusing on the Eastern part of Milos. Satellite data is essential for tracking Land Surface Temperature (LST) anomalies and radiative heat flux in this dormant but active volcanic field. Milos hosts a well-known shallow geothermal field mainly developed beneath the eastern part of the island. In this work we aim to establish the background surface temperature level for this volcano and observe possible fluctuations related to seasonal effects or changes in the shallow hydrothermal activity.Thermal sensors Landsat 8 and Landsat 9 (8-day sampling interval) at 100-m resolution during the year 2025 were used for this study. The final dataset contained 65 satellite images, each with cloud and shadow coverage below 40%. Initially, a pixel-based geostatistical analysis was done, where 12 monthly mean LST maps and 12 monthly standard deviation maps were produced to investigate the surface thermal conditions of the island. To mitigate climate change's influence, a further investigation was followed by producing 3 more maps, to detect and locate the accurate annual spatial distribution of valid clear-sky Landsat LST observations, derived by each pixel’s counts over 40oC and its relevance to the normalized annual frequency. The analysis was done completely on Google Earth Engine.The results showed that the monthly analysis of land surface temperature imagery consistently detects temperatures inside the Zephyria depression (eastern Milos) that are 5–25 °C warmer than the surrounding terrain, which can reach up to 58oC. Additionally, the final analysis succeeded in mitigating the external weather conditions and revealed that 34 observations (out of 65) present a land surface temperature over 40oC inside the Zephyria depression, with a clear spatial correlation to the shallow geothermal field of the island. Another important outcome was that despite the limitations due to atmospheric interference, the limited land-coverage of the island and the small scale of fumaroles onshore Milos, Landsat 8 and Landsat 9 are both able to detect the thermal anomalous pixels by using one year as a referenced period.

Reliable measurements of Galactic Cosmic Rays (GCRs) and Solar Energetic Particles (SEPs) requirewell-calibrated spaceborne particle instruments. This study presents a cross-calibration of theElectron, Proton and Helium Instrument (EPHIN) aboard SOHO with high-precision proton and heliummeasurements from PAMELA and AMS-02. The analysis extends the established $\Delta E$--$\Delta E$technique to later mission phases, accounting for detector aging and changes in instrument responseafter 2017.A GEANT4-based model of EPHIN, including a simplified representation of the SOHO spacecraft, is usedto derive energy response functions for penetrating protons and helium nuclei. Simulated detectorresponses based on force-field–modulated GCR spectra reproduce the observed EPHIN energy-lossdistributions within about 30\%. Effective energies and fluxes are obtained using a bow-tie inversionmethod and compared with AMS-02 and PAMELA observations during quiet solar conditions. The resultsshow agreement within the combined systematic uncertainties, demonstrating that SOHO/EPHINcontinues to provide valuable and reliable energetic particle measurements for long-termheliospheric studies.The EPHIN is supported under Grant 50~OC~2302 by the German Bundesministerium für Wirtschaft through the Deutsches Zentrum für Luft- und Raumfahrt (DLR). We acknowledge partial support from the Horizon Europe Program project SPEARHEAD (GA 101135044).

The Eastern Mediterranean and Middle East (EMME) region constitutes a critical domain for assessing the impact of atmospheric aerosols on solar photovoltaic (PV) power potential. The EMME region is characterized by exceptionally high solar resource availability and is affected by a variety of aerosol species transported from distant sources, while also hosting suspended particles of both anthropogenic and natural origin that are frequently recorded at high concentrations. Moreover, the Eastern Mediterranean, it is identified as a climate change hotspot, where projected changes in aerosol concentrations are expected to play a pivotal role. In this study, we analyze projections from the GFDL-ESM4 global climate model, participating in the 6th phase of the Coupled Model Intercomparison Project (CMIP6), to quantify the impact of aerosols’ and cloudiness’ spatiotemporal variability on PV power production in the EMME region, within the 21st century. To address this, we investigate trends and variability of radiation-related parameters – surface downwelling solar irradiance under all-sky and clear-sky conditions, under different Share Socioeconomic Pathways (SSP–based scenarios): SSP2-4.5, SSP3-7.0, SSP5-8.5. To simulate the PV power output, we employ the Global Solar Energy Estimator (GSEE), which incorporates a climate interface submodule designed to process gridded climate datasets with varying temporal resolutions, ranging from hourly to seasonal, as model input. Attenuation by cloudiness plays a significant role regarding future energy production, especially at the northernmost EMME regions. Nevertheless, the role of atmospheric aerosols is dominant during the sunniest months of the year, especially in the southeastern Mediterranean.

Solar Ultraviolet (UV) radiation plays a key role in many chemical and biological processes, and affects significantly human health. Excessive UV exposure may lead to adverse health effects, including sunburns, skin cancer, and cataracts, whereas moderate exposure is beneficial, e.g., by supporting vitamin D production and promoting mental well-being, among other benefits. UV radiation interacts with various atmospheric components before reaching the Earth’s surface. Photons with shorter wavelengths are absorbed at higher atmospheric layers by oxygen and tropospheric ozone, and practically only UV-A and a small part of the UV-B irradiance reach the troposphere. In the troposphere, UV is scattered by air molecules and is further attenuated by aerosols and clouds. Interactions between UV radiation and aerosols are not yet completely understood, and their parameterization constitutes a major uncertainty factor in models and satellite retrieval algorithms. Understanding these interactions is thus essential for accurately assessing UV exposure using modeled UV irradiance.Τhe purpose of this study is to evaluate satellite- and reanalysis-based retrievals of the effective dose for the cutaneous vitamin D synthesis using ground-based measurements over Athens and Thessaloniki, Greece. We evaluate data that are derived (1) using the methodology described in Fragkos et al., (2024, https://doi.org/10.3390/rs16111878), based on CAMS information in combination with satellite data from OMI and MSG, and (2) the UV climatology of which is also based on data from various sensors analyzing air quality. Ground-based spectral solar UV irradiance measurements performed with a MKIV single monochromator Brewer spectrophotometer in Athens, and a MKIII double monochromator Brewer spectrophotometer in Thessaloniki are used to validate the Satellite-based retrievals. AOD measurements from co-located CIMEL sun-photometers, part of the AERONET network are used to assess the effect of aerosols. The evaluation has been performed for the period 2004 - 2024. Further analysis yielded positive trends in the effective dose for vitamin D production in the last two decades, mainly due the decreasing trends in aerosols.