Institute of Atmospheric Sciences and Climate

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.

Wildfires are an increasing threat in Mediterranean regions, where extreme fire weather and long-term fuel accumulation are driving more frequent and severe events. In this context, fast and reliable fire spread simulations are essential to support both risk mitigation planning and real-time emergency management. PROPAGATOR is a stochastic Cellular Automata (CA) wildfire spread simulator designed to generate ensemble-based fire spread forecasts. The model, currently available as both an online application and open-source software, operates within a raster-based framework in which each cell is described by static attributes (e.g. fuel type, topography) and dynamic drivers (e.g. wind, fuel moisture). Fire propagation is modelled through a stochastic contamination process between burning and unburned cells, allowing the production of probabilistic maps of fire spread, as well as statistics on rate of spread and fireline intensity. PROPAGATOR also includes the capability to simulate the spotting phenomenon and suppression actions such as water drops or firebreak construction, making it suitable for both operational decision support during active fires and pre-event risk mitigation analyses. A current limitation of operational applications of PROPAGATOR is its focus on surface fire propagation, with no explicit representation of vertical fuel structure or transitions to crown fire. Crown fires, however, are characterized by higher spread rates, greater energy release, and increased unpredictability, with major implications for suppression effectiveness and ecological impacts. To address this limitation, an enhanced version of PROPAGATOR has been developed by extending the model to a quasi-three-dimensional (2.5D) representation of fuels, enabling the simulation of crown fire processes within the stochastic CA framework. The proposed Crown Fire Module relies on established empirical and semi-empirical formulations for crown fire initiation and spread that are compatible with a cellular automata approach. Crown fire initiation is governed by surface fireline intensity and crown base height, while crown fire rate of spread depends primarily on canopy bulk density and fire behaviour. These mechanisms have been integrated into the propagation rules of PROPAGATOR, allowing dynamic transitions between surface and crown fire behaviour within a probabilistic modelling framework. The implementation of these processes requires detailed information on both surface and canopy fuel structure and characteristics, which remains challenging at operational scales. To address this issue, we investigated the use of UAV-based LiDAR remote sensing to derive key fuel structure parameters using semi-automatic algorithms available in the literature. This approach offers a balance between spatial detail and areal coverage that is suitable for operational wildfire applications. A pilot study conducted in the Venafro area (Molise, Italy), based on a past wildfire event with a comprehensive dataset describing fire evolution, provided high-resolution inputs to test the enhanced model. By explicitly simulating surface-to-crown fire transitions, the upgraded version of PROPAGATOR aims to improve decision support for wildfire risk management, supporting applications ranging from fuel treatment planning to operational response under extreme fire weather conditions.

The ESA ExoMars mission will land at Oxia Planum to search for signs of life on Mars [1, 2]. In this study, we analyze aeolian linear features in the landing ellipse using CTX (6 m/pixel), CaSSIS (5 m/pixel), and HiRISE (25 cm/pixel) imagery.We identified bright wind streaks oriented towards the S-SSW (mean azimuth 189°), consistent with formative winds blowing from the N-NNE. Their orientation reveals slight variations, allowing us to distinguish distinct sub-populations that appear controlled by the local topography.In contrast, dark-toned stripes form a 'streaky' pattern with a main NE-SW trend. In the western sector, they consist of elongated dark patches covering the bright, clay-enriched unit (the mission’s main target [2]). Crucially, the presence of small scarps suggests a degree of material consolidation or cementation. These stripes are preferentially preserved in the lee of impact craters (~600 m diameter), suggesting formative winds from the NE, thus differing substantially from the orientation of the nearby bright streaks.In the SE sector, SSE-oriented dark stripes are associated with a ~2 km diameter impact crater. Both CaSSIS and HiRISE data confirm that these features consist of a dark ejecta blanket preferentially preserved along the crater's southern rim, directly overlying the bright clay-enriched bedrock. Their orientation is slightly divergent but comparable to the bright wind streaks in this area, suggesting control by the current regional wind regime.We propose that these findings indicate a new class of Martian aeolian feature. Unlike typically described wind streaks, the features presented here appear composed of consolidated material. Specifically, the dark ejecta stripes can be interpreted as 'aeolian preservation streaks'. This feature arises from the differential erosion of a consolidated unit (e.g., crater ejecta blanket) by winds from the N-NNW; the crater rim creates a wind shadow that preserves the ejecta downwind while the surrounding area is removed, exposing the underlying Noachian bedrock.The orientation of these preservation streaks suggests that a N-NNW wind regime has been dominant in shaping the landscape over geological timescales. Even the dark stripes in the western sector, particularly where clustered behind topographic obstacles, may share this origin. Although their degree of consolidation remains to be definitively determined, their divergence from bright streaks suggests either a different formation timeline or strong local topographic control. These hypotheses regarding consolidated aeolian features and paleo-wind regimes will require crucial in-situ validation by the ESA Rosalind Franklin rover.[1] Vago J. et al. (2017). Astrobiology, 17. [2] Quantin et al. (2021), Astrobiology, 21.

Developing climate-resilient pathways requires an integrated view of risk that combines physical hazards with socio-economic vulnerability and adaptive capacity under future uncertainty. Within the SPARCCLE project, this is achieved by developing a probabilistic climate risk assessment framework for Europe that highlights the highest and recurrent impact patterns of climate extremes, as well as the challenges these pose for adaptation planning.Building on the core components of risk, namely hazard, exposure, and vulnerability, we integrate the MESMER climate emulator with the CLIMADA risk assessment platform to generate large ensembles of spatially explicit hazard realizations for extreme temperatures under custom emission pathways and global mean temperature trajectories. These hazards are combined with detailed exposure data, focusing on population exposure to extreme heat and accounting for future demographic change. Location-specific socio-economic vulnerabilities, including age structure, gender, and income inequality, are incorporated through hazard-specific impact functions.The use of a climate emulator enables exploration of a wide range of plausible futures, capturing dominant and recurrent spatiotemporal risk patterns as well as low-probability, high-impact outcomes that are often missed by limited climate model ensembles. This probabilistic framework allows us to identify regional hotspots of risk, assess where adaptation needs are greatest, and explore where adaptation constraints and limits may emerge under different climate and socio-economic pathways, reflecting alternative future challenges for Europe.Using heat-related impacts as a detailed application, we assess whether projected adaptation efforts are sufficient to close future adaptation gaps across regions and scenarios. The framework is designed to be scalable to multisectoral analyses and to feed into integrated assessment models and decision-support tools. By linking physical hazards, socio-economic vulnerability, and adaptive capacity in a unified probabilistic approach, this work supports forward-looking climate risk management and strengthens Europe’s preparedness for diverse future climate and socioeconomic challenges.

The launch of the ESA Arctic Weather Satellite Path Finder Mission (AWS-PFM), forerunner of the EUMETSAT EPS-Sterna mission, equipped with a cross-track scanning radiometer (Microwave Radiometer, MWR) which covers frequency between 50 and 325 GHz, represents an important improvement in satellite meteorology. The MWR represents a significant innovation in microwave radiometry, due to its four channels in the 325.15 GHz band offering enhanced sensitivity to cloud ice, thus enabling precise cloud observation.Exploiting coincident overpasses over precipitation events between the AWS and spaceborne radars, such as the Dual-frequency Precipitation Radar (DPR) onboard the NASA/JAXA GPM-CO mission and the Cloud Profiling Radar (CPR) onboard the ESA/JAXA EarthCare mission, can improve our understanding on the relationship between the cloud structure and the signal observed by the radiometer. This work presents a case study concerning a nearly coincident overpass of GPM-CO and AWS-PFM over Hurricane Melissa, a tropical cyclone that developed into a Category 5 during the 2025 Atlantic season. The observations took place on October 30, 2025, when Melissa had weakened to Category 2. The possibility to observe this type of event combining dual-polarization microwave (PMW) channels — available from the GPM Microwave Imager — with the sub-mm channels — available from the AWS-PFM MWR — as well as the measurements and precipitation profiles available from the DPR provides unprecedented potential for improving our understanding of the dynamics and microphysics processes in tropical cyclones. An analysis combining DPR observations and GMI brightness temperature (TB) is carried out based on our previous work regarding the analysis of Mediterranean tropical-like cyclonic events, such as Medicane Ianos, classified as category 1 hurricane at its peak of intensity. In addition, the combination of multi-channel measurements from AWS-PFM MWR reveal the added value of the sub-mm channels at 325.15 GHz to relate the cloud top structure with precipitation features. The comparison between Medicane Ianos and hurricane Melissa shows remarkable similarities at the time of the GPM-CO/AWS-PFM overpass. In both cases, very high Ku-band radar reflectivity values (around 50 dBZ) are associated with very intense precipitation (around 100 mm/h), which does not correspond to extreme TB features usually observed in the presence of strong updrafts sustaining large frozen hydrometeors at the upper levels. This indicates that, even during extremely intense cyclonic phenomena, the development of intense convective cores is limited. This analysis is ancillary to the future launch of the EPS Sterna mission, a constellation of AWS-like small satellites, designed to improve weather forecasts by providing global measurements of atmospheric temperature, humidity profiles as well as cloud and precipitation features with frequent revisit times.

Benzene, toluene, ethylbenzene, and xylene (BTEX) are hazardous air pollutants with high toxicity and a strong potential for secondary pollutant formation. However, their occurrence and behavior in the Arctic remain poorly understood. During the Alaskan Layered Pollution and Chemical Analysis (ALPACA) field campaign in Fairbanks, Alaska in January-February 2022. Surface observations in downtown Fairbanks revealed two major pollution periods, with extremely cold (down to -35°C) and warmer temperatures (around 0°C), respectively. BTEX concentrations reached 4–12 times higher than those reported in the US and European countries under dark, cold Arctic winter conditions at breathing level, posing a significant health risk. We simulated BTEX atmospheric distributions in the Fairbanks region using the FLEXible PARTicle-Weather Research and Forecasting (FLEXPART-WRF) Lagrangian particle dispersion model and anthropogenic emissions at the surface and aloft. Due to limited photochemical loss in to the dark polar winter conditions, we treat BTEX as an unreactive tracer in the model. The control run with the emission inventory developed by Alaska Department of Environmental Conservation (ADEC) substantially underestimates BTEX concentrations compared to observations during both polluted periods, indicating deficiencies in winter emissions and near-surface mixing. Enhancing cold-start gasoline vehicle emissions by a factor of 2 during very low-temperatures substantially improved model results during the cold polluted period, while introducing a relative humidity dependence for mobile emissions improved simulated BTEX during the warm, humid pollution period. Addition of emissions of residential heating oil aromatics, not taken into account in the ADEC inventory, also reduced normalized mean biases by 5-10%.The improved model simulation was used to investigate contributing source sectors. While mobile traffic emissions were identified as the dominant source of BTEX across the Fairbanks North Star Borough, residential heating and non-point sources contributed substantially in downtown Fairbanks. Replacing residential wood burning in the inventory with oil heating during severe pollution periods, in line with air quality control guidelines, was found to effectively reduce BTEX concentrations, particularly benzene by up to 30%. While persistent surface-based temperature inversions largely confined BTEX below ~20 m, upward transport, induced by wind shear, during severe episodes, sometimes lofted near-surface pollutants to higher altitudes, potentially contributing to regional pollution and background Arctic haze.The findings of this study emphasise the need to accurately account for temperature and humidity dependent vehicle emissions, residential oil heating emissions, and winter boundary-layer dynamics for improved simulations of air quality in cold wintertime environments, not only in the Arctic but also in mid-latitudes.

Irrigation strongly influences land-atmosphere interactions and the terrestrial water cycle, yet its representation in land surface models (LSMs) remains highly uncertain. These uncertainties arise from both the scarcity of reliable irrigation benchmarks and the challenge of representing heterogeneous irrigation practices within coarse model grid cells (e.g., kilometer-scale resolutions). In this study, we examine structural limitations in the representation of irrigation within the Noah-MP LSM, implemented in the NASA Land Information System, by testing different calibration strategies. A sprinkler irrigation scheme is optimized using Sentinel-1-derived irrigation estimates and a genetic algorithm over an intensively irrigated region of northeastern Spain at a 0.01° spatial resolution. Two calibration approaches are evaluated: (i) adjusting the soil moisture threshold (Thirr) that triggers irrigation, and (ii) introducing a Scale Irrigation Coefficient (SIC) to account for sub-grid heterogeneity in irrigated area and applications’ timing. Results show that calibrating Thirr alone provides limited flexibility, resulting in unrealistic irrigation peaks and excessive water application. By contrast, the optimized SIC-based parameterization substantially improves irrigation dynamics, reduces model errors relative to benchmark in situ observations, and better captures interannual variability in surface soil moisture. Findings demonstrate that assuming uniform, full-grid irrigation at resolutions of ~1 km or coarser is physically unrealistic due to both operational constraints on irrigation practices and the fragmented structure of agricultural landscapes. Comparisons with satellite-based evapotranspiration and gross primary production datasets also reveal inconsistencies in simulated vegetation responses, highlighting remaining limitations in vegetation parameterization. Overall, this work underscores the importance of explicitly accounting for scaling effects in irrigation schemes and points toward future integration of satellite data assimilation to enhance representation of irrigation-water-carbon interactions.

A suite of high-resolution configurations of the coupled climate model IFS-NEMO to investigate recent and future climate variability and change has been recently developed within the project EERIE (European Eddy-Rich Earth System Models) and the Destination Earth initiative. This contribution emphasises the climate responses emerging from these simulations and their sensitivity to spatial resolution and the experimental protocol considered.The model hierarchy combines eddy-permitting (∼25 km) and eddy-rich (∼9 km) ocean components with convection-parameterised (∼25 km) and convection-permitting (∼4.5 km) atmospheric configurations, enabling a systematic assessment of resolution-dependent processes and feedbacks. Particular attention is given to how differences in model physics, resolution-aware tuning strategies, scenario forcing (i.e. SSP1-2.6 vs SSP3-7.0) and experimental design influence the simulated climate variability across configurations.Ongoing analyses of historical simulations show enhanced performance for the higher-resolution configurations in the representation of mean-state properties, with especially clear improvements in dynamical fields. We further assess the extent to which the shorter spinup approach employed in Destination Earth, compared to EERIE, can reliably capture internal variability and externally forced responses while substantially reducing computational cost.A systematically stronger future response of the Atlantic Meridional Overturning Circulation to external forcings is found in the eddy-resolving configurations compared to the eddy-permitting ones. Idealised control simulations with quadrupled CO2 forcing – inspired by the CMIP6 DECK experiments – also show a more pronounced temperature response at the highest resolution compared with the ∼25 km configuration, thus yielding stronger climate sensitivity.More generally, we also briefly outline emerging applications of the kilometre-scale IFS-NEMO model in other European research projects, including TerraDT, which focuses on land–atmosphere coupling, and PREDDYCT, which investigates the role of mesoscale ocean eddies in seasonal-to-decadal climate prediction. Together, these efforts highlight the added scientific value of high-resolution climate modelling for understanding forced responses and informing future climate projections.

The Mediterranean Sea is often affected by tropical-like cyclones, which cause heavy rainfall, strong winds, storm surges and flooding. The accurate classification of precipitation into convective and stratiform within these systems is essential for understanding storm dynamics and improving predictive models. In this study, we developed a deep learning approach based on U-Net architecture to classify convective and stratiform precipitations during Mediterranean cyclones using the Global Precipitation Measurement (GPM) IMERG product. We derived physically consistent labels for training through an exponential distribution-based thresholding of rainfall intensities. The trained U-Net model effectively reproduced the spatial structure of convective rainbands and surrounding stratiform regions within the cyclone structure. In addition to validating the convective precipitation detection using brightness temperature satellite observations and ERA5 reanalysis, we also incorporated pluviometer records. These ground-based measurements confirmed the model’s strong capability to identify areas affected by convective precipitation. This study demonstrates the potential of integrating a physics-based approach with deep learning for high-resolution characterization of precipitation in Mediterranean cyclones. While the segmentation of convective precipitation alone does not directly quantify coastal hazard, these results provide essential input layers for downstream coastal-impact assessments. In particular, the high-resolution identification of convective rainfall can be integrated into hydrological and hydraulic models (e.g., HEC-RAS or similar) to simulate surface runoff, flash-flood dynamics, and related coastal impacts under a changing climate.