Si presenta un nuovo dataset meteoclimatico ad alta risoluzione per l’Italia e la regione alpina, ottenuto tramite downscaling dinamico dei campi di rianalisi ERA5 con il modello meteorologico ad area limitata non idrostatico MOLOCH. Il prodotto, denominato MORE (MOloch-downscaled ERA5 REanalysis), ha una risoluzione spaziale di circa 1.7 km e copre in modo continuo il periodo 1990–presente, fornendo campi orari di numerose variabili sia al suolo che ai principali livelli isobarici.

La validazione di MORE è stata effettuata con un approccio multiscala per la precipitazione e la temperatura a 2 metri, utilizzando dataset osservativi densi e controllati. Il confronto con altre rianalisi convection-permitting e con prodotti a più bassa risoluzione spaziale evidenzia che MORE riproduce in modo realistico la variabilità spazio-temporale delle osservazioni, migliora la simulazione della frequenza e dell’intensità delle precipitazioni, degli estremi sub-giornalieri (in particolare in condizioni convettive) e di indicatori climatici rilevanti come il numero di notti tropicali, pur mostrando un bias freddo sistematico nella temperatura.

Come caso di studio applicativo è stata analizzata l’alluvione che ha colpito l’Emilia-Romagna nel maggio 2023. MORE ricostruisce in maniera realistica l’evoluzione meteorologica dei due episodi di precipitazione estrema, offrendo un valore aggiunto nella rappresentazione delle strutture a mesoscala che hanno determinato gli accumuli precipitativi localizzati. Le simulazioni idrologiche forzate con i dati MORE mostrano inoltre un miglioramento nella rappresentazione delle portate a scala di bacino e della dinamica dell’umidità del suolo.

Nel complesso, MORE rappresenta la rianalisi a più alta risoluzione attualmente disponibile per l’Italia e la regione alpina. La ricchezza informativa del dataset, con numerose variabili a risoluzione oraria, ne fa una risorsa di riferimento per studi idrometeorologici, analisi di impatto e adattamento ai cambiamenti climatici, e per lo sviluppo di servizi climatici in aree a complessa orografia e ad elevata esposizione agli eventi estremi.

I venti catabatici svolgono un ruolo determinante nel sistema climatico antartico, influenzando le dinamiche di interazione tra oceano e atmosfera. Essi favoriscono la formazione delle polynye costiere, siti di produzione di acque dense che alimentano la circolazione oceanica globale. L’obiettivo dello studio è analizzare i pattern di circolazione atmosferica che favoriscono gli eventi catabatici nell’area di Baia Terra Nova durante l'autunno, l'inverno e la primavera australi (marzo-novembre). Per l'identificazione degli eventi, è stata utilizzata la serie storica dei dati orari di vento della stazione meteorologica automatica “Eneide” (afferente all’Osservatorio meteo-climatologico antartico dell’ENEA), relativa al periodo 1995-2024. È stato applicato un criterio di selezione oggettivo che combina due condizioni simultanee: velocità del vento superiore al 90° percentile della distribuzione e direzione di provenienza compresa in un intervallo specifico, definito tramite l'analisi della rosa dei venti.

La caratterizzazione delle configurazioni atmosferiche si è basata sui dati ERA5, analizzando sia il campo di pressione al suolo sia i campi di geopotenziale, temperatura e vento a diverse quote isobariche. La classificazione dei regimi sinottici favorevoli agli eventi catabatici è stata ottenuta tramite un approccio che integra l'Analisi in Componenti Principali con un algoritmo di clusterizzazione k-means. Le configurazioni sinottiche identificate sono state successivamente esaminate in termini di frequenza di occorrenza, variabilità interannuale e caratteristiche medie degli eventi associati. Infine, è stata analizzata la potenziale connessione tra la frequenza di questi regimi e la variabilità climatica su larga scala, attraverso la correlazione con i principali indici teleconnettivi dell'Emisfero Sud, quali il Southern Annular Mode, il Southern Oscillation Index e il Dipole Mode Index.

Tropospheric ozone (O₃) is a well-known phytotoxic pollutant and can lead to reduced photosynthesis, accelerated leaf senescence and to other negative effects, thus threatening food security and impairing biomass growth and carbon sequestration in forest ecosystems. Traditional global assessments often rely on exposure-based metrics, overlooking how environmental and physiological factors regulate O₃ uptake by plants.

This study presents a global flux-based assessment of O₃ risk to wheat and to forests across the 21^st century, employing a dual-sink big-leaf dry deposition model to estimate the phytotoxic ozone dose (POD) and the associated effects on crop and forests productivity. Simulations were driven by meteorological and O₃ concentration data from the UKESM1 Earth System Model, under three contrasting Shared Socioeconomic Pathways (SSP1, SSP3, and SSP5). The study analyzed trends in POD from the early 2000s to the end of the century, with particular attention to the roles of soil water availability and rising atmospheric CO₂ concentrations, both of which are expected to influence stomatal conductance and thereby O₃ uptake.

Results indicate a general decline in global O₃ risk toward 2100, though regional and ecosystem differences persist. For wheat, strong O₃ precursors emission controls (SSP1-2.6) could reduce O₃-related global production losses to below 1.4%, while weaker controls (SSP3-7.0, SSP5-8.5) may exacerbate O₃ risks in key agricultural regions of Asia, South America, and Sub-Saharan Africa. For forests, reduced O₃ uptake is largely driven by notably lower stomatal conductance under elevated CO₂ and higher vapor pressure deficits, rather than decreases in ambient O₃ levels.

These findings highlight the value of flux-based frameworks to assess global O₃ risk under climate change, by providing a basis for prioritizing region-specific mitigation strategies to protect crop productivity and forest ecosystems from O₃ damage under future climate conditions.

To fully understand Earth's climate system, it is crucial to account for all atmospheric gases that have a high global warming potential or a significant impact on the ozone layer. Among these, nitrous oxide (N₂O) and halogenated carbon compounds—including CFCs, HFCs, HCFCs, and PFCs—stand out due to their long atmospheric lifetimes and considerable warming effects. Nitrous oxide and chlorine-containing compounds also play a key role in human-driven ozone depletion and are regulated globally under the 1989 UN Montreal Protocol.

Satellite-based instruments offer a powerful, multi-mission tool for tracking and analyzing the behavior of these so-called Other Long-Lived Greenhouse Gases (OLLGHGs) in the atmosphere. To support the use of these satellite datasets, the European Space Agency (ESA) launched the LOng-LIved greenhouse gas PrOducts Performances (LOLIPOP) CCI+ project in 2023 in the framework of the Climate Change Initiative (CCI) program.

The primary objective of LOLIPOP is to assess whether the current generation of satellite observations meets the quality requirements needed for climate research and related services, as well as to identify the needs of end users. To demonstrate their potential, the project includes five dedicated case studies. Results from these studies, user needs survey as well as datasets quality assessment will be presented.

Il progetto SCINTILLA, promosso dalla Regione Campania in collaborazione con il MASE, mira a sviluppare strumenti innovativi per il monitoraggio e la previsione della qualità dell’aria, con un focus sul particolato atmosferico e i suoi impatti sulla salute umana. Le attività hanno integrato modelli numerici (CAMx) su scala nazionale e regionale, alimentati da inventari emissivi aggiornati (ISPRA, EMEP, HERMESv3) e forzati con simulazioni meteorologiche WRF, validando le simulazioni tramite confronti con dati osservativi ARPAC per il 2017. I risultati evidenziano una buona capacità dei modelli di riprodurre i pattern stagionali e diurni di NO2, PM10 e PM2.5, seppure con una sottostima dei picchi invernali.

Per superare le limitazioni dei modelli deterministici, sono state adottate tecniche di Intelligenza Artificiale basate su reti neurali (AFNO), in grado di correggere il bias delle simulazioni e migliorare la previsione temporale delle concentrazioni di inquinanti. Sul fronte sperimentale, sono state condotte campagne di campionamento del particolato in due siti urbani (Napoli e Avellino), integrando misure in-situ, sensori a basso costo e dati meteorologici ad alta frequenza, al fine di una caratterizzazione chimico-fisica dettagliata e dell’analisi sugli effetti sanitari.

Una componente innovativa riguarda l’uso integrato di osservazioni satellitari (MAIAC MODIS AOD550) e modelli numerici per estendere la copertura del monitoraggio e calibrare stime spaziali di PM10 laddove scarseggiano le stazioni di misura. I risultati preliminari supportano la validità dell’approccio di data fusion per generare mappe continue di esposizione, più rappresentative per la valutazione del rischio sanitario e ambientale. Il progetto si pone così come riferimento per strategie regionali di tutela ambientale, offrendo metodologie trasferibili in altri contesti territoriali.

The latest generation of high-resolution, convection-permitting reanalyses, capable of representing atmospheric processes at small spatial scales (≤4 km), is crucial for studying the temporal and spatial evolution of phenomena such as convective storms and orographic precipitation. Leveraging long (>35 years) and continuous datasets over Italy, this study investigates the occurrence and characteristics of hourly precipitation extremes (HPE) and quantifies their potential increase over time. Previous studies have validated convection-permitting reanalyses against observations from climatological to daily scales, demonstrating their ability to capture fine-scale precipitation events, although spatial mismatches sometimes occur.

The work is based on the MERIDA HRES convection-permitting reanalysis (1986–2022). Spatially coherent hourly precipitation structures (~160,000 per year) are identified from hourly reanalysis fields through clustering techniques and percentile-based thresholds. Each of them is characterized by maximum spatial extent, timing, peak value, mean intensity. The resulting dataset allows calculation of seasonal climatological averages of their distribution, intensity, and spatial extent. HPE are then extracted using local annual maxima in hourly precipitation (RX1hour).

Results reveal a marked increase in HPE occurrences over Alpine and Prealpine regions during summer, and along some southern and insular coastlines in autumn. These spatial and seasonal patterns correspond to regions where convective processes dominate intense, localized precipitation, potentially amplified by climate change. This study provides detailed insights into hourly precipitation patterns over Italy and guidance for stakeholders to leverage reanalysis data for enhancing infrastructure resilience to extreme precipitation.

Rapid urbanization, deteriorating air quality, and climate change are increasingly interacting in ways that amplify risks for urban populations. Cities are both major sources of greenhouse gas emissions and hotspots of vulnerability, where dense populations face the compounded effects of air pollution and climate extremes. In the context of more frequent and severe weather events, urban areas must urgently design and implement adaptation strategies informed by emerging scientific evidence.

This study introduces a novel multiscale modeling framework that couples the ADMS-Urban dispersion model with the Weather Research and Forecasting (WRF) mesoscale model to simulate the combined effects of extreme heat events on thermal comfort and pollutant dispersion. Developed within the PRIN2022 project “Urban hEat and pollution iSlands inTerAction in Rome and possible mitigation strategies” (RESTART), the approach assimilates high-resolution WRF meteorological fields (up to 500 m) into the ADMS-Urban system to capture interactions between climate and air quality at the city scale.

Using the July 2022 heatwave in Rome (Italy) as a case study, we demonstrate the capability of this modeling chain to assess how extreme heat conditions influence both thermal comfort and pollutant concentrations in densely populated urban environments. Following sensitivity analyses to identify the most robust model configuration and validation against ground-based observations of meteorological and air quality variables (13 weather stations and 16 air quality stations), the framework is applied to evaluate two greening scenarios and their potential to mitigate heat stress and improve air quality across the metropolitan area.

Results highlight the applicability of this integrated modeling chain as a decision-support tool for assessing urban planning and climate adaptation strategies, with direct implications for enhancing resilience in cities facing growing environmental pressures.

Nella conservazione dei beni culturali rivestono particolare importanza il monitoraggio e la valutazione del microclima relativo all’opera stessa. In questo lavoro consideriamo le condizioni microclimatiche della Chiesa di San Panfilo di Tornimparte (AQ) dove è stata condotta una campagna di misura microclimatica (Ferrarese et al., 2023). La chiesa (XII-XIII secolo) è di grande interesse storico ed artistico in quanto ospita nel presbiterio un ciclo di affreschi del pittore rinascimentale Saturnino Gatti (1494).

Le condizioni microclimatiche sono state misurate per circa un anno in diversi punti all'interno della chiesa e in due siti all’esterno: un primo in prossimità dell'edificio e un secondo presso la più vicina stazione meteorologica. Il presente lavoro si propone di descrivere la campagna di monitoraggio e le misure effettuate durante tutto l’anno e quindi di analizzare le condizioni microclimatiche interne ed esterne. Il clima storico all’interno della chiesa è stato identificato applicando la normativa corrente e la discussione dei risultati ha permesso di identificare eventi potenzialmente pericolosi per la conservazione degli affreschi.

Le condizioni interne ed esterne sono state confrontate utilizzando alcuni indici statistici: PI (Performance Index), IME (Index of Microclimatic Excursion), IMV (Index of Microclimatic Variability), NDR (Normalized Diurnal Range), RHratio (ratio in Relative Humidity) e il raggio minimo dei micropori vuoti (Racca et al., 2024).

I risultati mostrano che tutti gli indici sono in grado di distinguere tra condizioni interne ed esterne, mentre IME, IMV e NDR sono anche sensibili alle diverse condizioni all'interno della chiesa. Tra gli indici, l'IMV sembra descrivere meglio le condizioni microclimatiche, poiché è definito utilizzando sia la temperatura che l'umidità relativa e non dipende da soglie basate sugli standard o sull'esperienza dei curatori. Gli indici si sono dimostrati uno strumento utile per confrontare diverse condizioni microclimatiche e potrebbero essere inclusi nelle pratiche per la valutazione del microclima.

Soiling deposition is a widespread issue affecting photovoltaic (PV) systems of all types, with varying characteristics depending on the geographical location, the season, the prevailing weather conditions, and the geometry of the system (e.g., tilt angle, panel type, surface treatments, etc). There are instruments capable of estimating the presence of soiling on PV panels, such as optical measurement sensors that analyze the type of deposited particles, or comparative techniques like the daily manual cleaning of radiometers. However, these methods are typically expensive and complex, making them unsuitable and economically unfeasible for Commercial & Industrial (C&I) systems. The objective of this study is to estimate the degree of soiling through a comparison between ground-based irradiance measurements using radiometers and irradiance data derived from MSG (Meteosat Second Generation) meteorological satellite observations. By analyzing these two data sources—one affected by soiling and the other independent from it—it was possible to develop a Performance Ratio Index capable of assessing the degree of soiling on the ground-based radiometer, and consequently, the level of soiling on the photovoltaic system itself. Post-processing techniques (such as filtering out low irradiance values, applying temporal moving windows of variable size, etc.) were required to isolate the information related to soiling while eliminating other sources of noise and interference present in the measured data. A comparison with dry and wet deposition events of atmospheric pollutants (dust and PM10), using data from the CAMS (Copernicus Atmosphere Monitoring Service) project, enabled the evaluation of the system’s ability to correctly identify major soiling events, contributing to its calibration and optimization. This automated system for detecting the soiling level in PV installations is particularly suitable for operational use in C&I systems, which can benefit from targeted cleaning interventions that offer a positive economic return when significant soiling is detected.

Wildfires have become increasingly frequent in southern Europe, particularly in Spain, Portugal, Italy, and Greece. Although fire is a natural component of Mediterranean ecosystems, the expansion of recreational use of natural and forest areas has increased the number of human-caused fires. Climate change further exacerbates this situation, intensifying extreme temperatures and droughts and altering two of the three primary drivers of wildfires: fuel and weather.
The Canadian Forest Fire Weather Index (CFWI) is a meteorologically based index widely adopted to estimate fire danger. It requires only temperature, wind speed, relative humidity, and precipitation as input, and consists of six components: three fuel-moisture codes and three fire behavior indices. Although originally developed for Canadian boreal conditions, the CFWI has been applied in Mediterranean regions due to its simplicity and reliance on standard meteorological data. Studies have shown its ability in capturing fire danger in Mediterranean environments, though further evaluation has been recommended, especially in drier landscapes. However, the CFWI is a fire weather index that does not explicitly resolve the moisture content in the basic fuel moisture classes (1h, 10h, 100h, 1000h) required for the initialization of the fuel moisture in modeling systems based on the Rothermel model, such as WRF-SFIRE. This work investigates the integration of fuel moisture into wildfire simulations of Italian case studies using the coupled atmosphere-fire model WRF-SFIRE. The model includes a predictive time lag dead fuel moisture model to resolve spatial and temporal variability in the fuel moisture content, and the influence of fuel flammability on fire spread. This study examines various methods for treating the fuel moisture in the model. We compare the results of simulations performed under three approaches: (i) uniform fuel moisture initialization kept constant during the simulation, (ii) dynamic fuel moisture modeling using a time-lag model initialized from the equilibrium moisture content, and (iii) dynamic fuel moisture modeling with initialization based on fuel moisture values derived from FWI moisture codes. The objectives of this study are multiple. First, to evaluate the skill of WRF-SFIRE in reproducing Italian wildfire events. Second, to quantify the impact of spatial fuel moisture variability in the fuel-moisture initialization on fire spread simulations. A further objective is to assess whether FWI codes can reliably be mapped to dead-fuel moisture classes for initializing WRF-SFIRE. Finally, the study aims to evaluate the capability of WRF to reproduce CFWI time series and thresholds, and to examine the potential of WRF-derived FWI as a diagnostic tool for fire danger in Mediterranean regions.

Research project implemented under the National Recovery and Resilience Plan (NRRP), Project title “National Biodiversity Future Center -NBFC”. CUP J33C22001190001

Micrometeorology plays a fundamental role in atmospheric science by providing the high‑frequency observations needed to quantify surface–atmosphere exchanges and to understand boundary‑layer processes across heterogeneous and complex landscapes. Current research priorities include improving our understanding of canopy–atmosphere interactions and integrating micrometeorological measurements with high‑resolution modeling and remote‑sensing products.

An area where micrometeorology is becoming increasingly important—but remains underrepresented, particularly in mid‑latitude regions such as the Mediterranean—is the quantification of dry and wet deposition of reactive and greenhouse gases. Long‑term, high‑quality deposition measurements are still rare, and micrometeorological techniques offer some of the few direct, ecosystem‑scale approaches for estimating deposition velocities and fluxes in real‑world, heterogeneous environments. Strengthening these measurements is essential for constraining nutrient and pollutant budgets, improving atmospheric chemical‑transport modeling, and deepening our understanding of biosphere–atmosphere coupling.

New tools and approaches—such as advanced turbulence sensors, distributed flux networks, machine‑learning methods for data gap‑filling, and large‑eddy simulations coupled with land‑surface models—are helping to better capture small‑scale processes and enhance weather and climate predictions. Yet many open questions remain, particularly when scaling observations from individual sites to broader regions or comparing data collected with different protocols across networks. Additional challenges, including irregular terrain, shifting airflow patterns, and heterogeneous land cover, continue to complicate flux estimation and the closure of the surface energy balance.

At the regional level, micrometeorology is gaining momentum across a wide range of applications: from agro‑ecosystem monitoring and carbon‑cycle research to urban pollution and climate studies, as well as investigations in mountain and coastal environments, emission inventories, and deposition assessments. National infrastructures—such as coordinated networks like ICOS, which provide consistent long‑term flux and concentration datasets—offer essential support for this growing research landscape.

The turbulent exchange of momentum within and above tall forested canopies plays a central role in regulating surface–atmosphere interactions, particularly in complex ecosystems such as the Amazon rainforest. The dense and heterogeneous canopy structure interacts strongly with the flow, challenging the applicability of classical boundary-layer concepts that assume the presence of a well-defined inertial sublayer. Under non-neutral stratification, these assumptions are further weakened as large coherent structures and submeso motions introduce additional variability and intermittency. Observations from the Amazon Tall Tower Observatory (ATTO) indicate that convective structures from the outer layer in unstable stratification and low-frequency submeso motions in stable conditions disrupt the conventional boundary-layer and mixing-layer frameworks.

To address these challenges, a scale-wise co-spectral budget model is developed to analyze the vertical velocity spectrum E_ww(k_x) and its relationship to vertical momentum transport. The model explicitly balances mechanical production, pressure decorrelation, and longitudinal buoyancy production or destruction in the momentum co-spectral budget. Results reveal that the momentum flux co-spectrum F_wu(k_x) depends not only on E_ww(k_x) but also on the longitudinal heat-flux co-spectrum F_uθ(k_x).

Across both stable and unstable regimes, the scaling of F_wu(k_x)​ is primarily governed by F_uθ(k_x), while E_ww(k_x) exhibits more variable behavior. The analysis identifies a robust k_x^−7/3​ scaling in F_uθ(k_x) across the inertial subrange, whereas the classical Kolmogorov k_x^−5/3​ scaling in E_ww(k_x) is not universally observed. Moreover, the de-correlation time between longitudinal and vertical velocity fluctuations follows ε^−1/3k_x^−2/3​ within the inertial subrange but remains nearly constant for larger scales, independent of stability. These findings highlight the critical role of stratification and canopy structure in shaping multi-scale momentum exchange in the roughness sublayer.

Dry deposition of ozone (O₃) can occur through different pathways, primarily stomatal and non-stomatal. While stomatal uptake is well studied, non-stomatal processes, such as chemical reactions with nitric oxide (NO) and volatile organic compounds, are often overlooked. In particular, the reaction with NO can represent a highly efficient sink, potentially accounting for up to ~20% of total O₃ deposition (Finco et al., 2018).

NO is mainly emitted from soils microbial processes, with forest soils constituting an important global source. Numerous studies have shown that following rainfall events, NO emissions may exhibit strong peaks lasting several days.

The work presented here aimed to investigate O₃ and NO dynamics within a forest ecosystem by comparing summer periods characterized by low and high NO emissions. Measurements were conducted at Bosco Fontana Nature Reserve, a mixed oak–hornbeam forest in the Po Valley. The analysis is based on data collected through eddy covariance flux measurements of O₃ and NO at two heights (above and below canopy), measurements of vertical concentration of gasses, and measurements of soil NO fluxes obtained by means of dynamic soil chambers.

Results provide new evidence of the role of soil-derived NO in forest–atmosphere O₃ exchanges. Periods of elevated soil NO emissions corresponded to increased NO concentrations near the surface (2 and 8 m), indicating a direct influence of soils on the lower atmosphere. At the same time, higher O₃ deposition velocities were observed at 8 and 40 m, consistent with the strengthening of the chemical sink.

Overall, findings emphasize the importance of soil-emitted NO in enhancing non-stomatal O₃ removal in forests, highlighting the need to integrate this process into dry deposition parameterizations.

DECIPHER (Disentangling mechanisms controlling atmospheric transport and mixing over mountains across space–time scales) investigates multi-scale exchanges of energy, momentum, and tracers between the surface and atmosphere, with a focus on thermally driven flows in the Italian Alps. We report the project’s initial observational phase, comprising two coordinated field campaigns along with an overview of topography, instrumentation, and weather during the field campaigns, and present selected case studies.

The first campaign (23 July-29 October 2024) took place at Col Margherita (2543 m ASL) in the Eastern Italian Alps. Here, optical particle counter (OPC) measurements were taken to characterize the fine (diameter < 1 µm) and coarse (diameter ≥ 1 µm) fractions of aerosols over the site. Two episodes of enhanced coarse-mode aerosol at Col Margherita were identified and further investigated using Doppler and Raman lidars and standard meteorological data from Passo Valles (2032 m a.s.l.), 3 km downslope.

The second field campaign (2 October-10 November 2024) was carried out in the Monte Baldo mountain range on a steep east-facing slope. A two-level eddy covariance tower, complemented by a four-way net radiometer, soil sensors, and thermohygrometers distributed along the slope, was deployed to investigate surface-layer processes and turbulent fluxes associated with slope-wind systems. A typical day with fully developed thermally-driven slope winds was highlighted using eddy covariance tower measurements.

Together, the two campaigns represent preparatory efforts aimed at informing and supporting a larger observational initiative scheduled for summer 2025 at the Monte Baldo site under the international cooperation initiative TEAMx (Multi-scale transport and exchange processes in the atmosphere over mountains - programme and experiment).

La chioma forestale rappresenta un’interfaccia cruciale per i processi di scambio tra atmosfera e biosfera, in cui la chimica atmosferica gioca un ruolo determinante nella regolazione della qualità dell’aria, del bilancio energetico e del ciclo del carbonio. In questo contesto, l’ozono troposferico emerge come un attore chiave: da un lato costituisce un ossidante reattivo in grado di degradare composti organici volatili biogenici (BVOC) emessi dalla vegetazione, modulando la formazione di aerosol secondari e radicali ossidrilici; dall’altro, rappresenta un fattore di stress per le piante, influenzando la fisiologia fogliare e gli scambi gassosi. Nonostante i progressi sperimentali e modellistici, la chimica intra-canopy rimane tuttora non completamente compresa, a causa della complessità delle interazioni tra microclima, emissioni biogeniche e processi di deposizione (Monson & Holland, 2001; Goldstein et al., 2004). La comprensione delle dinamiche dell’ozono all’interno della chioma, incluse le sue interazioni con superfici fogliari, emissioni biogeniche e processi microclimatici, è quindi essenziale per valutare l’impatto delle foreste sulla composizione atmosferica e, reciprocamente, gli effetti della qualità dell’aria sulla funzionalità ecosistemica.
La velocità di deposizione rappresenta infatti uno dei due principali driver della deposizione. In questo lavoro verranno analizzati i dati di velocità deposizione per l’ozono, ottenute tramite la tecnica dell’eddy covariance da misure effettuate al di sopra (40 m) e all’interno (8 m) della canopy forestale di Bosco Fontana (Marmirolo, MN). Verrà mostrata l’influenza dei principali driver ambientali (temperatura, umidità relativa, radiazione, friction velocity, accoppiamento/disaccoppiamento tra chioma e atmosfera), vegetativi (presenza o meno di foglie) ed emissivi (emissione di NO dal suolo) in due annate contrastanti dal punto di vista meteorologico.

Urban areas, responsible of over 70% of CO₂ emissions, are one of the most important sources of GHG gases. Accurate quantification of city emissions through direct observations is crucial for assessing the effectiveness the adopted mitigation strategies.

As part of the ICOS Cities project (https://www.icos-cp.eu/projects/icos-cities), four eddy covariance towers were installed in the Paris area to capture the variability of CO₂, heat and radiation fluxes across an urban-to-rural gradient. The selected sites were chosen to be representative of a highly urbanised and densely built-up area (Jussieu), an urban forest (Vincennes), a semi-urban area (Saclay) and a heterogeneous area combining highly urbanised zones with vegetated patches (Romainville). Additionally, the observations from the urban sites were integrated with the EC flux measurements from the ecosystem sites of Fontainebleau (FR-FON, forest) and Grignon (FR-GRI, crop).

CO₂ and heat flux measurements showed seasonal dynamics that reflected the respective degrees of urbanisation of the sites and the presence of biogenic sinks. At the urban sites of Jussieu and Romainville, the sensible heat flux, H, was generally higher than the latent heat flux, LE. At Jussieu in particular, H remained positive throughout the day and night, indicating the presence of a local heat source. In contrast, the forested site of Vincennes exhibited higher latent heat fluxes than in Jussieu and Romainville, often with similar intensity to sensible heat fluxes. Our analysis identified both Jussieu and Romainville as net sources of CO₂, with the highest daily emissions during the winter months, and slight daytime CO₂ uptake during summer. However, the two sites displayed distinct diurnal CO₂ flux patterns due to the different temporal variability of dominant emission sources (stationary combustion and traffic) or to people commuting from the city center (Jussieu) during the day to residential areas (Romainville) at night. The mixed urban forest of Vincennes showed instead strong biogenic signature, with CO₂ fluxes characterized by daytime uptake (down to -10 µmol m⁻²s⁻¹) during the growing season. A comparison between EC flux measurements and emission inventories estimates for the city of Paris will be presented.

Ozone dry deposition to vegetated surfaces is caused by stomatal uptake made by plant leaves and by ozone disruption on external vegetal surfaces, like leaf cuticles, and soil.

Although the stomatal deposition pathway has been widely studied because of its implications for the negative effect of ozone on plant growth and productivity, the non-stomatal deposition pathways have been scarcely addressed for long time, and they are not completely understood yet.

In 2020, Clifton et al. proposed for the first time a mechanistic model to predict non-stomatal deposition of ozone on dry and wet leaf cuticles as well as on soils. However, the model was purely theoretical, and no attempts were made to validate it using real measurements on the field.

This work presents the first attempt to calibrate and validate Clifton’s model of non-stomatal resistances for a broadleaf forest. For this purpose, the total ozone deposition resistance obtained by implementing the Clifton’s model was compared with that derived from the eddy covariance flux measurements made in 2021 and 2022 at Bosco Fontana (Italy). Moreover, the resistance partitioning allowed a closer comparison with the cuticular resistances predicted by the model.

The soil deposition resistance of ozone was then compared with that measured by means of automatic soil chambers at the same site in 2024 and 2025.

The results showed good agreement with the Clifton’s prediction for the cuticular resistance, and less satisfactory agreement with soil resistance to O3 deposition. This seems to be due to the litter that covers the soil beneath the forest canopy, which alters the expected responses of Clifton’s model for (bare) soils.

Clifton’s model has the potential to describe non-stomatal deposition in forests. However, it requires modification to account for real field conditions in which litter covers forest soils, and to account for chemical sinks in the trunk space, which are completely neglected by the model.

Il presente studio mira a valutare l’impatto degli elementi urbani e vegetativi sulla dispersione degli inquinanti provenienti da fonti di traffico all’interno dello strato di canopia urbana, con particolare attenzione alla microscala urbana e allo strato pedonale. L’inquinamento atmosferico urbano di origine veicolare rappresenta un rilevante rischio per la salute pubblica, soprattutto quando tende ad accumularsi al livello pedonale, dove i cittadini risultano maggiormente esposti. La complessità del tessuto urbano influisce in modo significativo sulle dinamiche del flusso d’aria, potendo determinare effetti inattesi sull’efficienza dei processi di dispersione degli inquinanti.

Come caso di studio è stato selezionato un quartiere del centro storico di Bologna (Italia), caratterizzato dalla presenza di strade ad alto traffico, alberature stradali e aree verdi. La via principale e le aree circostanti vengono analizzate in dettaglio per indagare la relazione tra ventilazione e complessità del tessuto urbano, con l’obiettivo di riprodurre realisticamente tale interazione. Le dinamiche del flusso d’aria e la distribuzione della concentrazione degli inquinanti sono state investigate combinando esperimenti di laboratorio e simulazioni numeriche ad alta risoluzione. Le dinamiche dello strato di canopia e i meccanismi di dispersione degli inquinanti sono riprodotti sperimentalmente sfruttando il canale idraulico del Dipartimento di Ingegneria Civile, Edile e Ambientale dell’Università di Roma “La Sapienza” e un modello in scala (1:1000) dell’area di interesse.I risultati sperimentali sono stati impiegati per la validazione del modello numerico. L’impiego di un modello Reynolds-Averaged Navier–Stokes (RANS) consente di approfondire il ruolo delle caratteristiche turbolente nelle interazioni tra vegetazione, vento e inquinanti. Le simulazioni sono inoltre condotte con dati di input realistici, ottenuti dalle stazioni di monitoraggio cittadine, per analizzare nel dettaglio l’interazione tra morfologia urbana e condizioni meteorologiche reali.

Lo studio presentato fa parte di un progetto più ampio volto alla creazione di un catalogo di Nature Based Solutions, considerando sia gli effetti positivi che quelli potenzialmente negativi (GREEN POLIS, PRIN 2022).

The surface energy balance (SEB), i.e., the partitioning of the energy exchange between the Earth’s surface and the atmosphere, is crucial for defining atmospheric boundary layer characteristics and evolution. An accurate assessment of its components is essential for a variety of applications. However, measurements of the SEB terms are still affected by uncertainties. In particular, eddy-covariance measurements of turbulent heat fluxes typically do not balance the available energy. Studies suggest this discrepancy primarily results from advection driven by secondary circulations, prevalent over heterogeneous and complex terrain as a consequence of differential heating.

This study aims to analyze the eddy-covariance measurements from a tower located in Mezzolombardo, in the Alpine Adige Valley (Trentino - Italy), to investigate the SEB closure. The analysis focuses on the assessment of the relationship between SEB non-closure, surface heterogeneity, and the resulting development of local and mesoscale thermally-driven circulations. Objective criteria to select days with the development of thermally-driven circulations are used, refining the method proposed by Lehner et al. (2019). The impact of various eddy-covariance data processing techniques, including averaging and rotation approaches, on flux estimates and SEB closure is quantified. Moreover, analyses conducted on other Alpine sites within diverse complex terrains (e.g., valley floor, valley slope, mountain top) will also be presented.

The overall results provide a systematic quantification of the non-closure of the SEB in several typical Alpine contexts, highlighting similarities and differences between sites located in various topographic and land cover settings and under different meteorological conditions.

The present work is part of the INTERFACE project (INvestigating ThE suRFACe Energy balance over mountain areas), which is performed in the framework of the TEAMx research programme.

Hailstorms represent one of the most damaging convective phenomena, with severe consequences for agriculture, infrastructure, and society. Their prediction remains challenging due to the multiscale nature of the processes involved, from mesoscale dynamics to microphysical growth mechanisms. In this study, Artificial Intelligence (AI) is employed to identify atmospheric conditions favorable to hail over the Continental United States (CONUS). Vertical atmospheric profiles from radiosonde soundings serve as predictors, while hail occurrences are derived from NOAA ground-based observations, allowing the problem to be framed as a binary classification (hail vs. no-hail).
An Extreme Gradient Boosting (XGBoost) model was trained using a balanced strategy to mitigate class imbalance and tested on the original unbalanced dataset. The model exhibited robust performance, successfully discriminating hail-producing environments. To assess the robustness and transferability of the approach, a comparison was performed between results obtained using real radiosonde data and synthetic soundings extracted from the GFS model. The analysis highlights a trade-off between model skill and data availability, suggesting opportunities for application in regions with limited sounding coverage, such as Italy.
Finally, SHAP-based interpretability analysis confirmed the physical consistency of key predictors and offered new insights into their role in hail formation.

Accurate representation of atmospheric dynamics at convection scale remains a major challenge for numerical models and a key factor in operational weather predictions. Reliable initial conditions, generated through data assimilation using observations from multiple platforms, are essential to improve forecasts in deep convection environments. In this study, the ICOsahedral Non-hydrostatic (ICON) model is applied at convection-permitting scale over Italy, following the operational configuration of Arpae Emilia-Romagna and adopted by the ItaliaMeteo Agency. ICON is coupled with the Local Ensemble Transform Kalman Filter (LETKF) within the Kilometre-scale Ensemble Data Assimilation (KENDA) system. Focusing on a poorly predicted extreme convective storm in the Marche region, we show the positive impact of convection-scale data assimilation based on conventional and radar observations. Nevertheless, precipitation remains substantially underestimated when relying solely on these datasets. We highlight the crucial role of low-level moisture convergence in convection initiation and the significant undersampling of humidity in conventional data. To address this, we investigate the added value of humidity-sensitive microwave radiances from polar satellites, still rarely employed in limited-area models worldwide. Assimilation of clear-sky observations from the Microwave Humidity Sounder (MHS) leads to notable improvements in precipitation forecasts. To extend further the rich multi-platform dataset, infrared all-sky radiances in water vapor absorption channels from the geostationary Meteosat Second Generation SEVIRI instrument are integrated, providing higher spatial and temporal resolution. The relative contributions of these observation types are analyzed, including their positive effects on surface and upper-level variables and convective indices. This study also supports the future operational assimilation of satellite radiances in the Arpae and ItaliaMeteo system.

Extreme precipitation events represent a growing global challenge, particularly affecting the Mediterranean basin, where their frequency and intensity continue to rise. Accurate numerical weather prediction models are crucial to effectively forecast these events; however, operational models often struggle with capturing the precise magnitude and location of intense convective storms. This limitation mainly arises from coarse model resolution and reliance on convection parameterizations. This study explores the potential benefits of hectometric-scale numerical modelling (500-meter resolution) by employing the ICON model to investigate the devastating floods that impacted Italy’s Marche region in September 2022 and another hail-driven case study affecting the Black forest and the Swabian-Jura region in Germany in August 2023. Through sensitivity tests and ensemble simulations, we demonstrate substantial improvements in the representation of extreme precipitation events. Our results show that hectometric-scale simulations, combined with explicit convection and the Smagorinsky turbulence scheme, significantly enhance the accuracy and realism of precipitation forecasts compared to traditional coarser-resolution operational setups. Further, a focus on perturbations of microphysical cloud parameters is analysed. The study highlights the crucial influence of initial and boundary conditions and choice of microphysics schemes on forecast quality, with specific ensemble members clearly outperforming others due to their accurate representation of critical atmospheric features, such as wind convergence and moisture transport. These findings underline the necessity and added value of very high-resolution ensemble modelling approaches, coupled with advanced turbulence parameterizations, for improving the predictability of extreme convective rainfall and hail.

Negli ultimi decenni, il cambiamento climatico ha portato a un aumento significativo della frequenza e dell’intensità di eventi meteorologici estremi, come forti precipitazioni e piene improvvise, con conseguente incremento del rischio idrogeologico e della vulnerabilità di ecosistemi e infrastrutture urbane. Tali fenomeni, caratterizzati da forte variabilità spaziale e temporale, risultano particolarmente impattanti nelle aree urbane, dove la copertura impermeabile del suolo, l’alta densità abitativa e la presenza di infrastrutture critiche amplificano le conseguenze di allagamenti ed esondazioni.

Il sistema idraulico di Milano rappresenta un caso emblematico: corsi d’acqua naturali e canali artificiali si intrecciano strettamente con il tessuto urbano. In particolare, le piene del Fiume Seveso causano allagamenti ricorrenti nel quartiere Niguarda, a nord della città, provocando danni diffusi a persone, infrastrutture e mobilità.

In questo scenario, la capacità di prevedere con precisione variabili meteorologiche e idrologiche a brevissimo termine risulta fondamentale per gestire il rischio e sviluppare sistemi di allerta tempestivi. Questo studio propone l’impiego di modelli di machine learning, come LDCast e GPTCast, sviluppati dalla Fondazione Bruno Kessler di Trento, per la previsione radar in chiave nowcasting. Le stime prodotte da questi modelli vengono poi utilizzate sia come input per modelli idrologici fisicamente basati sia in algoritmi di intelligenza artificiale sviluppati dal Politecnico di Milano.

L’obiettivo dello studio è valutare le prestazioni complessive del sistema previsionale e dimostrare come esso possa rappresentare un importante passo avanti nell’implementazione di sistemi di allerta a brevissimo termine.

Medicanes (MEDIterraean HurriCANES) are meteorological events with the potential to cause devastating floods, storm surges, and windstorms, often leading to significant disruption and casualties. During their mature phase, they exhibit phenomenological features typical of tropical cyclones, notably the warm core (WC), a warm area spanning the mid-to-upper troposphere. The critical need for rapid, automated identification of this feature motivates this work.

This study details the development of an automated WC identification system in the context of the ESA MEDICANES project (https://medicanes.isac.cnr.it/) that exploits passive microwave measurements from Low Earth Orbit (LEO) satellites, using four channels within the oxygen absorption band as input. The data corpus for this study was derived from approximately 30,000 satellite overpasses across three instruments from 2000–2020, encompassing 770 Mediterranean cyclones.

The base algorithm is a Convolutional Autoencoder-based Semi-Supervised Anomaly Detection (AE-SAD) model, trained primarily on "normal" (non-WC) atmospheric cases, complemented by a limited set of labelled “anomalies” (WC cases). The model’s efficacy is founded on reconstruction error: normal inputs are reconstructed with minimal error, whereas the distinctive features of WC anomalies are characterized by a significantly higher error, utilized as the anomaly score. Preliminary results suggest the AE-SAD system provides robust differentiation, with high reconstruction error scores reliably flagging WC events.

To rigorously validate the model's performance, accuracy, and overall utility, the study compares systematically the AE-SAD model against a comparative algorithm, quantified using standard metrics such as the F1-score and Area Under the Curve (AUC), implicitly prioritizing Recall, as both metrics reward models that correctly identify the minority class (the rare medicanes). This research is anticipated to provide a critical, automatic tool for the near-real-time identification of medicanes' WC, significantly improving lead time for hazard prediction and risk assessment in the Mediterranean region.

Methane (CH₄) monitoring is a global priority for climate mitigation strategies, as highlighted by initiatives such as the Global Methane Pledge. While satellite observations have advanced our ability to quantify methane emissions across scales, current products do not provide information on the vertical distribution of CH₄, a key parameter for identifying emission sources and understanding atmospheric processes. In this context, Physics-Informed Neural Networks (PINNs) offer new opportunities by embedding physical constraints into machine learning models, thereby enhancing both accuracy and efficiency compared to traditional retrieval methods. The PRIN-MVP (Methane Vertical Profiling) project develops a PINN-based approach to retrieve CH₄ vertical profiles from hyperspectral infrared observations. The methodology was trained and tested on about one million synthetic spectra simulated under clear-sky conditions with the σ-IASI/F2N radiative transfer model, supported by auxiliary atmospheric parameters. To optimize the information content, only the most sensitive spectral channels, identified through Averaging Kernel analysis, were retained. Both spectral data and atmospheric profiles were compressed using principal component analysis (PCA). The model was validated using real spectra relating to two case studies: the Mediterranean basin in spring 2025, during episodes of Saharan dust intrusion, and the sabotage of the Nord Stream gas pipeline in the Baltic Sea. Results demonstrate that the PINN-based approach accurately identifies anthropogenic methane emissions and consistently reconstructs vertical profiles of CH₄. This work highlights the potential of PINNs for regional-scale methane monitoring and contributes to the development of innovative tools for atmospheric greenhouse gas analysis. In the future, the model could be extended to CO₂.

Each austral spring, the Antarctic ozone hole develops and reaches its maximum extent between October and November, before disappearing in December as stratospheric temperatures rise. The seasonal warming inhibits the formation of Polar Stratospheric Clouds (PSCs), which provide the surfaces that catalyze the reactions responsible for ozone destruction. PSCs appear when temperatures fall below 195 K, enabling the condensation of nitric acid and water vapor into crystalline particles (mainly HNO₃·3H₂O or NAT). Ozone depletion is routinely monitored from space by UV–visible instruments such as OMI (the Ozone Monitoring Instrument) and TROPOMI (Tropospheric Monitoring Instrument). However, their dependence on reflected sunlight limits their capability during the polar night and under persistent cloudy conditions, typical of the Antarctic winter, when the interior of the continent remains largely unobserved. In addition, these sensors are insensitive to nitric acid and water vapor in the gas phase. Microwave limb sounders, such as MLS/AURA, provide complementary HNO₃ data but at coarse spatial resolution and without information on the thermodynamic state of the Upper Troposphere–Lower Stratosphere (UT/LS). Recent improvements in radiative transfer modeling have made it possible to retrieve temperature, ozone, and nitric acid simultaneously from IASI (Infrared Atmospheric Sounding Interferometer) infrared spectra, even in cloudy scenes. The model we developed is specifically designed to exploit IASI’s high spectral resolution, enabling the retrieval of key atmospheric parameters over Antarctica in both clear and cloudy conditions, thus extending observational coverage during the polar night. Analysis of IASI observations from 2021–2023 reveals a deeper and more extended ozone hole than shown by ECMWF analyses that assimilate OMI and TROPOMI data. The results indicate a clear relationship between decreasing HNO₃ concentrations and temperatures below 195 K, confirming the formation of NAT particles. Spatial patterns of HNO₃ retrieved from IASI closely match those from MLS/AURA, highlighting the robustness of our approach in capturing ozone–nitric acid interactions in the cold and cloud-covered Antarctic atmosphere.

Nell’ambito della convenzione tra il CNR-ISAC e il Dipartimento della Protezione Civile (DPC) è in fase di sviluppo un nuovo strumento per la validazione e il confronto continuo, in near real time (NRT), di diversi prodotti di precipitazione provenienti sia da piattaforme satellitari sia da misure, stime e osservazioni a terra. L’obiettivo principale è fornire un sistema integrato, automatizzato e scalabile in grado di monitorare e valutare in maniera costante l’affidabilità, la qualità e la coerenza dei diversi prodotti disponibili, con particolare enfasi sull’area italiana, dove la complessità orografica e climatica rende particolarmente interessante l’analisi proposta.

La metodologia adottata trae origine dall’esperienza consolidata nel progetto europeo H SAF di EUMETSAT ed è stata ampliata per includere un ventaglio di analisi a diverse scale temporali: dal singolo evento, all’analisi giornaliera, mensile, stagionale e di lungo periodo. Attualmente il sistema integra oltre 20 prodotti di precipitazione provenienti da sensori satellitari, reti pluviometriche e misure radar internazionali e consente di effettuare più di 60 confronti sull’intero globo.

I risultati ottenuti dimostrano le potenzialità dello strumento nell’evidenziare differenze sistematiche, complementarità e criticità tra prodotti, fornendo statistiche dettagliate sui singoli confronti (indici di accuratezza, bias, correlazioni, distribuzioni spaziali e temporali) e una visione d’insieme utile al monitoraggio e alla valutazione comparativa dei prodotti. Tale approccio fornisce al tempo stesso una base solida per attività di calibrazione e miglioramento dei prodotti di stima della precipitazione, oltre che per supportare decisioni operative e strategie di gestione del rischio idro-meteorologico.

L’obiettivo a medio termine è l’ampliamento progressivo del numero di prodotti gestiti e delle tipologie di confronti effettuati, così da costruire un quadro sempre più completo e robusto delle performance dei diversi prodotti di stima delle precipitazioni. L’architettura, concepita come flessibile ed estendibile, potrà essere ulteriormente potenziata con l’integrazione di nuovi dati, metodologie avanzate di confronto basate anche su tecniche di machine learning, e strumenti di visualizzazione interattiva atti a facilitare l’esplorazione e la disseminazione dei risultati verso comunità scientifica, enti istituzionali e utenti finali.

Clouds are a crucial regulator of the Earth’s radiation budget (ERB), making the detection and characterization of their properties essential for meteorological research. However, observing clouds in the Antarctic region is challenging: direct in situ data are limited by extreme environmental conditions, while satellites face difficulties in distinguishing clouds from the underlying snow or ice (Cossich et al., 2021). The scarcity of reliable measurements also contributes to the poor representation of Antarctic clouds in General Circulation Models (GCMs), where cloud microphysics parameterizations are often based on mid-latitude or tropical data (Lachlan-Cope, 2010; Bromwich et al., 2012).

This study aims at characterizing in detail the properties of Antarctic clouds by analyzing their seasonal variability and correlation with surface variables, and to develop a parameterization of ice cloud effective dimensions as a function of the thermodynamic conditions of the layer. To achieve this, we use an extensive dataset of spectrally resolved downwelling radiances in the far- and mid-infrared spectral range (200–1000 cm^-1), collected by the REFIR-PAD spectroradiometer (Radiation Explorer in the Far Infrared – Prototype for Applications and Development) installed at Concordia Research Station, Antarctica, spanning 2013–2020.

The radiances are processed using the Cloud Identification and Classification (CIC) algorithm (Maestri et al., 2019; Donat et al., 2024) to identify cloud layers and classify their phase (ice or mixed). These spectra are then used as input to the Simultaneous Atmospheric and Cloud Retrieval (SACR) algorithm (Di Natale et al., 2020), which retrieves cloud optical depth, effective dimensions, and atmospheric profiles of water vapor and temperature. Retrievals are further constrained by a-priori information on cloud-base and cloud-top heights, obtained from the Polar Threshold (PT) algorithm (Van Tricht et al., 2014) applied to collocated lidar backscatter profiles.

Results indicate that, despite strong seasonal temperature variations (213–257 K in summer; ~195–245 K in other seasons), the occurrence of both ice and mixed-phase clouds is consistently associated with relatively warmer near-surface air. Seasonal analysis further reveals a pronounced semi-annual cycle in cloud occurrence and ice cloud optical depth, superimposed on the expected annual variability. Finally, a parameterization of ice cloud effective diameters as a function of layer temperature and ice water content is developed and evaluated against existing literature.

The LUCE mission, formerly CALIGOLA, is an advanced multi-purpose space lidar mission, exploiting elastic (Rayleigh-Mie), depolarized, Raman and fluorescent lidar echoes from atmospheric and ocean constituents, with a focus on atmospheric and oceanic observation aimed at characterizing the Ocean-Earth-Atmosphere system and the mutual interactions within it. This mission has been conceived by the Italian Space Agency (ASI) with the aim to provide the international scientific community with an unprecedented dataset of geophysical parameters capable of increasing scientific knowledge in the areas of atmospheric, aquatic, terrestrial, cryospheric and hydrological sciences. The Italian Space Agency is partnering with NASA on this exciting new space lidar mission. The mission is planned to be launched in 2032, with an expected lifetime of 3-5 years. Scientific studies in support of the mission are ongoing, commissioned by the Italian Space Agency to University of Basilicata and ISMAR-CNR. A Phase A study, commissioned by the Italian Space Agency to Leonardo S.p.A. and focusing of the technological feasibility of the lidar payload, was carried out starting in October 2022 and has recently bridged into a Phase A/B1 study (kick-off in March 2025). Phase A/B1 activities for the platform and the end-to-end system, commissioned by the Italian Space Agency to Thales Alenia Space, have also started, with kick-off in March 2025. In September 2023, NASA-LARC initiated a pre-formulation study to assess the feasibility of a possible contribution to the LUCE mission focused on development of the detection system and sampling chain and the implementation of data down link capabilities. The pre-formulation study ended in September 2024, and, after a successful Mission Concept Review, a phase A/formulation study started in January 2025. This presentation will provide details on current status and future steps of this groundbreaking multidisciplinary lidar mission.

In anticipation of the forthcoming launch of ESA’s 9th Earth Explorer, the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) satellite, substantial effort is being devoted to the development of advanced algorithms and databases to fully exploit the mission spectrally resolved radiance measurements across the 100–1600 cm-1 interval.

Within this framework, we present recent advances in the sigma-FORUM fast radiative transfer model [Masiello et al., 2024], designed to provide accurate and computationally efficient simulations of FIR radiances under cloudy-sky conditions. A key new feature is the implementation of a temperature-dependent optical property dataset for column aggregate ice particles [Ren et al., 2025], covering the 160–270 K range. This addition enables a more consistent treatment of the radiative impact of ice clouds in the far-infrared, where sensitivity to ice particle microphysics is significant.

The relevance of the new dataset for the interpretation of FORUM observations is assessed through multiple-scattering simulations of far-infrared outgoing longwave radiation, performed across a range of representative ice cloud conditions.

Finally, the performance of the iota retrieval scheme (Martinazzo, Maestri et al., in prep.), integrating sigma-FORUM with a Tang Chou adjustment scheme [Tang et al., 2019], are evaluated both on simulated and measured cloudy-sky observations from the Infrared Atmospheric Sounding Interferometer, IASI.

This study investigates the relationship between Air Temperature (AT), tree canopy cover, and imperviousness in Rome, Italy, using a novel approach based on low-cost sensors mounted on public buses. The system operates autonomously, requiring no on-site personnel, and provides continuous mea- surements across the entire urban area and at all hours of the day. Data were collected over 53 clear-sky summer days under stable meteorological condi- tions and aggregated onto a 500 m grid after quality control and normaliza- tion. Results show a strong linear correlation between AT and canopy cover during morning hours, with an estimated cooling potential of up to −1.6°C at 100% cover. This effect disappears after the onset of the sea breeze, high- lighting the role of wind in suppressing local cooling. At night, AT exhibits a strong linear increase with imperviousness, with differences up to 3.6°C be- tween fully urbanized and non-urbanized areas. The diurnal cycle of Urban Heat Island (UHI) intensity, derived from the imperviousness-based method, is consistent with theory and previous studies, showing negligible values dur- ing daytime and peaks of 3–4°C at night. By leveraging automated, citywide measurements with low-cost sensors, this study provides new insights into the spatial and temporal variability of urban heat and supports the development of targeted adaptation strategies.

Accurate air temperature measurements in environmental monitoring networks are essential for various applications in meteorology, hydrology, climatology and ecology. Hence, manufacturers of weather stations have been continuously introducing new improvements to take advantage of technological progress and achieve precise measurements and, as much as possible, unaffected by spurious effects. However, while electronic temperature sensors have reached very high levels of accuracy and reliability, radiation shields (RS) remain the most critical component affecting measurement quality. In the last decades, following the increasing diffusion of electronic systems worldwide, the traditional Stevenson screens, as well as their American counterpart, the Cotton Regional Shelter (CRS), have been widely replaced either by Passive Radiation Shields (PRSs), i. e. shields that do not include any active device to prevent sensor overheating, or by Forces Aspiration Radiation Shields (FARS), which instead include systems ensuring suitable sensor ventilation. This study presents a field intercomparison of four of the most advanced PRSs commonly used in operational meteorological applications. During eight months (April-November 2024), identical temperature sensors, protected by different shields, were installed in an experimental field in Friuli Venezia Giulia (Italy). The resulting data were compared using objective statistical analyses based on multiple criteria. Results indicate that the BAR-3, while highly responsive, is prone to overcooling in wet conditions and overheating at low solar elevations. The RAD10 offers good moisture protection and balanced performance, particularly in humid climates, but responds more slowly. The SMART provides reliable behavior in most conditions, especially in dry or temperate climates, with good responsiveness and limited moisture retention. The COMET shows the best nighttime performance and strong moisture shielding, but is the most susceptible to daytime overheating and has the lowest responsiveness due to its thermal inertia. Results from this study provide scientific support for the identification of possible new reference instruments for operational air temperature measurements, as far as traditional shelters are no longer a global standard.

How does climate change impact extreme events and which is the future change of their dynamics? How will the ongoing and future changing climate control the evolution and intensification of severe storms? The Hail Hazard in the Mediterranean (H2Med) project tackles these open issues by investigating hailstorms in the Mediterranean region through the synergistic application of satellite observations, meteorological reanalysis and climatic modelling. Extending and refining the preliminary 22-year climatology proposed in Laviola et al. (2022; 2023) at daily scale, the large-scale and mesoscale atmospheric scenarios that trigger hail events in some regions of the central Mediterranean area are investigated through a cluster analysis using ERA5 reanalysis data. Hail-prone conditions associated with the optimization of a hail-proxy index (based on reanalysis products) are also investigated through the ensemble of climate model projections to outline the future evolution of hail events over the Mediterranean Basin.

The results of this project offer a new paradigm of knowledge and operative tools for better understanding the effects of climate change on hailstorms by using hail-bearing convective systems as a driver for evaluating the potential impact of future changes in the Mediterranean basin. Thus, a new vulnerability map, where past events, current occurrences and future scenarios will be stressed, will be generated to serve the scientific community, operational forecasters, stakeholders such as insurance companies and policymakers.

References

Hail Hazard in the Mediterranean (H2Med) website: https://h2med.isac.cnr.it

Laviola, S., G. Monte, E. Cattani, and V. Levizzani, 2022: Hail climatology in the Mediterranean Basin using the GPM constellation (1999–2021), Remote Sens., 14(17), 4320, https://doi.org/10.3390/rs14174320.

Laviola, S., G. Monte, E. Cattani, and V. Levizzani, 2023: How hail hazards are changing around the Mediterranean, Eos, 104, https://doi.org/10.1029/2023EO230070. Published on 27 February 2023.

Atmospheric transport processes over mountainous terrains are intrinsically affected by a variety of landforms, surface covers, atmospheric stability situations and interactions among different airflows, typical occurring on multiple space and time scales. Our understanding of the interplay of such factors is still far from being fully understood. In particular, the peculiar properties of atmospheric turbulence controlling the exchanges of momentum, heat and mass between the land surface, the atmospheric boundary layer and upper levels are still largely unexplored.

As a contribution to filling these gaps, the research project DECIPHER implemented observational and modelling actions aimed at disentangling mechanisms controlling atmospheric transport and mixing processes over mountain areas at different space- and timescales, in the framework of the larger international research effort TEAMx (Serafin et al. 2018, Rotach et al. 2021).

As part of the project, field measurements were performed at selected areas to investigate transport and exchange processes associated with thermally-driven slope winds and other local winds, and their connections with various ambient and weather conditions.

In particular, an intensive field campaign was organised over an east-facing steep slope of Monte Baldo (45°39'56.0"N, 10°49'10.9"E), an approximately north-south oriented mountain range in the Southern Alps. Several different instruments were operated from mid-June to mid-October 2025 to cover atmospheric processes at different scales. Turbulent processes were monitored through multi-level flux towers installed at different elevations, along with thermohygrometers distributed along the slope to capture the vertical structure of the ambient atmosphere. Mass and optical sensors monitored concentrations and properties of particulate matter. The 3-D structures of local along- and cross-slope winds and vertical temperature profiles were also observed with multiple wind lidars based at different points. Moreover, tropospheric profiles were obtained from a tethered balloon and a Raman lidar. Further non-conventional measurements included high-frequency profiling of turbulence near the surface and distributed soil moisture monitoring using a cosmic-ray neutron sensor.

Preliminary outcomes from the analysis of the resulting huge dataset allow for identifying interesting patterns of local circulations and their connections with turbulence structure, as well as with the surrounding flow and stability conditions, under different weather situations.

Besides research achievements, DECIPHER was also a remarkable and unique opportunity to consolidate relationships of scientific cooperation among the numerous partners involved, and to successfully test new observational techniques and logistic solutions for the nontrivial deployment of such a major field campaign. Hence it contributed to build-up It was a great opportunity for growing a scientific community characterised by complementary expertise, a remarkable amount of personnel involved, and the numerosity and diversity of instruments enabled to plan and perform the unusual effort adequate for phenomena, whose complexity would not be captured with any fewer instruments. Buildup of a scientific community

References

Rotach, M.W., Serafin, S., Ward, H.C., Arpagaus, M., Colfescu, I., Cuxart, J., De Wekker, S.F.J., Grubišic, V., Kalthoff, N., Karl, T., Kirshbaum, D.J., Lehner, M., Mobbs, S., Paci, A., Palazzi, E., Bailey, A., Schmidli, J., Wittmann, C., Wohlfahrt, G. and Zardi, D. (2022): A collaborative effort to better understand, measure and model atmospheric exchange processes over mountains. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-21-0232.1

Serafin, S., Adler, B., Cuxart, J., De Wekker, S.F.J., Gohm, A., Grisogono, B., Kalthoff, N., Kirshbaum, D.J., Rotach, M.W., Schmidli, J., Stiperski, I., Večenaj, Ž. and Zardi D. (2018): Exchange Processes in the Atmospheric Boundary Layer Over Mountainous Terrain. Atmosphere, 9, 102. https://doi.org/10.3390/atmos9030102

Studi recenti di eventi di precipitazioni estreme e alluvioni che hanno interessato l’Italia centro-settentrionale e l’area alpina in particolare hanno rivelato che oltre al contributo locale dovuto all’evaporazione dal Mar Mediterraneo, una quantità rilevante di umidità può giungere da regioni remote per mezzo di un intenso trasporto confinato all’interno di corridoi lunghi e stretti, noti come fiumi atmosferici.

Il progetto nazionale ARMEX, finanziato nell’ambito PRIN2022 dal Ministero dell’Università e della Ricerca, ha l’obiettivo di esplorare i fiumi atmosferici nel Mediterraneo e la loro connessione con eventi idrometeorologici estremi sull’Italia. Il progetto coinvolge competenze sia nella modellistica meteorologica e idrologica, sia nel monitoraggio da satellite. Vengono qui presentati alcuni risultati del progetto.

Utilizzando le rianalisi ERA5 e il dataset di precipitazioni ArCIS, e applicando un algoritmo di identificazione, opportunamente adattato alla peculiare e complessa morfologia della regione, è stato possibile evidenziare le principali caratteristiche climatologiche dei fiumi atmosferici nel Mediterraneo e la loro connessione con eventi idrometeorologici estremi sul centro-nord Italia dal 1960 ad oggi. Inoltre, attraverso l’analisi di diversi casi studio, simulazioni numeriche ad alta risoluzione hanno dimostrato che la presenza di un intenso fiume atmosferico – proveniente dalle aree tropicali dell’Africa o dall’Atlantico – rappresenta un elemento distintivo degli eventi estremi. Gli esperimenti modellistici hanno permesso di investigare le caratteristiche, i meccanismi dinamici e gli impatti dei fiumi atmosferici, i quali si sono rivelati un ingrediente fondamentale per il verificarsi di precipitazioni estreme. Infine, si sta esplorando la predicibilità a scala sub-stagionale di eventi estremi caratterizzati dalla presenza di fiumi atmosferici.

The thermal and moisture regimes of coastal cities arise from the interplay between mesoscale atmospheric circulations and local urban form, creating highly heterogeneous microclimates that directly affect heat exposure, energy demand, and outdoor comfort. This study presents an integrated multi-source approach to evaluate thermo-hygrometric variability in Bari (southern Italy), a representative Mediterranean coastal city characterized by strong land–sea interactions. The analysis combines in situ observations, remote-sensing data, and urban-canopy modelling to disentangle the relative roles of mesoscale forcing and microscale heterogeneity. Eight canyon-level sensors were deployed across districts with distinct morphology, vegetation density, and distance from the coastline, continuously monitoring air temperature and, at five sites, relative humidity during the summer of 2023. These measurements were complemented with high-resolution ECOSTRESS land-surface-temperature (LST) data and numerical simulations from the Multi-Layer Urban Canopy Model (MLUCM BEP+BEM) driven by ERA5 reanalysis. Morphological and radiative parameters around each site were quantified within a 500 m radius following the Local Climate Zone (LCZ) classification. Statistical analyses based on repeated-measures ANOVA quantified temporal and spatial variability in air temperature and humidity, enabling a robust assessment of the significance of observed differences. Results show that nocturnal temperature differences across Bari are relatively limited (< 2 °C), while after sunrise, the development of a sea–land-breeze circulation induces marked divergence among sites. Inland neighbourhoods warm and dry rapidly, reaching up to 5–6 °C higher air temperatures than coastal areas, which remain moderated by maritime ventilation. Conversely, relative humidity exhibits an inverse pattern, increasing along the coastline and decreasing inland. Evening cooling is slower in dense central districts due to greater heat storage, a pattern also captured by ECOSTRESS LST data. These findings highlight the dual control exerted by mesoscale dynamics and local morphology, with compact built-up zones and impervious surfaces amplifying daytime heat accumulation and delaying nocturnal release. Simulations with MLUCM BEP+BEM reproduce the observed intra-urban variability with notable accuracy, outperforming ERA5 near-surface reanalysis fields. Model-sensitivity tests demonstrate that the choice of boundary forcing significantly affects MLUCM BEP+BEM performance: sea-based forcings yield the best agreement near the coastline, while interpolated configurations perform better inland. Across all scenarios, the model captures microscale-driven variability of comparable magnitude to mesoscale contributions, confirming its suitability for reproducing canopy-layer processes under different urban and meteorological conditions. Overall, this research demonstrates that integrating mesoscale and microscale datasets provides a coherent framework for interpreting the spatial structure of thermal and moisture fields in coastal cities. The multi-source methodology—combining field measurements, satellite observations, and a physics-based urban-canopy model—offers a transferable approach for diagnosing urban heat exposure and informing climate adaptation. In particular, the MLUCM BEP+BEM model proves effective in distinguishing coastal–inland gradients and quantifying the influence of urban form and land cover on local climate. These results underscore the need for multiscale assessments to support energy-efficient urban design, improve outdoor comfort, and enhance resilience strategies in Mediterranean coastal environments increasingly exposed to heat stress and climate change.

Acknowledgements: This work is supported by ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union – NextGenerationEU (CUP F83C22000740001).

Reference: Pappaccogli, G., Zonato, A., Martilli, A., Buccolieri, R., and Lionello, P.: MLUCM BEP+BEM: An offline one-dimensional Multi-Layer Urban Canopy Model based on the BEP+BEM Scheme, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-219, 2025.

The project “Urban hEat and pollution iSlands inTerAction in Rome and possible mitigation strategies” (RESTART), funded by the Italian Ministry of University and Research within the PRIN program, investigates the interplay between the Urban Heat Island (UHI) and the Urban Pollution Island (UPI) in Rome over the period 2019-2024. At its concluding stage, the project provides new insights into the drivers and feedbacks linking these phenomena and proposes tailored Nature-Based Solutions (NBS) to improve urban liveability and resilience.

High-temporal resolution meteorological and air quality observations from monitoring networks and international observatories were analysed to assess the current state of UHI and UPI and their links under different meteorological conditions. The analysis confirms that their interaction is strongly modulated by meteorological conditions, with combined effects intensifying during heatwaves and calm wind days.

To capture these dynamics, RESTART employed an innovative multiscale modelling framework that couples the Weather Research and Forecasting (WRF) mesoscale model with the ADMS-Urban dispersion model. This chain proved effective in reproducing spatial patterns of meteorological variables and pollutant concentrations, and in evaluating the role of NBS under both current and mitigation scenarios.

In this contribution, the science-based recommendations derived from the project will be discussed. Specifically, the outcomes emphasize the potential of NBS (i.e., urban greening and tree planting) to simultaneously mitigate heat stress and pollution accumulation. By combining high-quality monitoring data with advanced numerical simulations, RESTART contributes to the design of tailored, evidence-based mitigation strategies supporting sustainable and climate-resilient urban planning.

To conclude, the integrated observation–modelling approach developed in RESTART represents not only a valuable decision-support tool for the city of Rome, but also a transferable methodology applicable to other urban areas facing similar climate and air quality challenges.

Le micro- e nanoplastiche (MNP) stanno emergendo come una nuova e pervasiva componente degli aerosol atmosferici, ma le loro origini, i meccanismi di trasporto e il destino finale nell’atmosfera rimangono in gran parte sconosciuti. A differenza delle particelle aerosol convenzionali, esse derivano da una complessa combinazione di sorgenti continentali, marine e secondarie, e il loro comportamento fisico dipende in modo critico da dimensioni, forma e tipo di polimero.
Nonostante la crescente attenzione scientifica, i dati osservativi disponibili sono ancora scarsi e frammentari, raccolti in ambienti eterogenei e mediante metodologie analitiche non uniformi, rendendo difficile ottenere una visione coerente e comparabile del fenomeno.

In questo lavoro vengono analizzate le possibili sorgenti e traiettorie di trasporto atmosferico delle MNP, con l’obiettivo di identificare i processi dominanti che ne controllano la variabilità spaziale e temporale. A partire da osservazioni provenienti da ambienti urbani, suburbani, montani e nuvolosi, lo studio combina l’utilizzo dell’ultima versione del modello di dispersione lagrangiano FLEXPART v11 (adattato per le MNP) con una analisi statistica multi livello, basata su correlazioni e modelli di regressione multipla, per esplorare le relazioni tra le concentrazioni osservate e un insieme di proxy su larga scala rappresentativi delle diverse sorgenti potenziali (suoli agricoli e aridi, aree popolate, spray marino), considerando differenti età di trasporto atmosferico (1–45 giorni).

I risultati indicano che gli indicatori antropici e continentali (popolazione, aree irrigate) presentano correlazioni positive e persistenti soprattutto a brevi tempi di trasporto, mentre le sorgenti a più lungo raggio sembrano essere legate a emissioni di tipo marino e da zone aride. I modelli di regressione evidenziano driver regionali eterogenei e il potenziale contributo del trasporto a lunga distanza evidenziato dai siti montani.

Nel complesso, i risultati dimostrano che, pur in presenza di forti limitazioni osservazionali, è possibile estrarre segnali fisicamente coerenti, fornendo nuove evidenze sui processi che governano la presenza e la trasformazione delle MNP nell’atmosfera, e sottolineando la necessità di miglioramenti nelle tecniche di osservazione e di una maggiore copertura spaziale dei dati.

The rising frequency and severity of compound hydro-meteorological extremes underscore the urgent need to better understand their dynamics and occurrence. Compound events, involving concurrent or sequential natural hazards, often lead to amplified impacts compared to individual events. A recent striking example is the exceptional sequence of heavy rainfall in northern Italy (2023–2024), which triggered widespread flooding in Emilia-Romagna. Flood severity and extent were compounded by prior soil saturation resulting from earlier rainfall, illustrating how antecedent conditions can exacerbate impacts. However, the rarity and unprecedentedness of such events limits their representation in observational records owing to their limited temporal coverage, hence posing substantial challenges for robust statistical characterization. To overcome this, the UNSEEN (Unprecedented Simulated Extremes using ENsembles) approach has recently emerged. UNSEEN is employed by pooling large ensembles of seasonal re-forecasts from numerical weather prediction models to create synthetic time series spanning thousands of years. This enables the analysis of low-probability, high-impact events and the investigation of their dynamical features within a statistically robust framework.

This study applies the UNSEEN methodology to compound flood events within a multivariate framework, focusing on the interaction between precipitation and soil moisture as a key preconditioning driver. Seasonal re-forecasts from the SEAS5 dataset (ECMWF) over 1994-2023 are considered to characterize unprecedented compound floodings in Emilia Romagna. As a first step, the UNSEEN ensemble's ability to represent univariate extremes is assessed to ensure the reliability of the pooled surrogate time series. The surrogate series is then analyzed to distinguish precipitation extremes occurring with and without soil-moisture pre-conditioning, enabling a detailed assessment of their interaction and the associated hydrological responses within the regional river catchments. Results indicate that the UNSEEN ensemble realistically reproduces historical extreme flood events, offering a more robust characterization than observational records alone. Additionally, the river discharge response further differentiates the two event classes, with pre-conditioned events consistently leading to higher river levels, underscoring the amplifying role of antecedent soil moisture to the hydrological system. These findings highlight the value of ensemble-based approaches for better understanding rare compound events and informing more effective adaptation and mitigation strategies in flood-prone areas.

Medicanes are high-impact cyclones whose frequency may decline but whose strongest events may intensify in a warming Mediterranean (Romero et al., 2017; Tous et al., 2016). Medicane Ianos (September 2020) stands out as the strongest event on record, causing severe flooding and coastal damage across the Ionian Sea.

We analyze Medicane Ianos using a vertically integrated Moist Static Energy (MSE) variance budget as a process-based diagnostic of convective–dynamical coupling. ERA5 fields are tracked objectively, phases are classified in Hart phase space, and the budget is evaluated over ~2.5×10⁵ km² along the storm’s track. The intensification of Ianos is explained by a delicate balance between vertical moistening and horizontal advection, with surface latent-heat fluxes and radiative tendencies reinforcing the MSE build-up during the mature stage. The energy structure is tropical-like within ~600 km of the center during peak intensity, supporting medicane classification (Flaounas at al., 2022).

We extend this framework by using MSE variance as a convective metric across ensembles to link energetics to track and phase uncertainty. With the ECMWF IFS ensemble with perturbed physical parameterizations, forecast spread in trajectories and transition timing correlates with early-time MSE-tendency components and with the interaction between upper-level PV streamers and near-surface thermodynamic disequilibrium (Saraceni et al., 2023, ACP). This combined approach—MSE-budget + ensemble diagnostics—clarifies when medicanes behave more “tropical-like” (dominant convective moistening) versus “subtropical” (strong baroclinic/advection control), informing the ongoing classification debate.

References

Saraceni, M., Silvestri, L., & Bongioannini Cerlini, P. (2025). Analyzing the Mediterranean Tropical-like Cyclone Ianos Using the Moist Static Energy Budget. Atmosphere, 16(5), 562. https://doi.org/10.3390/atmos16050562

Saraceni, M., Silvestri, L., Bechtold, P., & Bongioannini Cerlini, P. (2023). Mediterranean tropical-like cyclone forecasts and analysis using the ECMWF ensemble forecasting system with physical parameterization perturbations. Atmospheric Chemistry and Physics, 23, 13883–13909. https://doi.org/10.5194/acp-23-13883-2023

Flaounas, E., Davolio, S., Raveh-Rubin, S., Pantillon, F., Miglietta, M. M., Gaertner, M. A., Hatzaki, M., Homar, V., Khodayar, S., Korres, G., et al. (2022). Mediterranean cyclones: Current knowledge and open questions on dynamics, prediction, climatology and impacts. Weather and Climate Dynamics, 3(1), 173–208. https://doi.org/10.5194/wcd-3-173-2022

Romero, R., Gaertner, M. A., Sánchez, E., Domínguez, M., González-Alemán, J. J., Miglietta, M. M., Walsh, K. J., Gil, V., Padorno, E., Picornell, M. A., & Romero, R. (2017). Climate change projections of medicanes with a large multi-model ensemble of regional climate models. Global and Planetary Change, 151, 134–143. https://doi.org/10.1016/j.gloplacha.2016.10.008

Tous, M., Romero, R., & Ramis, C. (2016). Projected changes in medicanes in the HadGEM3 N512 high-resolution global climate model. Climate Dynamics, 47(3–4), 1357–1372. https://doi.org/10.1007/s00382-015-2901-7

A comparative study of Medicane Daniel (September 2023) is performed using two high-resolution models, WRF and ICON, both configured at ∼2 km spatial resolution with comparable domains, timesteps, boundary forcing and settings. A custom optimizer harmonizes the vertical levels discratization and sensitivity experiments test different cumulus parametrizations: fully explicit, shallow-convection, deep-cumulus parameterized and ICON’s gray-zone option. Diagnostics include an objective tracker combining mean sea-level pressure and lower-tropospheric geopotential, alongside intensity metrics (central pressure and 10 m wind), precipitation patterns and point validation at Benina (HLLB) for pressure, wind and rainfall. Structural evolution is assessed through Hart’s Cyclone Phase Space (CPS) and a novel Temporal Annular Symmetric Mean (TASM), describing the three-dimensional storm structure during its warm-core phase. Both models reproduce Daniel’s track, lifecycle and tropical-like features. Explicit convection deepens the cyclone and sharpens wind maxima, but enhances small-scale variability that complicates tracking. Deep-cumulus schemes weaken extremes and broaden rainfall, while shallow-convection options provide a balance, improving precipitation placement and core thermodynamics. Model internal differences also influence results: ICON shows lower efficiency in transferring diabatic heating upward, producing a shallower warm core, whereas WRF tends to generate a stronger vortex to better retain tropical-like characteristics. CPS and TASM consistently indicate a shallow-to-deep warm-core transition and a compact, symmetric structure at peak intensity. Overall, the study highlights the importance of harmonized configurations and suggests that, at gray-zone resolutions, shallow-convection treatments often offer a good compromise for simulating Mediterranean tropical-like cyclones.

We analyze 17 Mediterranean cyclones with tropical characteristics using ERA5
reanalysis data to investigate the role of upper-tropospheric processes and their
seasonal variations in cyclone development. Our results show that cyclones
occurring in September exhibit the strongest moist convection, with Ianos, one
of the strongest medicanes on record, representing an extreme case. The devel-
opment of the warm core is anticorrelated with the value of upper-tropospheric
potential vorticity (PV), and correlated with lower tropospheric PV associated
with latent heat release. Using back-trajectories analysis, we then investigate the
presence of dry intrusions in the early and mature cyclone phases. We show that
the Lagrangian definition of dry intrusion, characterized by a descent of 400 hPa
in 48 hours, is not satisfied in most of the cyclones considered here. Instead,
weaker PV streamers with shallower descent are often observed before cycloge-
nesis. While a dry intrusion may reduce static stability and intensify convection,
leading to an accelerated warm core formation, the eventual warm core intensity
appears to depend critically on pre-existing convection and diabatic heating in
a close vicinity of the cyclone center. This is the case for Medicane Ianos. In
fact, this cyclone presents two marginally descending flows of about 200 hPa
in 60 hours associated with shallow PV streamers, one in the early stage and
one before the main tropical-like phase. Despite the modest intensity of the
PV streamer, Ianos develops the strongest warm core among all cyclones. This
motivates us to examine Ianos in detail using a WRF simulation with a grid
spacing of 3 km. The simulation confirms the need for a high-resolution model
setup to correctly represent the cyclone intensity and capture key mesoscale
mechanisms and the dominant contribution of diabatic heating to the cyclone
deepening.

Wet-snow events are a major cause of severe winter outages in the Italian high- and medium-voltage power networks, due to the accumulation of ice and snow on overhead conductors. It is estimated that, in Italy alone, the annual economic impact of these events exceeds 200 million euros.

To address this issue, RSE initiated a research program more than a decade ago that led to the development of an operational alert system for snow accumulation on overhead lines. The system, known as WOLF (Wet-snow Overload aLert and Forecasting), is designed to forecast wet-snow loads on overhead lines during snowfall events and to provide timely warnings to Italian TSOs and DSOs, enabling them to implement appropriate measures to ensure the reliability and continuity of electricity transmission and distribution. WOLF integrates precipitation and temperature fields from the WRF model with the Makkonen accretion model, which estimates the growth of snow load on a reference conductor in each domain grid cell as a function of the prevailing meteorological conditions.

Over the past ten years, observations collected at the WILD (Wet-snow Ice Laboratory Detection) monitoring station, located in the Cuneo Alps, have supported the refinement of both meteorological forecasting models and snow-sleeve accretion models. Previous case studies have shown that the primary sources of uncertainty in snow-load forecasts stem from the intrinsic limitations of the meteorological fields simulated by the NWP models used to drive the accretion model. Sensitivity analyses performed using different model configurations and global drivers revealed variable performance, without identifying a single optimal setup across the analyzed snowfall events.

The recent availability of NWP model outputs from multiple providers through the Italian open-data hub Mistral (https://meteohub.mistralportal.it/app/datasets) offers new opportunities to address these limitations. In this work, the potential benefits of a multi-model approach are assessed by combining snow-load forecasts derived from different meteorological simulations and by exploring probabilistic post-processing techniques for wet-snow prediction. The results, validated against observations from recent snowfall events in the Alpine region, indicate that this approach is promising. Further research is planned, including evaluation over a larger set of case studies, with the aim of reducing the forecast uncertainty of the WOLF system starting from upcoming winter seasons.

The Solar Wind-Magnetosphere-Ionosphere coupling constitutes an important subject of scientific interest, in particular in the Space Weather context. Briefly, in this process, the energy is transferred from the solar wind to the magnetosphere by means of both the magnetic reconnection at the dayside magnetopause and the viscous-like interaction generated by micro or macro instabilities. On the other hand, the magnetosphere and the ionosphere, strictly connected through the magnetic field lines, can exchange energy and momentum, basically, through three main processes: (1) the transmission of electric fields, (2) the flows of electric charges by means of Field Aligned Current (FAC) and (3) the precipitation and/or outflow of particles. In this work, we study some aspects of the interaction of the interplanetary coronal mass ejections (ICME) of September 6-11, 2017 event with the magnetosphere-ionosphere system. In particular, we analyze the response of the magnetosphere to the impact of the interplanetary shock preceding the ICME, the magnetospheric and the ionospheric disturbance currents, and the geomagnetically induced currents (GIC) that developed over the entire northern hemisphere.