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.

The Monin–Obukhov Similarity Theory (MOST) provides the framework for describing turbulent exchanges of momentum and scalars in the atmospheric surface layer. Despite its wide application, systematic deviations between MOST predictions and observations persist under non-ideal conditions, such as strong instability, weak turbulence, surface heterogeneity, and the existence of sources and sinks for momentum or scalars. In this study, we quantify the magnitude and structure of these uncertainties using a Bayesian framework applied to virtual potential temperature gradients measured over Bosco Fontana, a deciduous broadleaf forest in Marmirolo (MN), Italy. We reformulate the classical MOST relationship for the mean virtual potential temperature difference as a hierarchical Bayesian model that explicitly separates random measurement noise from structured model error. The hierarchical term allows deviations from MOST to vary systematically with atmospheric stability, while still sharing a common underlying distribution. Posterior estimates are obtained using Markov Chain Monte Carlo sampling, and model diagnostics are used to identify regions of parameter space where MOST assumptions fail. Building on this, we introduce a latent process inference approach to simulate unobserved mechanisms that contribute to MOST shortcomings. These latent processes act as data-driven hypotheses, enabling the formulation of new parametric corrections and extensions of similarity relationships under weakened assumptions. Results and proposed model extensions will be presented in the poster.

Ozone deposition to vegetated surfaces is commonly estimated by 1-D dry deposition models which implement ozone uptake by leaf stomata and ozone disruption on leaf cuticles, branches and soil.

These models use resistance networks where ozone flux is considered as an electric current and ozone concentrations as electric potentials.

Simple models treat the vegetation as a single big-leaf with certain features, while more recent models divide the canopy vertically into multiple layers with a certain number of leaves or branches.

In this work, the total ozone deposition predicted by the two types of models was compared with the vertical ozone flux measured in a broadleaf forest from 2013 to 2022.

Moreover, the ozone concentrations calculated at the top of the canopy using the two model schemes were used to calculate the stomatal uptake and the phytotoxic ozone dose with the indicator (POD1) adopted by UN/ECE to assess the ozone risk for vegetation in Europe.

Aerodynamic resistances were calculated hour by hour according to the MOST and the stomatal resistances were modeled according to Jarvis (1976), while cuticular and soil resistances were kept as constant. Simulations were performed under well-mixed conditions and under different stability conditions, as well as under real soil water content and full water supply for roots, and under isothermal vertical profile of temperature or with measured temperature profile within the canopy.

Results revealed discrepancies between the modeled and the measured total ozone deposition which were attributed to the chemical sink of O3 due to the NO in the trunk space, as found by Finco et al (2018), but that were not modeled in both deposition schemes. Once this missing chemical sink was added, the multilayer model performed better than the single-layer one and predictions were close to the measured fluxes.

Instead, the ozone doses predicted as POD1 by the two models were similar and quite close to the measured ones. However, when the ozone dose absorbed by the whole plant is required - instead of the dose absorbed by a single leaf at the top of the canopy, as defined by UN/ECE- the multilayer model predicted doses that were 40% greater than those predicted by the single-layer model.

Data series from October 2005 to September 2025 from the ISAC-Lecce Micrometeorological Station database (www.basesperimentale.le .isac.cnr.it ) have been analyzed with main attention to the surface water budget and possible 20-years trends. The results are presented as 6-months averages dividing the hydrological year in wet season (October-March) and dry season (April-September), after applying post processing corrections on the half-hour surface fluxes in the database and using the energy budget closure as validation. 20-year trends in surface and soil temperature and surface fluxes show a pronounced increase of all temperatures and sensible heat fluxes, together with a decrease of the latent heat fluxes and a marked increase of the surface-air temperature difference. Together with the observed precipitation tendency to migrate towards the dry season, the observed trends imply a decrease in the calculated annual net infiltration, in agreement with the decreasing trend of groundwater levels measured in the last years in two wells of the underlying aquifer.

Direct measurements of greenhouse gas fluxes (CO₂ and N₂O) were conducted to evaluate the mitigation potential of agricultural management over a pear crop under real cultivation conditions in Emilia-Romagna. Net CO₂ fluxes were measured using the eddy covariance technique, while N₂O emissions were monitored via dynamic chambers coupled with a high-precision gas analyzer.

The pear orchard in Conselice (RA) was impacted in the first year by the May 2023 flood, which disrupted normal crop function. Post-flood, the system shifted from a CO₂ sink to a net source, and N₂O emissions increased due to anaerobic conditions promoting denitrification. The anaerobic soil conditions, elevated temperatures, and nutrient input from floodwaters (e.g., sewage, manure) likely enhanced denitrification, driving this response. Although nitrogen input during the flood could not be quantified, the event acted as a large-scale fertilization, disrupting the planned GHG balance assessment

Measurements continued through 2024, capturing critical data on flux variability and environmental response over a more representative year.

This contribution presents an overview of the activities and results of the INTERFACE project. The project aims to quantify the non-closure of the surface energy balance at different sites in the Alpine environment, where processes related to the lack of closure, i.e., advection due to the development of thermally-driven circulations, are expected to be particularly significant. This objective is addressed by combining flux station and unmanned aerial system (UAS) measurements. The use of the UAS allows spatially distributed measurements around the eddy-covariance sites, which are crucial for the estimation of advection.

The analysis of eddy-covariance data from various sites representative of different Alpine contexts (e.g., valley floor, valley slope, mountain top) and climatic settings (North and South of the main Alpine crest) allows a systematic quantification and comparison of the characteristics of the surface energy balance, including the lack of closure. Particular attention is given to the evaluation of the role of thermally-driven circulations in the non-closure of the surface energy balance, selecting, by means of objective criteria, days with well-developed slope and valley circulations.

The INTERFACE project contributes to the TEAMx international research programme, which aims to improve our understanding of exchange processes in the atmosphere over mountains.

GREEN-POLIS is a two-year research project involving the University of Rome ‘La Sapienza’, Trento and Bologna, founded in 2023 under the PRIN 2022 scheme by the Italian Ministry of University and Research. The project studies the efficacy of selected Nature-Based Solutions (NBSs) in mitigating the negative effects caused by urban climate change, specifically the Urban Heat Island (UHI) and the Urban Pollution Island (UPI). The analyses are conducted in a multiscale perspective, ranging from the street and building scale to the neighbourhood scale, up to the city scale. The final objective is to provide evidence-based analysis, grounded in a rigorous scientific approach, to build a robust and systemic knowledge of the most effective urban NBSs, their potential benefits, as well as the possible side effects.

Such a challenge is addressed by implementing an investigation that starts with the detailed analysis of microscale effects of NBSs on the in-city ventilation, temperature, and pollutant dispersion in prototypical urban geometries, through the use of laboratory experiments and high-resolution numerical simulations. Subsequently, specific building-scale models are applied to improve urban numerical parameterisations for mesoscale meteorological models, allowing for the analysis of an extensive implementation of NBSs within an urban area.

Selected neighbourhoods and the entire city of Bologna, a recognised hotspot of climate change and air pollution, are taken as a practical case study that will be investigated experimentally through an ad hoc experimental campaign, and numerically through building-resolved simulations as well as mesoscale simulations at the city scale. The overall objective of the project will be discussed, giving an overview of the different results obtained. Additionally, key findings obtained through high-resolution simulations at building scales will be discussed.

The Eddy Covariance method relies on the assumption that the wind, temperature, and scalar data series fed as input are stationary up to order 2.

Order 1 stationarity is usually enforced by subtracting the “trends” from the original signals and calculating the second-order moments (variances and covariances) on the residuals so obtained.

To date, many definitions are available for these trends, and various identification methods have been described for each.

The question then arises as to whether these different definitions and computing approaches impact the final eddy covariance results and to what extent.

Studies on this subject have been conducted in the past, for example, within the AmeriFlux community and with respect to the FLUXNET network.

There is still interest in extending these results to micro-meteorological networks for environmental protection, such as SHAKEUP by ARPA Lombardia.

This study was then conducted using data from SHAKEUP sites, characterized by heterogeneous contexts, including suburban and rural areas in Lombardy.

The best results were achieved when site and context dependencies were considered when choosing the method.

This study investigates the phenomenon of the surface urban heat island (SUHI), which refers to the elevated land surface temperatures (LST) observed in urban areas compared to surrounding rural landscapes. SUHI is one of the most obvious indicators of anthropogenic alteration of the environment and directly influences local microclimates, human health, and urban sustainability. Understanding its spatial and temporal patterns is essential for developing effective adaptation and mitigation strategies in the context of global climate change.
The research focuses on a comparative analysis of several large Italian metropolitan areas, using high-resolution thermal data acquired by ECOSTRESS (Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station). This sensor offers a spatial resolution of 70x70 meters and provides frequent observations throughout the diurnal cycle, allowing the detection of fine-scale thermal variations in different urban fabrics and times of day. These capabilities allow for a more detailed characterization of the intensity, persistence, and variability of the urban heat island effect than is possible with sensors with lower temporal and spatial resolution.

To better interpret the observed thermal patterns, LST data derived from ECOSTRESS are integrated with land use and land cover information from the Copernicus Urban Atlas. This approach allows for the assessment of SUHI gradients at different levels of urbanization and building density, distinguishing the specific thermal footprints associated with industrial zones, compact urban cores, and suburban or peri-urban areas.

In addition, a long-term temporal analysis is performed to assess the potential of ECOSTRESS data for monitoring SUHI dynamics over multiple seasons and under extreme weather conditions such as heat waves. By identifying areas most prone to excessive overheating, the study provides crucial insights into urban vulnerability and thermal inequality.

Ultimately, this work contributes to improving the understanding of urban thermal environments and their spatio-temporal complexity, providing a solid scientific basis for evidence-based urban planning. The results support the design of climate-sensitive strategies, including the enhancement of green infrastructure, cool materials, and adaptive urban morphologies, aimed at strengthening urban resilience in a time of global warming.

This work is supported by: Progetto “RETE - Resilience of the Electric Transmission grid to Extreme events” (PNRR innovation grants) (CUP F83C22000740001);

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).

This study investigates experimentally how surface roughness affects turbulence and vertical mixing at a sheared density interface forming at the top of a gravity current flowing on a flat surface. The experiments were conducted in a water channel using the lock-exchange technique. Two cases were considered: one in which the channel bottom was smooth and the other when it was made rough by means of a series of parallelepiped elements about one-sixth the height of the gravity current. Feature-Tracking and Planar Laser-Induced Fluorescence techniques were used to measure fluid velocity and
density. The results show a general sensitivity of the mean and turbulent flow variables on the surface characteristics. For the rough case, the density interface is thinner and some of the main parameters involved in the turbulence kinetic energy equation, e.g., buoyancy P, production B and its dissipation rate ε are higher. Similarly, turbulent diffusivities of mass and momentum also depend on roughness, although their ratio remains virtually unchanged going from the smooth to the rough surface. Furthermore, a balance between P, B, and ε was observed in both cases across the whole density interface, thus verifying the applicability of established buoyancy laws proposed in the past (Osborne
1980). Finally, some of the results are consistent with existing field data and numerical simulations, e.g., mixing coefficient agrees reasonably well with literature data irrespective of surface characteristics.

Snow cover plays an essential role in regulating the Earth’s climate but it has significant impacts on human well-being in several parts of the world (e.g. source of freshwater for agriculture and human consumption, source of energy for hydroelectric power). In this study the distribution of snow cover variables over the whole Italian territory which includes the southern part of the Alps and the Apennines chain between 2000 and 2022 using MODIS data acquired from Terra and Aqua platform are analyzed. After preprocessing the data to obtain a binary snow/no-snow field, the start (SOS), length (LOS), and end (EOS) of the snow season were calculated. The LOS mean values which range from 0 to 365 days show the highest values over the Alpine chain with a mean value of about 90 days for elevations above 500 m a.s.l. Conversely, the lowest values are seen over the Po Plain area with about 5 days for elevations lower than 500 m a.s.l. Moving to the south, the Apennine region show higher values again for higher elevations with a mean value of 6 days in the West region and to 10 days in the East region. For all regions LOS clearly depends on elevation, but the large variability in values at the same altitude highlights the influence of other factors (e.g., slope, aspect, latitude, and longitude). Regarding the temporal evolution, the east region of the Apennines is the only region where the series shows a significant trend of -3.2 days per decade. When different elevation bands are considered the LOS series shows a significant negative trend only at elevations higher than 3500 m a.s.l. especially due to the signal observed over the Alps of about -5.1 and -0.6 days per decade.

To further explore snow cover changes, ERA5-Land reanalysis snow cover was analyzed. A good correlation between MODIS-derived snow metrics and reanalysis over the 21-year period was found. Given this, ERA5-Land snow cover trends across its entire time (1951-2022) was further evaluated, offering a longer-term perspective on snow cover variability in the region.

I radar meteorologici sono strumenti fondamentali per la misura e la stima della grandine e sono in grado di fornire informazioni chiave per dedurre la severità delle precipitazioni convettive. Ad oggi, gli approcci più innovativi si avvantaggiano di radar a doppia polarizzazione, che permettono di misurare la precipitazione grandinigena attraverso la riflettività differenziale e altri parametri polarimetrici (e.g. Bechini and Chandrasekar, 2015). Allo stesso tempo, sono state proposte molteplici tecniche che si basano sulla singola polarizzazione. Questi metodi utilizzano misure di riflettività orizzontale per individuare alcune variabili “proxy” dei processi fisici connessi allo sviluppo della grandine. Tuttavia, le misure da radar sono influenzate da alcuni errori sistematici che, localmente, possono rendere incerta la stima del rischio grandine. Per superare queste limitazioni, sono stati sviluppati alcuni algoritmi basati su un’opportuna integrazione di dati radar con altre variabili meteorologiche, come i dati provenienti da strumentazione in-situ o da satellite (e.g. Kunz and Kugel, 2015; Capozzi et al., 2018).

In questo studio, si propone un metodo che possa sfruttare la combinazione dei dati radar con i dati di fulminazione, con il fine ultimo di individuare i temporali grandinigeni in evoluzione. Recentemente, è stata condotta un’analisi sulla relazione che si instaura tra i chicchi di grandine e l’attività di fulminazione nelle nubi convettive sul territorio italiano, dalla quale sono emersi risultati molto promettenti (Vermi et al., 2025). In particolare, l’analisi effettuata si è concentrata su una caratteristica specifica dell’attività di fulminazione, ovvero il cosiddetto “lightning jump” (LJ), che si può definire come un brusco aumento del numero totale di fulmini, che si verifica tipicamente nelle prime fasi di sviluppo di un temporale. Per il 77% dei casi di studio indagati, il segnale di LJ è in grado di classificare correttamente un temporale grandinigeno. Inoltre, la rilevazione del lightning jump permette di definire una serie di indicatori come il numero di LJ misurati, la loro intensità e l’anticipo con cui si prevede la grandinata (in minuti), utili ad ipotizzare quale sarà il diametro massimo dei chicchi di grandine. Infatti, tutti i trend di questi indicatori crescono all’aumentare delle dimensioni dei chicchi di grandine e divengono ancor più robusti in caso di grandine grossa (diametro dei chicchi ≥ 5 cm): in tali circostanze, si rilevano mediamente 7 LJ per evento, un aumento nell’attività di fulminazione di 35 volte in poche decine di minuti e circa 60 minuti di anticipo tra l’osservazione del primo LJ e la caduta dei chicchi di grandine al suolo. A partire da questi risultati, opportune combinazioni di radar a singola o doppia polarizzazione con le variabili connesse alle fulminazioni potrebbero condurre ad un miglioramento delle performance di algoritmi che si occupano del riconoscimento e del monitoraggio dei temporali grandinigeni in modalità operativa (e.g. Capozzi et al., 2022).

Riferimenti bibliografici

Bechini, R. and Chandrasekar, V., 2015. A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications. Journal Of Atmospheric and Oceanic Technology, 32, 22-47. https://doi.org/10.1175/JTECH-D-14-00097.1

Capozzi, V., Picciotti, E., Mazzarella, V. and Marzano, F. S., 2018. Fuzzy-logic detection and probability of hail exploiting short-range X-band. Atmospheric Research, 201, 17-33. https://doi.org/10.1016/j.atmosres.2017.10.006.

Capozzi, V., Mazzarella, V., De Vivo, C., Annella, C., Greco, A., Fusco, G. and Budillon, G., 2022. A Network of X-Band Meteorological Radars to Support the Motorway System (Campania Region Meteorological Radar Network Project). Remote Sensing, 14(9), 2221. https://doi.org/10.3390/rs14092221

Kunz, M. and Kugel, P.I.S., 2015. Detection of hail signatures from single-polarization C-band radar reflectivity. Atmos. Res., 2015, 153, 565-577. https://doi.org/10.1016/j.atmosres.2014.09.010.

Vermi, F., Capozzi, V., Monte, G., Budillon, G. and Laviola, S., 2025. Lightning jump as precursor of very large hail occurrence: first evidence in the Italian territory. Bull. of Atmos. Sci.& Technol, 6, 21. https://doi.org/10.1007/s42865-025-00104-2.

In the framework of the Hail Hazard in the Mediterranean (H2Med) project, the Multi-sensor Approach for Satellite Hail Advection (MASHA) technique is applied to study severe hail events occurred in Italy during the last years. MASHA reconstruction of hail patterns allows to identify the severity of events, the trajectory of storm and the lifetime of hail clusters into the clouds. Each event is then investigated through the Principal Component Analysis and cluster analysis in aim to identify the large-scale atmospheric conditions that trigger and reinforce hail and super hail events classified by MASHA. The multivariate statistical approach based on Principal Component Analysis and cluster analysis is applied to some atmospheric fields and thermodynamic indices derived from ERA5 reanalysis. Then, a set atmospheric types favouring hail formation is provided. Finally, future changes of the occurrence of large and extreme hail events over the whole Mediterranean basin is derived from CMIP6 climate model projections for the 21^st century under different SSPs scenarios.

The Alpine orography plays a crucial role in modulating convective storm dynamics, yet the initiation and development of severe convection, including hail-producing storms, remain only partially understood. This study presents a case study analysis of convective events observed in the Alpine region, using national composite radar data provided by the Department of Civil Protection, the MASHA satellite product (Laviola et al., 2025, in submission) for the estimation of hail probability, and ground reports from the European Severe Weather Database (ESWD). MASHA is a hybrid advanced satellite technique capable of detecting the hail-bearing convection developing in the Mediterranean basin every 5 min at very high spatial resolution (3-5 km). The selected cases, characterised by significant hail episodes, are analysed to study the spatio-temporal evolution of convective cells and to assess the capabilities of the MASHA method in detecting hail probability. By integrating these three methods, the aim is to assess the consistency and limitations of satellite algorithms in complex terrain environments. This research is part of the broader TIM (Thunderstorm Intensification from Mountains to Plains) project promoted by ESSL, with the aim of improving the understanding of convective phenomena in mountain environments and their implications for impact prediction and mitigation.

Building on this qualitative validation, the MASHA dataset is now being used to perform a preliminary climatological analysis of hail events over the Alpine arc for the recent 5–6 summer seasons. This extended investigation aims to identify spatial patterns and temporal variability of hail occurrence, providing new insights into the influence of complex orography on convective activity.

Dal mese di maggio 2025, gravissimi incendi boschivi hanno colpito vaste aree del territorio canadese, in particolare le province di Saskatchewan, Manitoba e Ontario. Tali eventi sono stati ricondotti alle severe condizioni di temperatura ed aridità del suolo connesse al cambiamento climatico. Come conseguenza, ingenti quantitativi di particolato derivante dagli incendi sono stati immessi in atmosfera, interessando la troposfera e la bassa stratosfera. Gli intensi venti occidentali che caratterizzano le alte quote delle medie latitudini hanno trasportato i fumi attraverso l’Atlantico settentrionale, fino ad interessare, a partite dai primi di giugno, l’Europa centro-occidentale, sia in quota che negli strati più bassi. L’analisi correlata delle back-trajectories ottenute dal modello Hysplit, delle elaborazioni di immagini satellitari e dei dati prodotti dalla modellistica CAMS, ha permesso di individuare alcune delle fasi salienti del trasporto di aerosol derivante dagli incendi canadesi, evidenziando poi il sopraggiungere di una non trascurabile componente di particolato di origine sahariana. Sfruttando i dati registrati dalla rete di stazioni AirQino (https://www.airqino.it), è stata svolta un’analisi delle serie temporali dei valori di concentrazione di PM10 e PM2.5 misurati durante le prime due decadi del mese di giugno. Le stazioni AirQino, che integrano sensori per la rilevazione dei principali inquinanti atmosferici, di gas serra e di parametri meteorologici, attualmente garantiscono una notevole copertura del territorio italiano e sono operativi con alcuni punti di misura in Francia (Cannes e Marsiglia), Spagna (Barcellona), Ungheria (Budapest e Debrecen) e Romania (Bucarest). In particolare, le misure su base oraria di concentrazione di PM10 e PM2.5 hanno permesso di valutare l’evoluzione temporale e spaziale degli eventi connessi al trasporto dei fumi provenienti dagli incendi canadesi e del contributo desertico sahariano.

The Boundary-layer Air Quality-analysis Using Network of INstruments (BAQUNIN) supersite has been operational since 2017, providing high-quality atmospheric composition data for both scientific research and satellite validation activities. BAQUNIN involves the coordination and the synergistic exploitation of a number of ground-based instruments operated at the Sapienza University campus (downtown Rome), CNR-ISAC (Tor Vergata), and CNR-IIA (Montelibretti), i.e., across urban, semi-rural, and rural sites around Rome. The supersite, promoted by the European Space Agency (ESA) and the EUropean METeorological SATellite system (EUMETSAT), is designed to validate current and future satellite products (trace gases, greenhouse gases, aerosols, clouds) and to investigate the physical processes driving boundary layer evolution in complex Mediterranean urban environments.

Within the BAQUNIN framework, the Raymetrics Aerosol Profiler (RAP) represents a key instrument continuously operating at the Sapienza site since 2021. RAP is a single-wavelength elastic lidar (1064 nm) acquiring vertical profiles of aerosol backscatter and extinction up to 15 km with high spatial (3.75 m) and temporal (10 s) resolution.

Following a 2024 refurbishment, RAP has been upgraded with full scanning capability, enabling novel observation strategies. In particular, the Horizontal Pointing Mode (HPM) allows the system to probe the lowest portion of the urban boundary layer over distances up to 5 km, a region typically inaccessible to conventional lidars and ceilometers, at very high spatial and temporal resolution (3.75m, 10 sec).

Operating RAP in HPM provides unique observational capabilities for the validation of aerosol loads and atmospheric corrections in very high-resolution satellite optical missions, as it captures spatial and temporal scales directly comparable to those of satellite products. Thus, RAP strengthens BAQUNIN’s role as a unique infrastructure for investigating aerosol dynamics in the urban boundary layer and for supporting the calibration and validation of new-generation satellite observations.

Convection plays a crucial role in redistributing energy within Earth’s atmosphere and is often associated with cloud formation and severe weather events, including hailstorms that can cause significant damage to infrastructure and property. In recent decades, Italy and the broader Mediterranean Basin have experienced a rising trend in such extreme events, highlighting the need for improved observational capabilities and retrieval methodologies to analyze convective storms, particularly those producing hail.

This is the primary objective of the PRIN 2022 project “Convection Characterization via Synergistic GEO and LEO Satellite Observations”, which aims to investigate convection using data from the EarthCARE (EC) mission—featuring the highly sensitive 94-GHz Cloud Profiling Radar (CPR) with Doppler capability—alongside observations from the METEOSAT Rapid Scan Service (RSS).

This study presents analyses of convective case studies that occurred over the Mediterranean region in 2024 and 2025. Convective clouds are examined using convection products from the EUMETSAT Satellite Application Facility in support of nowcasting and very short-range forecasting, derived from Meteosat RSS data. Additional insights into updrafts and overshooting tops are obtained through EarthCARE CPR measurements, including radar reflectivity and vertical velocity profiles. The Multi-sensor Approach for Satellite Hail Advection (MASHA), a novel multi-instrument technique designed for real-time tracking of hail-producing clouds, further complements the analysis.

Key case studies are discussed to assess the complementarity and effectiveness of combining active and passive, GEO and LEO satellite observations, with particular focus on the challenges of interpreting Doppler velocity data for identifying updrafts within convective systems.

Snow precipitation plays a crucial role in the global water cycle and energy balance of Earth's climate system, particularly in Polar regions, where it significantly impacts the ice mass balance of polar caps and ice sheets.

The EarthCARE mission, a collaborative effort between ESA and JAXA, is designed to capture the microphysical variability of clouds and precipitation. Its W-band (94 GHz) Cloud Profiling Radar (CPR) enables tracking the formation and evolution of precipitation. But quantitative snowfall remote sensing presents challenges due to the highly variable microphysical and electromagnetic properties of ice crystals and aggregates on small spatial and temporal scales, which is not fully captured but the retrieval algorithms. Moreover, spaceborne radars cannot sample the lowest atmospheric layers because of ground clutter. This makes the estimation of the snowfall at the surface very challenging when significant variations in precipitation occur within the few hundred meters above the ground (Bracci et al., 2022).

To address this, a methodology for estimating reflectivity and Doppler velocity at 94 GHz from K-band (24 GHz) Doppler spectra collected by a Micro Rain Radar (MRR, Metek) and coincident disdrometer observations has been developed and tested in Antarctica (Bracci et al., 2023). This method, known as K2W, exploits the synergy between two commonly available Antarctic instruments to validate satellite-based W-band radar products.

With the release of EarthCARE’s first L2 CPR products for December 2024 and January 2025, this approach has been replicated using EarthCARE overpasses near the Italian Antarctic station “Mario Zucchelli” (MZS), where an MRR and a disdrometer have been operational since 2016 in the framework of the project “Antarctic Precipitation Properties” (APP) of the Italian National Antarctic Research Program (PNRA), also integrating data from a weather station and a ceilometer from the ENEA Italian Antarctic Meteo-Climatological Observatory (IAMCO).

Snowfall events at MZS coinciding with an EarthCARE overpass (point-to-line distance < 20 km) happened on seven occasions since the start of EarthCARE data delivery, with five virga occurrences and at least one good match (April, 30th 2025). A comparison of retrieved physical quantities from ground-based and satellite observations is presented and critically analysed. A statistical analysis on the long time series quantifies the expected frequency of virga occurrence at MZS.

Negli ultimi anni, l’utilizzo dei segnali provenienti dalle costellazioni di Global Navigation Satellite Systems (GNSS) si è affermato come una tecnica affidabile per la stima continua del contenuto di vapore acqueo atmosferico, attraverso la determinazione del ritardo zenitale troposferico (Zenith Tropospheric Delay, ZTD). Nell’ambito di alcuni progetti INTERREG (PROTERINA-3 Évolution, PROTERINA4Future) è stata realizzata una nuova infrastruttura GNSS-meteo per il monitoraggio integrato delle condizioni atmosferiche su mare tramite sistemi installati a bordo di una flotta di navi di linea operativa sull’alto Tirreno. Tale infrastruttura combina reti di ricevitori GNSS permanenti con stazioni meteorologiche di superficie, consentendo la generazione in tempo quasi reale di prodotti come ZTD, Integrated Water Vapor (IWV) e parametri termoigrometrici superficiali, con elevata risoluzione temporale e spaziale.

Al fine di validare le prestazioni del sistema e quantificare l’accuratezza dei prodotti derivati, è stata organizzata una campagna di radiosondaggi sperimentali a basso costo presso le stazioni della rete, con il rilascio di palloni meteorologici dotati di sensori per la misura diretta dei profili verticali di temperatura, pressione e umidità relativa. Le osservazioni dei radiosondaggi, considerate lo standard di riferimento per la calibrazione e la validazione dei modelli atmosferici, sono state confrontate con le stime simultanee fornite dalla rete GNSS e dai sensori a terra.

Il presente lavoro descrive da un lato le prestazioni del sistema GNSS-meteo di osservazione, il primo operativo per un lungo periodo (oltre 4 anni), dall’altro il sistema di radiosondaggio a basso costo sviluppato appositamente per la validazione. Sono stati analizzati i dati di confronto per i lanci effettuati durante differenti stagioni e con diverse condizioni atmosferiche. I risultati mostrano una buona correlazione tra i valori di IWV derivati da GNSS e quelli stimati dai radiosondaggi. Il lavoro presenta infine anche un confronto tra queste osservazioni ed i dati ottenuti da modelli di rianalisi (MERRA-2, ERA5). Questa validazione dimostra l’affidabilità di entrambi i sistemi di misura - GNSS-meteo e radiosondaggi sperimentali - ponendoli come utili strumenti da utilizzare in maniera complementare o in alternativa a sistemi di misura convenzionali.

I pluviometri e i disdrometri sono strumenti fondamentali per ottenere informazioni dirette sulle caratteristiche delle precipitazioni in un determinato sito. Tuttavia, le loro misure possono essere influenzate dalla presenza del vento. In questo studio sono stati identificati e quantificati gli effetti indotti da questo fattore ambientale. In particolare, per valutare le prestazioni del disdrometro in condizioni di vento, sono stati analizzati i dati raccolti da due disdrometri Thies Clima LPM e da sensori di vento installati nelle città di Pescara e Roma.

Il set di dati copre per Pescara il periodo da luglio 2021 ad agosto 2024, sebbene includa interruzioni significative, mentre per Roma gli interi anni 2023 e 2024. In primo luogo, lo studio presenta le principali caratteristiche dei siti in termini di distribuzione del vento e della pioggia, nonché le loro distribuzioni congiunte. Quindi, vengono quantificati gli effetti del vento sulle misure dei disdrometri in termini di errore sistematico associato alla stima della DSD (Drop Size Distribution).

I risultati indicano che a Pescara le DSD non corrette dal vento differiscono, in media, del 10.6 % in termini di errore medio assoluto percentuale rispetto alle DSD corrette e a Roma del 6.3 %. Le correzioni sulle DSD si riflettono sui valori di intensità di precipitazione. In questo caso, le differenze tra le intensità di precipitazione ottenute da DSD non corrette per effetto del vento e da DSD corrette sono di 0.26 mm/h per Pescara e 0.23 m/h per Roma in termini della radice dell’errore quadratico medio. Tali differenze risultano statisticamente significative.

Successivamente, poiché il vento ha effetti anche sulla misura pluviometrica, e tale effetto varia al variare delle dimensioni delle gocce (e quindi della DSD) i dati disdrometrici corretti sono stati utilizzati per correggere le misure di precipitazione di un pluviometro posto a qualche metro dal disdrometro di Roma e di due pluviometri installati nei pressi del disdrometro di Pescara, nonché di una rete di 25 pluviometri nella regione Calabria. Gli effetti della correzione sono valutati confrontando i valori corretti con quelli non corretti. Queste differenze sono risultate statisticamente significative.

Understanding temperature variations within the complex Urban Canopy Layer (UCL) is challenging due to limitations and discrepancies between temperature measurements taken in urban canyons or in other more practical nearby positions, such as rooftops. The key question is then how much these measurements differ and what factors contribute to these differences. According to the guidance of the World Meteorological Organization (WMO), measurements within urban canyons are recommended, while rooftop observations are not encouraged for urban monitoring due to “potentially anomalous microclimatic conditions”. Questions about the representativeness of rooftop data are particularly relevant given the increasing number of rooftop sensors deployed through citizen science. This study aimed to address this knowledge gap by comparing temperatures within the UCL using two sensors: one located on a rooftop, and the other positioned within the canyon. The temperature difference between these two nearby locations followed a clear diurnal cycle, peaking at over 1 °C between 12:00 and 16:00 local time, with the canyon warmer than the rooftop. This daytime warming was primarily driven by solar radiation and, to a lesser extent, by wind speed, but only under clear-sky conditions. During the rest of the day, the temperature difference remained negligible.

This methodology was extended to a broader network of low-cost temperature sensors located near rooftop stations deployed in the city of Rome, and similar results were found.

In many practical applications (e.g., wind energy assessment, atmospheric pollutant dispersion modelling, and others), knowledge of wind speed is of paramount importance, and errors in its measurement may hamper the application usefulness of technical results.

To date, most wind speed measurements have been conducted using cup anemometers. These sensors are known to show measurement distortions under slow wind conditions, the frequency of which is significant at locations worldwide.

This study aimed to investigate the impact of these measurement distortions at sites characterized by different wind speed statistical distributions, and was conducted by comparing the 10-min average speed from cup and 3D sonic anemometers at various SHAKEUP sites (ARPA Lombardia).

The results show a cup anemometer-induced change in the measured wind statistics, particularly at low wind speed regimes. The medium and high wind speed regimes showed substantial agreement.

Possible strategies for mitigating the statistical perturbation are also considered and discussed in view of applications (e.g., dispersion modeling).

In recent years, data-driven approaches have emerged as alternatives to traditional physics-based retrievals, taking advantage of machine learning techniques such as learnable pseudoinverse, random forests, or deep learning architectures. These methodologies generally rely on training datasets derived from simulations or observational databases and exploit non-linear relationships between measured radiances and atmospheric parameters without explicit forward modeling. In this work, a deep learning architecture, based on the latent twins approach (originally introduced in theoretical works such as Chung et al., 2025), is applied to IASI spectra, with the goal to assess the robustness of this method for retrieving atmospheric profiles, including temperature, water vapor, ozone, surface emissivity, and surface temperature, in real-world clear-sky conditions. The algorithm is first applied on synthetic radiances derived from the NWP SAF database using the fast radiative transfer code sigma-IASI/F2N (Masiello et al, 2024). After validating the architecture on synthetic data, the algorithm is applied to IASI Level 1C observations, along with their corresponding Level 2 products which serve as reference to evaluate the reconstruction accuracy of the autoencoder-based retrieval. The retrieval performances are discussed along with possible strategies to provide an error analysis for the reconstructed thermodynamical profiles.

Continuous monitoring of pollutants and greenhouse gas vertical concentrations in the atmosphere allows for air quality studies of a region and for long-term studies of trends and seasonal behaviors. Ground-based remote sensing instruments are usually exploited for this scope.

Since 2021, CNR-ISAC has managed two MAX-DOAS SkySpec-2D, one in Rome Tor Vergata and the other in San Pietro Capofiume, located in the middle of the Po Valley. These instruments measure scattered atmospheric spectra in the UV-VIS range. From them, we retrieve the total vertical column densities of NO2 and O3. We also apply our algorithm DEAP to retrieve the tropospheric profiles of NO2 and aerosol extinction, and their integrated value of tropospheric concentration and aerosol optical depth. These instruments are compliant with the FRM4DOAS network, an ESA activity aimed at harmonizing operations from DOAS instruments all around the world. Since mid-2024, in the framework of the PNRR-EMM project, we expanded the net of SkySpec in Italy by installing two new instruments, one in Monte Cimone at 2165m a.s.l., and the other in Bologna at the CNR-ISAC facility. Here we also installed a Fourier Transform Spectrometer (FTS), the EM27/SUN. It measures direct-sun spectra in the NIR range, from which we retrieve total columns and dry-air mole fractions of CO2, CO, CH4, and H2O. The instrument is part of the COCCON network, which supports the TCCON network, made of IFS125 HR FTS. By the end of 2025, we plan to install one IFS125 HR in the Bologna CNR facility in the frame of the project PNRR-ITINERIS.
In addition to air quality studies, these measurements are useful for satellite validation purposes. This activity is important as satellite data quality must continuously be upheld by ground-based measurements.

Here we will report an overview of the instruments' operations, the retrieved products, and comparisons against satellites.

Meteonetwork (MNW) is a Citizen Weather Stations (CWSs) initiative established in 2002 and continuously operating since then. It provides high spatial-density meteorological coverage across Italy. This study evaluates the consistency between MNW and National Oceanic and Atmospheric Administration (NOAA) observations for temperature (T) and pressure (P). NOAA stations located in Italy were selected as reference sites, while MNW stations within a 10 km radius were paired accordingly. A total of 49 NOAA and 93 MNW stations were analyzed using data collected on five fixed days per month from January 2023 to April 2025. The average acquisition frequency was 5 minutes for MNW (for both T and P), and 20 minutes and 3 hours for NOAA temperature and pressure data, respectively.

To ensure comparability, MNW observations were synchronized both temporally and altimetrically. Temperature values were adjusted to match the elevation of the corresponding NOAA station, while pressure data, already reduced to mean sea level in both networks, required no further correction. Temperature (ΔT) and pressure (ΔP) differences were computed for each station pair. Outliers were filtered using the Interquartile Range (IQR) method, and the 90th percentile of the RMSE of the filtered differences was adopted as the performance threshold for MNW stations.

The comparison yielded the following statistics for ΔP: mean = 0.34, standard deviation = 0.44, RMSE = 1.13, and 90th percentile of RMSE = 2.08 hPa; and for ΔT: mean = –0.33, standard deviation = 1.10, RMSE = 1.27, and 90th percentile of RMSE = 1.90 °C. These results demonstrate the stability and strong coherence between the two networks, highlighting MNW potential for meteorological applications that require dense and real-time observations.

This study represents a preliminary step toward evaluating the consistency of the main environmental sensor networks in Italy, with future analyses planned to include MISTRAl portal data.

The phenomenon of the urban heat island (UHI) has been observed since 1810 when a British scientist, Luke Howard, observed that the city of London is warmer than the rural surrounding. Since then, the concept of UHI has been well documented a, with many studies investigating the factors responsible for its formation and development, namely a decrease in vegetation and evapotranspiration, a rise in low-albedo, dark surfaces, and an increase in anthropogenic heat output in the urban cores. However, since the UHI is significantly affected by the geographic features and climatic conditions, the understanding of the topic remains quite limited especially in some areas. For instance, a significant gap remains in understanding the interaction of the UHI with heat waves (HW) conditions, with contrasting results in different areas. This study examines how UHI patterns change in space and time during extreme heat events in the city of Bologna, situated in the Po Valley in Italy, a well-known climate change and pollution hotspot. By combining an LCZ-based approach with the calculation of thermal comfort indices to assess the degree of human heat stress among different types of urban morphologies, the study aims to provide a better understanding of the UHI and HW interactions at the urban scale. The results indicate that the UHI in the area is significantly higher at nighttime, when the historic core of Bologna with dense LCZs (layers 2 and 3) experiences higher thermal stress. The level of heat stress reaches maximum levels during the HW period, in which greener or more open LCZs mitigate thermal discomfort. These results may be useful for informing targeted urban planning strategies for climate adaptation in the area.

A multi-scale observational analysis of a 1.6 km wide IF3 tornado in Northern Italy is conducted using radar and sounding data, ground weather stations, and damage surveys. The tornado occurred close to Alfonsine, along the Adriatic coast, on July 22, 2023, in one of the most tornado-prone regions of Europe. An initially hail-bearing supercell (which produced hailstones up to 10 cm in diameter) became tornadic as it approached a dryline bulge. During the transition from a hail-dominant to tornadic storm, the long-lived supercell generated a damaging Rear-Flank Downdraft (RFD) surge, with unusually cold wind gusts reaching 40 m/s. A dry and hot air mass from the southwest was partially ingested by the mesocyclone just before the tornadogenesis occurrence. At the same moment, the storm was also ingesting from the east a maritime air mass with very high values of equivalent potential temperature. A seamless wind damage pattern, transitioning from damage caused by straight-line wind gusts to tornadic damage, suggests that the tornado may have developed from the stretching of small-scale pre-tornadic vertical vorticity maxima within the RFD. As in other case of significant tornadoes in Northern Italy, the environment was characterized by strong deep layer shear and conditional instability, but weak low-level wind shear. However, numerical simulations indicate that along the dryline the low-level storm relative helicity and vertical vorticity were stronger, suggesting a higher tornado potential. The tornado resulted in only 14 injuries, likely because it impacted a sparsely populated area. Considering that past significant tornadoes in the region affected much more densely populated areas, and since no tornado warnings or shelters are currently in place, there are growing concerns about the potential catastrophic consequences of a future significant tornado in the highly populated areas of northeastern Italy.

On 29 October 2024, an intense supercell formed along the Valencian coastal area. The storm brought about 600 mm of rainfall with extensive damage and casualties. Although the large scale atmospheric patterns that trigger the event is quite marked and relevant, the role of the sea structure is not totally clear. WRF (Weather Research and Forecasting system) simulations were conducted to examine how oceanic factors – sea surface temperature (SST), SST anomalies, upper-ocean vertical stratification, and ocean heat content – influenced the development of the extreme weather event over Valencia area. The model was run at a resolution of 3-1 km, with the SLAB Ocean model activated in order to model the structure of the mixed layer depth consistently with the atmosphere. Simulations, under this configuration, is capable of represent the phenomenon very realistically, in space and time. The intensity reached by the control simulation is about 590 mm/12hr, close to the observed reality. We used observed SST, mixed layer depth produced by the CMEMS model reanalysis and anomalies are based on the CMEMS dataset 1987-2010. The results suggest that ocean–atmosphere interactions accounted for around 25% of the storm’s peak intensity, while storm genesis and track remained largely unaffected to ocean structure. Among the tested factors, pronounced upper-ocean stratification and vertical lapse rate enhanced surface latent heat fluxes and invigorated convection, thereby intensifying the storm. In contrast, SST anomalies had only a minor, spatially inconsistent influence due to their patchy distribution prior to the event. These findings underscore the importance of accurately representing upper- ocean structure and heat content in mesoscale models to improve forecasts of extreme Mediterranean weather events.

In recent decades, Europe has experienced a marked intensification of extreme weather events, with notable implications for population thermo-hygrometric well-being. This study investigates time series of atmospheric variables from the ERA5-Land and ERA5-Heat datasets, provided by the Copernicus Climate Change Service (C3S), spanning the period 1961–2024. Extreme events are identified with respect to the 1961–1990 climatological baseline. The analysis examines spatial and temporal variations in the frequency and intensity of heatwaves, identified using 2-m air temperature, and droughts, quantified through the Standardized Precipitation Evapotranspiration Index (SPEI), as well as their co-occurrence as compound events. The associated impacts on thermo-hygrometric stress are assessed using the Universal Thermal Climate Index (UTCI), derived from ERA5-Heat data. Four daily event-based scenarios, identified from the analysed datasets, are considered: (i) a reference case without extremes, (ii) heatwaves only, (iii) droughts only, and (iv) compound heatwave–drought events.

The results reveal a clear temporal evolution of these events, with significant increases in frequency and severity, particularly in recent decades, and demonstrate that the co-occurrence of heatwaves and droughts amplifies the risk of thermo-hygrometric stress compared to single events. Therefore, this study provides an integrated assessment of extreme climate risks, contributing to a better understanding of the thermo-hygrometric stress under extreme conditions.

The findings of this research are particularly relevant for urban planners and policymakers, as they can highlight the social implications of extreme climate events and can guide the design of tailored adaptation strategies. In particular, the results can support the development of early warning systems, inform the planning of resilient urban infrastructures, and provide actionable insights to enhance the resilience of European communities.

This study investigates outdoor thermal comfort conditions in a selected area of Lecce (southern Italy) through the Universal Thermal Climate Index (UTCI), employing a microclimate modeling approach. The analysis is conducted using the ENVI-met software, a three-dimensional Computational Fluid Dynamics (CFD) model designed for simulating surface–plant–air interactions at the microscale.

In the first phase, two scenarios are simulated: (A) the current configuration of the study area and (B) a redesigned future scenario developed within an urban redevelopment plan approved by the Municipality of Lecce, as part of the Ministerial Experimental Program of Interventions for Adaptation to Climate Change in Urban Areas (Ministero della Transizione Ecologica, 2021). Meteorological inputs are derived from ERA5 reanalysis data for the period 1991–2020, from which a representative summer day is applied to both scenarios. This phase aims to assess the microclimatic effects and improvements in outdoor thermal comfort associated with mitigation strategies, including the introduction of permeable surfaces, increased vegetation and shading and water features (fountain).

In the second phase, Scenario B is further analyzed under projected future climate conditions, using climate projections as meteorological inputs. The objective is to evaluate the effectiveness of the proposed mitigation measures in enhancing outdoor thermal comfort across three future time horizons: near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) (Lee et al., 2021).

Overall, the study quantitatively highlights the potential benefits of climate-sensitive urban design strategies, showing how nature-based and structural interventions can mitigate urban heat stress and enhance thermal comfort under both current and future climatic conditions.

References

Lee, J.-Y., Marotzke, J., Bala, G., Cao, L., Corti, S., Dunne, J. P., Engelbrecht, F., Fischer, E., Fyfe, J. C., Jones, C., Maycock, A., Mutemi, O., Ndiaye, O., Panickal, S., Zhou, T., et al. (2021). Future global climate: Scenario-based projections and near-term information (Capitolo 4). In IPCC, Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.

Ministero della Transizione Ecologica. Decreto direttoriale n. 117 del 15 aprile 2021, Programma sperimentale di interventi per l’adattamento ai cambiamenti climatici in ambito urbano — Gazzetta Ufficiale della Repubblica Italiana, Serie Generale n. 135, 8 giugno 2021.

The production and chemical composition of Secondary Organic Aerosols (SOA) are key factors in urban air quality. Analyzing data from a summer campaign in Beijing, conducted in 2017, we found nocturnal peaks of NOz (NOy - NOx) up to 40 ppb, correlated with nocturnal high concentrations of NO and NO2 and SOA formation the following day. We employed the Framework for 0-D Atmospheric Modeling (F0AM), based on the Master Chemical Mechanism (MCM), coupled with the Washington Aerosol Module, based on SIMPOL mechanism for the particle phase modeling, to run simulations investigating the speciation of ONs in both gas and particle phases anf to correlate the nocturnal NOz peaks, which we suggested are mainly in the gas phase, with the diurnal particle growth events observed in Beijing.

Il presente lavoro è incentrato sullo studio delle caratteristiche meteorologiche del ciclone simil-tropicale Qendresa, verificatosi nel Mediterraneo centrale tra il 6 e l'8 novembre 2014, utilizzando il modello WRF ad area limitata. Qendresa ha avuto origine nelle prime ore del 6 novembre attraverso il processo di “lee cyclogenesis” sul versante orientale della catena dell'Atlante. Successivamente il ciclone si è approfondito spostandosi verso l'isola di Pantelleria, per poi traslare verso sud-est, attraversando l'isola di Linosa, dove il suo nucleo ha raggiunto il minimo assoluto di pressione al livello del mare. Il sistema ha proseguito poi verso Malta e, già indebolito, si è spostato al largo della costa orientale della Sicilia, descrivendo una traiettoria ad anello durante la mattina dell'8 novembre, prima di spostarsi definitivamente verso est, in direzione della Grecia. Complessivamente, l'evento ha provocato tre vittime e ingenti danni strutturali nelle aree colpite.

L'obiettivo principale di questo lavoro è quello di riprodurre la struttura e l'evoluzione del ciclone, raggiungendo la migliore coerenza possibile con le osservazioni disponibili, durante il periodo di 36 ore dalle 00 UTC del 7 novembre alle 12 UTC dell'8 novembre 2014. Per migliorare l'identificazione dei processi fisici chiave e aumentare l'accuratezza della traccia simulata del ciclone, nel modello sono stati assimilati dati osservativi e di rianalisi a diversi livelli verticali e intervalli temporali. Sono stati testati due approcci di assimilazione dei dati: il filtro di Kalman d'insieme (EnKF) e l'assimilazione dei dati variazionali tridimensionali (3DVar).

Sulle Alpi nord-occidentali, alla quota di 3480 m s.l.m., presso la stazione di Plateau Rosa, la concentrazione di diossido di carbonio atmosferico viene misurata dal 1989, continuativamente dal 1993. L’altitudine a cui si trova la stazione e la distanza da fonti antropogeniche permettono di ottenere misure di background di concentrazione di gas serra e di inquinanti.

A partire dall’analisi della serie pluritrentennale di concentrazioni di CO₂, sono stati selezionati i valori di background, che costituiscono circa l’80% dell’intero dataset. Tali valori sono stati utilizzati per il calcolo del growth rate della CO₂ atmosferica, con risultati in accordo con quelli di stazioni di rilevanza globale.

L’intera serie di misure di CO₂ rivela, inoltre, l’influenza di masse d’aria con concentrazione variabile rispetto al background, come conseguenza della circolazione alla scala locale o alla mesoscala. In questo lavoro è stato sviluppato un metodo per l'identificazione degli eventi estremi e delle aree di provenienza delle particelle d’aria; tali episodi, numericamente limitati, sono stati quindi studiati applicando due diversi modelli di dispersione lagrangiani: MILORD e FLEXPART-WRF. Il primo è stato utilizzato per simulazioni long-range per tutti gli episodi di concentrazione classificata come estrema, mentre il secondo è stato applicato allo studio del trasporto alla mesoscala e alla scala regionale durante alcuni eventi.

I risultati rivelano come, durante gli episodi di picco, le principali aree di provenienza dei traccianti siano ravvisabili sulle zone fortemente industrializzate dell’Europa, mentre la circolazione atmosferica, durante i medesimi episodi, sia tipicamente ciclonica.

Climate change plays a fundamental role in the intensification of extreme weather events, such as heatwaves (HWs), droughts and floods. In the last year, intensity, frequency and duration of such events have increased and climate projections indicate further worsening in the coming years. As concerns health risks, extreme heat exposure can exacerbate cardiovascular and respiratory diseases. The negative effects of HWs can be intensified by concurrent deterioration of air quality. This connection is a widely researched topic, especially for specific pollutants, like tropospheric ozone. However, so far, few studies have investigated the association between particulate matter (PM) enhancements and HWs.

This study concerns the analysis of an intense compound event of HW and PM enhancement occurred in Bologna (Italy) during July 2023. The multidisciplinary approach implemented offers both an original method for investigating such events and a detailed description of the phenomenon by using ground-based sensors, remote-sensing measurements, satellite products and reanalysis data. The identification of compound events is conducted through extreme heat indices, i.e. the Excess Heat Factor and the Warm Spell Duration Index, and through novel index for PM based on the seasonal variability of PM. Seven compound events are found and cover about the 25% of the study period, 85 days out of 334 between January and November 2023. The analysis highlights the role of the African anticyclone in driving both the HW and the increase in PM concentrations. Besides the detailed analysis of the large-scale synoptic pattern, this is confirmed by measurements from a ground-based optical particle counter along with aerosol chemical speciation and satellite aerosol products.

These results can help policy makers in organizing more suitable responses to such compound events. Integrating Urban Heat Island analysis can offer further insights about the different negative impacts these events pose over people living in distinct urban areas.

The northeastern Italian plains are highly prone to severe convective storms, often producing large to giant hailstones due to specific orographic features. This study wants to numerically simulate and to analyze two such extreme events: a supercell on 1 August 2021 that generated hailstones up to 9 cm in diameter, and the 24 July 2023 outbreak, during which two supercells developed—one producing a European record-breaking 19 cm hailstone. Remarkably, both events produced their largest hail in the same area, near Azzano Decimo.

Numerical simulations were performed with the WRF model at 1 km resolution, coupled with the HAILCAST hail growth scheme. For the 2021 event, several configurations were tested, showing that initialization with IFS data provided the best performance. Realistic hail sizes and storm structure were reproduced only when radiosonde data from Udine Rivolto were assimilated through nudging.

The 2023 event was also simulated using the same configuration, but this setup yielded suboptimal results. Nudging degraded the simulation, as the radiosonde profile was substantially less unstable than the simulated surrounding environment, driving the model away from equilibrium and producing unrealistic convection. The most accurate results (with hailstones of sizes around 10 cm) were obtained using ERA5 initialization without data assimilation.

Comparison of the two events shows that, despite similar synoptic-scale conditions, they differ markedly in low-level moisture advection and instability, limiting dynamic and thermodynamic analogies. Analysis of the simulated updrafts—based on maximum vertical velocity, updraft area, updraft helicity, and liquid water content—shows that the 2023 event reached values nearly three times higher than those of 2021.

These results confirm, for simulations based on real events, a hypothesis previously supported only by idealized studies: hailstone size is not directly proportional to convective instability (e.g., CAPE), but depends primarily on the residence time of hail within the updraft, which can be effectively estimated through the analysis of the updraft morphology (i.e. updraft area).

The Bayesian yacht sank in Porticello, Sicily, at 0206 UTC on 19 August 2024 during a thunderstorm. Of the 22 people on board, 7 lost their lives. An in-depth analysis of available observations highlighted that the ship was likely struck by a quasi-linear convective system. Satellite images showed a Mesoscale Convective System over the Tyrrhenian Sea between 2300 UTC on 18 August 2024 and 0300 UTC on 19 August 2024, with convective cells that lasted less than 1h. The storm motion of the cell that hit Porticello was not consistent with that expected for a right mover supercell, suggesting that supercells were not present during the event. A few videos taken along the coast captured very intense northwesterly wind gusts, with no evidence of rotating winds or waterspouts. Before sinking, the yacht drifted southeastward, pushed by the northwesterly wind. Data from weather stations revealed classic downburst features, such as an increase in pressure and a drop in potential temperature corresponding to the strongest gusts. No signs of mesocyclones (e.g. sudden pressure drop) were detected.

The predictability of the event was also investigated. Operational simulations performed one day ahead with the ICON-2I model, running at 2.2 km horizontal resolution over a domain centred on Italy, pointed out that a convective wind gust hazard could have been expected over the southern Tyrrhenian Sea that night. Furthermore, the satellite analysis showed that the storm developed 3h before the accident and kept a coherent trajectory during its lifetime, suggesting that there may have been enough time to warn people. Lastly, we remark that radar data were unavailable in the area affected by the storm, which is a significant limitation for nowcasting, early warning systems, post-event analysis and research.

This study investigates the interaction between simplified urban-like obstacles and boundary layer flows under rotational effects, through laboratory experiments in a rotating water tank. Idealized building arrays were used to analyze how obstacle geometry influences flow separation, vortex formation, turbulence, and momentum transfer at the urban scale. The Rossby number (Ro) was varied to explore different regimes where rotational effects compete with inertial forces. Results, are presented in terms of flow and turbulence fields. Then, vertical profiles are analysed in order to identfy the effect of rotation both inside and out side the canions. The results contribute to the understanding of how rotation modifies boundary layer flow interactions with urban geometries, providing experimental insights relevant for urban flow modeling and environmental applications.

Thermals arise from differential heating of the Earth's surface, producing buoyant updrafts that are essential for sustaining altitude in non-motorized aviation. This study investigates the development of thermals within the atmospheric boundary layer (ABL) using a very high-resolution numerical model.

The Weather Research and Forecasting (WRF) model is employed to simulate the formation and dynamics of vertical velocities over a specific pre-alpine area in the north of Italy. The model performance is evaluated using Global Positioning System (GPS) flight data collected by paragliders during several summer days in July 2023.

The results show that the meteorological model can reasonably reproduce the spatial distribution of thermals, particularly in identifying areas where they are less likely to occur. These findings highlight the potential of high-resolution numerical models for improving thermal forecasting in recreational aviation, with implications for flight planning and safety in non-motorized flight activities.

Medicanes are tropical-like storms with growing impacts in the Mediterranean basin. This study examines the multiscale dynamics of two representative events, Qendresa (2014) and Ianos (2020), using high-resolution WRF simulations at 1 km and sensitivity tests on physical parameterizations.

Proper Orthogonal Decomposition (POD) is applied to temperature and wind fields to identify dominant modes, while Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA) capture temporal variability and multifractal features. Results show a vertically stratified energy distribution, with stronger coherence in the boundary layer and more isotropic structures in the upper troposphere.

This data-driven approach highlights key mechanisms of rapid intensification and improves understanding of cyclone predictability and mesoscale turbulence in the region.

Le grandinate costituiscono uno dei fenomeni meteorologici più impattanti per la società e le attività umane. I cambiamenti climatici stanno modificando i contesti ambientali in cui queste si sviluppano, influenzando fattori cruciali come l’umidità nei bassi strati, l’instabilità convettiva, i processi microfisici e il wind shear verticale. Ciò assume particolare rilevanza nel bacino del Mediterraneo, riconosciuto come un “hotspot climatico” e tra le aree più esposte al rischio di grandine a livello globale. Nonostante l’elevata frequenza e i rischi associati, resta ancora incompleta la conoscenza delle condizioni meteorologiche su scala sinottica che favoriscono lo sviluppo di questi eventi.

In questo studio, basato sulla climatologia satellitare giornaliera delle grandinate proposta da Laviola et al. (2022), sono stati analizzati i principali pattern spaziali estivi di grandine di grandi dimensioni (>2 cm) nell’Italia settentrionale (44.0-47.0°N, 6.0-14.0°E) ed i relativi schemi di circolazione atmosferica associati. Per l’analisi sono state impiegate la Principal Component Analysis e la Cluster Analysis, utilizzando diversi campi atmosferici derivati da rianalisi ERA5. Per garantire coerenza e omogeneità nei dati, l’indagine è stata condotta sul periodo 2014-2023.

I risultati evidenziano che la distribuzione spaziale degli eventi di grandine in Italia settentrionale si organizza in tre clusters principali, ben distinti fra loro. Il primo riguarda prevalentemente il Nord-Est, il secondo interessa soprattutto la Pianura Padana, mentre il terzo è localizzato nel settore nord-occidentale. Gli schemi circolatori associati ai primi due cluster mostrano la presenza di una saccatura sul Mediterraneo occidentale, che convoglia sull’Italia settentrionale un flusso caldo-umido da sud-ovest, accompagnato da un marcato gradiente termico a 850 hPa. Il terzo schema è invece legato a un’area ciclonica sull’Europa occidentale, caratterizzata da un forte contrasto termico sulla Francia e da un intenso flusso sud-occidentale di origine subtropicale verso l’Italia. Tra gli elementi comuni e determinanti in tutti i casi figurano il trasporto anomalo di vapore acqueo tra 2 e 5 km di quota, che favorisce un’elevata disponibilità di acqua liquida per la convezione, e la divergenza dei venti in alta troposfera.

Dal confronto tra i sottoperiodi 2014-2018 e 2019-2023, emerge un aumento significativo del contributo del terzo schema al numero totale di eventi, passato dal 19.6% al 31.8%. Ciò indica un incremento, sia in termini assoluti sia relativi, della frequenza delle grandinate nel Nord-Ovest italiano.

References

Laviola, S.; Monte, G.; Cattani, E.; Levizzani, V. Hail Climatology in the Mediterranean Basin Using the GPM Constellation (1999–2021). Remote Sens. 2022, 14, 4320. https://doi.org/10.3390/rs14174320.

Nitrogen-containing organic compounds (NOCs) represent key light-absorbing components of atmospheric PM₂.₅, yet the sources and formation mechanisms of nitrophenolic species remain unclear. Thirty-six PM₂.₅ samples collected during winter and summer from Yangon and Mandalay, Myanmar, were analyzed using UHPLC–Orbitrap MS. A total of 562–1318 organic compounds (average 1064) were identified in the ESI– mode, with NOCs accounting for 14–21% of molecular numbers and 13–35% of total concentrations.

Nitrophenolic compounds, defined by O/N ≥ 3 and AI > 0.5, were mainly distributed in zones C, F, and G of the Van Krevelen diagram and dominated the aromatic NOC fraction. Two ubiquitous nitrophenols—nitrocatechol (C₆H₅NO₄) and dimethylnitrocatechol (C₈H₉NO₄)—were detected in all samples and exhibited strong positive correlations, suggesting similar sources and transformation pathways. Their relative abundances showed distinct humidity dependence, with C₆H₅NO₄ favored under dry conditions (RH < 50%) and C₈H₉NO₄ under humid conditions (RH > 60%).

These findings highlight the significant role of nitrophenolic compounds in brown carbon formation and secondary processes in tropical aerosols, providing key mechanistic insights for subsequent modeling of their humidity-dependent formation pathways.

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.