While elevation-dependent warming (EDW) has become a widely studied phenomenon referring to the systematic variation of warming rates with elevation (Pepin et al., 2015), recent research has highlighted the need to move beyond temperature alone toward the broader concept of elevation-dependent climate change (EDCC, Pepin at al., 2022, 2025). EDCC encompasses the diverse responses of multiple climate variables to climate change along elevational gradients, reflecting the complex interactions between atmospheric processes, surface conditions, and topography in mountain regions.

In addition to elevation-dependent temperature trends, EDCC includes changes in precipitation and climatic extremes, along with in the variables useful to disentangle the driving mechanisms of the observed change, such as in snow cover, cloudiness, radiation balance. These changes have profound implications for mountain hydrology, cryosphere dynamics, ecosystems, and downstream water resources, particularly in regions that depend strongly on meltwater and orographic precipitation.

This talk provides an overview of the state-of-the-art understanding of EDW and EDCC, drawing on observational evidence, reanalysis products, and climate model simulations (e.g. Palazzi et al., 2019 ; Ferguglia et al., 2024). Particular attention is given to major mountain systems, including the Alps, Himalayas, Andes, and Rocky Mountains, where elevation-dependent signals have been documented but also show regional variability.

The presentation further discusses the physical mechanisms proposed to explain elevation-dependent climate responses, as identified in the literature, including changes in snow–albedo feedbacks, cloud–radiation interactions, water vapor and lapse-rate effects, and land–atmosphere coupling. Emphasis is placed on how the relative importance of these mechanisms varies across regions and seasons, contributing to the uneven distribution of climate change impacts with elevation.

Overall, the talk highlights EDCC as a framework for understanding how climate change manifests in mountain environments, stressing the need for integrated observational strategies and high-resolution modeling approaches to better assess future risks and inform mitigation and adaptation in high-elevation regions.

References:

- Ferguglia, O., Palazzi, E. & Arnone, E. Elevation dependent change in ERA5 precipitation and its extremes. Clim Dyn 62, 8137–8153 (2024). https://doi.org/10.1007/s00382-024-07328-6

- Palazzi, E., Mortarini, L., Terzago, S. et al. Elevation-dependent warming in global climate model simulations at high spatial resolution. Clim Dyn 52, 2685–2702 (2019). https://doi.org/10.1007/s00382-018-4287-z

- Pepin, N; Bradley, RS; Diaz, HF; Baraer, M; Caceres, EB; Forsythe,; Fowler, H; Greenwood, G; Hashmi, MZ; Liu, XD; Miller, JR; Ning, L; Ohmura, A; Palazzi, E; Rangwala, I; Schoner, W; Severskiy, I; Shahgedanova, M; Wang, MB; Williamson, SN; Yang, DQ: “Elevation-dependent warming in mountain regions of the world”, Nature Climate Change, Volume 5, Issue 5, Pages 424-430, ISSN: 1758-678X, 2015

- Pepin, N. C., et al. (2022). Climate changes and their elevational patterns in the mountains of the world. Reviews of Geophysics, 60, e2020RG000730. https://doi.org/10.1029/2020RG000730

- Pepin, N., Apple, M., Knowles, J. et al. Elevation-dependent climate change in mountain environments. Nat Rev Earth Environ 6, 772–788 (2025). https://doi.org/10.1038/s43017-025-00740-4

Questo studio analizza gli eventi di temperatura estrema verificatisi negli Appennini tra il 1961 e il 2022, utilizzando dati giornalieri in situ e rianalisi ERA5. Gli obiettivi principali sono: i) valutare, tramite il test stagionale di Kendall, le tendenze delle ondate di calore estive (heat waves) e degli episodi di caldo anomalo che si verificano nelle altre stagioni (warm spells), in termini di frequenza, durata e intensità; ii) descrivere ed analizzare, su base stagionale, la climatologia sinottica di tali eventi. L’analisi mostra un aumento significativo degli eventi di caldo estremo. In particolare, nel trentennio 1991-2020, rispetto al 1961-1990, il numero di eventi è cresciuto del 134% in estate e del 102% in primavera, mentre in autunno e inverno l’aumento risulta di portata inferiore e spesso non significativo. Attraverso un approccio metodologico basato sull’analisi in componenti principali e sul metodo di clustering k-means, sono stati individuati diversi schemi sinottici su larga scala associati alle heat waves e alle warm spells. In estate, sta aumentando l’incidenza di particolari configurazioni atmosferiche, caratterizzate da un’area ciclonica (spesso nello stadio di cut-off) sull’Atlantico nord-orientale, in corrispondenza delle Isole Britanniche o al largo dell’Irlanda, e da un promontorio esteso dal nord-Africa alla penisola Balcanica. Tale schema favorisce l’afflusso verso l’Appennino di masse d’aria molto calde di origine subtropicale, soprattutto ai livelli medi della troposfera. È stata inoltre osservata una connessione tra le ondate di calore appenniniche e le temperature superficiali dell’Atlantico nord-orientale: queste ultime sono generalmente al di sotto delle medie climatologiche durante gli eventi di caldo estremo e nei giorni immediatamente precedenti, contribuendo al prolungamento e all’intensificazione degli stessi. Queste evidenze offrono nuove chiavi di lettura sui rapporti tra caldo estremo e circolazione atmosferica su larga scala, e costituiscono strumenti utili per migliorare la previsione delle ondate di calore. Lo studio mette infine in luce l’importanza di disporre di serie climatologiche lunghe, affidabili e ben distribuite anche in aree montane, indispensabili per comprendere come il cambiamento climatico stia trasformando gli ecosistemi d’alta quota e incidendo sulle attività umane.

Aviation systems face unprecedented challenges from climate-driven temperature extremes, with critical infrastructure particularly vulnerable due to dependence on atmospheric conditions. We present the first continental-scale analysis of projected heat extreme evolution across 30 major European airports using bias-corrected CMIP6 ensemble projections spanning 2035-2064. Daily maximum temperatures from 10 General Circulation Models under three emission scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) were processed using advanced Generalized Quantile Delta Mapping bias correction specifically designed for extreme events. Heatwaves were identified using percentile-based thresholds (99.7th percentile, minimum 3-day duration) with comprehensive analysis of frequency, duration, and intensity metrics.

Our analysis reveals systematic intensification of heat extremes across all studied locations, with southern European airports experiencing the most sistematic changes. Across the 30 airports, ensemble-median anomalies of daily Tmax rise by ~+4.1 °C (SSP1-2.6), +5.3 °C (SSP3-7.0), and +6.2 °C (SSP5-8.5), with a marked north–south gradient. Heatwave frequency, duration, and intensity all increase: southern hubs approach 4–5 events yr⁻¹ by SSP5-8.5 (e.g., Antalya 4.91 yr⁻¹), mean durations lengthen from ~4.1 to ~7.0 days, and a standardized heatwave intensity (SHI) typically exceeds 3.6, indicating sustained extremes relative to site climatology. Rare but recurring multi-month episodes (∼100 days) appear as statistical outliers at several Mediterranean airports in SSP3-7.0/5-8.5, signaling potential regime shifts. The tail of the distribution expands such that ≥45 °C enters the regular range at multiple airports, while 25–29 of the 30 sites exhibit significant positive trends in heatwave frequency under SSP3-7.0/5-8.5.

These findings demonstrate systematic evolution toward "new climatologies" across European aviation infrastructure, with relevant implications for flight operations, airport engineering standards. The continental-scale coherence of changes suggests pervasive impacts requiring transformative rather than incremental adaptation strategies. Results underscore urgent need for infrastructure resilience planning addressing conditions that exceed historical design assumptions and operational experience.

Multi-­ year droughts pose a significant threat to the security of water resources, putting stress on the resilience of hydrological, ecological and socioeconomic systems. Motivated by the recent multi-­ year drought that affected Southwestern Europe and Italy from 2021 to 2023, here we utilise two indices—the Standardised Precipitation Evapotranspiration Index (SPEI) and the Standardised Precipitation Index (SPI)—to quantify the temporal evolution of the percentage of Italian territory experiencing drought conditions in the period 1901–2023 and to identify Widespread Multi-­ Year Drought (WMYD) events, defined as multi-year droughts affecting at least 30% of Italy. Seven WMYD events are identified using two different precipitation datasets: 1921–1922, 1942–1944, 1945–1946, 2006–2008, 2011–2013, 2017–2018 and 2021–2023. Correlation analysis between the time series of Italian drought areas and atmospheric circulation indicates that the onset and spread of droughts in Italy are related to specific phases of the winter North Atlantic Oscillation (NAO), the Scandinavian Pattern (SCAND), East Atlantic/Western Russia (EAWR) pattern and the summer East Atlantic (EA) and East Atlantic/Western Russia (EAWR) patterns. Event-­ based analysis of these drought episodes reveals a variety of atmospheric patterns and combinations of the four teleconnection modes that contribute to persistently dry conditions in Italy during both winter and summer. This study offers new insights into the identification and understanding of the meteorological drivers of Italian WMYD events and serves as a first step toward a better understanding of the impacts of anthropogenic climate change on them.

Coastal lagoons, such as the Venice Lagoon, are ecologically significant yet highly vulnerable ecosystems facing increasing pressure from climate change. Accurate projections of key hydrographic variables—such as water temperature and salinity—are crucial for developing effective adaptation and management strategies. However, traditional process-based hydrodynamic models, while physically robust, are often computationally prohibitive for performing the long-term, large-ensemble simulations required for comprehensive climate impact assessments. This study addresses this challenge by introducing a novel data-driven framework that leverages a Convolutional Neural Network (CNN) to efficiently simulate and project monthly temperature and salinity dynamics at key locations within the Venice Lagoon, representing distinct marine, riverine, and intermediate regimes. The core of the methodology is a CNN architecture specifically designed to capture the complex, non-linear relationships between large-scale environmental drivers and localized lagoon responses. A major methodological hurdle was the limited observational dataset, comprising only four years of irregular, approximately monthly measurements. To overcome this data scarcity, we implemented a non-standard training protocol based on a sequential optimization strategy, which enhanced the model’s ability to learn robust dependencies and generalize effectively. The model was trained using a minimal yet physically meaningful set of predictors: 2 m air temperature, precipitation, offshore sea level, and offshore sea surface salinity. For future projections, the validated CNN was forced with a set of synthetic climate scenarios representing global warming levels (GWLs) of 1.5, 2.0, and 3.0 °C relative to pre-industrial conditions. These scenarios were constructed by perturbing the historical driver data according to established climate sensitivities for the Mediterranean region. The results demonstrate the framework’s high predictive accuracy, with the CNN successfully reproducing historical observations (R-squared > 0.96 for temperature; R-squared> 0.85 for salinity). Sensitivity analyses confirmed that the model learned physically plausible dynamics, correctly identifying atmospheric forcing as the primary driver for temperature and recognizing the distinct roles of oceanic exchange and terrestrial freshwater input in controlling salinity across the lagoon’s spatial gradient. Projections under the 3.0 °C GWL scenario reveal substantial future changes: lagoon water temperature is projected to increase by up to 6 °C in summer, while salinity is expected to rise by more than 4 psu at the riverine station. These changes are not uniform throughout the year, leading to a pronounced amplification of the annual cycle for both variables and, consequently, to increased seasonal stress on the ecosystem. In conclusion, this work highlights the potential of tailored CNNs as powerful and computationally efficient tools for downscaling climate information and generating actionable projections in complex coastal systems. The proposed framework provides a viable alternative to resource-intensive models and offers critical insights into the future hydrographic evolution of the Venice Lagoon, underscoring the urgent need for climate-resilient management.

Acknowledgments: FB was funded from NBFC – National Biodiversity Future Center, funded by European Union – NextGenerationEU, Project code CN_00000033, CUP F87G22000290

Reference: Bozzeda, F.; Sigovini, M.; Lionello, P. Neural Network Modelling of Temperature and Salinity in the Venice Lagoon. Climate 2025, 13, 189. https://doi.org/10.3390/cli13090189

In recent decades, the frequency and intensity of extreme weather events have increased. Heavy precipitation is of major scientific and societal relevance, yet its analysis is especially challenging due to spatiotemporal variability and multiscale interactions among processes of different nature. In this work, precipitation and its extremes are investigated using data from heterogeneous sources, including weather-station observations, reanalysis products, and outputs from downscaling techniques based on Artificial Intelligence algorithms. In particular, the latter comprise results from new machine-learning methods that employ convolutional architectures for climate downscaling, developed through a collaboration between FBK and ENEA. Designed to provide high spatial resolution forecasts, these methods yielded four historical precipitation datasets that form the basis of the present analysis. This study examines seasonal mean precipitation by comparing these datasets with the ERA5 reanalysis after regridding to a common reference grid and assessing the reproduced spatial patterns. In doing so, it quantifies the differences between the reanalysis and the AI-generated datasets. Finally, return period estimates obtained via extreme-value methods highlight the strengths and limitations of the various datasets relative to the reference reanalysis.

Viene presentato un nuovo dataset operativo climatico di temperature minime e massime giornaliere esteso all'Italia centro-settentrionale per il periodo dal 1991 ad oggi, creato dal Gruppo di lavoro ArCIS (Archivio Climatologico per l’Italia Centro-Settentrionale). Il dataset copre l’area di studio con una griglia regolare di circa 5 km di risoluzione ed è costruito a partire da dati osservativi raccolti e controllati per la qualità dai servizi meteorologici regionali locali. I dati sono stati controllati per qualità e omogeneità statistica. Ai fini dell’interpolazione, il ruolo delle stazioni è stato determinato in base alla loro rappresentatività dal punto di vista termico e in base alla loro appartenenza a serie climatiche storiche; inoltre, il territorio è stato diviso in 28 macroaree, con caratteristiche meteorologiche e geografiche specifiche e, partendo dalle mappe di uso del suolo rese disponibili dal Servizio Corine, sono state create mappe di frazione urbana e una mappa statica dei corpi idrici principali. In ogni macroarea l'interpolazione è stata quindi eseguita prima identificando la dipendenza della temperatura dalla quota, poi identificando la dipendenza lineare dei residui dalla frazione urbana e dalla distanza dai corpi idrici; il prodotto finale è stato completato interpolando i residui sull’intero territorio in base alla distanza tra le stazioni.
Il dataset è descritto mostrando mappe giornaliere per eventi meteorologici specifici, anomalie mensili, descrivendo le caratteristiche dei valori climatologici e delle tendenze annuali e stagionali sul periodo coperto dal dataset. Particolare attenzione sarà dedicata alla descrizione dei valori medi e delle tendenze di specifici indici climatici, costruiti a partire dai dati giornalieri di temperatura minima e massima, che descrivono la frequenza degli eventi estremi nell'area di studio.

An increase in precipitation extremes is one of the most robust aspects of anthropogenic climate change, but the latest assessment of the IPCC still reported low confidence on projected changes in the Mediterranean region. Yet, a number of Mediterranean cyclones, i.e intense mid-latitude storms, have caused considerable precipitation extremes and economic damage in the most recent years, including in Italy. The role played by climate change in these events remain poorly quantified.

In this work we first show that the CERRA regional reanalyses show a significant upward trend (1985-2024) in the number of annual daily precipitation extremes within the Warning Areas of the Italian Civil Protection, and that the upward trend is largely robust to the driving large-scale weather type. To better understand these trends, we then take a storyline approach and by looking at circulation analogs we analyse the response to climate change of selected past high-impact Italian storms, such as storm Vaia and Cyclone Minerva, in large ensembles of regional and global climate model simulations. This enables us to cleanly separate the contribution of internal climate variability from the forced response to climate change. A probabilistic framework is introduced to isolate the role of changes in the large-scale atmospheric circulation, cyclone-development and precipitation intensity in the risk of precipitation extremes. Results show a clear increase in the risk of intense cyclone-associated Italian precipitation extremes, though internal variability is large, and it can mask the climate change signal at individual grid points in single climate realisations. We conclude suggesting new storyline-based and statistical approaches that might help to generalise the results to the different weather types that cause Italian precipitation extremes.

The atmospheric monitoring station at Plateau Rosa, situated in in the north-western Italian Alps near Mt. Cervino, is part of the WMO/GAW (World Meteorological Organisation/Global Atmospheric Watch, Identification Code: PRS) program since 1989 and part of the ICOS (Integrated Carbon Observation System) framework since 2021. At the station carbon dioxide (CO₂) and methane (CH₄) mole fractions have been measured since 2018 with a cavity ring down spectrometer (Picarro G2301). Concentration measurements at this site, 3480 meter AMSL, are particularly valuable for tracking the atmospheric background and global trend of greenhouse gases but are also impacted by various source areas in Europe.

In this study, we analyzed the seven years (2018-2024) record of CO₂ and CH₄ mole fractions at the station. We focused on the past five years, since the station has been part of the ICOS network, to analyse periods of enhanced CO₂ and CH₄ levels over the background that are associated with pollution events at regional scale. We identified 30 pollution events, when air masses were coming mainly from the Po Valley and central Europe. We used the FLEXPART atmospheric transport model coupled to the high resolution (1 km x 1 km) output of the numerical weather prediction model COSMO to produce concentration footprints and simulate regional CO₂ and CH₄ contributions. We assessed how well this transport model, coupled with different surface fluxes (EDGAR, CAMS), captures the selected pollution events and reproduces the continuous CO₂ and CH₄ record at the station.

We finally demonstrate how CO₂ and CH₄ mole fraction data measured continuously at the station at Plateau Rosa can be used to attribute pollution events to specific regional source areas in Europe that might not be accounted by the inventories.

Long-term weather records are often stored in archives still in paper format. These data come from different sources, and they represent a key starting point to understand the climate of the past, a reference to validate climate models and an input data for reanalysis. This problem concerns both rich regions and data-sparse areas. In recent decades, numerous climate data rescue programs have begun in many countries worldwide. These projects aim to preserve data by digitizing, transcribing and analyzing them, in order to make them accessible to the scientific community. One of these projects is presented in this study, ReData (Recovery of Data), launched by the Meteonetwork Association in collaboration with the University of Milan in 2017. The aim of this project is to scan and digitize the daily weather bulletins available from 1879 to 1940 relative to the Italian country plus some colonies and surrounding territories edited by the Italian Royal Central Meteorological Office. The scanning process is finished, resulting in a collection of 99,518 pages (about 200GB) that will be soon available. The digitization is in progress through the Zooniverse platform (https://www.zooniverse.org/projects/meteonetwork/redata). Here, volunteers from all over the world can contribute to the digitization of a station with data for 12 variables in about 1 minute per day only. Since December 2024, when the project has been launched on Zooniverse, with a mean of about 5000 classification per week and a number of active participants per week stabilized between 50 and 100, we have digitized more than 10 years for 37 stations. Moreover, considering the good quality of the digitization (for about 99% of the data two of the three digitization are equal) it has been decided to move from three digitizations for each day to two digitizations to reduce the whole transcription.

Precipitation extremes are a significant natural hazard that has caused considerable destruction in Italy over the past decade. However, our understanding of the effects of climate change on these extremes remains incomplete, with unclear trends in the intensity and frequency of precipitation extremes. Part of this uncertainty results from internal variability in atmospheric circulation, which is key in triggering high-­ impact precipitation events. To address this issue, here we develop a comprehensive classification of the Weather Types (WTs) associated with November 1984 to October 2024 Extreme Precipitation Events (EPEs) from CERRA in the 156 operational warning areas used by the Italian Department of Civil Protection, by applying Self-Organising Maps to sea level pressure and 500 hPa geopotential height. We identify six different WTs associated with Italian EPEs, corresponding to different large-­ scale dynamical drivers: Atlantic cyclone over France/northern Tyrrhenian Sea (WT1), Mediterranean cyclone over Central Italy (WT2), Western Mediterranean cyclone associated with upper level trough over Iberia (WT3), Westerly zonal flow (WT4), upper-­ level cut-­ off low (WT5), and Mediterranean cyclone centered over the Tyrrhenian Sea (WT6). The relevance of these WTs for different warning areas is evaluated through composites of moisture transport, the probability of EPEs given a specific WT and a seasonality analysis. The annual frequency of extreme precipitation events exhibits a statistically significant increasing trend (Mann-­ Kendall, p < 0.05), corresponding to an average rise of more than two additional events per year per decade. The positive trend in precipitation extremes occurs for five out of the six WTs, with the largest increase occurring for WT4. These results add to the existing knowledge of drivers of extreme precipitation events in Italy, providing an understanding of underlying large-­ scale atmospheric circulation and a database of weather types to investigate the role of anthropogenic climate change in climate model simulations.

Understanding the interplay between meteorological and agricultural droughts is crucial for assessing the impacts of compound climate extremes. While the link between temperature, precipitation, and soil moisture has been largely investigated for drought assessment, their physical properties and role in drought type are region-dependent and influenced by different drivers that are still not fully understood. In this study, we combine satellite and reanalysis datasets to understand the atmospheric drivers for the transition from meteorological to agricultural drought, and address the physical characteristics of major drought events in Europe. Particular attention is given to the role of land-atmosphere interaction for compound drought events. Preliminary results highlight where and under what conditions the persistence indices can serve as robust proxies for linking meteorological and agricultural droughts, analyze favorable weather conditions that contribute to drought amplification, and finally address knowledge gaps in this aspect.

Anthropogenic climate change is profoundly altering the global water cycle, increasing both the frequency and severity of extreme precipitation events. This issue is particularly acute in the Mediterranean basin, a recognized climate-change hotspot where heavy rainfall and flooding already impose severe societal risks, as demonstrated by the May 2023 Emilia-Romagna and October 2024 Bologna floods.

This study validates and applies the newly produced Destination Earth IFS–FESOM dataset to investigate the thermodynamic contribution of anthropogenic warming to mean and extreme precipitation over Italy. First, it examines how climate change influences precipitation intensity, assessing whether heavy rainfall events are intensifying at rates consistent with thermodynamic expectations. Second, it explores how this intensification is modulated by large-scale circulation patterns, evaluating differences across Weather Types (WTs) associated with Italian extremes. Finally, it analyzes how anthropogenic warming affects the magnitude and spatial structure of recent flood events, using spectrally nudged storyline experiments to isolate the thermodynamic contribution of climate change in the Emilia-Romagna (May 2023) and Bologna (October 2024) floods.

Two background-climate simulations of the Destination Earth IFS–FESOM dataset were analyzed: Control, representing a 1950s climate, and Historical, representing present-day conditions. Both were produced using spectral nudging toward ERA5, ensuring consistent large-scale circulation while allowing small-scale processes to evolve freely. The simulations were validated against multiple benchmarks (ERA5, CERRA, and ArCIS), confirming their ability to realistically reproduce Mediterranean precipitation variability.

Analyses of precipitation changes employed complementary diagnostics, including quantile behavior, weather-type composites, and storyline case studies. Results reveal a clear thermodynamic amplification of rainfall under warming, particularly in northern Italy and during autumn. The precipitation response depends on the quantile, with the strongest increases occurring in the most extreme events, and is modulated by atmospheric circulation patterns associated with different Weather Types.

Overall, this work confirms that extreme precipitation is intensifying under anthropogenic warming and demonstrates the explanatory power of the storyline framework in attributing observed flood magnitudes to climate change. These findings highlight the urgent societal need to anticipate and adapt to escalating hydrological risks in the Mediterranean region.

Historical meteorological data are crucial for understanding a region’s climate. This research analyzes spatial patterns and trends of daily precipitation in a transect between the Po Plain near Verona, Italy, and Tyrol.

A dataset of daily precipitation was created spanning 1923-2024. Many station’s data were manually digitized from historical annual reports. The entire dataset was quality-checked with suitable procedures, and suspicious data were verified. The time series were homogenized using the R library Climatol.

Climatological precipitation indices were calculated on the homogenized dataset. Additionally, some indices were spatially interpolated for three normal reference periods (1931-1960, 1961-1990, and 1991-2020). Data normality was first verified. Then, an iterative procedure calculated experimental semivariograms to determine the best interpolation parameters. Finally, the kriging with external drift algorithm was executed.

Results confirm established climatic features of pre-alpine precipitation distribution. Notably, however, novel spatial patterns have emerged. The Precipitation Concentration Index suggests that, over the past century, locally the domain has seen an increase in highly irregular precipitation distribution. Furthermore, R95pTOT, R99pTOT, R95p, and R99p indices indicate spatial shifts and significant variations within the domain. The synoptic-scale weather regime variability is currently hypothesized to be the driving force behind this observed pattern.

In conclusion, the present study underlines the importance of reliable historical data from meteorological measurements and encourages the systematic digitization and publication of paper-based annual reports.

Reanalyses are widely used by the scientific community to assess precipitation trends across a broad range of disciplines. In particular, high-resolution regional reanalyses offer unique opportunities to monitor small-scale processes and evaluate the impacts of extreme precipitation events. Nevertheless, previous studies based on ERA5 and other global reanalyses have highlighted limitations in the temporal consistency of long-term annual precipitation estimates, calling for caution regarding their suitability for detecting robust climatological signals.

This study extends the analysis of temporal consistency to four state-of-the-art convection-permitting reanalyses specifically developed for the Italian domain (MORE, CHAPTER, MERIDA HRES, and VHR-REA_IT) by dynamically downscaling ERA5 using different numerical models (MOLOCH, WRF, and COSMO). Long-term seasonal precipitation trends are evaluated against a homogenized observational dataset (UniMi/ISAC-CNR), specifically designed for climate analysis and ensuring temporal consistency. This comparison allows for a detailed quantification of uncertainties in precipitation trends across regions and seasons, helping to disentangle the climate signal from potential artefacts. Such inhomogeneities may be inherited from ERA5 in some cases or further introduced during the assimilation of local observations through techniques such as observational nudging. Finally, similarities and differences among the various reanalyses are discussed in the context of the specific architecture and configuration of each product.

By providing an uncertainty assessment of seasonal precipitation trends, this work will deliver crucial information to reanalysis users on the extent to which trend results can be reliably employed in climate studies and impact assessments. At the same time, it will offer valuable feedback to reanalysis developers, supporting the design of reanalysis products that meet the highest standards of temporal consistency for Italy.

El-Niño Southern Oscillations (ENSO) is the most important variability mode of the climate system on the interannual time scale. In addition to its significant impact on the climate system, it has been suggested that ENSO will play a crucial role in shaping the effects of climate change on future weather and climate extremes. The ability of climate models to correctly reproduce ENSO variability is a strict test of the reliability of their future projections. Particularly, ENSO provides the context for the study of the climate system response to unforced radiative feedbacks, which in turn provides information on the long-term feedbacks related to climate change. This study evaluates the variability of outgoing longwave radiation (OLR) driven by El Niño-Southern Oscillation (ENSO), as simulated by a subset of climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). This is achieved by using spectral OLR fluxes derived from satellites observations. The first part of the work uses two datasets of spectral fluxes derived from the Infrared Atmospheric Sounding Interferometer and the Atmospheric Infrared Sounder, to investigate the spectral ENSO feedback. Then, ENSO spectral signature is calculated for CMIP6 models using spectral radiative kernels and climate models outputs of water vapor, surface and air temperature fields. The results obtained are compared with those from observations to identify potential biases in the climate models reproduction of the time lag and peak of the radiative response to ENSO driven sea surface temperature anomalies that were not apparent from the broadband observations.

In the climate system many components interact with each other in a nonlinear manner, producing variability over a wide range of spatial and temporal scales. These variabilities, called internal, cannot be traced back to direct responses to external forcings, and are generally chaotic in nature. However, despite the holistic nature of the climate, modeling some of its components separately from others can be fully justified by the immense complexity of the system. Of course, modeling studies of this kind must focus on phenomena and processes that are known not to interact through strong feedbacks with other climate components. Modeling the ocean system through a hierarchy of models (from low-dimensional ones up to high-resolution three-dimensional ones) is, in fact, of great interest in order to disentangle internal variability (here more commonly referred to as “intrinsic”) from that directly connected to atmospheric forcing.

In this context, this communication presents modeling results regarding the intrinsic variability of the Gulf Stream (GS) in the North Atlantic and the Mediterranean Sea (MS). In the first case, the focus is on the separation of the GS at Cape Hatteras due to inertial overshooting, on its dependence on the jet intensity, on the presence of a critical transition and on its possible effect on the AMOC in a scenario of global climate change. As regards the MS, preliminary results are presented based on ensemble simulations using the FESOM-C finite element model.

This research is carried out within the INVMED (“Investigation of the intrinsic variability of the Mediterranean Sea”) project funded by PRIN-2022.

The C3S2_520_CNR project improves the evaluation and quality control (EQC) function of the Copernicus Climate Change Service (C3S) by providing efficient and transparent quality assurance of climate data sets in the Climate Data Store (CDS). The main objective is to answer the question “How, and how well, can I use these data for my purposes?” To this end, the EQC material is organised in a hierarchical structure so that users can find high-level information on fitness for purpose with application examples, detailed requirements that data, metadata and documentation must meet, and scientific assessments with explicit examples of data use. The approach is strongly user-oriented and combines interactive self-assessment tools, stakeholder consultation and continuous feedback to ensure the reliability, usability and long-term sustainability of quality information for climate datasets.

Di fronte a eventi meteorologici intensi sempre più frequenti, determinati dal riscaldamento globale in atto, diventa indispensabile aumentare la consapevolezza collettiva sul sistema climatico. In questo contesto, la scuola e l’università assumono un ruolo chiave nel processo di educazione climatica, offrendo a studenti e insegnanti l’opportunità di comprendere il sistema climatico terrestre, le sue cause e le conseguenze dei cambiamenti che lo caratterizzano. Le attività di recupero e digitalizzazione dei dati meteorologici storici possono rappresentare un importante strumento educativo. A tale scopo, tra i Percorsi per le Competenze Trasversali e l'Orientamento (PCTO) di Sapienza Università di Roma, è stato avviato nel 2023 il progetto Non perdiamo il “Tempo”: adotta una serie storica di dati meteorologici di Roma.

L’attività proposta riguarda la trascrizione di un anno di dati del Registro Meteorologico dell’osservatorio centenario del Collegio Romano (fondato in Roma nel 1787, come Torre Calandrelli), e il successivo confronto dei dati di temperatura con quelli misurati a distanza di 100 anni. Tale percorso si propone di: (i) avvicinare gli studenti e studentesse alla riscoperta dei dati meteorologici di elevato interesse storico-scientifico conservati su supporti cartacei ormai dimenticati; (ii) contribuire al recupero del dato storico mediante la digitalizzazione e, quindi, rendere le persone coinvolte coscienti dell’importanza della raccolta delle osservazioni a lungo termine per studiare la tendenza evolutiva del clima. Ad oggi sono stati coinvolti circa 65 liceali provenienti da due scuole superiori di secondo grado di Roma e provincia.

Il presente contributo descriverà la struttura delle attività del PCTO, i risultati emersi nel biennio di realizzazione e una valutazione del loro impatto formativo.

Italy has long played a pivotal role in the development of meteorology, from inventing key instruments to establishing one of the earliest international observation networks. Over the past three centuries, this legacy has generated a vast and valuable archive of meteorological data preserved in Italian repositories. While numerous initiatives have helped to safeguard parts of this heritage, a large portion of the records still exists only in paper form. These collections are vulnerable to deterioration, placing at risk data of inestimable scientific value for meteorology, climate science, and climate change assessments.

This study highlights recent projects in which the Italian Association of Atmospheric Sciences and Meteorology (AISAM) has been central to national data rescue efforts.

The first is Cli-DaRe@School, a Citizen Science initiative launched in 2022 to digitize previously untapped Italian meteorological observations not yet available in digital format. The project focused on four monographs published by the Italian Hydrographic Service containing monthly temperature (1926–1955) and precipitation (pre-1950) data. Over two academic years, more than 500 students from 10+ high schools contributed to the digitization, producing around 7,931 station records.

The second, Dieci e Lode (2023–2025), targeted the recovery and digitization of meteorological records from former Italian colonies and territories. These included data collected in regions administered by Italy at various times between the late 19^th and early 20^th centuries, such as Eritrea, Somalia, Ethiopia, Libya, the Dodecanese Islands, Albania, Dalmatia, and Istria. The project carried out extensive archival searches to retrieve meteorological observations from these areas and periods.

The third initiative, Cli-DaRe@Images, launched in 2024 and still ongoing, combines education and awareness-raising on climate change with historical data recovery. It engages high school students in the Trentino region to digitize meteorological records preserved in the San Bernardino Library in Trento.

In current practice, short-range atmospheric dispersion modelling is typically conducted using meteorological data obtained from local measurements, typically gathered using "conventional" electro-mechanical sensors, such as cup-vane anemometers and other similar devices.

Recently, wind sensors, such as three-dimensional ultrasonic anemometers, have become more widely available, allowing for higher-resolution measurements.

In a recent paper, the authors investigated the effect of using meteorological data from conventional or ultrasonic anemometers on modelled results in the case of a short Summer-time measurement campaign at Como and found significant ground concentration differences, especially evident in low-speed regimes.

This study extends this result by evaluating the modelled concentration differences over a longer time span at multiple sites in Lombardy equipped with both cup-vane and 3d sonic anemometers, the same used in the previous work.

The new test sites are characterized by different wind statistics and this allowed exploring the effect of different wind speed regimes. The new results confirm the former paper conclusions and reinforce them showing to what extent model outputs may change due to the use of wind sensors based on different technologies.

Volcanic ash released during Etna’s paroxysms poses a significant hazard to aviation at nearby airports, with Catania Airport being the most affected. This is because volcanic ash has a relatively low melting temperature compared to the operational temperature of jet engines, and its ingestion can cause engine failure. Consequently, accurate forecasting the volcanic cloud height, ash concentration, and atmospheric dispersion is crucial. To achieve this, plume-rise models are employed, with one-dimensional (1D) models commonly used for operational forecasting. Among these, FPLUME (Folch et al., 2016) is considered the most comprehensive, accounting for the effects of wind, moisture, latent heat, and variable entrainment coefficients. FPLUME requires several input parameters for the initial conditions, including the mass eruption rate (MER), exit ash velocity, exit ash temperature, exit water fraction, and meteorological variables.

Here we present the results from the analysis of 34 eruption events that occurred between 2011 and 2024. MER was retrieved using the Volcanic Ash Radar Retrieval (VARR) technique (Marzano et al., 2012; Mereu et al., 2015), based on data from an X-band weather radar located in Catania. Meteorological soundings were obtained from ERA5 reanalysis dataset. To account for uncertainties in the initial conditions, a Monte Carlo simulation was performed, running FPLUME 10,000 times for each event while perturbed input parameters. The results show that for events with MER > 8 × 10⁵ kg/s, FPLUME accurately predicts volcanic cloud heights. However, for weaker events, FPLUME consistently underestimates the cloud height. This discrepancy is more pronounced during eruptions occurring under unstable atmospheric conditions, characterized by high Convective Available Potential Energy (CAPE) and very low wind speeds.

These findings suggest that for weak paroxysms, atmospheric processes—particularly convection, which FPLUME does not currently represent—play a dominant role in plume evolution. To address this limitation, a modified version of FPLUME was developed, incorporating a parameterization of convective vertical velocity through CAPE, as well as a revised formulation of vortex entrainment that depends on wind speed rather than the Richardson number. This new model reduced the error between observed and simulated cloud heights, especially for weaker eruptions.

Climate services connect atmospheric science to societal decision-making in the face of rapid climate change. Current provision reveals fundamental tension between commercial interests and equitable access, prompting our advocacy for transformation from market-only models toward a knowledge commons approach that balances innovation with universal accessibility.

Commercial trends in climate services create concerning information disparities. Our analysis across multiple countries demonstrates that profit-driven models frequently exclude vulnerable populations from essential climate data due to cost barriers, creating a paradox where those most exposed to climate risks have least access to adaptation knowledge. This commercialization of publicly-funded research fundamentally weakens the social contract between science and society. In several Western nations, data access restrictions deliberately create lucrative markets, enabling corporations to develop superior risk assessments while constraining public access, thereby reinforcing existing socioeconomic disparities.

However, our research recognizes the legitimate role of private sector innovation in climate services. Drawing on team members' business experience, we acknowledge that commercial applications can flourish alongside equitable data access principles, particularly when specialized tools and interfaces build upon freely accessible core datasets.

Empirical evidence illustrates these inequities' real-world consequences. Asymmetric forecast access undermines adaptation capacity (Lemos et al., 2007), while agricultural insurance applications can paradoxically disadvantage resource-poor farmers despite actuarial benefits (Carriquiry and Osgood, 2012). Developing nations face additional institutional barriers that further widen access gaps (Buontempo et al., 2018).

Promising solutions emerge from European models, particularly the Copernicus Climate Change Service (C3S), which successfully balances open data provision, rigorous quality control (Dee et al., 2024), and stakeholder engagement (Buontempo et al., 2022) while enabling commercial innovation.

Through examination of restriction ethics, successful governance frameworks, and differential adaptation outcomes, we propose a tiered system ensuring universal core service access complemented by specialized commercial offerings. This approach recognizes that publicly-funded scientists must serve broader public interests while accommodating private engagement through mechanisms like open data mandates.

Our institutional analysis, economic modeling, and comparative case studies provide guidance for funding agencies and policy frameworks, advancing both knowledge commons theory and climate justice implementation to ensure climate services enhance collective resilience, particularly for the most vulnerable populations.

Cities are typically affected by the urban heat island (UHI) effect, that is the positive temperature difference between the urban area and the surrounding rural areas. Furthermore, in the context of a changing climate the increase in frequency and intensity of heat waves add up, exacerbating the overheating to which city dwellers are subjected, with severe consequences on health and society.

A full understanding of the distribution of meteorological variables within the perimeter of a metropolitan area is therefore essential to investigate the several issues affecting the urban population (e.g., outdoor thermal stress), so as to identify the neighbourhoods that suffer most from each of these issues, especially during heat waves. Numerical models such as the Weather Research and Forecasting (WRF) model are fundamental tools for this purpose as they guarantee thermodynamic fields with high spatial-temporal resolution and with continuous spatial coverage.

This work presents an innovative WRF configuration recently developed and tuned for the city of Rome (Italy), including a detailed characterization of the urban land use based on the Local Climate Zones at high resolution (grid horizontal size of 500 m in the innermost domain). Such configuration has been applied here to the simulation of a heat wave event that occurred in Rome during the exceptionally hot year 2022 testing different planetary boundary layer schemes and urban canopy models available in WRF.

Simulated values of temperature at 2 meters height and wind speed at 10 meters height were compared with the acquisitions of the 13 weather stations of the ASTI-Network, showing a good agreement for the different Local Climate Zones. Average bias is mostly less in module than 0.5°C, RMSE is about 1°C and the Pearson correlation coefficient is higher than 0.95 for temperature; average bias is mostly less in module than 1 m/s, RMSE is about 1m/s and the Pearson correlation coefficient is higher than 0.8 for wind speed.

These statistical parameters reveal that this tool is suitable for the exploration of UHI-related vulnerabilities in the city of Rome, even during heat waves.

The high thermal inertia of massive buildings (thick masonry, concrete, or stone walls with minimal openings) naturally attenuates and delays outdoor climate variability, offering a potential sustainable solution for the passive preservation of climate-vulnerable heritage collection. This research proposes an approach combining multiple statistical techniques for indoor climate (named also as microclimate) characterization, and for assessing existing buildings as passive or low-energy conservation spaces. The approach is applied to multi-years series of indoor temperature (T) and relative humidity (RH), including the mixing ratio (MR) of moist air, available at the Library Section for Special Collections (LSSC), located within the WWII massive bunker “Dora I” (Trondheim, Norway). Five years of microclimate data (2020-2024), are analyzed to investigate the intrinsic properties of the time series relevant for the microclimate characterization in conservation spaces housing climate vulnerable collections (paper-based objects housed within Dora I). It is found that indoor climate conditions remain stationary, with no discontinuities or trends over five years. Indoor T and MR are delayed of 70 and 50 days and attenuated by a factor of 0.2 and 0.4 compared to outdoor values, demonstrating high thermal inertia and moisture buffer. Findings confirm the suitability of heavyweight, passively conditioned massive buildings in ensuring stable indoor conditions for heritage conservation, supporting sustainable reuse strategies as an energy-saving solution. Given the widespread availability of similar massive structures (often former industrial, military, or infrastructural buildings), their potential is particularly promising.

In questo studio si indaga la variazione a lungo termine dell’esposizione della popolazione al biossido di azoto (NO2) nell’area urbana di Napoli, Italia, durante il periodo 2013–2022. La ricerca integra dati ad alta risoluzione temporale dalla rete regionale di monitoraggio di riferimento con misure ad alta risoluzione spaziale raccolte durante la campagna di citizen science “NO2, NO grazie!”, condotta nel febbraio 2020 con campionatori passivi a basso costo. L’obiettivo principale è stato quello di generare stime ad alta risoluzione delle concentrazioni di NO2 per valutarne l’andamento temporale, l’esposizione della popolazione e l’impatto sanitario in un ambiente urbano densamente popolato.

E' stato utilizzato un modello Random Forest basato sull’uso del suolo (Land Use Random Forest, LURF) per estrapolare i dati sperimentali della campagna di citizen science e per calibrare e armonizzare questi dati con la rete di monitoraggio ufficiale. Questo approccio ha permesso di stimare la concentrazione media annuale di NO2 su una griglia spaziale fine per ciascun anno del periodo di studio. La metodologia ha tenuto conto delle incertezze delle stime di esposizione e ha permesso di valutare la conformità agli standard della Comunità Europea (EC) e alle linee guida sulla qualità dell’aria dell’Organizzazione Mondiale della Sanità (OMS).

I risultati evidenziano che, nonostante una lieve tendenza al ribasso delle concentrazioni di NO2 nel decennio, una larga parte della popolazione napoletana è esposta a livelli superiori sia al nuovo valore guida dell’OMS (10 ug/m3) sia agli obiettivi intermedi, sempre indicati dall'OMS. Nella maggior parte degli anni, circa due terzi della popolazione ha sperimentato valori medi annuali di NO2 superiori a 40 ug/m3, con una riduzione temporanea osservata solo nel 2020 durante il lockdown per l'epidemia da COVID, quando la percentuale di esposti sopra questa soglia è scesa al 6%. La valutazione dell’impatto sanitario, basata su funzioni di risposta concentrazione-effetto consolidate, ha stimato che circa 1.300 decessi all’anno a Napoli sarebbero attribuibili all’esposizione a NO2 sopra la soglia di 10 ug/m3, con una riduzione significativa osservata solo nell’anno del lockdown.

Lo studio dimostra che le campagne di citizen science co-progettate, se integrate con il monitoraggio ufficiale e la modellazione statistica avanzata, possono migliorare notevolmente la risoluzione spaziale e l’affidabilità delle valutazioni sull’esposizione all’inquinamento urbano da NO2. I risultati sottolineano la persistenza di un’elevata esposizione a NO2 a Napoli e l’importanza di politiche di riduzione delle emissioni più rigorose e continuative per raggiungere la conformità agli standard internazionali sulla qualità dell’aria e tutelare la salute pubblica.

Urban Heat Island (UHI) and Urban Pollution Island (UPI) are two interlinked phenomena that exacerbate the vulnerability of cities to climate change, extreme weather events, and deteriorating air quality. While UHI refers to the excess temperature in built-up areas compared to their rural surroundings, UPI describes the accumulation of atmospheric pollutants within the urban canopy. Understanding their interaction is crucial for improving thermal comfort, air quality, and urban sustainability.

This study investigates the UHI–UPI relationship in Rome (Italy) by combining in-situ measurements of meteorological variables and pollutant concentrations using an integrated approach that combined multiple statistical techniques. Data collected over the period 2018–2023 include air temperature, humidity, and wind speed from dense meteorological stations, together with PM₁₀, PM_2.5, NO, NO₂, and O₃ from the ARPA Lazio air quality network.

Results reveal marked temporal variability of UPI intensity, with aerosol and nitrogen oxides peaking in winter under stagnant conditions, and ozone reaching maxima in summer. Seasonal cycles are mirrored by UHII, which is strongest in summer and weakest in winter. Lag-correlation analysis reveals the most significant coupling when nocturnal UHII is shifted backward by one day, indicating that daytime emissions accumulate overnight. Regression analyses identify daily mean air temperature and wind speed as key drivers of UHI–UPI interaction. Spearman correlations indicate negative associations of UHII with NO, PM₁₀, NO₂, and PM_2.5, and a positive correlation with O₃. Case studies (atmospheric stagnation, calm wind days, cold-air pooling nights, and heatwaves) confirm that atmospheric dynamics strongly modulate the intensity and persistence of both phenomena.

These findings highlight the complexity of UHI–UPI feedback and stress the importance of integrated monitoring and modelling frameworks. Science-based insights from this analysis provide valuable guidance for urban planning and environmental management aimed at reducing combined thermal and pollution stress in Mediterranean cities.

Solar ultraviolet (UV) radiation is a major risk factor for a variety of short- and long-term health conditions affecting the skin, eyes and the immune system. At the same time, controlled UV exposure is essential for human health, as it is the primary source of vitamin D. Individual exposure levels are influenced not only by ambient UV irradiance, typically monitored by fixed broad band radiometers, but also by the orientation of exposed body parts, outdoor exposure duration, activity patterns and interactions with the surrounding environment. Reliable quantification of personal UV exposure therefore requires the use of dosimeters, portable UV-sensitive devices - based on radiation-sensitive materials or electronic sensors such as photodiodes - that can be worn by individuals. At high altitudes, where solar UV irradiance is enhanced by elevation, snow cover, and clean atmospheric conditions, both residents and visitors are at increased risk of overexposure. To investigate this issue, a field campaign was carried out from July to August 2025 in the Mont Avic Natural Park, an Alpine protected area in the Aosta Valley (northern Italy). Ten volunteers, including park rangers and seasonal outdoor workers, wore calibrated electronic dosimeters at the wrist to measure personal UV exposure during their daily activities in high-altitude (1500–2700 m a.s.l.) environments. The collected exposure data are analyzed in relation to ambient UV levels measured by a fixed broadband radiometer and classified by type of outdoor activities. The individual exposure was compared to the occupational threshold limit value of UV radiation of 1 SED (1 standard erythemal dose is equivalent to an erythemal effective radiant exposure of 100 Jm^-2) as recommended by the International Commission on Non Ionizing Radiation Protection (ICNIRP). To our knowledge, this study represents one of the first assessments of occupational UV exposure among park rangers, a group that can also serve as a representative proxy for other cohorts of population such as mountain hikers. The results are beneficial for developing targeted sun protection guidelines for both occupational groups and mountain visitors.

TRIGGER (Solutions for mitigating climate-health induced risks) https://project-trigger.eu/ is an Horizon Europe project started in September 2022 dealing with the complex interlinkages between extreme weather and health effects. It involves 22 partners from several European countries working together to understand how to mitigate the incidence of several diseases such as cardio-vascular disorders in a warming climate. Specifically, the project focuses on achieving a better integration between personal health protection and the environment in which choices at personal level can be made to mitigate climate-related health risks. TRIGGER's engines are the Climate-Health Connections Labs (CHC Labs): five selected Labs built in European cities, strategically distributed from south to north Europe to capture the diversity in climate. The role of CHCL is to act as hub for the various TRIGGER activities. Each represents a specific environment and climate-related risks ranging from heat waves to air pollution. Each Lab co-design and implement clinical studies, namely the CrossCLAVIS (cross-sectional study), the LongCLAVIS (longitudinal study) and a retrospective study (RetroCLAVIS) to gather new information about climate-related health conditions and use refined climate and health indicators to understand criticalities and work on mitigation of those. TRIGGER is one of the 6 Horizon Europe projects, BlueAdapt, CATALYSE, CLIMOS, HIGH Horizons, IDAlert, and TRIGGER, form the climate change and health cluster. All cluster projects address climate change-induced health risks and help increase preparedness and adaptation by creating synergies, sharing experiences and knowledge, and developing aligned communication actions to maximise impact of climate change. In this presentation we focus on the findings of the first 3 years of projects with emphasis on the identifications of meteo-climate indicators used to infer health effects as well as the sensitivity to spatial and temporal scales. Starting from heat-waves cases, a methodology is presented to downscale relevant variables to formulate fine-grain novel indicators of heat exposure. The presentation will provide some insight on the relevance of data requirements to advance climate science in the health domain.

The monitoring of atmospheric water vapor is crucial in understanding and predicting severe weather conditions such as heavy rainfalls. This work presents a cost-effective and near real-time approach to estimate Precipitable Water Vapor (PWV) using low-cost dual frequency Global Navigation Satellite System (GNSS) receivers and open-source software to create a near real-time monitoring and alerting service for extreme weather conditions. The positioning method is based on real-time Precise Point Positioning (PPP) with State Spatial Representation (SSR), a messaging standard provided by the International GNSS service (IGS) to disseminate corrections for orbits, clocks, phase-biases and ionospheric delays in real-time. In this process, the Zenith Total Delay (ZTD), i.e., the delay experienced by GNSS signals while crossing the atmosphere, is estimated and PWV can be computed, thanks to the availability of ZTD estimates, and surface pressure and temperature data that are estimated through Global Pressure and Temperature (GPT-3) models. The service allows users to receive warnings about potential extreme weather events based on the sharp increase of PWV in a short period of time, which often indicates an imminent severe weather event. The system structure integrates GNSS data processing, atmospheric data estimation and dissemination of timely and reliable outputs. It also uses cloud computing for data logging to enable further analysis, and supports the potential integration of additional data sources for scalability. This framework offers an affordable and adaptable solution for enhancing weather monitoring, ensuring global applicability, thanks to the use of PPP technique.

The study was carried out within “PAIN AND GAIN - Positioning and INtelligent Alarms supported by a New Dense GNSS Affordable Infrastructure” Project n. 2022P8C7ZA, funded by European Union-Next Generation EU within the PRIN 2022 program (D.D. 104-02/02/2022 Ministero dell’Università e della Ricerca).

Nontuberculous mycobacteria (NTM) are opportunistic agents, with main sources of infection being soil and water. Higher population densities and tropical/subtropical climates were associated with increased pulmonary NTM infection (pNTM). Aim of this study was to assess whether pNTM was associated with climate parameters (temperature, humidity, wind speed, and precipitation) and urbanization.

We conducted a case-control study evaluating meteorological conditions (in the 12 months before the first NTM isolate for every patient) and urbanization in the municipalities of residence of 1,061 adults with pNTM enrolled in an Italian multicenter observational study (2013-2022), compared with a random sample of 10,000 adults from the Italian population.

Climate conditions of the municipalities involved in the study for the period of interest (January 2013- December 2022) were reconstructed using an interpolation technique called MLRLI (Multi-linear regression with local improvement).

The input data comes from various networks of meteorological weather stations, spread all over the Italian territory, measuring temperature at 2-meter height, relative humidity, wind speed and direction, barometric pressure and precipitation. Among these networks it is notable to mention the MNW network (Meteonetwork). Other networks (regional, private and aerodrome networks) are also included in the present analysis. For the rain precipitation field, satellite data are also included, from the GPM (Global Precipitation Measurement) mission.

Results show that densely populated areas were at higher risk for pNTM, even after adjusting for age, sex and meteorological parameters in a multivariable mixed-effects logistic regression model. Hotter, poorly ventilated climates with less precipitation were at higher risk for NTM infection, with OR (Odds Ratio) 95%; CI (Confidence Interval) 0.90 for each km/h increase in mean annual windiness; CI 0.99 for each 10 mm increase in mean annual precipitation; CI 1.21 for each °C increase in mean annual temperature.

This contribution presents the Three-Dimensional Urban Canopy Model (3DUCM), a novel three-dimensional urban parameterization, and its implementation in the Weather Research and Forecasting (WRF) model (WRF/3DUCM). Traditional urban sub-grid scale parameterizations implemented into mesoscale meteorological models typically consider a simple two-dimensional canyon geometry representative of the average urban morphology of the mesoscale model grid cell. On the other hand, 3DUCM is a single-layer urban canopy model that takes into account every single building of the urban area, also considering canyon orientation and explicit crossroads modeling, i.e., three-dimensional effects. Model output incorporates not only average grid cell variables, but also predicted fields down to the building scale.

In this work, WRF/3DUCM is tested in the city of Rome during a summer heat wave. Simulations benefit from detailed information on the geometric characteristics of the city, whereas physical properties of urban materials are assigned based on the local climate zone (LCZ) framework. Simulation results obtained with WRF/3DUCM are compared with those from simulations performed with WRF coupled with the default single-layer and multi-layer urban canopy parameterizations and evaluated against an observational dataset. The results highlight the potential of incorporating single-building details to improve the representation of urban microclimates with mesoscale meteorological models.

A rigorous definition for risk as the product of three factors (hazard, vulnerability and damage) is proposed. The application of this formalism requires: (1) the determination of the hazard as the probability density function (pdf) of a set of variables that correspond to the quantities that are potentially harmful for the conservation of cultural heritage; (2) the determination of the vulnerability as the conditional pdf of the damages produced by the potentially harmful conditions; (3) the damage, i.e., the value of the elements at risk or the costs to their restoration. Examples are given to test the application of this approach, through the processing of indoor and/or outdoor data and the use of dose-response (or loss, or damage) functions, to different study cases.

Recognizing the growing urgency of climate change, many regional administrations are developing tailored adaptation strategies based on the specific characteristics of their territories, including climate patterns, local atmospheric dynamics, topography, and, importantly, environmental and socio-economic factors. However, effectively leveraging this wealth of information to design robust climate strategies remains a challenge. This study presents the comprehensive approach used to establish the knowledge foundation for the mitigation and adaptation strategy of the Trentino region. We highlight three main steps in this process, emphasizing the collaborative effort of multiple stakeholders: 1) Preparing a scientific report reviewing current knowledge and identifying research gaps regarding climate change impacts in the region; 2) Disseminating a synthesis of this information to the general public; 3) Creating reference climate scenarios.
The literature review revealed substantial variation in both the scope and depth of analyses across different scientific sectors concerning climate change impacts in Trentino. This heterogeneity was reflected in the informative report, which employed a structured methodology to simplify and synthesize technical information without compromising scientific accuracy. For the climate scenarios, we considered multiple factors, including topography, data availability, and stakeholder needs. A layered approach was applied, incorporating spatially aggregated results, statistical downscaling, and high-resolution numerical simulations using a convection-permitting model. The study projects a general increase in temperatures, with more pronounced rises in winter minima and summer maxima. Extreme precipitation events are expected to become more frequent and intense, while seasonal patterns vary depending on the emissions scenario considered.

Preventive conservation involves strategies and practices that aim to minimise the risk of damage and losses to cultural heritage before deterioration occurs. Within this framework, microclimate — the climate conditions surrounding an object — plays a key role as it directly influences preservation.

Therefore, investigating microclimates is not merely about collecting data; it requires a meticulous analysis of datasets rather than simply calculating statistical parameters or verifying compliance with thresholds. It demands a thorough understanding of the complex interactions between the environment and the materials, along with an awareness of the climate-induced mechanisms responsible for deterioration.

This integrated approach is essential for assessing past and present conditions that have led to the object's current conservation status, and for guiding the identification of the most appropriate preservation actions in view of future scenarios. In this context, it is crucial to consider the growing impact of climate change and the pressure of mass tourism, both of which pose new challenges to the cultural heritage protection.

The Climate4Heritage laboratory of the Physics Department at Sapienza University of Rome has investigated the microclimate in more than 20 case studies over the past 15 years. This contribution analyses the challenges addressed during this period, from instrumental issues to data analysis, and discusses how combining theory with experiences in microclimate studies can lead to effectively and scientifically support preventive conservation of cultural heritage.

Insurance claim verification is the process through which insurance companies assess and validate the legitimacy and accuracy of claims before approval. This often involves complex procedures aimed at identifying inconsistencies, errors, or potential fraud.

Catwink is a new tool developed for the Italian context, designed to improve the efficiency of claim verification related to natural hazards such as floods, landslides, and earthquakes.

In this work, we present the Catwink components focused on evaluating the plausibility of claims caused by floods and landslides. The methods leverage multiple rainfall datasets and have been calibrated using recent extreme weather events. System performance has been assessed both through selected case studies and statistical analyses.

I medicane sono cicloni mediterranei che acquisiscono caratteristiche tipiche dei cicloni tropicali. Sebbene si verifichino mediamente pochi eventi ogni anno, i medicane hanno riscosso un crescente interesse poiché sono spesso associati a forti impatti (piogge intense, tempeste di vento, mareggiate) che colpiscono soprattutto le coste densamente popolate del bacino del Mediterraneo.

I prodotti satellitari non solo permettono di monitorare l’evoluzione e identificare le caratteristiche chiave dei medicane, ma forniscono anche preziose stime di precipitazione sopra il mare, dove questi cicloni si sviluppano e dove le osservazioni sono solitamente scarse. Nell’ambito del progetto “MEDICANES – Earth Observations as a cornerstone to the understanding and prediction of tropical-like cyclone risk in the Mediterranean” finanziato dall’Agenzia Spaziale Europea (https://medicanes.isac.cnr.it), vengono raccolti diversi prodotti di stime di pioggia da satellite. Lo scopo di questo studio è indagare le potenzialità dell’assimilazione di questi dati in un modello meteorologico ad alta risoluzione “convection-permitting”, al fine di migliorare la simulazione numerica dei medicane. L’assimilazione sfrutta una tecnica di nudging che modifica progressivamente i profili di umidità specifica del modello a seguito del confronto tra pioggia osservata e simulata.

Utilizzando due intensi eventi recenti come casi di studio, i medicane Ianos e Apollo, l’assimilazione si è mostrata capace di migliorare le simulazioni della traiettoria e dell’intensità dei cicloni, inducendo una modifica nei processi diabatici, che può propagarsi e influenzare anche la dinamica a larga scala.

Il radar meteorologico è uno degli strumenti che si presta maggiormente per supportare le tecniche per la previsione di eventi precipitativi con un’elevata risoluzione spaziale e temporale (ossia il nowcasting). Attualmente, esistono numerosi metodi di nowcasting che utilizzano in input serie temporali di echi radar, sfruttando anche tecniche di intelligenza artificiale. Le performance di tali metodi, tuttavia, sono influenzate da vari fattori e le loro capacità previsionali devono essere investigate localmente per applicazioni specifiche a livello nazionale. Questo lavoro si propone quindi di confrontare varie tecniche di nowcasting (tradizionali, basate sulla stima del campo di moto, e più recenti, considerando algoritmi di intelligenza artificiale) sul territorio italiano, al fine di stabilire una metodologia per fissare una soglia minima accettabile di performance (MAP) da utilizzare nell’analisi delle prestazioni di futuri metodi di nowcasting per quantificare il miglioramento rispetto allo “stato dell'arte”.
I risultati presentati sono ovviamente validi per l'Italia, anche se l'approccio è generale e potrà essere riprodotto in altre aree geografiche. Più nel dettaglio, come dataset di test sono stati adoperati circa due anni di stime radar di precipitazione, da gennaio 2022 a febbraio 2024, rivelate dal mosaico radar nazionale ogni 5 minuti, con una risoluzione spaziale di 1 km. Il dataset è stato filtrato mediante l’introduzione di un nuovo processo di screening che consente di selezionare in modo automatico le immagini contenenti un numero minimo di punti griglia distribuiti in modo omogeneo e consistente con i pattern spaziali tipici della precipitazione. Il risultato più evidente è la grande variabilità nelle prestazioni di ogni metodo testato. Inoltre, è stata rilevata una dipendenza stagionale e geografica delle performance. Durante la stagione estiva, infatti, quando i temporali convettivi sono più frequenti, le capacità predittive di tutti i metodi risultano ridotte e ciò suggerisce la necessità per il futuro di implementare moduli di nowcasting specifici per la convezione, possibilmente ottimizzati localmente, per migliorare le capacità di previsione in caso di instabilità atmosferica.

Il progetto DiToNA (Digital Twin of North Adriatic) mira a implementare una piattaforma di gemello digitale per la regione dell’Alto Adriatico, integrando modelli numerici avanzati di previsione meteorologica, oceanografica e ondosa, in grado di assimilare dati osservativi, inclusi quelli satellitari ad alta risoluzione, e produrre simulazioni ad altissima fedeltà spazio-temporale. DiToNA è cruciale per un’area vulnerabile a eventi meteo-marini estremi, quali cicloni mediterranei, la Bora, mareggiate e fenomeni di acqua alta che colpiscono città costiere e infrastrutture dell’Adriatico settentrionale.

La piattaforma combina i modelli WRF (atmosfera), ROMS (oceano) e SWAN (onde) tramite il framework COAWST, consentendo simulazioni tridirezionali e processi multi-scala tra atmosfera, oceano e onde. Le griglie di simulazione prevedono risoluzioni fino a 1 km nelle aree costiere più sensibili e una dettagliata stratificazione verticale nei primi metri sopra e sotto la superficie, per una rappresentazione accurata di scambi termici e turbolenze. L’innovazione chiave di DiToNA risiede nell’uso di tecniche di data assimilation variazionale (3DVAR) e nell’integrazione di dati da radar satellitari SAR, che migliorano significativamente l’inizializzazione dei modelli in aree marine.

La validazione, effettuata tramite indicatori quantitativi (bias, MAE, RMSE, CSI, POD), evidenzia benefici dell’assimilazione soprattutto nelle fasi iniziali della simulazione e nei modelli accoppiati, pur rilevando persistenti criticità nelle previsioni di precipitazioni estreme e una tendenza alla sovrastima degli eventi più intensi. Le analisi condotte su casi di studio emblematici, come il ciclone Detlef (novembre 2019), confermano la capacità del sistema di riprodurre eventi eccezionali, sottolineando la necessità di ulteriori miglioramenti, in particolare tramite reti osservative più dense e strategie di calibrazione dinamica. DiToNA si propone dunque come riferimento nazionale ed europeo per la previsione e la mitigazione degli impatti meteomarini nelle coste mediterranee.

As a result of greenhouse gases mitigation strategies to face climate change, renewable energy plants have greatly expanded during the last decades. Concurrently, extreme weather events are increasing in frequency and intensity, posing significant challenges for renewable energy generation and grid stability. Deep convection, capable of generating strong wind gusts, heavy rainfall and hailstorms, is often responsible for substantial damage to electrical infrastructure. As a result of the small temporal and spatial scales in which convection typically develops, its associated phenomena are difficult to predict accurately.

It follows that improving the ability of weather forecasting models to predict them is essential for making disaster prevention measures more effective.

This study used the regional model WRF-ARW to investigate the capabilities of forecasting a convective event that occurred in Northern Italy on August 28th, 2025. During the event, strong wind gusts uprooted several panels from a large PV plant in Verretto (PV). This event was analyzed using several high-resolution simulations conducted in both reanalysis and forecast mode, with three nested domains at resolutions of 12 km, 4 km and 1.3 km, respectively. As far as reanalysis mode is concerned, ERA5 was used, while two simulations using GFS and IFS drivers were carried out in forecast mode to assess the relative performance of the different boundary conditions in reconstructing this event.

Various meteorological variables were examined to evaluate the performance of each simulation. Convective parameters related to the genesis and development of thunderstorms, as well as surface wind speed and vertical velocity, were analyzed to assess the simulation’s relative performance in accurately reconstructing this event in space and time.

This study has also deepened the ability of regional downscaling in the detection of this localized event at different spatial resolutions.

These preliminary results are useful for guiding the selection of the most appropriate model configuration, such as grid settings and parameterizations, and boundary conditions for describing destructive and localized convective phenomena, which pose an even greater threat to energy infrastructure.

Urbanisation is responsible for deep modifications to the environment, altering the energy balance and local microclimate. In particular, urban areas are known for the so-called heat island effect, associated with a reduction in thermal comfort and, more generally, a series of negative effects on human physical and mental health.

Vegetation (shading, evapotranspiration), soil (support for vegetation, interface between vegetation and water, water reserve), water (integrated management returned to the soil directly or indirectly) and surface cover (thermoradiative properties, evapotranspiration) are considered the main factors capable of counterbalancing the heat island effect, contributing to the creation of urban cool islands. However, the impact of these factors on the urban microclimate is variable and is affected by the mutual influence of the various factors.

In this study, we will examine the impact of the main factors contributing to the development of urban cool islands, using the TEB-Surfatm model on several case studies in France. Parameters such as the colour and thermal properties of coatings, the height and the rate of coverage of the vegetation, water requirements associated with the presence of vegetation will be taken into account in order quantify their impact on the microclimate. The results of this research, developed in collaboration with Vinci Constructions, will be used to create resilient urban environments, reducing the impact of the urban heat island effect.

We present a computationally efficient regional weather prediction system based on the Weather Research and Forecasting (WRF) model and its data assimilation component, WRFDA. The system generates twice-daily 24-hour forecasts (00 and 12 UTC) over the European Alps and surrounding regions at 3.5 km resolution. Surface observations are assimilated using the 3D-Var algorithm, and forecast performance is evaluated against independent surface and radiosonde measurements. The assimilated observations are obtained from both conventional sources (SYNOP observations) and the dataset hosted by Hypermeteo S.r.l., which comprises observations from publicly and privately owned surface stations that are usually not included in assimilation routines. Assimilating these observations results in substantial improvements in near-surface temperature and humidity forecasts compared to control simulations without assimilation. In the first six forecast hours, mean temperature and humidity errors are reduced by up to 0.26 K and 0.18 g kg⁻¹, while the spread of errors decreases by 7–10%. The comparison of forecasts against radiosondes indicates, however, that these surface-based adjustments can increase forecast biases within the planetary boundary layer (PBL). At the same time, the assimilation of surface observations leads to an overestimation of the accumulated precipitation. We show that these side effects can be effectively mitigated by considering a different assimilation algorithm. The assimilation also exhibits different effects across terrain types, with greater benefits in lowlands than in mountainous regions, likely due to limitations in how static covariances spread observational information along terrain-following coordinates. Moreover, the forecast skill displays a clear diurnal pattern, with larger temperature errors occurring over mountains during the day and over plains at night.

Mediterranean Hurricanes (hereafter Medicanes) are high-impact, short-fused events whose rapid intensification and small spatial extent challenge conventional nowcasting systems. Reliable, automatic detection and tracking from geostationary satellite imagery could improve situational awareness and contribute to earlier warnings. Within the MEDICANES project funded by the European Space Agency (ESA), we apply a selfsupervised vision transformer called VideoMAE for learning high-level spatiotemporal features from geostationary satellite IR image sequences. Our aim is to both automatically detect Medicanes and track them from short video clips. The dataset consists of airmass RGB composites over the Mediterranean basin, built by means of the IR channels images collected by the Rapid Scan Service (RSS) of the Spinning Enhanced Visible InfraRed Imager SEVIRI on board the Meteosat Second Generation (MSG) geostationary satellite. We perform self-supervised pretraining of VideoMAE for representation learning, and fine-tune the pretrained model for downstream tasks: binary classification for cyclone presence, and regression for the cyclone-eye center localization. The pretrained-then-fine-tuned VideoMAE model yields robust detection task performance on the imbalanced test set, with overall accuracy exceeding 90% and POD above 95%. The model captures key cyclone dynamics, such as rotational cloud patterns and evolving airmass contrasts without manual feature engineering. The study shows that VideoMAE can provide a scalable and efficient approach to satellite video analysis, demonstrating robust transfer to cyclone detection despite severe class imbalance and delivering consistent center localization. These results suggest that transformer-based video models are well suited for operational use in rare-event detection, offering a practical path toward automated monitoring and early warning of Medicanes.

La previsione meteorologica probabilistica è una tecnica che utilizza ensemble forecasting, ovvero insiemi di simulazioni con condizioni iniziali e parametrizzazioni leggermente diverse per quantificare l'incertezza previsionale.

L'Ensemble Prediction System (EPS) dell'European Centre for Medium-Range Weather Forecasts (ECMWF) genera 50 membri ogni 6 ore. L'utilizzo operativo di questo ensemble per fornire condizioni iniziali e al contorno a modelli regionali ad alta risoluzione richiede una riduzione del numero di membri compatibile con le risorse computazionali disponibili. Si pone quindi il problema della selezione ottimale dei membri da utilizzare.

Questo studio presenta e valuta un metodo di riduzione dell'ensemble che preserva la consistenza statistica del campione originale. La metodologia si basa sulla riduzione dimensionale tramite Principal Component Analysis (PCA) delle variabili prognostiche del modello globale a livelli isobarici selezionati, seguita da clustering nello spazio latente. Da ogni raggruppamento viene estratto il membro più prossimo alla centroide. La numerosità dell’ensemble ridotto è stabilita priori con il numero di raggruppamenti prescritto. I membri selezionati vengono utilizzati come condizioni iniziali e al contorno per altrettante simulazioni con il modello ad area limitata WRF-ARW, mantenendo invariati i parametri del modello per ogni membro.

Il metodo è stato testato su cinque eventi meteorologici severi che hanno coinvolto il territorio italiano. Lo stesso dominio è stato usato per ogni evento. I risultati dimostrano che l'approccio proposto mantiene meglio la dispersione statistica dell'ensemble originale rispetto a una selezione casuale, offrendo una soluzione computazionalmente efficiente e facilmente implementabile per sistemi di previsione probabilistica operativi con numero arbitrario di membri.

Ensemble-based data assimilation algorithms can be used for the objective estimation of the optimal values of uncertain empirical constants in parameterization schemes. This is accomplished by state augmentation: empirical parameters are appended to the model state vector and updated on the basis of flow-dependent ensemble covariances with observable quantities. The method has been used so far as a way of increasing ensemble spread and accounting for model errors in the assimilation process. In this study, we show that parameter estimation results can be useful also for parameterization design, but only under rather restrictive conditions. The error variance of the assimilated observations needs to be as low as that of the state perturbations induced by the estimated parameter, and parametric uncertainty must be the dominant contributor to the total forecast uncertainty. We illustrate the methodology with examples dealing with parameterizations of boundary-layer turbulence and gravity-wave drag.

Accurate seasonal forecasting of water table elevation is essential for the effective management of water resources in unconfined aquifers, especially in the context of climate variability and human-induced pressures. This study presents a novel methodology for predicting water table elevation on seasonal timescales. This approach couples reanalysis and seasonal forecast data of soil moisture with a calibrated non-linear transfer model. This approach uses ERA5 reanalysis and SEAS5 seasonal forecasts to estimate the flux towards the aquifer and predict the water table elevation. A case study in the Umbria region of central Italy demonstrates the model's ability to simulate and predict monthly water table fluctuations. Two modelling strategies were compared: a static calibration approach (OPT 1) and a dynamic calibration approach (OPT 2), in which model parameters were updated by considering different time periods. Both options yielded skilful forecasts across lead times of 1 to 6 months, with OPT_2 showing slightly improved stability in forecast performance metrics. The results confirm the feasibility of incorporating seasonal climate forecasts into operational groundwater prediction frameworks. As expected, the accuracy of the forecasts is limited by the accuracy of the precipitation predictions, particularly during autumn and winter. The proposed framework establishes the basis for anticipatory aquifer management and early warning systems in the context of changing hydroclimatic conditions.

The interpolation of sparse ground data for the initialization of numerical weather forecast, for model validation, and for data assimilation is sometimes performed with geostatistical interpolation. Standard theoretical variograms are often applied. The characteristics of some intense convective events have triggered the idea of a new formula for a theoretical variogram. Strengths and possible difficulties occurring in the application of this variogram model are analyzed.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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