Massimo Milelli

Peripheral-station and representative-year weather files can misrepresent urban thermal conditions. This study quantifies how reference, weather construction, and temporal definition condition urban heat-island (UHI) effects on residential performance in Turin to support proper sustainable climate design studies. Seven-station observations (2014–2023) underpin processed actual meteorological years (AMYs), calendar-hour-average years (AvMYs), and regenerated typical meteorological years (TMYs). We introduce a new method, the Controlled Urban Weather Forcing (CUWF), which substitutes declared psychrometric variables within a common Caselle background; full-field and al, it is correct. Since polito is also the affiliation of mine (Chiesa), do we also need my email here, or is it ternative-reference cases test the boundaries. A DOE–TABULA apartment archetype, with two envelope states and three operating modes, was evaluated through 452 annual EnergyPlus simulations. Across ten matched AMYs, urban forcing increased useful sensible cooling by 2.8–12.3% and reduced heating by 5.3–25.8%. Original-envelope full-field Consolata relative to Caselle changed heating by −13.7% and cooling by +17.8%. TMY needs remained near AvMY values, but original-envelope cooling peaks increased by 38.7% at Caselle and 57.5% at Consolata. The AMYs contained 26 Caselle-defined persistent hot events, whereas the AvMY contained none under the same threshold. Rank-based, leave-one-year-out, daily aggregation, and lag analyses retained response directions but confirmed that degree-hours, dynamic loads, and overheating exposure are not interchangeable. Climate-resilient design should therefore specify the reference, weather representation, envelope, and control mechanism rather than treat UHI as a uniform offset, thereby properly supporting building and urban sustainable design and policy-correlated aspects.

The agricultural sector heavily relies on weather forecasts for informed decision-making. However, existing forecasting methods often lack localized precision, leading to significant uncertainties in predicting extreme weather events crucial for agricultural planning and management. The MAGDA H2020 project aims to address these challenges by developing a modular system deployed to farms, integrating observations from European space-based and ground-based assets to enhance tailored weather forecasts. This project represents a significant advancement by synergizing spaceborne, airborne, and ground-based measurement technologies, including GNSS and meteodrones observations, with meteorological models to benefit agriculture and water management operations. The project targets improvements in weather forecast accuracy, particularly concerning severe weather events like heavy rain, hail, windstorms. Inaccurate predictions of these events can lead to substantial crop damage, over-irrigation, or water shortages. Key challenges in Numerical Weather Models (NWM) stem from uncertainties in initial atmospheric conditions at small scales, necessitating enhanced observational data for improved model performance. Recent advancements in forecasting heavy rainfall events through data assimilation techniques show promising results. Studies have demonstrated the positive impact of integrating reflectivity data and in situ observations into meteorological models for predicting severe weather phenomena in various regions. Additionally, experiments incorporating Sentinel-derived and GNSS-derived products into high-resolution NWM have shown positive outcomes, particularly in predicting convective processes. The MAGDA project employs a cloud resolving modeling approach with a grid spacing of 2-3 km, coupled with rapid update cycles every 1-3 hours, to address uncertainties in weather prediction. The assimilation process integrates GNSS data to monitor integrated water vapor content, weather radar reflectivity to reconstruct the 3D cloud field, in situ weather stations for capturing near-surface atmospheric conditions, and meteodrones observations to collect information about the vertical profiles. By leveraging a combination of advanced technologies and data assimilation techniques, the project aims to enhance the accuracy and usefulness of weather forecasts tailored for agriculture and water management applications. The weather forecasts will be used as an input for the irrigation advisory, next to being used for generating warnings for extreme weather events. The warnings and irrigation advisories will ultimately be channeled through a Farm Management System to ensure the capability to effectively reach farmers and agricultural operators.

Citizen science is a powerful tool for promoting the understanding of urban climate and potential climate change hazards. By engaging citizens in scientific research and data collection, we can harness the collective power of communities to gather valuable information and contribute to our knowledge of urban climate dynamics.The H2020 I-CHANGE project (Individual Change of HAbits Needed for Green European transition, https://ichange-project.eu/) faces the challenge of engaging and promoting the active participation of citizens for addressing climate change, sustainable development and environmental protection in the framework of the European Green Deal, the European Climate Pact and the European Biodiversity Strategy for 2030.Also the HE project AGORA (A Gathering place to cO-design and co-cReate Adaptation, https://adaptationagora.eu/) has the ambition to contribute in building climate change adaptation roadmaps by fostering the participation and engagement of citizens and stakeholders in the co-design and co-creation of innovative problem-oriented climate adaptation solutions.In this framework, citizens can be trained, in the I-CHANGE project, to collect weather data using simple, low-cost monitoring tools. This data can then be aggregated and analysed to identify trends, patterns, and potential climate change hazards specific to urban areas. On the other hand, in the AGORA project, citizens can contribute to populate inventories on climate data, climate risks and climate adaptation and to develop the Digital Academy focused on a proper usage of climate data. Engaging citizens in this process fosters a sense of ownership and responsibility for their environment while also increasing their awareness of the impacts of climate change. By actively participating in data collection, citizens gain firsthand experience of how climate variables affect their daily lives and communities. Moreover, citizen science initiatives can be integrated into educational programs at schools, allowing students to actively participate in scientific research and contribute to the understanding of urban climate.Leveraging technology can enhance citizen engagement and data collection. Mobile applications and online platforms can be developed to facilitate data entry, provide real-time information, and encourage collaboration among participants. These digital tools make it easier for citizens to contribute to ongoing research efforts, access educational resources, and visualize the collected data, further promoting understanding of urban climate dynamics. All these “non-standard” measurements can be used (after a strict quality control) to enrich the official network in areas where it is more needed. In this work we will focus on urban areas to show how data collected by citizens can help in understanding the Urban Heat Island effect, also in relation to Heat Waves.

Weather forecasting is essential for agriculture, yet current methods often lack the localized accuracy required to manage extreme weather events and optimize irrigation. The MAGDA Horizon Europe/EUSPA project addresses this gap by developing a modular system that integrates novel European space-based, airborne, and ground-based technologies. Unlike conventional forecasting systems, MAGDA enables precise, field-level predictions through the integration of cutting-edge technologies: Meteodrones provide vertical atmospheric profiles where traditional data are sparse; GNSS-reflectometry offers real-time soil moisture insights; and all observations feed into convection-permitting models for accurate nowcasting of extreme events. By combining satellite data, GNSS, Meteodrones, and high-resolution meteorological models, MAGDA enhances agricultural and water management with precise, tailored forecasts. Climate change is intensifying extreme weather events such as heavy rainfall, hail, and droughts, threatening both crop yields and water resources. Improving forecast reliability requires better observational data to refine initial atmospheric conditions. Recent advancements in assimilating reflectivity and in situ observations into high-resolution NWMs show promise, particularly for convective weather. Experiments using Sentinel and GNSS-derived data have further improved severe weather prediction. MAGDA employs a high-resolution cloud-resolving model and integrates GNSS, radar, weather stations, and Meteodrones to provide comprehensive atmospheric insights. These enhanced forecasts support both irrigation management and extreme weather warnings, delivered through a Farm Management System to assist farmers. As climate change increases the frequency of floods and droughts, MAGDA’s integration of high-resolution, multi-source observational technologies, including GNSS-reflectometry and drone-based atmospheric profiling, is crucial for ensuring sustainable agriculture and efficient water resource management.

Urban Heat Island (UHI) is a phenomenon where urban areas experience significantly higher temperatures than their rural surroundings. This effect is primarily due to heat retention in buildings and roads, reduced evapotranspiration, and heat generated by vehicles, industrial activities, and air conditioning. Accurate UHI modelling is crucial for understanding its impacts and helping urban planners and policymakers in developing effective strategies to mitigate heat stress in cities. Advanced urban canopy schemes within atmospheric models play a key role, as they aim to realistically represent the complex interactions between urban surfaces and the atmosphere. The TERRA_URB (TU) model, developed within the COSMO Consortium, parametrizes the effects of buildings and streets on energy and moisture exchanges between the surface and atmosphere. Additionally, it accounts for the anthropogenic heat flux as a heat source from the surface to the atmosphere. TU requires the definition of parameters describing the geometrical and thermal urban features, such as street aspect ratio, building surface fraction, building height, impervious surface area, anthropogenic heat flux. These parameters were derived from the Local Climate Zone (LCZ) classification, using both global and local dataset. This study evaluated the performance of the TU scheme, implemented within the ICON model, by simulating past heat waves over Turin at 500 m resolution in hindcast mode. The ability of the TU scheme to reproduce UHI effects was assessed by comparing ICON simulations, with and without TU, against observational data. These included ground-level measurements from the Arpa Piemonte meteo-hydrological network and vertical temperature profiles from radiometers located in both the city center and surrounding areas. Furthermore, a denser crowdsourced air temperature network, NetAtmo, was evaluated to assess the potential of the citizen science data for improving urban climate studies. The results demonstrated that the TU scheme significantly improved the representation of the UHI effect.

Raising the awareness and interest of young generations for climate change phenomena and its impacts is a topic of great importance from a social and cultural standpoint. Along these lines, education plays a crucial role to meet the goals of the EU's Green Deal both through direct interplay with students and information spillover towards families and in general stakeholders at large. Inspired by these ideas, the H2020 I‐CHANGE project (Individual Change of HAbits Needed for Green European transition, 2021–2025, https://ichange‐project.eu/ ) aims at engaging and promoting the active participation of citizens for addressing climate change, sustainable development, and environmental protection. By taking into consideration the ongoing fast socio‐cultural change, the vertical growth of digital media, and, therefore, the new ways children and young people learn, this paper presents a possible path towards the practical implementation of key EU's Green Deal concepts in a secondary school, building on top of Episode of Situated Learning (ESL) methodology.

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues.

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect and analyze meteorological measurements. This paper presents the results of our analysis, demonstrating the effectiveness of citizen science in generating valuable environmental data. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. Our findings underscore the potential of citizen science to augment traditional monitoring efforts and inform decision-making processes in environmental research and management. The paper discusses the methodology employed, including data collection and statistical analysis techniques. Results highlight the reliability and accuracy of citizen-generated data, as well as the strengths of the MeteoTracker platform. The implications of our findings for environmental research, policy development, and public engagement are discussed, along with recommendations for enhancing the effectiveness of citizen science initiatives in environmental monitoring.

Snow in mountainous regions is a key water resource from local to continental scales. However, the link between increasingly frequent snow droughts and socio-hydrologic impacts from headwaters to lowlands is not fully understood. To untangle this relationship, here we combine snow and climate reanalyses with a range of data of terrestrial water storage, streamflow, and emergency water-use restrictions for the archetypal 2022 snow drought in the Po basin (Italy). We find that a persistent high-pressure ridge during winter 2021–2022 translated into a −88% anomaly in peak snow water equivalent, intraseasonal snowmelt, and earlier melt-out dates. Compounded by reduced summer precipitation and importantly increased temperatures, this snow deficit led to the lowest terrestrial water storage on record in summer 2022. Emergency water-use restrictions were concurrent with the peak in snowmelt deficit in early summer, rather than the peak in precipitation deficit in mid-winter. This study highlights the contribution of snowmelt deficit in driving the 2022 socio-hydrologic drought in the Po Basin.