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Climate change and land use are major drivers of environmental and socioeconomic transformations in landscapes and in coastal areas. The objective of this study was to present an interdisciplinary and participatory research methodology for analysing the evolutionary process of a coastal case study, the Trabocchi Coast in the Abruzzo Region (Italy), from multiple perspectives, including climate change, technological history, conservation protocols, and social function. The goal was to assess the resilience of these coastal structures, i.e., their ability to cope and adapt to changes while maintaining their identity and recognition. The adopted approach combined qualitative and quantitative data from the meteorological analyses, literature review, and field investigations involving a participatory workshop, from which a significant portion of the analysed results presented here emerged. The results indicate that the Trabocchi Coast has undergone significant changes in recent decades, posing a serious threat to cultural heritage due to climate change (sea-level rise, coastal erosion, storms, flooding and salinisation), changes in use and mass tourism. However, these changes have also presented challenges and opportunities for coastal development, stimulating diverse resilient responses from local communities, ranging from resistance to innovation. The paper discusses the factors that may make the Trabocchi Coast a model of resilience considering these changes. This is supported by the role of local institutions as guarantors of the cultural heritage value of the trabocchi in that specific landscape context, as evidenced by the approval of the "Costa dei Trabocchi Special Territorial Project" by the Abruzzo Region in 2023. [ABSTRACT FROM AUTHOR]

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model's precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. [ABSTRACT FROM AUTHOR]

The estimate of precipitation from satellite measurements is an indirect estimate if compared to rain gauges or disdrometer measurements, but it has the advantage of complete coverage over oceans, mountainous regions, and sparsely populated areas where other sources of precipitation data (e.g., weather radar) are unavailable or unreliable. Among the satellite-based precipitation estimates, geostationary (GEO) data ensure the highest spatial and temporal resolution. At the same time, the IR/VIS channels deployed on GEO satellites have lower capabilities than microwave (MW) channels in characterizing the cloud structure. Machine learning (ML) techniques can be considered a powerful tool to overcome the limitations related to the physical relationship between IR/VIS channels and precipitation estimation. This study describes the development of a convolutional neural network (U-Net) to retrieve the precipitation rate using IR measurements only from the Meteosat Second Generation (MSG) satellite. Its performances are evaluated through a comparison with H SAF and NASA operational products (e.g., H60B or H03B and IMERG-E, respectively), of which the algorithms are based on different principles. The results highlight a lower error in precipitation rate estimates for the U-Net with respect to the other products but also some issues in correctly estimating the more intense precipitation (>5 mmh−1). On the other hand, the precipitation detection capabilities of the U-Net outperform the H SAF products for lower precipitation rate, while IMERG-E shows the best performance regardless of the precipitation regime. Furthermore, the U-Net is able to account for and correct the parallax displacement that affects the measurement as the satellite viewing angle increases. [ABSTRACT FROM AUTHOR]

A destructive V-shaped thunderstorm occurred over the Marche Region, in Central Italy, on 15 September 2022. Twelve people died during the event, and damage to properties was extensive because the small Misa River flooded the area. The synoptic-scale conditions that caused this disastrous event are analysed and go back to the presence of tropical cyclone Danielle in the eastern Atlantic. The performance of the weather research and forecasting (WRF) model using lightning data assimilation (LDA) is studied in this case by comparing the forecast with the control forecast without lightning data assimilation. The forecast performance is evaluated for precipitation and lightning. The case was characterised by four intense 3-h (3 h) periods. The forecasts of these four 3-h phases are analysed in a very short-term forecast (VSF) approach, in which a 3 h data assimilation phase is followed by a 3 h forecast. A homemade 3D-Var is used for lightning data assimilation with two different configurations: ANL, in which the lightning is assimilated until the start of the forecasting period, and ANL-1H, which assimilates lightning until 1 h before the 3 h forecasting period. A sensitivity test for the number of analyses used is also discussed. Results show that LDA has a significant and positive impact on the precipitation and lightning forecast for this case. [ABSTRACT FROM AUTHOR]

The impact of assimilating GNSS-ZTD (Global Navigation Satellite Systems - Zenith Total Delay) on the precipitable water vapor and precipitation forecast over Italy is studied for the month of October 2019, characterized by several moderate to intense precipitation events. The WRF (Weather Research and Forecasting) model 4.1.3 is used with its 3DVar data assimilation system to assimilate ZTD observations from 388 GNSS receivers distributed over the country. The dataset was built collecting data from all the major national and regional GNSS permanent networks, achieving dense coverage over the whole area. Results show that WRF underestimates the atmospheric water vapor content for the period, and GNSS-ZTD data assimilation improves this underestimation. The water vapor forecast is verified for the forecast hours 1-6h after the last data assimilation time, while the precipitation forecast is verified in the phases 0-3h and 3-6h after the last data assimilation time. More than 3000 rain gauges spread over Italy were used to verify the precipitation forecast. The application of GNSS-ZTD data assimilation to a case study showed an improvement of the precipitation forecast in different ways, the main drawback being the prediction of false alarms. Considering the study over the whole period, GNSS-ZTD data assimilation had a positive impact on the precipitable water vapor and rainfall forecast, with an improvement of the performance up to 6 hours, and with statistically significant results for moderate to intense rainfall thresholds (25-30 mm/3h). [ABSTRACT FROM AUTHOR]