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A new methodology to map Italian coastal areas at risk of flooding is presented. This approach relies on detailed projections of the future sea level from a high-resolution, three-dimensional model of the Mediterranean Sea circulation, on the best available digital terrain model of the Italian coasts, and on the most advanced satellite-derived data of ground motion, provided by the European Ground Motion Service of Copernicus. To obtain a reliable understanding of coastal evolution, future sea level projections and estimates of the future vertical ground motion based on the currently available data were combined and spread over the digital terrain model, using a GIS-based approach specifically developed for this work. The coastal plains of Piombino-Follonica and Marina di Campo (Tuscany Region), Alghero-Fertilia (Sardinia), and Rome and Latina-Sabaudia (Lazio Region) were selected as test cases for the new approach. These coastal stretches are important for the ecosystems and the economic activities they host and are relatively stable areas from a geological point of view. Flood maps were constructed for these areas, for the reference periods 2010–2040, 2040–2070, and 2040–2099. Where possible, the new maps were compared with previous results, highlighting differences that are mainly due to the more refined and resolved sea-level projection and to the detailed Copernicus ground motion data. Coastal flooding was simulated by using the "bathtub" approach without considering the morphodynamic processes induced by waves and currents during the inundation process. The inundation zone was represented by the water level raised on a coastal DTM, selecting all vulnerable areas that were below the predicted new water level. Consequent risk was related to the exposed asset. [ABSTRACT FROM AUTHOR]

This study quantifies the vegetation impact on urban meteorology by means of the numerical model WRF (Weather Research and Forecasting model). The assessment was made for two months: July and January. These were considered as representative for the summer and winter seasons, for the reference year 2015 in three European cities: Bologna, Milano, and Madrid. Two simulations at 1 km resolution were conducted over the cities with and without the actual urban vegetation, called VEG and NOVEG, respectively, in the model input. Then, the impact of vegetation was evaluated as the difference between the two simulations (VEG-NOVEG) for temperature, relative humidity, and wind speed fields. In general, we found that, as can be expected, urban vegetation tends to cool the atmosphere, enhance the humidity, and reduce the wind speed. However, in some cases, areas with the opposite behaviour exist, so that no a priori results can be attributed to the presence of urban vegetation. Moreover, even when major impact is confined around grid cells where urban vegetation is present, changes in meteorological quantities can be observed elsewhere in the city's area. The magnitude of urban vegetation impact is higher in summer than in winter and it depends on the city's morphological peculiarities, such as urban texture and vegetation types and distribution: average July temperature variations due to the presence of urban vegetation reach peaks of −0.8 °C in Milano, −0.6 °C Madrid, and −0.4 °C in Bologna, while in January, the values range between −0.3 and −0.1 °C. An average heating effect of ca. +0.2 °C is found in some parts of Madrid in January. For relative humidity, we found increments of 2%–3% in July and 0.5%–0.8% in January, while a decrease in wind speed was found between 0.1 and 0.5 m/s, with the highest occurring in Madrid during July. [ABSTRACT FROM AUTHOR]

Air pollution represents a global threat leading to large impacts on health and ecosystems and many European areas still show a poor air quality. Many measures and policies have been adopted in the past decades at European, national, regional and even local level and many tools have been developed to tackle this issue. Among these tools, the European Air Quality Directive places more emphasis on the use of models for air quality assessment and management. Within this context, air quality forecasting systems play an important role in supporting decision makers when short-term actions are required to reduce human health risks by limiting population exposure. In this framework, at European level within the Copernicus Atmosphere Monitoring Service (CAMS), the regional air quality models participating to the service provide 4-day daily forecasts of the main atmospheric pollutants concentrations, in the lowest layers of the atmosphere. This work presents the development and the performances evaluation of FORAIR_IT, an high-resolution air quality forecasting system operating at both European (20 km) and Italian (4 km) scales. Its skill results are compared with CAMS_50 interim ensemble reanalysis (IRA) ones and a long lasting PM10 exceeding event, occurred in Emilia Romagna region in October 2017, is studied in more detail. Results show similar skill scores between FORAIT_IT and CAMS_50. Comparing the annual average of the monthly Root Mean Square Error Difference (RMSED) between FORAIT_IT first forecast day and CAMS_50, over the European domain the RMSED is 0.6, 1.7, 1.4 and 7.4 µg/m³ for daily mean PM10 and PM2.5 and daily maximum for NO2 and O3, respectively, while over the Italian domain it is 1.2, 0.3, 4.3 and 3.8 µg/m³. The importance of increasing model resolution in the region of interest is highlighted by the lower values of RMSED over Italy with respect to Europe. The results obtained by the detailed analysis of the PM10 exceeding event suggests the crucial role of the meteorological forcing in capturing both the timing and the intensity of the exceedances. As far as we know FORAIR_IT is the first forecasting system at high spatial resolution at Italian National level. [ABSTRACT FROM AUTHOR]

A growing health concern, due to poor air quality, recently led to an increased number of studies regarding air pollution effects on public health. Consequently, close attention is paid to estimation methods of exposure to atmospheric pollutants. This paper aims to meet a specific requirement of epidemiological researchers, that is providing annual air pollution maps at municipality scale for health impact assessment purposes on national basis. Firstly, data fusion through kriging with external drift is implemented, combining pollution data from two different sources, models and measurements, in order to improve the spatial distribution of surface concentrations at grid level. Then, the assimilated data of air pollution are upscaled, so as to obtain concentrations at municipality level. This methodology was applied to Italy and Spain (in Spain, only the second step was carried out since the modeled concentration already included an assimilation procedure). In both countries, for each municipality, an estimate of the concentration value for atmospheric pollutants of major concern for human health (PM and NO) was provided, offering more relevant information from a surveillance point of view. [ABSTRACT FROM AUTHOR]

Air backward trajectory calculations are commonly used in a variety of atmospheric analyses, in particular for source attribution evaluation. The accuracy of backward trajectory analysis is mainly determined by the quality and the spatial and temporal resolution of the underlying meteorological data set, especially in the cases of complex terrain. This work describes a new tool for the calculation and the statistical elaboration of backward trajectories. To take advantage of the high-resolution meteorological database of the Italian national air quality model MINNI, a dedicated set of procedures was implemented under the name of M-TraCE (MINNI module for Trajectories Calculation and statistical Elaboration) to calculate and process the backward trajectories of air masses reaching a site of interest. Some outcomes from the application of the developed methodology to the Italian Network of Special Purpose Monitoring Stations are shown to assess its strengths for the meteorological characterization of air quality monitoring stations. M-TraCE has demonstrated its capabilities to provide a detailed statistical assessment of transport patterns and region of influence of the site under investigation, which is fundamental for correctly interpreting pollutants measurements and ascertaining the official classification of the monitoring site based on meta-data information. Moreover, M-TraCE has shown its usefulness in supporting other assessments, i.e., spatial representativeness of a monitoring site, focussing specifically on the analysis of the effects due to meteorological variables. [ABSTRACT FROM AUTHOR]

This work illustrates the contribution of persistent scatterer interferometry (PSI) from radar satellites ERS (European Remote Sensing satellite) and ENVISAT (Environmental Satellite) for the updating of a pre-existing landslide inventory (LSI) map: the main purpose is to change or confirm the landslide state of activity and geometry and to identify new landslides. Radar data have been integrated with optical images and ancillary data in a 1320 km2 wide river basin (Biferno Basin) located in the central-eastern part of Italy. The geological setting of the area is characterized by clay and alternated clayey, silt and sandy formations that are affected by slow landslides. Field validation confirmed the results and the capabilities of multi-interferometric synthetic aperture radar data, integrated and coupled with conventional techniques, to support landslide investigation at the regional scale thanks to the available archive of repeated satellite data, which provides measurements of ground displacements with a millimetre-scale accuracy. In the study area, about 9% of the pre-existing LSI has been modified by means of permanent scatterer (PS) information, 15% of which have changed the state of activity from dormant to active and 95 new landslides were detected. The radar interpretation method applied in Biferno Basin confirms its high capability of detecting and mapping landslides at basin the scale: the information acquired from radar interpretation is the basis of the proposed method to evaluate the state of activity and the intensity of slow landslides. However, it is clear that limitations exist and this method does not always support the updating of LSI for the whole study area. We consider this methodology and procedure as a portable and suitable one for different geological and geomorphological environments. [ABSTRACT FROM AUTHOR]

An application of the Weather Research and Forecasting model aiming to estimate wind and photovoltaic energy resources over Lesotho is presented. To this scope, the whole year 2015 was simulated in a two-way nesting mode reaching 1 km horizontal resolution. Different model parameterizations were tested and compared to available observations, then the best performing simulation was used to elaborate wind and photovoltaic potential maps over Lesotho. Concerning the photovoltaic potential, Lesotho presents a good potential countrywide, having values ranging from around 1600 kWh/kWp to 1750 kWh/kWp, with maxima in the highlands. The results also show that there are many promising areas for wind power exploitation. Zones with higher wind potential are mainly found all along the eastern border with South Africa, from north to south and in general in the highlands. The wind potential exhibits much more variability respect to the photovoltaic one, with estimated energy production ranging from 1000 kWh/kW up to 4000 kWh/kW. Finally, an evaluation of complementarity of wind and solar electricity generation was done at different time scales, showing that some areas with negative correlation coefficient exist, where it is possible to minimise intermittent production and allowing an optimal integration in the electricity grid. • Assessment of photovoltaic and wind energy resource over Lesotho is presented. • A numerical atmospheric model is tuned and used for the evaluation. • For the first time 1 km resolution energy potential maps are produced over Lesotho. • Complementarity of wind and photovoltaic electricity generation in time is explored. [ABSTRACT FROM AUTHOR]