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The ICON (ICOsahedral Nonhydrostatic) is a joint project between the Deutscher Wetterdienst (DWD) and the Max-Planck-Institute for Meteorology (MPI-M) for the development of a unified global numerical weather prediction system. In 2018, COSMO (COnsortium for Small-scale Modeling) started the migration from the COSMO-LM to the ICON-LAM (ICON Limited Area Model) as the operational model. The main aim of this work is the presentation of a sensitivity study performed over a domain located in southern Italy (including the northern part of the Campania region and the southern part of Lazio) aimed to provide a contribution to the definition of a model configuration suitable for accurate weather forecasts over this area. Following the work performed by the authors with COSMO on a similar domain, the week 19-25 November 2018 has been selected as test case, when a low-pressure system coming from Western Mediterranean determined intense storms and gusts.A computational grid R2B11, characterized by a very high resolution (about 1.2 km), has been adopted. Initial and boundary conditions are provided by the ECMWF IFS model at a spatial resolution of about 8.5 km. The sensitivity was carried out starting from a model configuration optimized by the authors (in a joint effort with the CMCC Foundation, Italy) over the whole Italian area, employing a grid with different resolution (R2B10, about 2.5 km). The reference configuration assumes that the shallow convection parameterization is active whereas the parts treating deep and mid-level convection are switched off. Moreover, a single moment cloud microphysics scheme and a diagnostic Kohler cloud cover scheme are employed.A first sensitivity was performed with respect to the domain size, considering a reference domain (11.38° – 15.38° E; 40.25° - 42.25° N) and two additional domains respectively larger (in both directions) than 50% and 100% with respect to the original one. Then, a sensitivity to the numerical parameters, which have been shown to play a significant role in determining model response, has been carried out, e.g tkhmin (minimal diffusion coefficient for heat), rlam_heat (factor for laminar resistance for heat) and v0snow (factor for vertical velocity of snow).Model evaluation has been conducted against ground observation data provided by CIRA instrumentation and by the SCIA system developed by ISPRA (Italy). Moreover, a comparison with forecasts provided by the COSMO model at 0.009° (about 1 km resolution) forced by the same driving data has been performed, in order to highlight the differences between the performances of the two models.

This work provides a significant contribution on the open debate in the climate community to establish the added value of very high-resolution configurations, characterized by a horizontal resolution below 4 km with respect to current state-of-the-art climate simulations (10–15 km). Specifically, it aims at assessing quantitative gains and losses in the performance of climate models caused by an enhancement in temporal and spatial resolution by evaluating the capability of different climate simulations in reproducing daily and sub-daily present precipitation dynamics over a complex orographic context such as the Alpine region. In this perspective, the results of three experiments (EURO-CORDEX ensemble mean, CCLM 8 and CCLM 2.2) at different spatial (~ 12, 8 and 2.2 km) and temporal (daily, 6 h and 3 h) scales are compared to gridded and point-scale observational datasets. Precipitation data are analyzed by mean of the Expert Team on Climate Change Detection and Indices indicators, as well as with statistical models able to evaluate the precipitation distribution and the extreme values for different durations of precipitation events. To objectively assess gains and losses in adopting high-resolution RCMs, data are elaborated assuming the distribution added value as metric, particularly focusing on the role of orography. The work returns, at daily scale, a gain in climate model performances moving from lower to higher horizontal resolution. At the same time, investigating the effect of the orography the simulation with the finest grid proves to better capture local precipitation dynamics at higher altitudes in terms of both sub-daily precipitation and extreme events.

One of the most severe weather hazards to aviation is in-flight airframe icing, i.e. the accretion of ice on airplane’s surfaces during flight. In order to increase margins of aviation safety, the early detection of regions affected by icing conditions is a challenging and desirable goal. In the framework of the H2020 EU project SENS4ICE (SENSors and certifiable hybrid architectures for safer aviation in ICing Environment), CIRA has developed a remote detection tool of icing conditions based on satellite data. Specifically, high-resolution satellite products, based on Meteosat Second Generation (MSG) data, have been considered, with spatial and temporal resolutions of about 3 km and 15 minutes respectively. The aim of this tool is to identify areas potentially affected by in-flight icing hazard, giving information about the severity of the phenomenon (light, moderate or severe) and an estimate of the altitude at which this hazard can occur. The developed algorithm also takes into account supercooled large droplets (SLD), which pose a serious threat to aviation and have been the cause of tragic accidents over the last decades. The tool relies on satellite data, to remotely infer the properties of clouds, and a set of experimental curves and envelopes that describe the interrelationship of cloud liquid water content, mean effective diameter of the cloud droplets and ambient air temperature. These curves, provided by the Federal Aviation Administration (FAA), define the atmospheric icing conditions and represent the reference legislation in this field. Furthermore, a nowcasting algorithm based on the extrapolation in time of the current icing conditions has been implemented, in order to perform a forecast over a short period ahead, responding to the great need for timely and location-specific forecasts that are relevant for aviation, e.g. for safety reasons or for planning and routing air traffic. This presentation will provide a preliminary analysis of the performance of the implemented tools, which will be evaluated in relevant icing conditions in the framework of SENS4ICE flight campaigns, planned for 2023.Acknowledgment: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement N° 824253 (SENS4ICE project).

Extreme hydro-meteorological events are often defined by the statistical analysis of some parameter that measures the strength of the event over a long enough time series. The parameter could refer to the intensity of the event in terms of energy or to the impact of the event on the environment. This attribution becomes even more relevant when used as reference for future climate projections, suggesting a possible increase in the number of extreme events considering the attribution applied to the past database. In the literature concerning storm-surge, the use of significant wave height (Hs) percentiles to define thresholds of an extreme event is a common practice when dealing with sufficiently long datasets. Usually, this value ranges from 90th up to 99.5th trying to highlight about 3-6 Hs peaks per year. But, in fact, thresholds should provide a benchmark for how much a region can withstand an extreme event. The Italian coast of the northern Adriatic is recently increasing its sensitivity to such episodes, that threaten one of the most active touristic hub of Italy, the highly valuable Po Delta UNESCO Biosphere Reserve and city of Venice fragile structure. Recently in late 2019, a strong event hit Venice with high tides flooding the city's main monument, St. Mark's Basilica, for the 6th time in 1200 years, with levels very similar to the worst event in history in 1966. Attempting to better understand the distribution of these extreme events throughout last decades and how reanalysis products can be useful for storm-surge studies, this paper presents a climatological comparison of significant wave height data extracted from ECMWF ERA5 against the entire historical series available to the Nausicaa wave buoy. This station, owned and managed by ARPAE, is located about 8 km offshore the Municipality of Cesenatico, where the seabed is about 10m, and since 2007 has been used to monitor and prevent sea level related events. In the last 12 years, at least 10 extreme events have been reported based on hourly measured data in Nausicaa and the damage observed along the coast, allowing the local authorities to define Hs thresholds as 1.5 m to significant events and 3.0 m for extreme events. However, analysing the measured data in this period, at least 26 events that exceeded the 3 m threshold were observed, representing the percentile 99.81th of the historical series, whereas only 10 storm-surge events resulted in damage to cities or environmental protection areas. When analysing Hs extracted from ERA5 at the nearest grid point to Nausicaa (~ 30 km) for the same 26 events, all events were correctly identified by reanalysis and represented with an averaged correlation of 0.96. For Hs series extracted from ERA5, values above 3 m reached the 99.83rd percentile for the same period from 2007 to 2018, and 99.84th when expanded to the last 30 years (since 1989), showing that, although quite restricted, the 99.8th percentile seems to be a good value for identifying extreme events of storm-surge in the northern Adriatic Sea.

Weather conditions and Air Traffic Management (ATM) operations are strongly related, due to the relevant influence that the observed and forecasted weather conditions into assigned airspace have on the operational conditions that are therefore possible for such airspace. In the framework of the SESAR JU funded project CREATE (Innovative operations and climate and weather models to improve ATM resilience and reduce impacts), therefore, a dedicated study has been carried out in order to investigate the relation between weather and ATM. Such study both investigated the consequences that adverse weather conditions can have on ATM and, on the other side, addressed the currently and future available tools that can support ATM in the prediction and management of weather conditions from the aeronautical operations point of view. In this paper, the results of such literature study are outlined. More in details, in the paper first the main outcomes of the study are summarized, in terms of identification of the most relevant weather phenomena that affect the ATM operations, indication of their main impacts on them in terms of operational disruptions and, finally, identification of the associated level of severity. In particular, the above indicated aspects are addressed by taking into specific consideration the enroute and the TMA (Terminal Manoeuvring Area) flight phases and, for each of them, the main affecting weather phenomena and the main affected operations are identified. Then, in the paper the results of the study are summarized about the identification of the most relevant meteo tools that are expressed by state-of-the-art technologies to support the ATM operations in order to properly take into account the weather conditions in a precise and timely manner. This study addresses both the review of available and perspective tools for weather reporting and of the available and perspective numerical models supporting ATM in terms of weather models and air quality models.

In order to increase the weather awareness on board, at CIRA (Italian Aerospace Research Center) it is under development a general system able to provide, in near-real time, forecast and monitored weather information along the flight route, gathering and integrating data from different sources. MET4F (METeorological data for FLARE) is a new tool, within this system, devoted to provide the over mentioned weather information through the dispatch of a binary message from an on ground control station to an Unmanned Air Vehicle (UAV). The message format follows the protocol adopted by the CIRA Remotely Piloted Aircraft System and allows an automatic flight reconfiguration, also on the basis of the weather information.

This study presents a detailed analysis of the present and expected future extreme climate conditions over Italy through the use of some extreme indicators. Climate data for this analysis were provided by the regional climate model COSMO-CLM, using different grid spacing to ascertain the real importance of using higher resolution climate data, especially over such a complex topography as Italy. Four simulations were carried out at spatial resolutions of 0.125° and 0.0715°, driven by ERA-Interim Reanalysis and the CMCC-CM global model. We investigated the ability of the model to represent realistically the climatology of a subset of climate indicators defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) for precipitation and temperature. Several high-resolution observational data sets available over some Italian regions were therefore used in order to offset the limited number of observations available over Italy in the E-OBS data set and its coarse grid. We found that the increase in resolution could have interesting benefits in representing such extreme indices, especially in the more orographically complex areas. Finally, we investigated future climate changes regarding extreme weather events expected under anthropogenic climate change scenarios, employing the IPCC RCP4.5 and RCP8.5 greenhouse gas concentrations, showing that such events are expected to increase over Italy.

This study presents the results of dynamically downscaled climate simulations over Italy produced with the COSMO-CLM model. Three simulations forced by ERA-Interim Reanalysis were conducted respectively at a spatial resolution of 0.22°, 0.125° and 0.0715° over the period 1979–2011. The results were analysed in terms of 2-m temperature and precipitation with the aim of assessing the model's ability to reproduce these important features of the Italian climate. The results were validated by comparing model output with different independent observational datasets. Values of temperature and precipitation show a general good agreement with observations, with a fair reduction of errors in all seasons as the resolution is increased. Two simulations at a spatial resolution of 0.0715°, driven by the global model CMCC-CM, were performed over the period 1971–2100, employing the IPCC RCP4.5 and RCP8.5 scenarios. Climate projections show a significant warming expected in Italy at the end of the 21st century, along with a general reduction in precipitation, particularly evident in spring and summer.

Snow cover maps from Earth Observation (EO) satellites are valuable datasets containing large-scale information on snow cover extent, snow cover distribution and snow cover duration. In evaluating the performances of Regional Climate Models, EO data can be a valid piece of information alternative to in-situ measurements, which require a dense network of stations covering the entire altitudinal range and techniques for interpolating the values. In this context, MODIS snow products play a leading role providing several types of snow cover maps with high spatial and temporal resolutions. Here, we assess snow cover outputs of a high resolution Regional Climate Model (RCM) using MODIS maps of snow covered area over the Po river basin, northern Italy. The dataset consists of 9 years of MODIS data (2003–2011) cleaned from cloud cover by means of a cloud removal procedure. The maps have 500 m spatial resolution and daily temporal resolution. The RCM considered is COSMO-CLM, run at 0.0715° resolution (about 8 km) and coupled with the soil module TERRA_ML. The ERA-Interim reanalyses are used as initial and boundary conditions. The results show a good agreement between observed and simulated snow cover duration and extension. COSMO-CLM is able to reproduce the inter-annual variabilities of snow cover features as well as the seasonal trend of snow cover duration and extension. Limitations emerge when the RCM simulates the progressive depletion of the snow cover in spring. Simulated snowmelt occurs faster than the observed one. Then, we investigate the influence of the spatial resolution of the climate model. The simulation at 0.0715° (about 8 km) is compared to a simulation performed at 0.125° (about 14 km). The comparison highlights the benefits provided by the higher spatial resolution in the accumulation season, reflecting the improvements obtained in temperature and precipitation fields.